Journal of Technology and Informatics (JoTI)

Development of Quibot Technology-Based Learning Media to Improve Student Motivation and Learning Outcomes

2026-01-06T14:47:10+07:00

This study examines the effect of Quibot, a chatbot-based learning medium, on students’ learning outcomes and learning motivation in mathematics learning at the junior high school level. The study was motivated by the limited integration of interactive digital media and its impact on students’ academic performance. A quantitative quasi-experimental design was employed involving 60 eighth-grade students of Yayasan Perguruan Sultan Iskandar Muda, divided into an experimental group using Quibot and a control group using conventional learning methods. Learning outcomes were measured using a validated multiple-choice mathematics achievement test, while learning motivation was assessed through a Likert-scale questionnaire. Data were analyzed using normality and homogeneity tests, followed by one-way ANOVA for learning outcomes and Welch’s t-test for learning motivation. The results showed a significant difference in learning outcomes between the two groups, F(1,58) = 4.65, p = 0.035, with a moderate effect size (η² = 0.074). However, no significant difference was found in learning motivation (p = 0.871). These findings indicate that Quibot effectively improves students’ academic achievement but does not significantly influence learning motivation within a short intervention period. This study highlights the role of chatbot-based learning media as an instrumental tool in supporting cognitive learning outcomes.

Implementation of The Topsis Algorithm In A Car Purchase Decision-Making System

2026-01-28T14:34:39+07:00

Private vehicles such as cars and motorcycles are crucial modes of transportation for the movement of goods and people. With technological advancements, car manufacturers offer a wide range of vehicles. Therefore, prospective buyers face challenges in selecting a vehicle that best suits their preferences and criteria. To tackle the issue, this study develops a practical decision support system (DSS) as a user-friendly tool for buyers, with theoretical contributions in the form of a more adaptive TOPSIS application and systematic analysis in car selection. This study focuses on collecting car-related data using 12 criteria, such as price, fuel consumption, safety, and design. The TOPSIS method is then normalized to ensure a fair and objective comparison between criteria. The results show the top alternative ranking, Suzuki 2002 (closeness score of 0.7089 in position 1), and the SUS test result of 85.6, indicating that the system is easy to use and capable of providing recommendations that align with user preferences. Therefore, this study highlights that the TOPSIS method can be an effective tool in supporting car purchase decision-making and making it easier for prospective buyers to choose the car that best suits their needs.

Brush-shaped Motion Gesture of UGV Using Hand Gesture Recognition

2026-01-23T13:53:19+07:00

Manual observation of corn leaf diseases in agricultural fields often faces challenges related to time, effort, and accuracy. To address these challenges, brush-shaped motion patterns, such as zig-zag and boustrophedon trajectories, provide an effective solution by enabling uniform area coverage while reducing redundant traversal, energy consumption, and sensing gaps, making them well-suited for precision agriculture applications. Building on this approach, the system utilizes the MediaPipe framework for hand landmark tracking and the K-Nearest Neighbors (KNN) algorithm to recognize six navigation commands: forward, backward, stop, turn_right, turn_left, and capture. These commands are transmitted via Wi-Fi with an average latency of 0.001964 s. To ensure navigation accuracy during pattern execution, corrections are made using rotary encoders. Gesture classification experiments on 6,000 samples achieved a maximum accuracy of 99.46% across two participants, with stable KNN performance under both indoor and outdoor lighting variations, as well as hand distances ranging from 50 cm. Furthermore, the capture gesture produced an average image acquisition latency of 0.3037 s at various UGV observation positions. In summary, these results demonstrate that integrating real-time gesture control with UGV maneuvers enables systematic field surveys for maize leaf disease monitoring and supports Sustainable Development Goal (SDG) 2 through precision agriculture technology.

Blocking Delay Effects on Microcontroller Speed and Responsiveness in Industrial IoT Devices: A Systematic Review

2026-03-20T15:36:11+07:00

This review synthesizes research on the impact of blocking delay on microcontroller speed and responsiveness in IoT devices for industrial automation. It evaluates blocking delay effects on microcontroller performance. The review benchmarks scheduling and edge computing techniques, identifies mitigation strategies, compares case study outcomes, and analyzes architectural and software factors influencing blocking delay. A systematic analysis of experimental, simulation, and co-design studies was conducted. The analysis focused on real-time scheduling, interrupt handling, network-induced latency, and edge computing integration. Key findings reveal that advanced scheduling algorithms and interrupt nesting significantly reduce blocking delays and improve task responsiveness. Edge computing and hardware optimizations also minimize network-induced latency and enhance local processing capabilities. Multiple sources of blocking delay, including resource contention and network overload, are mitigated through adaptive scheduling and hardware-assisted mechanisms. Real-world case studies confirm substantial latency reductions and improved control performance in industrial IoT contexts. These findings underscore the interplay of software and hardware factors in shaping microcontroller responsiveness. The review highlights the necessity for scalable, integrated solutions that address dynamic industrial environments. It informs the design of more responsive and efficient microcontroller-based IoT systems for industrial automation.

