J-Intech (Journal of Information and Technology) is a journal published by the Research & Community Service Institute (LPPM) of the UBHINUS (Universitas Bhinneka Nusantara), Malang. The scope of this journal includes the fields of Informatics Engineering, Information Systems, and Informatics Management. Its purpose is to accommodate the growing needs in Information Technology development. J-Intech is published four times a year, in March, June, September, December, with ISSN (print): 2303-1425 and ISSN (online): 2580-720X. It also has a DOI: https://doi.org/10.32664/j-intech.
Design and Implementation of an Android-Based Scholarship Search Application Using Web Scraping Techniques
Education is a vital need for humans; however, high education costs often become a barrier for students, particularly those from underprivileged backgrounds. Although scholarships offer an alternative solution, scholarship information is commonly scattered across multiple platforms and difficult to access efficiently. This study aims to design and implement an Android-based scholarship search application using web scraping techniques to integrate and centralize scholarship information from various reliable sources. The main contribution of this research lies in the application of automated web scraping using Google Cloud Function to support a mobile-based scholarship information system, enabling periodic data collection and real-time access to updated scholarship data. The scraping process runs every 24 hours and stores structured data in a Firestore database, which is then presented through an Android application developed using Kotlin and Jetpack Compose. Analytical results from usability testing indicate that 90% of users rated the application as easy or very easy to use, while system performance evaluation shows that all core features functioned successfully with fast data retrieval and display. Black-box testing confirms that searching, filtering, sorting, and favorite management features operate as expected. This application provides an effective solution for students to efficiently search and manage scholarship information through an integrated platform. Future development may focus on improving scraping performance, enhancing the user interface, and adding notification features for newly available scholarships
Copyright (c) 2025 J-INTECH https://creativecommons.org/licenses/by-sa/4.0
An Integrative Conceptual Review of Federated Learning Ontology: Reconceptualizing the Global Model as Distributed Intelligence
Federated Learning (FL) is widely recognized as a privacy-preserving architecture, yet the ontological status of the "global model" within this distributed system remains under-theorized. This study challenges the purely technical view by reinterpreting FL as an ontological regime of distributed intelligence, where the model exists as a causally effective informational entity. By synthesizing Informational Realism and Actor-Network Theory into a unified Ontological Mediation Framework (OMF), we argue that the global model acquires reality through continuous mediation among algorithms, local data, and institutional actors. To contextualize this framework, the study examines the Federated Tumor Segmentation (FeTS) Initiative, illustrating how a non-local model exerts tangible causal influence within a real-world medical consortium. Furthermore, the paper proposes two normative instruments, specifically the Ontological Impact Assessment (OIA) and the Ontological Audit Framework (OAF), to translate these philosophical insights into practical governance mechanisms for transparency and accountability. This research contributes to the foundations of Responsible AI by positioning Federated Learning not merely as a computational tool, but as a socio-technical process where existence, knowledge, and ethics are intrinsically linked.
Designing a Web-Based Dropship Management System for PT Xarana Djaya Motor
Indonesia’s online retail is expanding alongside the national digital economy: 56.1% of global internet users shop on weekends, with Indonesia ranking ninth at 59.3%. The digital economy is projected to reach IDR 5,800 trillion by 2030 (USD 360 billion), and in 2023 Indonesia accounted for 40% of ASEAN’s e-commerce market. In this context, PT Xarana Djaya Motor, a multi-brand motorcycle spare-parts distributor, runs a dropshipping model with multiple online sellers. Yet orders and shipment confirmations are still coordinated via chat and reentered manually, causing duplicate entries, processing delays, and limited real-time stock visibility. This study designs and implements a web-based Dropship Management Application that centralizes catalog, order, shipment, and return workflows. Analysis uses UML/ERD; implementation employs Laravel (PHP) and MySQL. Alpha/black-box testing validates key rules (one shipment per order) with multiple items and partial returns at the order-item level. Results indicate reduced manual handling, improved data accuracy, and clearer status visibility as supported by centralized workflows and functional (black-box) acceptance tests. The work contributes a reusable reference architecture for SMEs, with future extensions to marketplace API integration, automated label generation, analytics dashboards, and mobile access.
