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
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Avocado Ripeness Classification Based on Digital Imagery Using an Artificial Neural Network
Traditional methods for determining avocado ripeness rely primarily on subjective visual observation, which is highly prone to error. This study aims to develop an automated classification system for avocado ripeness using a sequential Artificial Neural Network (ANN) based on digital image data. The dataset consisted of 1,500 balanced avocado images distributed across three classes: Ripe (500 images), Rotten (500 images), and Unripe (500 images). A total of 1,200 images were used for training and 300 images for validation. Images were preprocessed through resizing to 128 × 128 pixels and pixel intensity normalization. The proposed ANN architecture consisted of a Flatten layer, two hidden Dense layers with ReLU activation, and an output layer with Softmax activation. Experimental results showed that the model achieved an overall accuracy of 89.00%, with macro-average Precision, Recall, and F1-Score values of 0.92, 0.89, and 0.89, respectively. The best classification performance was achieved for the Rotten class, with a Precision of 1.00 and a Recall of 0.96. Classification errors mainly occurred between the Unripe and Ripe classes, where visual similarities during the ripening transition stage led to cross-class predictions. Overall, the proposed ANN model demonstrated reliable performance for avocado ripeness classification using digital image data and showed its potential as a simple image-based decision-support tool for post-harvest quality assessment.
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Implementation of K-Means Clustering with Attribute Adjustment and Cluster Validation in a Web-Based TPQ Student Assessment Information System
This study aims to implement K-Means Clustering with attribute-level adjustment in a web-based TPQ student assessment information system and evaluate the quality of the resulting clustering results. The study was conducted at TPQ Ashfiya' Kauman Ngoro Jombang using a dataset of 40 students at the Al-Qur'an level obtained from learning assessment data. The variables used include fashohah, tajwid, ghorib and musykilat, as well as voice and melody. The clustering process was carried out using the K-Means algorithm to group students based on similarity in scores, while attribute adjustment was applied in the system design to prevent unused assessment components from being interpreted as low ability scores. Evaluation of cluster quality was carried out using the Sum of Squared Error (SSE), Silhouette Score, and Davies-Bouldin Index (DBI). Based on the evaluation results, K=3 was selected because it formed an interpretable grouping structure and was supported by the Elbow Method. The results show that the application of the K-Means method in the information system can help group students' abilities in a more structured and objective manner and support assessment data management by TPQ teachers. However, the results of this study are a case study in the TPQ environment studied and have not been intended for generalization to a wider population.
Web-Based Attendance System Using Arcface, GPS Validation and Liveness Detection
The development of digital attendance systems requires authentication mechanisms that are not only flexible but also secure against identity and location manipulation. Conventional fingerprint-based attendance systems have limitations in supporting employee mobility and still require dedicated hardware at each workplace location. Existing online attendance systems generally implement GPS validation or face recognition independently and often lack mechanisms to prevent identity spoofing attacks. In addition, computationally intensive models may reduce system efficiency in real-time web environments. Therefore, this study proposes a web-based attendance system that integrates lightweight facial authentication, GPS-based geofencing validation, and passive liveness detection to improve both security and computational efficiency.This study aims to develop a web-based attendance system integrating ArcFace-based facial authentication, GPS-based geofencing validation, and Passive Liveness Detection using MiniFASNet. Experimental evaluations were conducted to assess authentication accuracy, computational efficiency, and spoofing detection capability. The results showed that MobileFaceNet achieved an authentication accuracy of 95.00% with an average inference time of 0.0216 seconds per face. In addition, the implemented liveness detection mechanism effectively detected most spoofing attempts involving photographs and replay videos. The system also achieved a False Acceptance Rate (FAR) of 0% under the evaluated experimental conditions and successfully fulfilled all functional requirements. Therefore, the proposed system provides a secure, efficient, and centralized attendance solution suitable for organizations with high employee mobility.
Real-Time Crowd Counting for Smart City Park Monitoring Using YOLOv11 and a Microservices Architecture
The provision of accurate and easily accessible public information regarding the condition of urban parks remains a challenge in the management of public open spaces. People still do not have a system that allows them to monitor park crowd levels directly, making real-time observation of park conditions difficult. Previous studies on crowd counting have mainly focused on improving object detection performance, while the implementation of crowd monitoring systems as public information services remains limited. Therefore, this research integrates the YOLOv11 algorithm with a microservices architecture to provide real-time crowd information through a public park monitoring website. This research develops a park visitor counting information system based on computer vision using the YOLOv11 algorithm to detect and count crowds from CCTV video streams. The system is designed using a microservices architecture with the FastAPI framework to support real-time detection and data integration into the Surabaya park monitoring website. The research process involves several stages, including dataset preparation, data labeling using Roboflow, YOLOv11 model training, selection of the most optimal optimizer, and implementation of the system on the detection backend. The results show that the YOLOv11m model with the SGD optimizer achieved the best performance, obtaining an mAP@50 score of 92.76%, a recall value of 89.75%, and an F1-score of 90.07%. In addition, the system successfully performed real-time crowd detection and counting under various crowd density levels, lighting conditions, and CCTV camera angles.
