SMATIKA JURNAL
type: channel
description: SMATIKA: STIKI Informatika Jurnal is a journal published by Lembaga Penelitian & Pengabdian kepada Masyarakat (LPPM) of Sekolah Tinggi Informatika & Komputer Indonesia (STIKI) Malang. The scope of this journal in the field of Computer Science, Information Systems, and Information Management. SMATIKA journal published four times a year, in March, Juny, September and December with an ISSN (printed): 2087-0256 and ISSN (online): 2580-6939. SMATIKA has accredited Sinta in S4 and has had DOI 10.32664.
publisher: LPPM UBHINUS MALANG
language: en
publication name: SMATIKA JURNAL
issn: 2087-0256
copyright: The writer agreed that the article copyright by Smatika journal and the writer has the right to disseminate the paper published without permission in advance.
items: Indoor Human Presence Detection System Using Histogram of Oriented Gradients Algorithm
items: Improving User Experience and Information Delivery through Motion UI in a Single-Page Campus Website
items: Evaluation of User Experience of Yamaha R15 AR Tutorial Application Using User Experience Questionnaire (UEQ)
items: Virtual Reality Game of Sorghum for Food Resilience
items: Analysis of Build Time in Continuous Integration on Multi-Node Jenkins Using Caching Methods
items: Building RT/RW Net With Pointing Points Between Regions
items: Classification of Fake and Real using Vision Transformer and EfficientNet-B0 on Real and AI-generated Images
items: Transfer Learning Based CNN Model Optimization for Pneumonia Classification in Chest X-Ray Images
items: Web-Based Cattle Feed Monitoring and Management System at PT XYZ Central Lampung
items: Security Gap Analysis of the Sorong Ministry of Health Polytechnic Website Using the Penetration Testing Method
items: Classification of Pneumonia in X-Ray Images Using CNN ResNet50V2 with Transfer Learning
items: Sentiment Analysis of Indonesian Audio Transcription Results Using T5 (Text-to-Text Transfer Transformer)
items: WordPress Application Satisfaction Level Sentiment Analysis Using K-Nearest Neighbor and Naive Bayes Methods
items: Aplikasi Augmented Reality Untuk Pengenalan Panca Indera Manusia
items: Application Types Of Drugs For Young Generation Based On Augmented Reality
items: Implementation of Internet of Thing to Automatically Monitor Water Filling and Usage
items: Mapping Literature and Research Trends on AI Applications in Journalism: A Bibliometric Review
items: Simple Additive Weighting Method for Selection of Social Media in Digital Marketing for Culinary MSMEs in Banjarmasin City
items: Employee Performance Assessment in the Field of Public Infrastructure and Facilities Handling Using the C4.5 Decision Tree Algorithm
items: Design and Construction of an Environmental Monitoring System in a Cow Shed Based on the Internet of Things
items: Prototype of Weather Monitoring System and Early Rain Warning Based on Internet of Things
items: Home Page SMATIKA Journal Volume 14 Number 02, Desember 2024
items: Implementation Of The K-Nearest Neighbors (KNN) Algorithm For Malnutrition Prediction
items: Decision Support System In Selecting Automatic Motorcycles using the Preference Selection Index (PSI) Method
items: Design and Development of a Water Quality Recommendation System Prototype for Nila Aquaculture
items: Modeling of a Malaria Parasite Detection System on Microscopic Images of Blood Cells Using Deep Learning Methods
items: Implementation of Tsukamoto Fuzzy Inference System on Loan Eligibility Determination
items: The Use of K-Means and K-Medoids Algorithms for Developing New Student Admissions Promotion Strategies
items: Implementation of Web-Based Accounting Information System at Military Institutions in Kolatmar Puslatmar-8 Teluk Ratai
items: Emotion Detection on Facial Images Using Deep Learning as a Support Tool for Therapy for Individuals with Alexithymia

Indoor Human Presence Detection System Using Histogram of Oriented Gradients Algorithm
type: item
description: This study aims to develop a web-based monitoring system utilizing computer vision and the Histogram of Oriented Gradients (HOG) algorithm to detect and count the number of people in a room. The research was motivated by the need for automated systems to replace manual methods, particularly during the COVID-19 pandemic, where contactless attendance tracking is critical. The HOG algorithm extracts visual features as image gradients representing human shapes, which are then classified using a machine learning model. The research methodology involved testing various camera configurations and angles, such as above door placement and room corners, with 80 and 60-degree camera angles. The system was built using Python, OpenCV for image processing, and Flask for web interface development. Data was collected through 200 trials, capturing live images with a camera. The results showed that the camera positioned above the door at an 80-degree angle achieved the highest detection accuracy at 66%, while other configurations showed reduced accuracy, averaging around 52%. The system also logs data, including time, detected human count, and captured images for further analysis. The findings highlight the importance of camera placement and angle in ensuring effective human detection. These results support the development of computer vision-based systems for practical applications such as crowd management, attendance counting, and public space monitoring. Future research is proposed to improve detection accuracy by integrating facial recognition technology and developing a more intuitive user interface. This study contributes substantially to improving the efficiency of automated systems for counting individuals in enclosed spaces, addressing various industrial and social needs.
subject: Articles
  topic: section
subject: computer vision
  topic: keywords
subject: HOG
  topic: keywords
subject: human detection
  topic: keywords
subject: OpenCV
  topic: keywords
subject: machine learning
  topic: keywords
creator: Hendry Anggono
creator: Jozua F Palandi
creator: Bagus K Kristanto
creator: Zusana E Pudyastuti
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-08-01
publication date: 2025-08-01
starting page: 240
ending page: 247
doi: 10.32664/smatika.v15i01.2077