Development of the “Scentify” Application as a Web and Mobile-Based Perfume Recommendation System Using Content-Based Filtering Based on User Preferences
This study presents the development of Scentify, a web and mobile-based perfume recommendation system employing the Content-Based Filtering (CBF) algorithm. The research aims to assist users in identifying perfumes aligned with their personal preferences such as fragrance type, concentration, gender, age, and price range. The development methodology includes literature review, user preference data collection via questionnaires, system design, and implementation using Flutter for mobile and Flask for backend API. User preference data were integrated with curated perfume datasets obtained from reliable online sources to form the recommendation base. The results demonstrate that Scentify can produce personalized recommendations with relevant accuracy. It features login, registration, perfume questionnaire, favorites, and admin dashboard modules. The system was validated using black-box testing, proving its reliability and user-friendliness. This work confirms that Content-Based Filtering is effective in building personalized digital recommendation systems and contributes to digital innovation in the perfume and e-commerce industries.
VibeScreen: A Mood-Based Movie and Music Recommendation Mobile Application
The advancement of digital technology has driven the emergence of various innovations in delivering personalized entertainment content. One promising approach is a mood-based recommendation system, which enables users to receive suggestions for movies or music that match their emotional state. This study designed and developed VibeScreen, a prototype application for recommending movies and music based on user mood using sentiment analysis of text inputs. The system applies Natural Language Processing (NLP) techniques to classify user sentiment, which is then used to generate relevant entertainment recommendations. The application was developed using Flutter for the mobile interface and Flask for the backend services, with Firebase supporting user authentication and data storage. The dataset was collected through online questionnaires and secondary sources such as IMDb and Spotify. Testing results show that the system can provide mood-relevant recommendations with an interactive and responsive interface. This research contributes by integrating movies and music recommendations in a single platform, offering a more adaptive and emotionally relevant entertainment experience.
Application of Web and Mobile-Based Information System for Diabetes Patient Management at Bandar Lor Clinic
Diabetes mellitus is a chronic disease that requires continuous monitoring and management. Klinik Pratama Bandar Lor Kediri faces challenges in managing information for diabetic patients, including recording medical history, monitoring blood glucose, facilitating patient–doctor communication, and providing continuous education. Limited access to complete and up-to-date patient data hinders service delivery and complicates clinical decision-making, while patients often struggle to record examination results, adhere to treatment schedules, and obtain optimal consultations. To address these challenges, this study developed DiaCare, a web and mobile based information system built using the prototyping method with Flutter and Firebase. The system integrates key features such as health record management, medication reminders, online consultations, activity tracking with calorie estimation, digital delivery of laboratory results, dietary monitoring, and diabetes-friendly menu recommendations. Functional testing using the black-box method achieved a 100% success rate, validating that all features performed as expected. By enabling patients to self-monitor their condition while maintaining real-time communication with doctors, DiaCare improves efficiency, data accuracy, and patient engagement. The findings suggest that DiaCare has the potential to enhance diabetes management and optimize healthcare services for patients requiring regular monitoring at Klinik Pratama Bandar Lor Kediri.
Design and Implementation of FitSphere as an Android Application for Gym Membership Management at Chain Gym
The advancement of information technology in the digital era has driven transformation across various sectors, including the fitness industry. Chain Gym, a fitness center located in Klaten, faces challenges in managing data and membership services, which are still handled manually and separately. The lack of an integrated system has led to inefficiencies in processes such as class registration, fitness progress monitoring, and member information management, thereby affecting service quality and member satisfaction.This study designs and develops a mobile-based Android application named FitSphere, built using the Kotlin programming language with Firebase as the backend, to serve as an integrated platform supporting membership management at Chain Gym. The development process adopts the Rapid Application Development (RAD) methodology, which includes several stages: requirements planning through observation and interviews, system design using UML models and table relationship diagrams, construction involving coding and system integration, and the cutover phase that encompasses testing and deployment of the application.The application is equipped with key features such as membership registration, gym-related news, membership subscription, consultation, workout history, class registration, and Body Mass Index (BMI) calculation. The use of the RAD approach enables a faster and more adaptive development process that accommodates user needs and feedback.The results show that the features in FitSphere function as intended and have the potential to improve operational efficiency, making it easier for members to access services. Black Box Testing conducted on both member and admin features showed a 100% success rate, confirming that all functionalities operate correctly. The application also provides strategic benefits for management by offering deeper insights into user needs. Overall, the implementation of this application delivers an effective digital solution that enhances Chain Gym’s competitiveness in the rapidly growing fitness industry.