Improving the Performance of Convolutional Neural Networks (CNN) in Identification of Agricultural Plant Diseases
This study aims to improve the performance of the Convolutional Neural Network (CNN) algorithm in detecting coffee leaf diseases using the Inception V3 architecture. The main challenge of this classification is the subtle visual similarity in color, texture, and symptom patterns between diseases. To overcome this, Inception V3 is implemented because of its superiority in multi-scale feature extraction through convolution factorization which reduces parameters while increasing accuracy. The dataset used consists of 1,120 images, evenly distributed into four classes (three types of diseases and one healthy class, each with 280 images), with a training, validation, and test data split ratio of 896:112:112. As a comparison, a conventional basic CNN architecture consisting of 3 convolution layers (3 X 3, stride 1), 3 max-pooling, and 1 dense layer, trained with the same hyperparameters (Adam optimizer, learning rate 0.001, batch size 32) is used. The experimental results show a significant performance improvement; Model accuracy increased from 74.4% on a standard CNN to 97.0% after integrating Inception V3. The scientific contribution of this research lies in mapping overlapping visual characteristics of coffee diseases through multi-scale feature optimization, which demonstrates that computational efficiency can go hand in hand with accuracy improvements on complex agricultural image datasets. These findings confirm that the Inception V3 architecture provides a robust and efficient solution for automating plant disease diagnosis in the field.
Implementation of Ridge Regression and SHAP for Analyzing Anxiety Levels Based on the Digital Behavior of Social Media Users
Advances in technology have led to an increase in the frequency of smartphone, social media, and various digital app usage in daily life. These digital activities give rise to various digital behaviors—such as screen time, number of notifications, social media usage, and sleep patterns—which can be evaluated to understand users mental health. High digital device usage is suspected to be associated with increased anxiety levels among users. Therefore, this study aims to analyze the impact of digital behaviors on anxiety levels by utilizing explainable machine learning approaches and AI. This study uses the dataset, comprising 500 data points obtained from Kaggle. The research stages consist EDA, feature engineering, target leakage evaluation, comparison of several machine learning algorithms, cross-validation, and model interpretation using SHAP. Based on the EDA results, the variables social_media_time_min, notification_count, and digital_addiction_score showed a positive relationship with anxiety_level. During the model-building process, the variable digital_wellbeing_score was removed because it had a very high correlation with anxiety_level -0.84, which could lead to potential target leakage. The algorithm comparison revealed that the Ridge Regression model performed best compared to Random Forest, SVR, and XGBoost Regression, with an R² score of 0.221 and an RMSE of 1.627. Additionally, the results of 5-Fold Cross Validation showed an average R² Score of 0.158 with a standard deviation of 0.062, indicating that the model demonstrated fairly consistent performance. The SHAP interpretation reveals that notification_count and social_media_time_min are the variables that most strongly influence the prediction of anxiety_level. The results of this study indicate that digital behavior affects users anxiety levels, although the relationships among the variables remain quite complex. This study also emphasizes the importance of evaluating the target leakage and understanding the model in the development of machine learning-based mental health analysis to ensure that the prediction results are more objective and clear.
Implementation of the MARCOS Method and Entropy Weighting for Selecting the Best Teacher at Madrasah Ibtidaiyah
Selecting the best teacher in a madrasah requires an assessment mechanism that is transparent, reproducible, and able to process several performance criteria simultaneously. This study evaluates 20 active teachers at Madrasah Ibtidaiyah Al-Huda using five benefit criteria: attendance, pedagogical competence, personality competence, social competence, and professional competence. The criterion weights were calculated objectively using Shannon Entropy, and the alternatives were ranked using the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The Entropy calculation produced weights of 0.011 for attendance, 0.266 for pedagogical competence, 0.266 for personality competence, 0.245 for social competence, and 0.212 for professional competence. The MARCOS results show that Wasilul Gaffar obtained the highest utility value of 0.783 and ranked first. Sensitivity testing and Simple Additive Weighting (SAW) comparison indicate that the leading alternative remains stable under several weighting scenarios. The findings show that the integration of Shannon Entropy and MARCOS can support evidence-based teacher performance evaluation in a small madrasah context without relying solely on subjective judgment.
Usability Evaluation of RAD-Based Production Monitoring Systems in Automotive Manufacturing Using System Usability Scale Method
The rapid adoption of digital monitoring tools in the automotive sector often prioritizes technical deployment speed over user-centric design, leading to potential operational inefficiencies. This study evaluates the usability of a production monitoring system developed through Rapid Application Development (RAD) at an automotive manufacturing plant. Utilizing a purposive sampling of 20 active users comprising 15 shop-floor operators and 5 supervisors. This research employs the System Usability Scale (SUS) to quantify user experience post-implementation. Data integrity was rigorously verified through SPSS, yielding a Cronbach’s Alpha of 0.842, which confirms the high reliability of the evaluation instrument. The findings reveal an overall average SUS score of 71.25, categorized as Acceptable with a Good adjective rating and a Grade C classification. Despite this favorable total score, a notable disparity exists between user groups; supervisors rated the system at 74.0, while operators, who manage high-frequency data entry, provided a lower average of 68.5. This result underscores a usability friction inherent in rapidly developed systems that may hinder long-term industrial sustainability. The study concludes that while the RAD-based architecture meets professional functional standards, iterative refinements focusing on reducing interface complexity for operators are essential to minimize human error and optimize production performance.