Improving User Experience and Information Delivery through Motion UI in a Single-Page Campus Website
type: item
description: This research aims to develop a campus introduction website interface for STIKI Malang by implementing Motion UI to enhance information delivery effectiveness and user experience. Currently, the STIKI website uses a multi-page structure that requires visitors to open sub-menus separately to access information, resulting in significant resource consumption and long loading times. By transforming the website structure into a single-page application (SPA) and integrating dynamic animation elements through Motion UI, it is expected to increase engagement and conversion rates. Qualitative analysis results show significant improvements in user engagement and information comprehension, with page loading times drastically reduced to an average of 2.6 seconds. Evaluation using the User Experience Questionnaire (UEQ) also demonstrates significant improvements in attractiveness, clarity, efficiency, and stimulation aspects. This research concludes that implementing Motion UI on STIKI's single-page website successfully enhances user experience, access speed, and information delivery effectiveness. The results of this research can serve as a reference for developing interface design strategies for other educational institution websites
subject: Articles
  topic: section
subject: Motion UI
  topic: keywords
subject: Single-page application
  topic: keywords
subject: User engagement
  topic: keywords
subject: User experience
  topic: keywords
subject: Website interface design
  topic: keywords
creator: Jihan Arifia Ramadhanti
creator: Laila Isyriyah
creator: Rakhmad Maulidi
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-07-30
publication date: 2025-07-30
starting page: 226
ending page: 239
doi: 10.32664/smatika.v15i01.2039

Evaluation of User Experience of Yamaha R15 AR Tutorial Application Using User Experience Questionnaire (UEQ)
type: item
description: This study aims to evaluate the user experience (UX) of an Augmented Reality (AR) application designed as a tutorial medium for introducing components of the Yamaha R15 VVA motorcycle. The evaluation was conducted using the User Experience Questionnaire (UEQ) method, covering six aspects: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The research sample consisted of 30 students who have taken or are currently taking the Human-Computer Interaction course at Universitas Bhineka Nusantara. The analysis results showed that the pragmatic quality had an average value of -0.092 and the hedonic quality 0.108, with an overall score of 0.008, categorized as neutral. The average standard deviation of 0.666 indicates a high level of agreement among respondents. Although the application met some visual and interactive aspects, weaknesses were found in navigation features, usage guidance, and audiovisual support. Reliability testing showed that the UEQ instrument is appropriate for use, with a Cronbach’s alpha value of 0.88 (pragmatic) and 0.77 (hedonic). These results recommend further development in usability aspects and supporting features to enhance the effectiveness of the AR-based technical learning application.
subject: Articles
  topic: section
subject: Augmented Reality (AR)
  topic: keywords
subject: User Experience (UX)
  topic: keywords
subject: User Experience Questionare(UEQ)
  topic: keywords
creator: Setiabudi Sakaria
creator: Subari Subari
creator: Evy Poerbanintyas
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-07-28
publication date: 2025-07-28
starting page: 214
ending page: 225
doi: 10.32664/smatika.v15i01.2055

Virtual Reality Game of Sorghum for Food Resilience
type: item
description: Sorghum is a vital staple food crop widely consumed in Asia and Africa, ranking among the top five food crops globally after wheat, corn, rice, and barley. It is rich in carbohydrates and low in sugar, making it a suitable dietary option for individuals with diabetes. Sorghum can be cultivated on marginal or unproductive land; however, improper cultivation techniques may lead to sub-optimal yields or even crop failure. Survey findings indicate that public awareness and knowledge about sorghum cultivation in Indonesia remain limited, contributing to difficulties in effective planting and crop management. To address this issue, a simulation game utilizing virtual reality (VR) technology was developed to provide an interactive and immersive learning experience for sorghum cultivation. The Unity game engine was employed in the development process, incorporating freely available assets from various sources. The application was validated through expert interviews and user testing within the community. Post-testing results demonstrated a 31% increase in user knowledge regarding sorghum cultivation. These findings suggest that the VR-based game effectively enhances public understanding of the procedures involved in planting and maintaining sorghum crops.
subject: Articles
  topic: section
subject: Agricultural education
  topic: keywords
subject: simulation game
  topic: keywords
subject: sorghum
  topic: keywords
subject: staple food
  topic: keywords
subject: virtual reality
  topic: keywords
creator: Delta Ardy Prima
creator: Mikhael Ming Khosasih
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-07-28
publication date: 2025-07-28
starting page: 203
ending page: 213
doi: 10.32664/smatika.v15i01.2049

Analysis of Build Time in Continuous Integration on Multi-Node Jenkins Using Caching Methods
type: item
description: In the era of modern software development, the speed and efficiency of application build processes have become critical factors in enhancing the productivity of development teams. Jenkins, as a popular CI/CD tool, can be utilized to perform distributed builds across multiple servers. However, long build times remain a significant challenge, especially for container-based applications that require numerous dependencies and compilation stages. This study proposes a method to optimize build time by leveraging caching mechanisms. The proposed method utilizes cache storage to save frequently used build artifacts, thereby reducing the time required for recompilation in subsequent builds. In this research, experiments were conducted to compare the build time of container-based applications before and after implementing the caching method. The results of this study indicate that applying caching techniques to container-based applications can improve build speed by approximately 43% to 76%. This finding demonstrates that caching is highly effective in enhancing build time efficiency within Jenkins’ multi-node architecture. Consequently, this optimization not only accelerates the build process but also reduces computational resource consumption, thereby supporting more efficient and cost-effective CI/CD practices.
subject: Articles
  topic: section
subject: cache
  topic: keywords
subject: Continuous Integration
  topic: keywords
subject: Continuous Deployment
  topic: keywords
subject: Docker
  topic: keywords
subject: Jenkins
  topic: keywords
creator: Febriyan Adji Saputro
creator: Muhammad Agung Nugroho
creator: Eka Sahputra
creator: Alon Jala Tirta Segara
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-07-28
publication date: 2025-07-28
starting page: 193
ending page: 202
doi: 10.32664/smatika.v15i01.2034