Classification of Stinging Nettle Plants Based on Leaf Images Using the CNN Method (Case Study: Biru-Biru Village)
The high number of skin irritation cases among residents in Desa Biru-Biru due to direct contact with stinging nettle plants highlights the need for an automatic identification system to distinguish plant types. This study aims to develop a leaf image classification model for stinging nettle plants using the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture. The image dataset was collected directly from the study area and classified into four categories: Jelatang Ayam, Jelatang Gajah, Jelatang Niru, and Non-Nettle plants. The research stages include data collection and analysis, pre-processing (resizing, normalization, augmentation), data splitting (70:10:20), model training, performance evaluation (accuracy, precision, recall, and F1-score), and web-based system implementation. The test results show that the model achieved an accuracy of 98%, with the highest precision score of 0.98, recall score of 0.98, and F1-score of 0.98. The system has also been successfully implemented as an interactive web application that allows users to identify nettle plant types quickly and accurately. This research contributes to risk mitigation efforts related to harmful plants in rural environments through the application of digital image processing technology.
Blockchain Adoption Challenges for SMEs: A Systematic Literature Review
Blockchain technology has emerged as a transformative force in various industries, with increasing interest from Small and Medium-sized Enterprises (SMEs). However, the adoption of blockchain by SMEs faces unique challenges that warrant comprehensive examination. This systematic literature review analyzes research on blockchain adoption challenges for SMEs published between 2019 and 2024. We reviewed 48 papers from major databases, including Scopus, Web of Science, and IEEE Xplore. Our analysis reveals three primary challenges: limited financial resources, lack of technical expertise, and regulatory uncertainty. The review also highlights a shift from conceptual to empirical studies over the period, indicating a maturing field of research. Geographically, 60% of studies focus on developing countries, particularly in Asia. Industry-wise, the supply chain sector dominates blockchain adoption research in SMEs (40%), followed by finance (25%) and manufacturing (20%). Despite challenges, significant benefits are identified, including enhanced supply chain transparency, improved operational efficiency, and better access to financing. This review contributes to the understanding of blockchain adoption in SMEs by synthesizing current knowledge, identifying research gaps, and proposing future research directions. Our findings provide valuable insights for researchers, practitioners, and policymakers involved infacilitating blockchain adoption among SMEs.
Small and Medium-sized Enterprises (SMEs)
Copyright (c) 2025 J-INTECH ( Journal of Information and Technology) https://creativecommons.org/licenses/by-sa/4.0
Development of an Indonesian Hoax Detection System Using Logistic Regression Based on TF-IDF
The massive spread of fake news (hoaxes) on digital platforms has become a serious challenge in Indonesia, with the potential to disrupt social stability and undermine public trust. This background drives the urgency of developing an automated system to combat disinformation. Unlike previous works relying on deep learning with high computational cost, this study demonstrates that a lightweight approach remains highly effective for Indonesian hoax detection. This study aims to develop and evaluate a lightweight and effective automatic classification system to detect Indonesian-language hoaxes using a machine learning approach. The method used is Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction to represent text content numerically, which is then classified using the Logistic Regression algorithm. This approach was chosen for its computational efficiency and ease of interpretation. The study utilizes a dataset collected from verified sources, consisting of 7,075 Indonesian-language news articles, which were divided into 80% training data and 20% test data. The evaluation results on the test data show excellent model performance, achieving an accuracy of 94.98%, a precision of 0.95, and an average F1-Score of 0.95. Specifically, the model demonstrated a strong ability to identify hoaxes with a recall value of 98% for the hoax class. This study concludes that the combination of TF-IDF and Logistic Regression is an efficient and accurate approach for Indonesian hoax detection, offering a practical solution that can be further developed to combat disinformation.