Design of a Web-Based QR Code Attendance System with Real-Time Notifications at Darul Arqom Junior High School Karanganyar
The rapid development of information technology has encouraged educational institutions to implement digital transformation in administrative management, including student attendance systems in Islamic boarding schools. SMP Darul Arqom Karanganyar still uses a manual paper-based attendance system that often causes recording errors, delays in attendance recapitulation, difficulties in monitoring student attendance, potential data manipulation, and delays in delivering attendance information to parents. These problems indicate that the attendance management process is inefficient and requires a more integrated digital solution. This study aims to design and develop a web-based attendance system using QR Code technology integrated with real-time notifications to improve the efficiency, accuracy, and transparency of student attendance management. The novelty of this research lies in integrating QR Code-based attendance validation with automatic real-time notifications for parents in a system specifically designed for the Islamic boarding school environment. Unlike previous attendance applications that mainly focus on digital attendance recording, the proposed system emphasizes attendance transparency, fraud prevention, and direct communication between schools and parents. The system was developed using the Agile Development method through iterative sprint-based stages, including planning, design, development, testing, evaluation, and implementation. The system utilizes the CodeIgniter framework, MySQL database, and QR Code technology for attendance validation. The results show that the system accelerates the attendance process, minimizes fraudulent attendance practices, improves attendance data accuracy, and assists administrators in monitoring attendance data efficiently. In addition, the system increases information transparency through direct real-time notifications to parents. Therefore, the system can support administrative digitalization and student discipline management in Islamic boarding schools.
Performance Evaluation of Naive Bayes and SVM in Classifying Public Opinion toward Game-Based Learning Policy
This study aims to analyze public sentiment toward the EDUBLOX program on Instagram comments using the Naive Bayes and Support Vector Machine (SVM) algorithms. The research process consisted of data collection, text preprocessing, manual sentiment labeling, feature extraction using TF-IDF, model training, and performance evaluation. The labeling process was conducted manually by two independent annotators based on predefined sentiment guidelines, and annotation reliability was evaluated using Cohen’s Kappa coefficient. The obtained Cohen’s Kappa value was (\kappa = 0.7907), indicating substantial agreement and good consistency between annotators. The dataset was divided into training and testing data using an 80:20 ratio. The evaluation process used confusion matrix metrics, including accuracy, precision, recall, and F1-score, while the McNemar test was applied to determine whether the performance difference between the two models was statistically significant. The results showed that the SVM model achieved a testing accuracy of 76.67%, marginally outperforming the Naive Bayes model with a testing accuracy of 75.29%. In addition, SVM demonstrated slightly better precision, recall, and F1-score values compared to Naive Bayes. However, the McNemar test produced a p-value of 0.1366, indicating that the performance difference between the two algorithms was not statistically significant. Therefore, both models can be considered to have relatively comparable classification capabilities, although SVM showed a slight numerical advantage in sentiment classification performance on Instagram comments related to the EDUBLOX program.
Development of a Digital Signature System for Electronic Certification Services Based on Public Key Infrastructure
Digital transformation in the modern era shifts documents from physical to electronic formats, improving administrative efficiency but simultaneously introducing new vulnerabilities like identity fraud, content manipulation, and signature repudiation. To mitigate these risks, this research successfully designs a robust digital certification service system ensuring data integrity, sender authenticity, and non-repudiation. The methodology adopted an experimental observational approach through software development based on the Waterfall model. It was evaluated using thirty PDF certification documents on a server environment running Go 1.26, an AMD Ryzen 7 4700 processor, and 16 GB RAM. The system architecture integrates a React.js frontend for visual interaction and a Golang backend focused purely on cryptographic computation. Its security infrastructure employs a Public Key Infrastructure (PKI) model, integrating the 2048-bit RSA algorithm for asymmetric key encryption and SHA-256 for document identity hashing. Testing revealed the signature placement mechanism succeeded with an average single-signature execution time of 8.05 ms, displaying zero failures across all documents. Employing binary byte structure analysis and the Force Binary Download method successfully eliminated false positive anomalies caused by browser metadata modifications, which were independently validated via OpenSSL and Python scripts in Google Colab. In conclusion, this electronic certification service platform effectively facilitates real-time document verification, provides absolute protection of data integrity, and ensures the validity of authentication in accordance with Indonesian legal standards for electronic transactions.