Building RT/RW Net With Pointing Points Between Regions
type: item
description: The digital divide between urban and rural areas in Indonesia remains a significant challenge in the current digital era, with limited internet access in remote regions hindering economic, educational, and social progress. This research aims to develop a wireless LAN-based RT/RW Net system as a solution to enhance efficient and affordable internet access. The system utilizes point-to-point access between regions using specialized routers to overcome geographical and physical barriers, thereby enabling internet access in hard-to-reach areas. By leveraging WiFi 5 technology as a solution for better range and performance, along with ease of operation and affordable construction costs, the system is designed for efficiency. Device configuration is carried out using the built-in application of the TP-Link EAP 110 router, facilitating easier and more efficient management. The PPDIOO (Prepare, Plan, Design, Implement, Operate, Optimize) research methodology is chosen as the approach for developing the RT/RW Net network. The advantages of this method include structured and systematic design capabilities, greater flexibility in meeting design needs, and the ability to optimize the quality of the network once it is operational. This method can also analyze and build networks according to local geographical conditions. Expected outcomes include improved quality and reliability of the RT/RW Net network, provision of cheaper and more convenient internet access for communities, and contributions to a more equitable dissemination of information and communication technology across Indonesia, enabling communities to enjoy better access to information, broader educational opportunities, and enhanced economic prospects.
subject: Articles
  topic: section
subject: Modem Isp
  topic: keywords
subject: Network RT/RW
  topic: keywords
subject: Network Topology Star
  topic: keywords
subject: PPDIOO method
  topic: keywords
subject: Speed test
  topic: keywords
creator: Adam Laxmana Mufti
creator: Azmuri Wahyu Azinar
creator: Hindarto Hindarto
creator: Yunianita Rahmawati
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-26
publication date: 2025-06-26
starting page: 91
ending page: 101
doi: 10.32664/smatika.v15i01.1520

Classification of Fake and Real using Vision Transformer and EfficientNet-B0 on Real and AI-generated Images
type: item
description: Advances in artificial intelligence (AI) technology have enabled the creation of synthetic images that resemble real images, posing challenges in detecting and classifying such images. This study aims to develop an EfficientNet-B0 and Vision Transformer (ViT) based classification model to distinguish between real images and images generated by generative AI. The data used consists of 30,401 original images from the MSCOCO 2017 dataset and 30,401 generative AI-generated images from the SyntheticEye AI-Generated Images Dataset on Kaggle. The results showed that the ViT model achieved 98% accuracy and EfficientNet-B0 achieved 96% accuracy in classifying the images. The conclusion of this research is that both models have great potential in detecting digital media manipulation, with ViT showing superior performance. The practical implication of this research is the development of more advanced technologies for detecting generative images, which can be used in various real applications such as digital security and media verification.
subject: Articles
  topic: section
subject: AI-Generated Image
  topic: keywords
subject: CNN
  topic: keywords
subject: EfficientNet
  topic: keywords
subject: Image classification
  topic: keywords
subject: Image processing
  topic: keywords
subject: Vision Transformer
  topic: keywords
creator: M. Syahrul Anwar Aria
creator: Cepy Slamet
creator: Muhammad Deden Firdaus
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-30
publication date: 2025-06-30
starting page: 179
ending page: 192
doi: 10.32664/smatika.v15i01.1531

Transfer Learning Based CNN Model Optimization for Pneumonia Classification in Chest X-Ray Images
type: item
description: Pneumonia is a leading cause of child mortality worldwide, and its diagnosis often relies on chest X-ray interpretation, which is prone to human error. This study aims to optimize a Convolutional Neural Network (CNN) model based on transfer learning using the DenseNet-121 architecture for pneumonia classification in chest X-ray images. The model was trained on a Kaggle dataset consisting of two classes: Normal and Pneumonia. Preprocessing included class balancing and data augmentation. Five fine-tuning strategies were tested, ranging from training only the classifier to unfreezing the entire pretrained layers. Evaluation metrics included accuracy, precision, recall, F1-score, and ROC-AUC. Results showed that the strategy of unfreezing Block 3–4 yielded the best performance with 94.39% accuracy, 95.61% F1-score, and 98.04% ROC-AUC. This study demonstrates that selective fine-tuning strategies significantly improve classification performance compared to training only the classifier or the entire network.
subject: Articles
  topic: section
subject: DenseNet-121
  topic: keywords
subject: Fine-Tuning
  topic: keywords
subject: Pneumonia
  topic: keywords
subject: Transfer Learning
  topic: keywords
creator: Rasikh Khalil Pasha
creator: Kholiq Budiman
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-28
publication date: 2025-06-28
starting page: 167
ending page: 178
doi: 10.32664/smatika.v15i01.1985

Web-Based Cattle Feed Monitoring and Management System at PT XYZ Central Lampung
type: item
description: This research discusses the design and implementation of a web-based cattle feed monitoring and management system at PT XYZ Lampung Tengah. The main problem faced is the manual recording in cattle feed management, which causes inaccuracies in feed consumption data, incorrect cost calculations, and the risk of inventory shortages or excesses. The research uses the Rapid Application Development (RAD) method in system development, which consists of four phases: Requirements Planning, RAD Design Workshop, Implementation, and Maintenance and Evaluation. The developed system has features for managing user data, feed type data, feed weight recaps, as well as generating recap and stock reports. The implementation results show that the system successfully automated the feed inventory recording process with access rights distribution between admin and user, data visualization through stock and feed expenditure graphs, and the ability to generate organized reports. In making the final report on animal feed, the admin takes up to 7 days because it adjusts warehouse data with feed expenditure records. But with this Web-based monitoring and animal feed system, the admin can present the final report on the use of feed in real-time. This system helps improve operational efficiency and the accuracy of feed usage recording at PT XYZ Lampung Tengah.
subject: Articles
  topic: section
subject: Digital Transformation in Agriculture
  topic: keywords
subject: Inventory
  topic: keywords
subject: Monitoring System
  topic: keywords
subject: Real Time
  topic: keywords
subject: Web-based
  topic: keywords
creator: Sania Media Nosa
creator: Heni Sulistiani
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-28
publication date: 2025-06-28
starting page: 151
ending page: 166
doi: 10.32664/smatika.v15i01.1717