Application of K-Nearest Neighbor Algorithm for Estimating Fishery Product Quality at BPPMHKP Pontianak
Microbiological quality is a key parameter in ensuring the safety and overall integrity of fishery products. One of the primary indicators in microbiological testing is the Total Plate Count (TPC), locally known in Indonesia as Angka Lempeng Total (ALT), which measures the concentration of aerobic microorganisms in a sample. This study aims to develop a predictive system for ALT values in fishery products using the K-Nearest Neighbor (KNN) algorithm as the main classification approach. The dataset was derived from microbiological test results conducted at the BPPMHKP Laboratory in Pontianak between 2020 and 2024. Data preprocessing included converting ALT values from scientific text format to numeric values, applying Min-Max normalization, splitting the dataset into training and testing subsets, and implementing the KNN algorithm with K = 3. Predictions were generated by calculating the Euclidean distance between each test sample and the training set, selecting the three nearest neighbors, and averaging their ALT values. The proposed system achieved a prediction accuracy of 98.66% compared to actual ALT measurements. Based on the microbiological threshold of 5.0 × 10âµ colony-forming units per gram (CFU/g) used by BPPMHKP, the system effectively estimated product quality according to safety standards. These findings indicate the potential of the system to be developed into an application-based decision-support tool for government laboratories and quality control agencies. Keywords: Total Plate Count, K-Nearest Neighbor, Prediction, Fishery Products, Accuracy
Improving Software Defect Prediction Performance Using C4.5 Based Ensemble Learning with AdaBoost and Bagging Techniques
Software defect prediction (SDP) plays a crucial role in improving software quality by enabling the early detection of faulty modules during the development phase. However, class imbalance within software defect datasets remains a significant challenge that adversely impacts prediction accuracy. This study aims to address this issue by implementing ensemble learning methods—specifically Bagging and AdaBoost—combined with the C4.5 decision tree algorithm to enhance classification performance. The research utilized five well-known datasets from the NASA MDP Repository (CM1, JM1, KC1, KC2, and PC1), each containing comprehensive software metrics and defect labels. The methodology involved several stages: data preprocessing (normalization and discretization), model training using 10-fold cross-validation, and performance evaluation through metrics such as accuracy and Area Under the Curve (AUC). Results indicate that both ensemble methods outperformed the standalone C4.5 algorithm across all datasets. Notably, the AdaBoost + C4.5 model yielded the highest accuracy in most scenarios, with the PC1 dataset reaching 97.20% accuracy. In comparison, C4.5 alone and C4.5 with Bagging recorded lower values, demonstrating the significant impact of adaptive weighting in AdaBoost. These findings affirm that ensemble learning, particularly AdaBoost, effectively mitigates the impact of class imbalance and improves prediction performance in SDP tasks.
Identification of Palm Oil Fresh Fruit Bunches Worth Selling with K-Nearest Neighbors Algorithm
Indonesia is the world's largest palm oil producer, with annual production reaching more than 45 million tons. The quality of oil palm fresh fruit bunches (FFB) determines the quality of the oil produced. The quality of FFBs can be seen through their maturity and health. Fruit that is not ripe, overripe, or contaminated with mold can reduce oil quality due to high levels of free fatty acids (FFA). This research aims to build a classification model of FFB marketability using the K-Nearest Neighbors (K-NN) algorithm with RGB and GLCM features. Image data was collected from the plantation, then processed through the stages of preprocessing, feature extraction, and normalization. The model was tested in three approaches, namely using RGB-GLCM combination features, RGB only, and GLCM only, with various data sharing scenarios, namely 70:30, 80:20, and 90:10, as well as varying k values, namely k = 3, 5, 7, 9. The evaluation results show that the RGB-GLCM feature combination model in the 80:20 data sharing scenario and k = 5 value is the most optimal model, with accuracy reaching 88%. In addition to providing high accuracy, this model also shows good stability compared to the RGB and GLCM models alone. This proves that the use of a combination of features is more effective and reliable in identifying the marketability of oil palm FFB compared to the use of a single feature.
Application of the K-Nearest Neighbor (K-NN) Algorithm for Detecting Banana Harvest Feasibility
This study focuses on detecting banana harvest feasibility at the green-ripe stage, an area often overlooked in previous studies that focus only on general ripeness. The objective of this research was to develop a system based on the K-Nearest Neighbor (K-NN) algorithm to classify bananas as “Ready for Harvest” or “Not Ready for Harvest” using digital image processing. The system utilizes Hue Saturation Value (HSV) for color analysis and Gray Level Co-occurrence Matrix (GLCM) for texture identification. Unlike other methods, the combination of HSV and GLCM provides richer, complementary features, improving classification accuracy. The study was conducted at a banana plantation in Kwala Bekala Village, Medan Johor District, with 200 banana images taken from five different locations. The K-NN algorithm, with a value of K = 3, was chosen to avoid tie votes and ensure computational efficiency. The system achieved an accuracy of 94%, with precision of 93.5%, recall of 92.8%, and an F1-score of 93%. In beta testing with 33 respondents (18 farmers and 15 non-farmers), the system achieved a user satisfaction rate of 90%. Misclassifications occurred due to factors such as lighting variations and background noise. The study demonstrates the practical benefit of using the K-NN algorithm for determining the optimal harvest time, helping farmers make more accurate decisions, reducing waste, and increasing market competitiveness. This research fills the gap in detecting green-ripe bananas, providing an innovative solution to optimize harvest timing in the agricultural industry.