Implementation of Naive Bayes Method and Natural Language Processing in Web-Based Online Hoax News Detection System
The rapid growth of digital media in Indonesia has accelerated the spread of hoax news, which poses serious threats to public trust and social stability. Based on data from the Ministry of Communication and Informatics of the Republic of Indonesia, a total of 12,547 hoax contents were identified from 2018 to 2023, while 1,923 new hoax contents emerged in 2024 alone. This research aims to design and build a web-based hoax news detection system by implementing the Multinomial Naive Bayes algorithm combined with Natural Language Processing (NLP) techniques. The system processes text through five NLP stages: sentence splitting, case folding, tokenizing, stopword removal, and stemming using PySastrawi. Feature weighting is performed using TF-IDF (Term Frequency–Inverse Document Frequency), and classification is executed using the Multinomial Naive Bayes algorithm enhanced with Laplace Smoothing and Log Posterior Probability. The output is converted using the Softmax function to produce probability percentages for each sentence. A manual calculation simulation using 10 training sentences and one test narrative containing four sentences was conducted to verify the algorithm. The system successfully classified the test narrative as hoax with a probability of 62.00% hoax and 38.00% non-hoax, consistent with the actual label. The system was evaluated using a Confusion Matrix on a 50-sentence test dataset, achieving Accuracy of 90.00%, Precision of 91.67%, Recall of 88.00%, and F1-Score of 89.80%. The resulting system provides a practical tool for the public to verify the credibility of news information in the digital era.
YOLOv11-Based AIoT System for Automated Size Detection and Counting of G0 Seed Potatoes Using MQTT Protocol
The increasing demand for G0 seed potatoes in Indonesia, reaching approximately 143,740 tons in 2021 while only 8.6% of the demand could be supplied, highlights the need for more efficient production and monitoring systems at the early stage of the potato seed supply chain. Current manual sorting and counting processes are labor-intensive, time-consuming, and prone to human error, creating a need for an automated and reliable monitoring solution. This study develops and implements an Artificial Intelligence of Things (AIoT) system based on the YOLOv11n object detection algorithm for real-time detection, size classification, and counting of G0 seed potatoes. The proposed system integrates a conveyor belt, a Logitech C922 Pro USB webcam for image acquisition, and a laptop as the edge computing unit running the YOLOv11n model. Detection results are transmitted through the MQTT protocol to a Node-RED dashboard for real-time remote monitoring. Unlike conventional approaches, the system combines a lightweight YOLOv11n model with MQTT communication to support simultaneous multi-category size classification and synchronized dashboard visualization. Detected potatoes are classified into three size categories (small, medium, and large) based on calibrated bounding-box pixel areas validated with potato farmers. The model was trained and evaluated using four epoch configurations (25, 50, 75, and 100 epochs) with Precision, Recall, F1-score, mAP@0.5, and mAP@0.5–0.95 as evaluation metrics. The 100-epoch model achieved the best performance, with precision approaching 1.00, recall of approximately 0.98, mAP@0.5 of 0.986, and mAP@0.5–0.95 of 0.96. Validation confirmed that calibrated geometric measurements matched the physical potato dimensions, while dashboard data were fully consistent with edge-computing outputs. These findings demonstrate that the proposed YOLOv11n-based AIoT system provides accurate, reliable, and real-time monitoring of G0 potato production, offering a practical solution to improve operational efficiency and data accuracy in Indonesia's potato seed supply chain.
Design and Implementation of an ESP32-Based Smart Home Electricity Monitoring and Control Module Using MQTT Dash and Telegram Integration
This study presents the design and implementation of an ESP32-based smart home box module that integrates multi-channel load control, real-time electrical energy monitoring, and automated user reporting within a single compact and modular system. The proposed module employs an eight-channel relay for household appliance control using the MQTT communication protocol, with MQTT Dash providing real-time visualization and manual control. Electrical parameters, including voltage, current, active power, and cumulative energy consumption (kWh), are measured using a PZEM-004T sensor and transmitted continuously via MQTT. The main contribution of this work lies in the unified system architecture that consolidates device control, energy measurement, and user interaction without reliance on proprietary cloud platforms. In addition to dashboard-based monitoring, a Telegram bot is integrated to deliver real-time system status notifications, on-demand energy reports summarizing average power and total consumption, and fully automated 24-hour periodic reports. This dual interaction mechanism enables both active real-time supervision and passive long-term monitoring, addressing limitations commonly found in prior IoT smart home implementations. Experimental evaluations confirm that the proposed system operates reliably, responsively, and with acceptable measurement accuracy for residential applications. All relays on the eight-channel module respond stably to ON/OFF commands from the MQTT Dash interface with delays of less than one second, ensuring dependable real-time control. The PZEM-004T-based monitoring subsystem provides voltage, current, power, and energy data with power measurement errors ranging from 0.83% to 2.66%, which is suitable for daily household energy monitoring. Tests under various single and combined load conditions demonstrate consistent detection of power variations. Overall, the results indicate that the proposed smart home box module provides a practical, low-cost, and scalable solution for integrated IoT-based residential energy control and monitoring.