Security Gap Analysis of the Sorong Ministry of Health Polytechnic Website Using the Penetration Testing Method
type: item
description: Currently, information technology is very necessary, in the digital era cybersecurity is one of the main factors for an institution to survive and be recognized for its credibility, including educational institutions, the Poltekkes Sorong Website is used as the main portal or media to disseminate academic information, interaction between students and the public, However, the existence of security gaps is something that allows cyber attacks to be carried out which can endanger data confidentiality. This study aims to analyze the security gaps in the Poltekkes Kemenkes Sorong website, the testing carried out using the penetration testing method which has several stages including collecting scanning information, vulnerability analysis. So that this study can provide guidance and practice for information system managers at Poltekkes Kemenkes Sorong to reduce the risk of cyber attacks and protect sensitive data held by the institution.
subject: Articles
  topic: section
subject: Acunetix Web Vulnerability
  topic: keywords
subject: Owasp Zap Vulnerability Assessment
  topic: keywords
subject: Penetration Testing
  topic: keywords
subject: Website Security
  topic: keywords
creator: Gunawan Yudi Jayanto
creator: Julius Panda Putra Naibaho
creator: Alex De Kweldju
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-28
publication date: 2025-06-28
starting page: 136
ending page: 150
doi: 10.32664/smatika.v15i01.1549

Classification of Pneumonia in X-Ray Images Using CNN ResNet50V2 with Transfer Learning
type: item
description: The utilization of technology to build models that can classify pneumonia medical images automatically is needed for early diagnosis. This study aims to implement a Convolutional Neural Network (CNN) model with ResNet50V2 architecture that has been proven to have high accuracy in medical image classification. The model adopts a deep and efficient residual architecture, which facilitates deeper training of the model without suffering from vanishing gradient problem. This study went through four main stages: pneumonia and normal X-ray image data collection, data pre-processing (including set division, transformation, and augmentation), modeling using CNN with hyperparameter tuning, and model evaluation. Evaluation was performed using accuracy, F1-score, and Confusion Matrix metrics. The CNN model with ResNet50V2 as the backbone achieved 97% accuracy, showing excellent performance in differentiating between pneumonia and normal despite a small amount of misclassification. Although this model showed impressive results, challenges such as potential misclassification in cases with unclear or ambiguous images remain. Compared to previous approaches, this model offers advantages in accuracy and processing efficiency thanks to the use of a deeper and more sophisticated ResNet50V2. These advantages are expected to improve the precision of automated diagnosis in future medical applications.
subject: Articles
  topic: section
subject: CNN
  topic: keywords
subject: Pneumonia Classification
  topic: keywords
subject: ResNet50V2
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: X-ray Images
  topic: keywords
creator: Muhammad Afian Anwar
creator: Yana Aditia Gerhana
creator: Undang Syaripudin
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-28
publication date: 2025-06-28
starting page: 126
ending page: 135
doi: 10.32664/smatika.v15i01.1538

Sentiment Analysis of Indonesian Audio Transcription Results Using T5 (Text-to-Text Transfer Transformer)
type: item
description: In the digital era, sentiment analysis has become a vital tool for understanding public opinion, particularly from data derived from digital media such as videos. However, voice-based sentiment analysis in the Indonesian language remains uncommon. This research aims to develop the T5 model for sentiment analysis of Indonesian generated from speech using speech-to-text technology. The primary advantages of the T5 model lie in its ability to process lengthy texts, comprehend natural language context, and adapt training for specific tasks such as sentiment analysis. The research dataset was obtained from 20 YouTube videos, segmented into clips of a maximum duration of 15 seconds, resulting in a total of 300 sentences consisting of 150 positive sentiments and 150 negative sentiments. The generated text data was processed using the T5 model, which was specifically trained to detect positive and negative sentiments through the optimization of specific hyperparameters. The results demonstrated that the T5 model achieved an accuracy of 83%, with a precision of 0.85, a recall of 0.83, and an F-measure of 0.83 when tested on datasets different from the training data. This research indicates that the T5 model can be adapted for voice-based sentiment analysis in the Indonesian language with satisfactory results. These findings contribute to the development of voice-based sentiment analysis technology, which can be applied to opinion analysis or product reviews. In the future, improving the pre-processing stage and using more diverse datasets are expected to improve the overall performance of the model.
subject: Articles
  topic: section
subject: Natural Language Processing (NLP)
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Speech-to-text
  topic: keywords
subject: T5
  topic: keywords
subject: Transformer
  topic: keywords
creator: Hilman Suhendar
creator: Cepy Slamet
creator: Undang Syaripudin
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-28
publication date: 2025-06-28
starting page: 115
ending page: 125
doi: 10.32664/smatika.v15i01.1521

WordPress Application Satisfaction Level Sentiment Analysis Using K-Nearest Neighbor and Naive Bayes Methods
type: item
description: User satisfaction reflects emotions when comparing services received with expectations, so understanding user satisfaction is important for app development. This research aims to evaluate user satisfaction with WordPress apps on the Google Play Store and identify areas for improvement. Sentiment analysis with KNN and Naïve bayes algorithms as the method used to extract information from 5,000 user reviews downloaded from Google Play Store,. The results showed the majority of reviews had positive sentiments, with Naïve Bayes providing better results than KNN, achieving 88% accuracy, 89.45% precision, 88% recall, and 83% F1-Score on a 90:10 data split. The word cloud of positive reviews featured words such as “greatâ€, “goodâ€, “helpfulâ€, “appâ€, and “goodâ€, reflecting user satisfaction with the ease and benefits of the app, while negative reviews featured words such as “difficultâ€, “tryâ€, and “failâ€ indicating technical difficulties and user dissatisfaction. This study concludes that WordPress apps have provided a satisfactory experience for most users, but some technical areas need improvement. The results of this study will provide valuable information for app developers in efforts to improve service quality and the app's reputation
subject: Articles
  topic: section
subject: KNN
  topic: keywords
subject: Naïve Bayes
  topic: keywords
subject: Satisfaction Level
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Wordpress
  topic: keywords
creator: Moch Siddiq Hamid
creator: Ade Eviyanti
creator: Hindarto Hindarto
creator: Novia Ariyanti
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-26
publication date: 2025-06-26
starting page: 102
ending page: 114
doi: 10.32664/smatika.v15i01.1522