Implementation and Analysis of Cloud-Native Approaches to Enhance Scalability and Performance in Dwi Jaya Understel Workshop Information System
This study aims to analyze the differences in scalability and performance between a traditional monolithic system hosted on a Virtual Private Server (VPS) and a cloud-native serverless architecture using AWS services for an automotive workshop information system. An experimental method was employed using a post-test only control group design. Performance testing was conducted with K6 as the stress testing tool under a ramp-up load pattern of up to 60 Virtual Users (VU) to simulate peak traffic conditions, while Grafana was used for real-time monitoring and visualization of system metrics.The results indicate that under peak load scenarios, the cloud-native architecture reduced the average response time by 89.1% (from 6.05 seconds to 657.10 milliseconds) and eliminated the error rate completely (from 0.154% to 0%), compared to the monolithic system. Additionally, the throughput improved by 38.2%, demonstrating better responsiveness and stability. These findings confirm that serverless cloud-native systems offer superior scalability and reliability in handling dynamic and high-demand workloads, making them well-suited for public service platforms such as automotive workshop information systems.
Traffic Accident Severity Classification System Using Random Forest Algorithm
Traffic accidents pose a major concern in many countries, including Indonesia, causing considerable losses, injuries, and fatalities each year. Properly classifying the severity of these incidents is essential for authorities to establish preventive actions, apply effective countermeasures, and improve overall road safety. Conventional statistical techniques often fall short in capturing the intricate relationships among multiple influencing variables, such as weather, driver experience, vehicle type, number of vehicles, and casualty figures. To address this limitation, this study proposes a machine learning–based classification method using the Random Forest algorithm, known for its robustness in handling complex and high-dimensional data while identifying nonlinear patterns. The model was trained on a traffic accident dataset from Kaggle and incorporated important features, including driver age group, driving experience, type of vehicle, lighting and weather conditions, type of collision, number of vehicles involved, and casualties. The proposed system achieved 81% accuracy, 75% weighted precision, 81% weighted recall, and a weighted F1-score of 77%, demonstrating reliable performance in predicting accident severity levels Slight Injury, Serious Injury, and Fatal Injury. And providing useful insights for data-driven planning in traffic safety management.
Webqual 4.0 Analysis of Java Dancer Resto and Cafè Ordering Website
Java Dancer Resto and Cafè is one of the restaurants in Malang City that has utilized a website as a means of online ordering, this study aims to analyze the quality of usability, information, interaction services, and the overall ordering website of Java Dancer Resto and Cafè using the WebQual 4.0 method. The research method is quantitative descriptive using a survey method to 100 consumers, data analysis using the average and percentage of user satisfaction with the Kaplan and Norton interval scale. The results showed that the overall quality of the Java Dancer Resto and Cafe ordering website was considered very good by users with an average score of 4.34. The usability aspect received the highest rating with a score of 4.41, indicating ease of use and intuitive navigation. The quality of information received a score of 4.35, indicating high accuracy and relevance of information. The service interaction aspect received a score of 4.25 and is still in the very good category, but requires improvement, especially in service personalization and transaction security.
Copyright (c) 2025 J-INTECH ( Journal of Information and Technology)
Preventive Attendance Record using Photo from Mobile Phone and Printed Paper using CNN
Face-based attendance systems are increasingly popular for their ease of use, but they are susceptible to fraud, such as using photos or videos for unauthorized attendance. This study introduces a digital attendance system that combines facial recognition with liveness detection powered by Convolutional Neural Networks (CNN). Liveness verification is achieved by analyzing subtle movements and responses to ambient lighting. The dataset includes 30 facial images, encompassing both authentic and fraudulent samples. Testing demonstrates a facial recognition accuracy of 91.3% and effective spoofing detection in static and dynamic settings. This system provides a secure, fraud-resistant attendance solution ideal for educational and corporate settings. Further enhancements are suggested to improve performance across diverse facial expressions and lighting conditions.