Design and Development of a Web-Based E-Survey System with Speech-to-Text Feature Using the Waterfall Method
Web-based electronic survey systems are widely used in higher education due to their efficiency and flexibility in data collection. However, many existing survey platforms still rely on conventional text-based input and provide limited accessibility support for users with disabilities. This study aims to develop a web-based E-Survey system integrated with a speech-to-text feature to improve accessibility and support inclusive participation in digital survey activities. The system was developed using the System Development Life Cycle (SDLC) with the Waterfall method, covering requirements analysis, system design, implementation, and testing. Accessibility considerations were based on the Web Content Accessibility Guidelines (WCAG) 2.1, while speech-to-text functionality was implemented using Automatic Speech Recognition (ASR) technology. System evaluation included black box testing, WCAG-based accessibility assessment, and speech-to-text performance testing using Accuracy and Word Error Rate (WER) metrics. The results indicate that all selected accessibility criteria evaluated in this study were successfully implemented and were consistent with the selected WCAG 2.1 principles assessed through developer-based inspection. The speech-to-text feature achieved an accuracy rate of 85.71% and a Word Error Rate (WER) of 5.41%. These findings demonstrate that the proposed system provides a more accessible and inclusive survey environment by enabling respondents to complete surveys through text or voice input, while highlighting the potential of speech-to-text technology in supporting inclusive participation in higher education.
Cybersecurity Awareness and Community Response to Phishing Threats: An Indonesian Social Behavior Analysis
This research discusses the increased levels of connectivity and digital engagement experienced by the people of Indonesia due to the development of digital technology as well as the risks associated with the growing prevalence of phishing cyber crimes in the country despite having the third-highest population of internet users globally. Although the level of digital literacy varies across different regions in the country, younger users in Indonesia appear to be the most vulnerable to phishing attacks. In view of the above, this paper seeks to identify and explore the behavioral dimensions that determine vulnerability to phishing amongst young internet users in Indonesia through a qualitative exploration of behavioral exposure towards such cyber crime. The research employs a quantitative cross-sectional survey method using structured questionnaires provided to 53 participants in their teens who were frequently involved in using digital platforms for purposes such as engaging on social media, making purchases from e-commerce sites, and executing payments via digital platforms, all chosen using a purposive sample method. Validity and reliability were measured using the Principal Component Analysis, the Kaiser-Meyer-Olkin validity test, Bartlett's Test of Sphericity and Cronbach's Alpha test respectively. Validity tests produced a KMO measure of 0.587 with a statistically significant Bartlett test result (χ²=71.940, df=10, p<0.001). Using PCA, two factors contributing 70.997% cumulative variance were identified with Eigenvalue values of 2.299(45.98%) and 1.251(25.02%). The first factor measures active reception of links via digital platforms and the second measures exposure to bypassing attempts via OTP channels. The four-item scale had a Cronbach Alpha of 0.743 implying acceptable internal consistency.
Design and Development of a Web-Based Audit Clearance Letter Application System Using PHP Framework at the Karanganyar Inspectorate
This research aims to design and develop a web-based Audit Clearance Letter application system at the Karanganyar Regency Inspectorate using the Laravel framework and MySQL database. The previous manual administrative process caused delays in verification, difficulties in tracking application status, and risks of data recording errors. This study adopted the Research and Development (R&D) methodology in conjunction with the Waterfall development model, which encompasses five sequential phases: requirements specification, system design, implementation, verification, and operational maintenance. The architectural foundation of the system was established upon the Model-View-Controller (MVC) pattern, selected to ensure a more organized, secure, and sustainable application structure. The results indicate that the system successfully integrates application submission, document upload, verification, letter issuance, and real-time status tracking within a single digital platform. Black Box Testing conducted on 14 testing scenarios achieved a 100% success rate and improved administrative efficiency from 2–3 working days to less than 1 working day in the verification and document monitoring process. In addition, User Acceptance Testing (UAT) achieved a User Satisfaction Index (USI) score of 88.2%, categorized as “Very Satisfactory.” The novelty of this research lies in the integration of a web-based Audit Clearance Letter administrative service specifically designed to improve transparency and efficiency in government supervisory institutions.
Comparative Analysis of Transfer Learning-Based Deep Learning Models for Jatropha Leaf Disease Classification
Plant disease identification is essential for enhancing agricultural productivity and promoting sustainable crop management practices. Jatropha curcas has considerable potential as a biofuel-producing plant; however, its growth and productivity can be significantly affected by various leaf diseases. Conventional disease diagnosis often requires substantial time and relies heavily on expert knowledge, creating a need for automated solutions based on deep learning techniques. Although deep learning has been widely applied in plant disease recognition, comparative studies focusing on transfer learning models for Jatropha leaf disease classification remain limited, particularly for datasets characterized by distinctive visual features and relatively small sample sizes. This research conducts a comparative assessment of several deep learning architectures to determine the most effective model for classifying Jatropha leaf diseases. The evaluated architectures include MobileNetV2, EfficientNetB0, ResNet50, DenseNet121, and VGG16. All models utilized ImageNet pre-trained weights and were adapted through fine-tuning of the final classification layers to accommodate a dataset containing healthy and diseased Jatropha leaf images. Experimental findings reveal that ResNet50 achieved the highest classification accuracy of 93.81%, followed by VGG16 at 93.58% and EfficientNetB0 at 90.49%. In comparison, DenseNet121 and MobileNetV2 attained accuracies of 85.40% and 74.56%, respectively. Model effectiveness was assessed using accuracy, training duration, confusion matrix analysis, and ROC curve evaluation to examine classification capability across categories. The results demonstrate that ResNet50 offers the most balanced combination of predictive accuracy and performance stability. Overall, the study confirms that transfer learning-based deep learning models are highly effective for Jatropha leaf disease classification, with ResNet50 emerging as the most suitable architecture among those investigated. These findings may serve as a valuable reference for the development of reliable and efficient plant disease detection systems in agricultural environments.