Aplikasi Augmented Reality Untuk Pengenalan Panca Indera Manusia
type: item
description: This study aims to design a maekerless Augmented Reality based application as a interactive learning media for the introduction of the five human senses for elementary school students. This application uses the Multimedia Development Life Cycle (MDLC) method because it is in accordance with the manufacturing stage. Features in This application displays 3D visualization of the organs of the five senses that can be manipulated by the user, complete with additional information in the form of text and audio. Feasibility testing conducted on elementary school students and teacher showed that the app had an excellent succes rate, with an average score of 98,1% based on a Likert Scale. This result shows that the application succesfully increasses student interest and effectively improves students understanding of the aterial of the five senses.
subject: Articles
  topic: section
subject: Augmented Reality
  topic: keywords
subject: Elementary School
  topic: keywords
subject: Five Sense
  topic: keywords
subject: Learning Media
  topic: keywords
subject: Markless Detection
  topic: keywords
creator: Ilham Wisnu Bachtiar
creator: Cindy Taurusta
creator: Azmuri Wahyu Azinar
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-24
publication date: 2025-06-24
starting page: 81
ending page: 90
doi: 10.32664/smatika.v15i01.1503

Application Types Of Drugs For Young Generation Based On Augmented Reality
type: item
description: Drugs are a serious threat to public health, especially the younger generation. Drug education has been carried out by the national narcotics agency but the reduction is not optimal, so a technology known as Augmented Reality (AR) is needed. The aim of research on educational applications on types of drugs for the young generation based on Augmented Reality is to make it easier for the young generation to understand about drugs and make users more interactive and interesting. This research was developed using the Multimedia Development Life Cycle research method. This method has six stages starting from the concept to the distribution results of the applications that have been created. From the research stages that have been carried out, the results of the Augmented Reality Education application on the types and side effects of drugs were obtained. This application still has limitations on Samsung Android devices. It doesn't support the Easy AR plugin. This application was created using the Markless Bassed Tracking method and uses black box testing with a very good category. This application can be used for education for the younger generation and the wider community.
subject: Articles
  topic: section
subject: 3 Dimensional Model
  topic: keywords
subject: Augmented Reality
  topic: keywords
subject: drugs
  topic: keywords
subject: education
  topic: keywords
subject: Markless Bassed Tracking
  topic: keywords
creator: Achmad Fauzi Makarim
creator: Cindy Taurusta
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-24
publication date: 2025-06-24
starting page: 70
ending page: 80
doi: 10.32664/smatika.v15i01.1497

Implementation of Internet of Thing to Automatically Monitor Water Filling and Usage
type: item
description: This research aims to address efficiency issues in household water management through the implementation of the Internet of Things (IoT) on an automated water reservoir system. The problem identified is the lack of efficient control and monitoring of water replenishment and usage, which often leads to wastage and mismatch with homeowners' needs. This research design adopts an IoT-based approach that enables real-time monitoring and remote control via a web application. The system is also equipped with sensors to detect pipe leaks and monitor water pressure. Analysis of the developed system shows that the device is able to provide accurate and responsive data regarding the condition of the water reservoir, and allows users to control water replenishment as needed. This finding shows a significant improvement in water usage efficiency, as well as reducing potential waste due to leaks and inaccuracies in filling the reservoir. The conclusion of this research is that the designed IoT system can be an effective solution for household water management, providing convenience and better control for users, and has the potential to be widely applied in water management at the home scale.
subject: Articles
  topic: section
subject: Automatic Water Reservoir
  topic: keywords
subject: Internet of Things
  topic: keywords
subject: Leak Detection
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Water Efficiency
  topic: keywords
creator: Yeris Ari Sandi
creator: Heni Sulistiani
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-24
publication date: 2025-06-24
starting page: 60
ending page: 69
doi: 10.32664/smatika.v15i01.1477

Mapping Literature and Research Trends on AI Applications in Journalism: A Bibliometric Review
type: item
description: An emerging field of interdisciplinary research that needs more focus is the application of AI in journalism. The bibliometric analysis of the most recent research on implementation in digital journalism is presented in this paper. From Scopus, publications from 2014 to 2023 were examined to determine subjects, performance, impact, and collaborations. The findings indicate a rise in yearly production, with over 88 publications released in 2023 attesting to heightened research activity. Additionally, more recent publications had a more significant citation effect, demonstrating their relevance today. The majority of the prolific writers are from America, China, and Spain. International collaborations are centered around the West, between the United States and China, and the United States and Korea. This offers a standard that will guide future research in this enormously promising field. For AI to be implemented in digital journalism in a responsible manner, under-represented groups must be more involved.
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: bibliometric
  topic: keywords
subject: journalism
  topic: keywords
creator: Masyitha Nur Shabrina
creator: Nabil Muttaqin
creator: Dio Lingga Purwodani
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-24
publication date: 2025-06-24
starting page: 48
ending page: 59
doi: 10.32664/smatika.v15i01.1467

Simple Additive Weighting Method for Selection of Social Media in Digital Marketing for Culinary MSMEs in Banjarmasin City
type: item
description: Digital marketing has become very important because the world of marketing has changed through advances in information technology. Interactive and integrated marketing that allows market intermediaries, potential consumers and producers to interact with each other. Instagram, WhatsApp, Twitter, Facebook, Telegram, TikTok, YouTube, and Shopee are social media that are often used in digital marketing. In this research, the data used is Banjarmasin City Culinary UMKM data obtained through a questionnaire totaling 174 respondent data which was distributed to the Banjarmasin City Culinary UMKM group. Data obtained from decision support system calculations using the Simple Additive Weighting method shows that many social media that are popular in society may not be suitable for use as digital media for culinary marketing. The results of the Simple Additive Weighting research show that it can be used to help culinary MSMEs in Banjarmasin City in making decisions about the use of social media through social media ranking results. This decision is reviewed based on several criteria such as length of business, length of use of social media, income and age. The length of business is because the longer the business lasts, the more people will know it. The length of use of social media is because the longer people use social media, the more they understand how to use the features and benefits of the features on social media. The greater the income in the business, the faster the business will develop. If you are at a more ideal age for running a business, your business will develop quickly because you have creative ideas and broad insight into business. The results of the Simple Additive Weighting method research show that WhatsApp has an accuracy value of 0.92, while Telegram has an accuracy value of 0.715. The concrete impact of the results of this research is that social media can help increase sales and the Simple Additive Weighting method can be used as a decision maker in …
subject: Articles
  topic: section
subject: Decision Support Systems
  topic: keywords
subject: MSMES
  topic: keywords
subject: Simple Additive Weighting
  topic: keywords
subject: Social Media
  topic: keywords
creator: Rusidah Rusidah
creator: Nurhaeni Nurhaeni
creator: Ahmad Hidayat
creator: Mambang Mambang
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-24
publication date: 2025-06-24
starting page: 37
ending page: 47
doi: 10.32664/smatika.v15i01.1461