NLP Implementation For AI Generated Text Detection (ChatGPT) Using Naive Bayes Method
The development of artificial intelligence (AI) technology, especially large language models like ChatGPT, presents challenges related to the authenticity and validity of digital content. AI's ability to produce human-like text opens up opportunities for misuse, such as plagiarism and information manipulation. This study aims to develop an AI text detection system using the Multinomial Naive Bayes algorithm, due to its ease of use and high effectiveness algorithm has become a popular choice for text classification.. The dataset used is the Human ChatGPT Comparison Corpus (H3C), sourced from the ELI5 subreddit on Reddit, consisting of 800 entries of questions and answers from both humans and AI. The labeling process involves combining answers into a single column and assigning labels based on the source. Preprocessing steps include case folding, removal of digits and punctuation, tokenization, stopword removal, normalization, and text finalization. Text features are extracted using the TF-IDF method, limited to the top 1000 features. The model is trained on 80% of the data and tested on the remaining 20%. The evaluation shows an accuracy of 93%. These findings suggest that the Naive Bayes method is effective in distinguishing AI-generated from human-generated text and has potential as an automatic AI content detection tool.
K-Means Clustering Algorithm Measuring the Satisfaction Level of MNC TV Muslim I'murojaah Program Viewers
Television remains one of the most influential mass media platforms for disseminating information and entertainment to the public, with one notable example being the I’Murojaah program aired by Muslim TV on MNC Channels. The objectives of this study are to identify the level of viewer satisfaction with the I’Murojaah program, determine the indicators influencing viewer satisfaction, classify viewers based on their satisfaction levels, and provide recommendations to program managers to improve quality and viewer satisfaction. This study employs a qualitative approach using the K-Means Clustering algorithm. The data used in this study were obtained through a survey distributed to 100 respondents, covering several viewer satisfaction indicators such as content quality (6 questions) and program presentation (6 questions). The collected data were then grouped and analyzed. The results of the first cluster iteration distance calculation consisted of 84 viewers who were very satisfied, while the second cluster consisted of 16 viewers with relatively low satisfaction levels. The Davies Bouldin Index values were -0.674 for class 2 clustering, -2.001 for class 3 clustering, -1.961 for class 4 clustering, and clustering class 5 (-2.000). In conclusion, the best clustering performance results were for classes 2, 3, 4, and 5. The smallest Davies Bouldin Index value was for clustering class 2. Recommendations for program improvement include enhancing image and sound quality, ensuring that the equipment and technology used can produce optimal quality.
Quality Analysis of an Interactive Programming Learning Platform Based on ISO/IEC 25010 Using a String-Matching Approach on User Reviews
This study aims to analyze user perceptions of Quality in Use of the Khan Academy e-learning platform, focusing on two key characteristics defined in the ISO/IEC 25010 standard: satisfaction and efficiency. Based on the analysis of user review data, there is a clear difference in volume between the two aspects: 539 reviews (65%) reflected the satisfaction aspect, while only 290 reviews (35%) were related to efficiency. This indicates that users are approximately 1.86 times more likely to comment on satisfaction than on efficiency. For satisfaction, most of reviews were positive (420 reviews, or 77.9%), while 119 reviews (22.1%) expressed negative sentiments. These results suggest that most users are satisfied with their experience using Khan Academy, particularly due to factors such as flexible access time, user convenience, and the wide availability of learning materials. In contrast, the efficiency- related reviews exhibited a more even distribution, with 154 positive reviews (53.1%) and 136 negative reviews (46.9%). This closer balance indicates that while some users appreciate the platform's performance, others report encountering technical issues, including slow access speeds, navigation difficulties, and system instability. Overall, user perception of the Khan Academy e-learning system is generally positive, especially regarding satisfaction. However, the findings also underscore the importance of addressing technical performance challenges to improve efficiency and ensure a seamless learning experience. These insights provide a valuable basis for the development of user-centered e-learning systems and contribute to the evaluation of system quality from the Quality in Use perspective.
Developing Kindergarten Management System for Supporting the Regional Educational Report
Administrative tasks in early childhood education centers often face challenges when conducted manually, leading to delays in reporting, data inconsistencies, and limited access for parents. Unlike conventional school management systems, this study specifically targets kindergarten-level reporting that aligns with regional government formats (Korwilkersatdik) in Indonesia. To address these issues, this study focuses on designing and developing a web-based information system to help manage administrative and reporting activities at TK Dharma Wanita 91 Pesanggaran, a kindergarten in Banyuwangi, Indonesia. The research applied a descriptive qualitative method and followed a structured software development process, including system analysis, design, implementation, and evaluation. The system was built using PHP with the CodeIgniter framework and MySQL as the database. It includes modules for student registration, payment validation, attendance tracking, lesson planning, and monthly report generation. Each module was tested using black-box testing and successfully met functional requirements. The implementation of the system significantly reduced manual workload, improved reporting accuracy, and facilitated better communication with parents. The results indicate that the system effectively supports the institution’s operational needs and aligns with reporting standards set by local education authorities. Future development may focus on adding mobile accessibility and analytical features to provide broader access and deeper insights for school management. This study demonstrates how a domain-specific information system can significantly improve the efficiency of kindergarten administration while meeting government reporting standards.
Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks
Natural disasters are events caused by nature such as earthquakes, tornadoes, tsunamis, forest fires, and others. The impacts of natural disasters are significant and varied across various sectors, including the economy, health, and primarily, infrastructure. Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. The data utilized consists of aerial images sourced from xView2: Assess Building Damage, comprising 50 aerial images with 5 classes: no-damage, minor-damage, major-damage, destroyed, and unlabeled. The steps undertaken in this research include data preprocessing using patchify and data augmentation. Subsequently, feature extraction is performed using convolution, followed by the training process using a neural network with the proposed architecture. This study proposes an architecture with 27 hidden layers, with feature extraction utilizing average pooling. The model evaluation process will employ Mean Intersection over Union (MIoU) to assess how closely the segmentation prediction results resemble the original data. The proposed architecture demonstrates the best MIoU result with a value of 0.31 and an accuracy of 0.9577.
Application of K-Nearest Neighbor Algorithm For Sentiment Analysis On Free Fire Online Game Based On Google Play Store Reviews
The swift expansion of the digital gaming sector, especially online games like Free Fire, has produced extensive user feedback via platforms like the Google Play Store. This research utilizes the K-Nearest Neighbor (KNN) algorithm to conduct sentiment analysis on 5,000 user reviews, with the goal of assessing its classification effectiveness. Following preprocessing (case folding, Text Cleaning, tokenization, stopword Removal, stemming), the data was converted using TF-IDF and balanced through SMOTE. Experimental findings indicate that KNN attained a peak accuracy of merely 36.53% (at k = 14), reflecting weak performance with high-dimensional textual data. In contrast, Logistic Regression attained a notably higher accuracy of 88%, showcasing its dominance for this task. The results offer perspectives for game developers to assess user feelings and emphasize the significance of selecting suitable machine learning models. Future research should investigate advanced classifiers like SVM, Random Forest, or deep learning methods to enhance accuracy.
Decision Support System for Determining Social Assistance Recipients in Petuaran Hilir Village Using the SMART Method
The distribution of social assistance in rural areas is a strategic government effort to reduce social inequality and improve the welfare of underprivileged communities. However, in Petuaran Hilir Village, the process of determining aid recipients is still conducted manually, leading to various issues such as a lack of objectivity, potential unfairness, and mistargeting. Therefore, this study aims to design and implement a Decision Support System (DSS) using the Simple Multi-Attribute Rating Technique (SMART) method to determine social assistance recipients in a more systematic and transparent manner. The SMART method was chosen due to its effectiveness in simplifying multi-criteria decision-making and its practicality for implementation at the village level. The system was developed as a web-based application and tested using the black-box method, as well as validated against the manual selection results conducted by village officials. Testing results showed that the system can objectively identify and rank aid recipients based on final scores from five main criteria: income, number of dependents, home ownership status, housing condition, and type of employment. The system achieved 100% consistency with manual selection results and reduced the selection process time by up to 70%, enabling a fairer and more targeted distribution of aid based on systematically calculated scores. By eliminating manual bias in the selection process, the system significantly improves the accuracy of recipient rankings. This study also opens opportunities for further development, such as integrating real-time population data and advanced analytical features to support more responsive social policies.
Development of a Web-Based Decision Support System for Selecting the Optimal Duck Feed Using the Analytic Hierarchy Process (AHP)
The selection of optimal livestock feed is essential for improving animal health and productivity. This study developed a web-based Decision Support System (DSS) using the Analytic Hierarchy Process (AHP) to help farmers choose the best feed based on nutritional content, price, and availability, including sub-criteria like protein, energy, fat, minerals, and vitamins. The system ranks alternatives using pairwise comparisons and priority weights. Validation against manual calculations showed high accuracy (correlation coefficient: 0.9987; errors <2.5%), with Fermented Feed (A3) as the top choice (score: 0.3611). Both methods produced identical rankings. The system reduces evaluation time by ~85% while maintaining accuracy, proving AHP’s effectiveness in digital livestock feed management tools.