Segmentation Analysis of Tourist Visits Using K-Means Algorithm at the Kutai Kartanegara Tourism Office
Kutai Kartanegara Regency possesses abundant and diverse tourism potential that can contribute significantly to regional economic development. Nevertheless, tourism visitation data has generally been utilized only for administrative documentation and reporting purposes, limiting its role in supporting strategic decision-making. Therefore, a data-driven approach is required to identify the characteristics and performance of tourism destinations more effectively. This study aims to analyze and segment tourism destinations in Kutai Kartanegara Regency using the K-Means Clustering algorithm. The research employed secondary data obtained from the Kutai Kartanegara Tourism Office covering the period from 2023 to 2025. Three variables were used in the clustering process, namely total domestic tourist visits (Wisnus), total international tourist visits (Wisman), and seasonal visitation fluctuations. Prior to clustering, the data underwent preprocessing and normalization to improve clustering performance. The Elbow Method was applied to determine an appropriate number of clusters, resulting in a three-cluster solution representing high-, medium-, and low-visitation categories. The clustering process successfully grouped 56 tourism destinations into 28 destinations in the medium-visitation cluster, 12 destinations in the high-visitation cluster, and 16 destinations in the low-visitation cluster. Furthermore, model evaluation using the Silhouette Score produced a value of 0.4288, indicating a moderate and acceptable level of cluster quality. The findings provide a comprehensive overview of tourism destination performance and can support policymakers in prioritizing destination development, improving resource allocation, and formulating more targeted tourism promotion strategies to enhance regional tourism competitiveness.
Development of an Android Based Personal Financial Management Application for Financial Monitoring and Student Expense Notifications
This development was carried out because many students still experience difficulties in managing their finances, resulting in waste and uncontrolled financial conditions. This is usually caused by a lack of awareness in financial recording and the absence of adequate tools to monitor income and expenses regularly. As a result, students often experience difficulties in meeting daily needs or sudden needs. The purpose of this research is to develop an Android-based personal financial management application to help students manage their finances better, in a structured, and efficient manner. With this application, it is hoped that students can make financial recording easier and more structured. The research was conducted by collecting data through observation and interviews to identify user needs in managing finances and understand the problems often faced by students in daily financial activities. The method used in this development is the Agile method, using the Scrum framework, which allows the development process to be carried out in stages and flexibly so that it can adapt to user needs. This application was developed on the Android platform by utilizing Firebase Authentication which is used as a security system to maintain the confidentiality of user data, while the Firebase Cloud Firestore service helps in data storage so as to reduce the risk of data loss. Black Box Testing showed that all application features functioned successfully according to system requirements. Based on usability testing using the System Usability Scale (SUS) involving 22 respondents, the application obtained a score of 79.09 which indicates that the application has good usability and is acceptable to users. Based on these results, the developed application can help students better manage their finances.
Design and Evaluation of a Virtual Reality Learning Game for Interior Design Styles
Public understanding of interior design styles is still limited because conventional two-dimensional (2D) media cannot adequately represent spatial depth, perspective, and the distinguishing characteristics of each style. This limitation often leads to misconceptions when identifying interior design concepts and elements. Therefore, this study aims to design and evaluate a Virtual Reality (VR)-based learning game as an immersive medium for introducing interior design styles. The system was developed using the Mechanics, Dynamics, and Aesthetics (MDA) framework and implemented in Unity with the XR Interaction Toolkit for the Meta Quest 2 platform. The application includes five interior design styles—modern, minimalist, industrial, natural, and rustic—and provides interactive tasks that allow users to explore, manipulate, and apply interior elements in a virtual environment. To evaluate its effectiveness, a pretest–posttest experiment was conducted with 30 participants. The results show that the average score increased from 31.25% in the pretest to 56.25% in the posttest, indicating an improvement of 25 percentage points. A paired-samples t-test also showed a statistically significant improvement, with p < 0.001. These findings suggest that the proposed VR learning game has the potential to improve users’ understanding of interior design styles.