Employee Performance Assessment in the Field of Public Infrastructure and Facilities Handling Using the C4.5 Decision Tree Algorithm
type: item
description: One way that should be finished to decide representative execution is to decide the worth that has been gotten equitably founded on the discipline measures of participation, obligation regarding finishing work, and consistence with commitments and denials, utilizing the C4.5 choice tree calculation. The data studied was the Worker Performance Evaluation Form with a total of 108 workers, in 2023-2024 based on certain criteria with the data selected being 96 active workers. This data has several criteria, namely attendance discipline, responsibility for completing work, and compliance with obligations and prohibitions. The main aim of this writing is to determine employee performance from each criterion. The C4.5 decision tree algorithm can assist in the decision-making process regarding improving the performance and training of workers in the field of handling public infrastructure and facilities. You can see the results of the research that has been carried out as training data, the gain and entrophy values have been calculated. In the first partition, the highest gain value was obtained for Compliance with Obligations and Prohibitions with a value of 0.489943 and the smallest was Responsibility for Completion of Work with a value of 0.172824. This algorithm can be used to determine employee performance as a candidate for employee performance which will be determined by the relevant agency.
subject: Articles
  topic: section
subject: C4.5
  topic: keywords
subject: Decision Tree
  topic: keywords
subject: Prestasi Pekerja
  topic: keywords
creator: I Gusti Ngurah Adytya
creator: Budi Sudrajat
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-19
publication date: 2025-06-19
starting page: 24
ending page: 36
doi: 10.32664/smatika.v15i01.1414

Design and Construction of an Environmental Monitoring System in a Cow Shed Based on the Internet of Things
type: item
description: Cows are livestock that are generally kept by the community, especially in rural areas. According to data from the Central Statistics Agency (BPS), in 2021 the beef cattle population reached 17,977,214, an increase compared to the previous year which was recorded at 17,440,393. Environmental factors in cow sheds can directly affect livestock, causing stress due to extreme temperatures, both hot and cold, resulting in decreased feed consumption and discomfort. High levels of ammonia gas from cow dung waste can affect cow resistance to disease and reduce cow productivity and can cause health problems for the surrounding community. This study aims to design an Internet of Things (IoT)-based cow shed environmental monitoring system. The method in this study uses several stages, namely data collection, needs analysis, design, planning and testing. The system created can monitor temperature, humidity and ammonia gas in the cow shed environment and display this information on a mobile application. In the mobile application, information from sensor values can be displayed in high, medium, and low categories during monitoring. The tool testing technique was carried out with 10 samples, the results showed that the temperature detector had an accuracy level of 91.13%, the humidity detector showed a value of 94.52% and the ammonia gas detector showed the highest value of 34.8 ppm at a distance of 10 cm and the smallest of 6.6 ppm at a distance of 100 cm. The results of this study indicate that the developed IoT system is effective in monitoring important parameters such as temperature, humidity, and ammonia gas in real-time. This can increase efficiency for farmers in monitoring environmental conditions in the cage, which was previously done manually.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: cow shed
  topic: keywords
subject: health
  topic: keywords
subject: monitoring system
  topic: keywords
creator: Fendi Untoro
creator: Nurchim Nurchim
creator: Pramono Pramono
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-19
publication date: 2025-06-19
starting page: 14
ending page: 23

Prototype of Weather Monitoring System and Early Rain Warning Based on Internet of Things
type: item
description: The condition of the air at a certain time and at a certain location over a short period of time is called weather. The term "time" refers to a mixture of meteorological factors that change over time. For example, the weather in the morning may be different from the afternoon and late hours. It is very important for humans to monitor, understand, and prepare for weather events. Weather information can be seen from weather information providers such as BMKG, but weather information on BMKG covers a wide area, so local areas such as villages have different weather even though they are in the same district. In addition, each weather information is presented in three-hour intervals and only covers a wide area, and there are no notifications in each area. Therefore, the purpose of this study is to create a prototype of a real-time weather monitoring system as well as an early warning of rain based on the internet of things (IOT) that can be accessed anywhere and anytime and the results are accurate and in accordance with environmental conditions, where the coverage is local areas such as villages so that there is no difference in weather information and early warning notifications. This research method uses the waterfall method with research stages carried out from data collection, needs analysis, design, creation of tools and program codes, and testing. This research produces weather information such as temperature, humidity, air pressure, wind speed, light conditions, and rain conditions, this research also produces weather notifications that can be used as early warnings of rain. The results of the study were also compared with data from BMKG, namely there was a significant difference between sensor values and notifications. This system can also be an alternative as a tool that can be used for weather monitoring and providing early warning notifications.
subject: Articles
  topic: section
subject: blynk
  topic: keywords
subject: early warning
  topic: keywords
subject: internet of things
  topic: keywords
subject: monitoring
  topic: keywords
subject: notification
  topic: keywords
subject: rain
  topic: keywords
subject: weather
  topic: keywords
subject: website
  topic: keywords
creator: Achmad Sholichin
creator: Rudi Susanto
creator: Nurchim Burchim
rights: Copyright (c) 2025 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-06-19
publication date: 2025-06-19
starting page: 1
ending page: 13
doi: 10.32664/smatika.v15i01.1395