Application of Naive Bayes Algorithm for Analysis of User Reviews on Mobile Legends Game: Bang Bang
Mobile Legends: Bang Bang is a highly popular MOBA game, especially among students, which generates a large volume of user reviews on the Google Play Store. These reviews provide a valuable data source for understanding user sentiment. This study conducts sentiment analysis on user reviews using three variants of the Naïve Bayes algorithm: BernoulliNB, GaussianNB, and MultinomialNB. From an initial 5,000 reviews collected via web scraping using Python, 4,428 reviews were used after neutral reviews were removed to focus solely on positive and negative sentiments.The preprocessing steps included case folding, word normalization, tokenization, stopword removal, and stemming. Sentiment labeling was carried out using a lexicon-based approach, comparing the frequency of positive and negative words in each review. The dataset was split in an 80:20 ratio for training and testing.The results show that MultinomialNB achieved the highest accuracy at 75%, followed by BernoulliNB with 74%, and GaussianNB with 50%. MultinomialNB demonstrated superior performance in detecting positive sentiments, while BernoulliNB offered more balanced results. GaussianNB performed poorly due to its assumption of normally distributed continuous data, which is unsuitable for text classification. This study concludes that Multinomial Naïve Bayes is the most effective model for sentiment analysis of user reviews when working with word frequency-based representations.
Development of Geolocation-Based Employee Attendance Application on Android Mobile
The development of mobile-based systems in Indonesia has provided innovative solutions to improve the efficiency of conventional administrative processes, especially in employee attendance. This research aims to develop an Android-based employee attendance application that is integrated with geolocation technology to enable accurate and real-time attendance monitoring. This system is built using the Waterfall method, which includes the stages of needs analysis, system design, implementation using Flutter and Dart programming language, and testing using black box testing techniques. Black-box testing was conducted on six main functions, resulting in a 94% overall success rate. Most functions achieved a 100% pass rate, but two test cases for attendance check in/out failed due to GPS location inaccuracies, highlighting the impact of device and environmental factors. The average response time was 1.28 seconds, and the average GPS delay was 2.1 seconds. The implementation of real-time notifications and admin verification improved transparency and minimized attendance fraud. The results demonstrate that the application provides an effective and efficient solution for employee attendance management. Future work should focus on enhancing location accuracy, conducting non-functional testing, and expanding features to ensure broader adoption and system robustness.
Development of Academic Community Recommendation System Using Content-Based Filtering at UIN Malang Informatics Engineering Study Program
The mismatch between the number and quality of Information and Communication Technology (ICT) talents and industry needs in Indonesia creates significant challenges, especially for Informatics Engineering students who often experience difficulties in determining the appropriate professional field. This research aims to develop a content-based filtering-based academic community recommendation system to help students choose communities that are relevant to their interests, skills and experience. The system uses TF-IDF and cosine similarity methods to match student profiles with community descriptions. Data was collected from 48 students and 10 academic communities in the Informatics Engineering Study Program of UIN Malang, and processed through preprocessing stages before modeling. Evaluation results using the System Usability Scale (SUS) resulted in a score of 76, which is categorized in the “goodâ€ level, However, users indicated the need for improved guidance in navigating the system. This system is expected to be an innovative solution to increase student participation in appropriate academic communities, as well as support the development of their potential and readiness for the world of work
Designing a Web-Based Laundry Service Application For Strala Laundry
As daily activities become more hectic, the need for efficient laundry services is increasing. Currently, Starla Laundry relies on a manual system for operations, leading to challenges such as storing customer data in logbooks, creating complex reports, and slow transaction processes due to manual calculations. These issues make it difficult for the owner and admin to manage the business effectively amidst growing competition. To address this, a new application is proposed using PHP programming, a MySQL database, and Microsoft Visual Studio Code for data processing. The application aims to improve operational efficiency, simplify data management, and support business growth. It is expected to enhance customer satisfaction and open opportunities to optimize the business in the digital era. This study focuses on designing a laundry service information system using the System Development Life Cycle (SDLC) methodology, which includes planning, analysis, design, and implementation. The system was tested using Black Box Testing to ensure the software meets its requirements. The results confirm that all application functions work as intended, making it ready to streamline Starla Laundry’s operations efficiently and professionally.