Sentiment Analysis of User Reviews on the Bus Simulator Indonesia Application Using Machine Learning Approaches
User reviews on the Google Play Store serve as a primary source of information for assessing the quality and satisfaction levels of an application. As expectations for game quality and realism continue to rise, Bus Simulator Indonesia still faces a range of technical issues — including bugs, glitches, visual disturbances, and suboptimal controls — that directly impact user comfort and overall experience. This study aims to classify the sentiments expressed by Bus Simulator Indonesia users into three categories: positive, neutral, and negative. In addition, it compares the performance of three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. The research pipeline consists of data collection, text preprocessing, word weighting, modeling, and model evaluation. The dataset comprises 19,482 Indonesian-language reviews gathered from the Google Play Store between 2021 and 2025, evaluated across three data-splitting scenarios: 90:10, 80:20, and 70:30. The results show that Support Vector Machine consistently achieved the highest accuracy across all scenarios, recording 84.3%, 82.0%, and 81.0%, with weighted F1-score values of 0.83, 0.82, and 0.81, respectively, indicating a more stable classification performance. Logistic Regression followed with accuracies of 81.1%, 79.9%, and 79.0%, with weighted F1-score values of 0.79, 0.78, and 0.77, respectively, while Naive Bayes produced the lowest scores overall. These findings confirm that Support Vector Machine is the most effective algorithm for sentiment classification of Bus Simulator Indonesia user reviews.
Sentiment analysis on public opinion trends on #MakanBergiziGratis programs on platform X using Long Short-Term Memory (LSTM) networks
The Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) has sparked diverse public reactions on X (formerly Twitter), making it important to understand sentiment dynamics at scale. This study analyzes sentiment in 5,516 Indonesian-language tweets collected between January to November 2025 using a hybrid embedding of Bidirectional Encoder Representations from Transformers (BERT) with a Long Short-Term Memory (LSTM) model. Initial labeling using a lexicon-based approach reveals an imbalanced distribution (positive 34.34%, neutral 60.80%, negative 4.86%). To address this issue, the Synthetic Minority Over-sampling Technique (SMOTE) is used to build a synthetic representation for minority classes in the embedding space. This approach helps improve class balance while maintaining the overall data representation. The proposed model achieves good performance, with 82.98% accuracy, 83.27% precision, 82.98% recall, and an F1-score of 83.01%. Cross-validation indicates relatively consistent performance, with accuracy ranging from 80.51% to 84.41%. Despite these results, the negative class remains challenging (F1-score 0.57), highlighting the impact of linguistic complexity, including implicit and nuanced expressions in social media text. Overall, the findings suggest that integrating BERT embeddings with LSTM and feature-level SMOTE can be a suitable approach for handling imbalanced sentiment classification tasks. However, further improvements, particularly in advanced transformer fine-tuning and deeper linguistic modeling, are needed to better capture subtle sentiment patterns.
Development and Evaluation of an E-learning System using the CodeIgniter framework to Support Informatics Learning at SMA Negeri 1 Tanjung Raja
This study aims to develop and evaluate a web-based e-learning system to support Informatics learning at SMA Negeri 1 Tanjung Raja. The research employed a Research and Development (R&D) approach using the Waterfall model, consisting of requirements analysis, system design, implementation, testing, and maintenance. The system was developed using the CodeIgniter 4 framework with the Model–View–Controller (MVC) concept. Functional testing was conducted using the Black Box Testing method, executed on 13 different test scenarios by three expert validators with information technology education lecturers with backgrounds in software engineering. All 13 test items were evaluated independently by each validator, resulting in a 100% pass rate (13 out of 13 items were functional in the third validator). Usability testing using the System Usability Scale (SUS) instrument with 30 respondents comprising 29 Grade X students and 1 Informatics teacher, resulted in an average SUS score of 89, which is classified as Very Good. These findings indicate that the system has robust functionality and is considered very easy to use in this particular school context. Limitations include the single-school setting and relatively small sample size, and further validation across different educational contexts is recommended.
Nutritional Status Classification of Children Using a Decision Tree Method (Case Study at RS AN-NISA) Based on a Web Application
This study develops a web-based classification model for the nutritional status of children aged 0–5 years using the Decision Tree (C4.5) algorithm applied to anthropometric data obtained from RS AN-NISA Tangerang during the period of January–December 2024. The attributes utilized include age (in months), gender, weight, and height, with nutritional status categorized into three classes: undernutrition, normal nutrition, and overnutrition. The dataset underwent a preprocessing phase consisting of data cleaning, removal of extreme values, attribute selection, categorical encoding, normalization, and dataset splitting with an 80% training and 20% testing ratio. The model was constructed using entropy-based splitting criteria with hyperparameter tuning to minimize the risk of overfitting, and its performance was evaluated using accuracy, precision, recall, and F1-score metrics, with Logistic Regression employed as a baseline model for comparison. The results demonstrate that the Decision Tree achieved an accuracy of 96.12% and a macro recall of 89.49%, outperforming the baseline model. The trained model was subsequently serialized and integrated into a Flask-based web application to enable real-time data input and nutritional status prediction. Black-box testing and User Acceptance Testing (UAT) yielded a user satisfaction rate of 88%, indicating that the system is feasible as a practical tool for early detection of child nutritional status in healthcare services.