Home Page SMATIKA Journal Volume 14 Number 02, Desember 2024
type: item
subject: Front Cover
  topic: section
creator: Siti Aminah
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2025-01-06
publication date: 2025-01-06

Implementation Of The K-Nearest Neighbors (KNN) Algorithm For Malnutrition Prediction
type: item
description: Malnutrition is a serious problem in developing countries, caused by a lack of food intake containing essential substances such as protein and energy. The implementation of machine learning algorithms can provide an accurate diagnosis of malnutrition health conditions in toddlers, facilitating early detection and appropriate interventions. The purpose of this study is to determine the performance of the K-Nearest Neighbors (KNN) algorithm in predicting malnutrition based on clinical characteristics possessed by toddlers. The data used are clinical characteristics of malnutrition sourced from a nutritionist. From the research results, the most optimal accuracy value in predicting malnutrition is 87%. With the existing dataset, it can be proven that the K-Nearest Neighbors (KNN) algorithm is able to classify malnutrition into 2 conditions, namely marasmus and kwashiorkor.
subject: Articles
  topic: section
subject: K-Nearest Neighbors
  topic: keywords
subject: kwashiorkor
  topic: keywords
subject: machine learning
  topic: keywords
subject: malnutrition
  topic: keywords
subject: maramus
  topic: keywords
creator: Dian Hasna Ramadhani
creator: Jumadi Jumadi
creator: Gitarja Sandi
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2024-12-16
publication date: 2024-12-16
starting page: 326
ending page: 336
doi: 10.32664/smatika.v14i02.1360

Decision Support System In Selecting Automatic Motorcycles using the Preference Selection Index (PSI) Method
type: item
description: This research aims to design a Decision Support System for selecting automatic motorbikes using the Preference Selection Index (PSI) Method. In the midst of the rapid development of the automotive industry, especially automatic motorbikes, the PSI method is applied without weighting criteria to help consumers choose motorbikes based on five main criteria. By normalizing data according to the type of cost or benefit criteria, the PSI method offers a simple and effective solution in decision-making processes involving various criteria. The system developed is expected to be able to provide recommendations for automatic motorbikes that suit the user's preferences and needs. The challenge in choosing an automatic motorbike lies in the complexity of considering various important criteria for consumers, such as price, completeness of documents, year of manufacture, condition and authenticity of spare parts. Therefore, this research aims to develop a decision support system that can provide effective motorbike recommendations based on these criteria without weighting. The PSI method is applied to achieve this goal by normalizing data according to the type of cost or benefit criteria, so that it is hoped that the resulting solution can meet the user's preferences and needs in choosing an automatic motorbike.
subject: Articles
  topic: section
subject: Automatic Motorbike
  topic: keywords
subject: Decision Support System
  topic: keywords
subject: Preference Selection Index
  topic: keywords
creator: Afiq Alghazali Lubis
creator: Josua Pinem
creator: Muhammad Agus Syaputra
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2024-12-11
publication date: 2024-12-11
starting page: 227
ending page: 238
doi: 10.32664/smatika.v14i02.1272

Design and Development of a Water Quality Recommendation System Prototype for Nila Aquaculture
type: item
description: In fish farming, specific processes are required for fish intended for food or ornamental purposes. In addition to food and weather, water quality must also be considered. Water quality parameters include ammonia content, water temperature, pH, turbidity, and Total Dissolved Solids (TDS). Poor water quality can result in the presence of toxic compounds, leftover feed, organic materials, and substances that cause diseases in fish. Conversely, good water quality can reduce water turbidity, allowing sufficient sunlight penetration and potentially increasing fish productivity. This study discusses a water quality detection device using five sensors: a temperature sensor, pH sensor, ammonia sensor, TDS sensor, and turbidity sensor, all connected to an Arduino Nano ATmega-328 to read the sensor data. Testing was conducted under five different water conditions: tilapia pond water, clean water, tilapia pond water mixed with clean water, catfish pond water, and tilapia pond water mixed with catfish pond water. The standard deviation for temperature, pH, and ammonia for all water conditions was less than 0.1. The standard deviation for TDS in catfish pond water and tilapia pond water mixed with catfish pond water was less than 1.0, and the turbidity values were below 7.
subject: Articles
  topic: section
subject: ammonia
  topic: keywords
subject: Arduino Nano ATmega-328,
  topic: keywords
subject: PH
  topic: keywords
subject: Rule Based Reasoning
  topic: keywords
subject: Water quality
  topic: keywords
creator: Mochamad Subianto
creator: Ernanda Kusuma Wardhana
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2024-12-16
publication date: 2024-12-16
starting page: 417
ending page: 425
doi: 10.32664/smatika.v14i02.1352

Modeling of a Malaria Parasite Detection System on Microscopic Images of Blood Cells Using Deep Learning Methods
type: item
description: Microscopic examination is the most common malaria examination technique used in health facilities. However, microscopic examination requires special skills and quite a long time. This research aims to develop a malaria parasite detection system model in blood cell images using deep learning technology to increase the accuracy and speed of detection with the Convolutional Neural Network (CNN) algorithm. This research was carried out in several stages: data collection, image preprocessing, dividing training data and validation data, creating a model using CNN, and evaluating the model. A CNN model was created to classify blood cell images into two classes, namely infected and uninfected. The dataset used as a reference in forming a detection system model uses blood cell images from the open-source Kaggle as many as 11.312 images. The CNN model evaluation results obtained an accuracy value of 97.17% in detecting blood cell images. These results show that the CNN model created can be used to detect malaria parasites using blood cell images.
subject: Articles
  topic: section
subject: Convolutional Neural Network
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Malaria
  topic: keywords
subject: Detection System
  topic: keywords
creator: Nurhaeni Nurhaeni
creator: Septyan Eka Prastya
creator: Ahmad Hidayat
creator: Fadhiyah Noor Anisa
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2024-12-16
publication date: 2024-12-16
starting page: 409
ending page: 416
doi: 10.32664/smatika.v14i02.1475