Design and Development of a Web-Based Daily Surgery Schedule Information System at Al-Ihsan Hospital
This research focuses on the design and implementation of a web-based daily surgery scheduling information system at Al-Ihsan Regional Hospital to overcome the limitations of the previously used manual scheduling process, which often caused data entry errors, scheduling conflicts, delays in information updates, and difficulties in accessing accurate schedule information. The system was developed using the Rapid Application Development (RAD) methodology, consisting of requirements analysis, system design and modeling using Unified Modeling Language (UML), and system implementation using the Laravel 8 framework (PHP based) to support application logic and rapid web development, Bootstrap for responsive and user-friendly interface design, and MariaDB as the database management system for storing and managing scheduling data. The application was deployed on an Apache web server through the Laragon environment. Data were collected through direct observation of hospital operational workflows, interviews with medical and administrative staff, and a review of relevant literature. The results of this study indicate that, based on User Acceptance Testing (UAT) involving five respondents with an average usability score of 4.4 out of 5, users perceived that the proposed system supported more efficient daily surgery scheduling activities, facilitated schedule monitoring, and reduced the likelihood of manual scheduling errors. In addition, users positively evaluated the usability of the interface for schedule management and monitoring activities. The implementation of authentication mechanisms and role-based access control was also perceived to support secure access management for sensitive scheduling information within the evaluated environment. Overall, this research demonstrates the practical application of information systems concepts in supporting digital transformation initiatives and improving operational performance within the hospital environment effectively and sustainably.
Home Page SMATIKA Journal Volume 15 Number 02, Desember 2025
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Large Language Models for JSON-Based Function Call Planning from Indonesian Natural Language: A Restaurant Search Chatbot Case Study
Large Language Models are increasingly adopted as planning components that translate natural language into structured representations for tool invocation, enabling executable interaction with backend systems through JSON based function calling. However, empirical studies focusing on Indonesian natural language remain limited. This paper presents a restaurant search chatbot case study that investigates JSON based function call planning from Indonesian user queries, with emphasis on the upstream planning task rather than conversational response generation. A synthetic dataset of 33,470 Indonesian restaurant search queries paired with ground truth JSON plans was constructed based on a predefined tool set and database schema. Supervised fine tuning with parameter efficient adaptation was applied to a pretrained language model. The fine tuned Mistral 7B model was evaluated using multiple metrics measuring JSON structural validity, tool sequence correctness, and parameter accuracy at different granularities. The results show strong performance, achieving a JSON structure validity rate of 0.97, tool sequence exact match accuracy of 0.92, column level accuracy of 0.97, and value level accuracy of 0.94. More stringent evaluation at the session level reveals remaining challenges in composing all parameters correctly within a single planning instance. Overall, the findings demonstrate that with carefully designed datasets and strict supervision, Large Language Models can reliably perform structured JSON based function call planning from Indonesian natural language, providing a practical foundation for extending this approach to other structured application domains where execution correctness is critical.
Mountain Climbing Safety Induction for Beginners Using Augmented Reality
The growing popularity of mountain climbing among novice participants has been accompanied by an increased risk of accidents, largely attributable to inadequate understanding of safety procedures. Conventional safety induction methods—primarily verbal briefings and printed materials—lack interactivity and fail to adequately simulate real climbing conditions. While prior research has examined general safety education and conventional multimedia learning approaches, the application of markerless augmented reality (AR) as an interactive, mobile-based safety induction tool specifically designed for beginner mountain climbers remains underexplored . Accordingly, this study aims to develop and evaluate an Android-based markerless AR application to enhance pre-climbing safety induction for novice climbers. The application was developed following the Multimedia Development Life Cycle (MDLC), encompassing the stages of concept development, design, material collection, assembly, testing, and distribution. Functional performance was assessed using black-box testing, while feasibility was evaluated through a Likert-scale questionnaire administered to 20 beginner climbers. Black-box testing confirmed that all system functionalities operated as intended. The feasibility evaluation yielded a score of 87%, indicating a good to very good level of acceptance among users. By integrating markerless AR technology into mobile-based safety induction, this study addresses a critical gap in outdoor safety training research. The findings demonstrate the feasibility and practical potential of immersive AR applications to enhance safety awareness and improve the effectiveness of preparatory training for beginner mountain climbers.
A SMIL-Based Approach for Designing Interactive Multimedia Film Players
Typically, an HTML-style application requires extensive scripting to coordinate user interactions, manage display layout, and synchronize the timing of all interaction events. With Synchronized Multimedia Integration Language (SMIL), an alternative approach to developing an interactive multimedia film application has been defined, supporting time-based multimedia cooperation and user interactivity without the need for complex scripts. This study presents the development and evaluation of an SMIL-based interactive multimedia film application. The application allows for user-selected films to be automatically selected, played, and managed (synchronized) through an interactive GUI. The two measures, the Layout Consistency Score (LCS) and Screen Utilization Efficiency (SUE), were evaluated. Experimental evaluations have shown that the average LCS for the application = 0.92, indicating that the application maintains a consistent and predictable layout across multiple instances of the interface. In addition, the average SUE score for the application was calculated to be 0.79, indicating that it uses the available screen space efficiently and does not create a visually competing cluster. These results reinforce the concept that the region-based approach of SMIL and its declarative style for creating applications enable the development of multimedia that is consistently and efficiently presented across multiple contexts. As a result, the findings of this study indicate that the SMIL approach will simplify and allow for a practical methodology to build interactive multimedia film player applications, while preserving layout quality and interactivity.