Implementation of Tsukamoto Fuzzy Inference System on Loan Eligibility Determination
type: item
description: Loans are an important financial instrument in modern life for both individuals and business groups. Consideration of prospective borrowers is very important to reduce the risk of default. However, in Gapoktan Makmur Sejahtera the consideration process is still done manually. Therefore, it is necessary to develop a more systematic and measurable approach in assessing the feasibility of granting loans to minimize the risk of default. The data source of this research comes from loan data at the Makmur Sejahtera Farmer Group Association (Gapoktan). This research uses the Tsukamoto Fuzzy Inference System (FIS) method with 3 input variables, namely income, collateral, and character of prospective borrowers. The output variable in this study is eligibility in the form of a percentage of the results of the FIS Tsukamoto calculation. The application in this study was developed with the Python programming language and the Flask framework. The database used in this research is MySQL. The results showed that the application of determining the feasibility of granting loans was successfully made and could help in the process of determining prospective borrowers at Gapoktan Makmur Sejahtera so as to reduce the risk of default.
subject: Articles
  topic: section
subject: FIS
  topic: keywords
subject: Fuzzy
  topic: keywords
subject: Loan
  topic: keywords
subject: Tsukamoto
  topic: keywords
subject: web
  topic: keywords
creator: Evi Nur Hidayah
creator: Sulistyo Dwi Sancoko
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2024-12-16
publication date: 2024-12-16
starting page: 399
ending page: 408
doi: 10.32664/smatika.v14i02.1463

The Use of K-Means and K-Medoids Algorithms for Developing New Student Admissions Promotion Strategies
type: item
description: New student admission is a crucial activity for universities, especially private universities, in obtaining new students. FTI Unmer Malang has implemented various promotional techniques, but still experiences obstacles in achieving student admission targets. The number of new students fluctuates, with a peak in admission in 2019 and a significant decrease of 23% in the last three years. One of the main problems is the lack of information dissemination to remote areas. To overcome this problem, this study applies a data mining method with clustering to group new student data based on their area of origin. Two clustering algorithms, namely K-Means and K-Medoids, are used to compare clustering results to find the optimal promotion strategy. The data used includes new students from the 2016 to 2022 academic years. The results of the study show that the K-Means algorithm shows better performance than the K-Means algorithm with DBI index accuracy level of 0.344. The results of the study are expected to help FTI Unmer Malang in determining a more effective promotion strategy based on the student's area of origin.
subject: Articles
  topic: section
subject: K-Mean
  topic: keywords
subject: K-Medoids
  topic: keywords
subject: New Student Admissions
  topic: keywords
subject: Promotion Strategies
  topic: keywords
creator: Devita Maulina Putri
creator: Asri Samsiar Ilmananda
creator: Nadita Prisanta
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2024-12-16
publication date: 2024-12-16
starting page: 388
ending page: 398
doi: 10.32664/smatika.v14i02.1474

Implementation of Web-Based Accounting Information System at Military Institutions in Kolatmar Puslatmar-8 Teluk Ratai
type: item
description: This research aims to design a web-based Accounting Information System (SIA) to optimize cash book management in Kolatmar Puslatmar-8 Teluk Ratai. The method used is Rapid Application Development (RAD) which consists of four stages: needs planning, user design, construction, and cutover. The system was developed using PHP as a programming language and MySQL as a database management system. Research results show that the web-based SIA designed has features such as transaction data management, categorization, financial reporting, and debt-debt management. The system uses a three-tiered access structure consisting of the Treasury Staff, Administrator, and Chief. Black box testing shows that the system has met the basic needs of Kolatmar Puslatmar-8 Teluk Ratai in cash book management. Implementation of this system has the potential to improve efficiency and accuracy in the financial management of institutions. However, further development and testing are needed to ensure the reliability, security, and scalability of the system in the long term.
subject: Articles
  topic: section
subject: Accounting Information Systems
  topic: keywords
subject: Cash Book
  topic: keywords
subject: Kolatmar Puslatmar-8 Teluk Ratai
  topic: keywords
subject: Rapid Application Development
  topic: keywords
subject: Web-based
  topic: keywords
creator: Yosi Khoirunnisa
creator: Heni Sulistiani
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2024-12-16
publication date: 2024-12-16
starting page: 375
ending page: 387
doi: 10.32664/smatika.v14i02.1410

Emotion Detection on Facial Images Using Deep Learning as a Support Tool for Therapy for Individuals with Alexithymia
type: item
description: Alexithymia is a condition characterized by difficulty in identifying and verbally expressing emotions, which can hinder an individual's ability to understand and manage their emotions. This study aims to implement and develop a model that can detect emotions using the MobileNetV2 architecture for therapy purposes for individuals experiencing alexithymia. The method uses the FER-2013 dataset, which consists of 35,887 grayscale facial images in 7 emotion categories: anger, disgust, fear, happiness, neutral, sadness, and surprise. Using a deep learning approach based on CRISP-DM, the research begins with normalization and data augmentation to improve the model's resilience to image variations. The developed model achieved a training accuracy of 67.7% and a validation accuracy of 65.3%, demonstrating significant capability in recognizing and classifying emotions from facial images. Evaluation using a confusion matrix showed that the model produced a precision of 64.9%, a recall of 65.4%, and an F1-score of 63.7% for each emotion class. This research implies the potential for developing systems that can support psychological therapy, especially to help individuals with alexithymia understand and manage their emotions through facial expression analysis, providing technology sensitive to emotional expressions.
subject: Articles
  topic: section
subject: Alexithymia
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Emotion Detection
  topic: keywords
subject: FER-2013
  topic: keywords
subject: MobileNetV2
  topic: keywords
creator: Alfin Yogi Setyawan
creator: Jumadi Jumadi
creator: Eva Nurlatifah
rights: Copyright (c) 2024 SMATIKA JURNAL : STIKI Informatika Jurnal
date: 2024-12-16
publication date: 2024-12-16
starting page: 353
ending page: 362
doi: 10.32664/smatika.v14i02.1368