Comparative Performance of Machine Learning Algorithms for Diabetes Prediction

2026-03-26T12:52:46+07:00

Early detection of diabetes mellitus is crucial to prevent severe complications. This study evaluates three machine learning algorithms for diabetes prediction using a quantitative comparative experimental design. The algorithms are k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest. These methods were chosen to compare distinct learning paradigms. k-NN is distance-based, SVM is margin-based, and Random Forest is an ensemble method. The goal is to find the optimal model for clinical use. The Pima Indians Diabetes dataset was used. It includes 390 patients and 15 clinical features. Performance was measured by accuracy, precision, recall, and F1-score. Random Forest had the highest accuracy (89.7%) and F1-score, providing the most balanced classification. SVM followed with 84.6%, and k-NN achieved 76.9%. Although k-NN had the highest recall (0.750), its precision was low (0.375), showing a high false-positive rate. Feature importance analysis pointed to blood glucose levels as the most significant predictor, which matches clinical knowledge. In summary, ensemble techniques like Random Forest offer the most reliable results. This highlights the importance of selecting the right algorithm for early diabetes detection in clinical applications.

Network Monitoring Using Zabbix ICMP Ping and Telegram Notifications Using the Network Development Life Cycle Model

2026-02-05T09:50:47+07:00

This study addresses the critical need for proactive network management in MSMEs, where reliance on stable connectivity is high but existing monitoring tools are often costly or reactive. We propose a low-cost, proactive monitoring framework that integrates the open-source Zabbix platform with ICMP Ping detection and real-time Telegram Bot notifications. Developed using the Network Development Life Cycle (NDLC) methodology, the system’s novelty lies in its practical integration of instant messaging to achieve near real-time fault notification in a scalable environment. Implemented at an ISP (CV Media Computindo) managing over 250 active clients, the framework was evaluated using 61 client devices on a virtualized Ubuntu Server. Experimental results demonstrate high operational impact: failure notifications were delivered in under two seconds with a 100% success rate, significantly reducing average device downtime from 30 minutes to just 3 minutes. Despite minor limitations regarding polling intervals and external messaging dependencies, the system proved highly effective and cost-efficient. This research provides a scalable foundation for resource-constrained organizations to enhance network reliability through open-source tools and offers a benchmark for future comparative studies with enterprise platforms like PRTG, Nagios, and Prometheus.

A Real-Time Human-Drone Interaction System for Cornfield Perimeter Monitoring Using Hand Gesture Control

2026-02-05T09:55:54+07:00

Perimeter monitoring in agricultural fields is essential for maintaining security and ensuring continuous observation of field conditions. This study develops a real-time human–drone interaction system using hand-gesture recognition based on MediaPipe Hands and a Support Vector Machine (SVM) classifier. A custom dataset of 24,000 images across 12 gesture classes was collected and converted into 42 hand landmarks (x, y, z), normalized relative to the wrist point. The SVM model with an RBF kernel was trained using an 80:20 split and achieved a testing accuracy of 99.18%. The system operates at 109 FPS with an average latency of 9.16 ms, enabling rapid and reliable drone responses to gesture commands. Field testing in a cornfield with FPV camera visualization demonstrated that the system consistently recognized gestures in varying outdoor lighting, allowing drones to execute precise perimeter checks and maneuvers. These results highlight the significant potential of integrating gesture recognition with drone control, providing a practical, real-world solution that advances smart farming, increases agricultural efficiency, and supports technological progress toward Sustainable Development Goals. The proposed system thus offers a lightweight, responsive, and impactful tool for modern agricultural perimeter monitoring.

Web-Based Automatic Code Evaluation System Using Claude AI for Programming Education

2026-03-13T13:40:10+07:00

Algorithm and Programming learning face challenges in providing fast and personalized feedback to students. The manual evaluation process conducted by lecturers requires considerable time, hindering students' iterative learning process. This study aims to develop a web submission platform prototype with automatic feedback based on Claude AI to support and enhance the programming learning process. The research method employs a Research and Development (R&D) approach with four stages: needs analysis, system design and planning, platform implementation, and testing and evaluation. The platform was developed using a PHP backend, MySQL database, and Claude AI integration through RESTful web services with a cascading AI evaluation strategy. Evaluation was conducted on 9 students with 39 submissions for three Java assignments with different difficulty levels. Results show the system successfully provides high-quality feedback with an average response time of 2.8 seconds and 100% evaluation success rate. Score distribution shows average improvement from the first assignment (82.3) to the third assignment (87.1), indicating a positive trend in iterative learning. A satisfaction survey of 8 respondents shows the system interface is user-friendly, and AI feedback helps identify syntax and program logic errors. Students made an average of 3.2 attempts per assignment, demonstrating high engagement in the learning process.

K-Means Algorithm Application for Clustering Recent University Graduates According to Work Readiness Indicators

2025-12-03T17:27:00+07:00

Graduate work-readiness segmentation is essential for data-driven career services in universities. This study applies K-Means clustering to tracer-study data using four input indicators: GPA (IPK), TOEFL, soft-skill points (SSKM), and study duration, while employment status and waiting time are treated as external outcomes. Records from 669 graduates (2020–2023) were preprocessed via deduplication, range checks, and z-score standardization. The number of clusters was determined data-driven over K=2–10 using the Elbow Method (SSE) and Davies–Bouldin Index; the optimal K=9 was selected at the DBI minimum. PCA visualization indicated a distinguishable cluster structure. Clusters C0, C3, C5, and C7 exhibited faster transitions (median waiting time 2 months) with high employment proportions (up to ~90%), whereas C2 and C8 showed longer waiting times (≥4 months). Cluster C4 was characterized by the longest study duration and a comparatively lower employment proportion. These results demonstrate that unsupervised learning can reveal actionable readiness segments, supporting targeted interventions (e.g., CV/portfolio clinics, interview practice, structured internships) and providing a foundation for subsequent predictive modeling of graduate outcomes.

Performance Measurement of the Info BMKG Application Using the Information Technology Infrastructure Library (ITIL) V.4 Framework

2026-03-20T15:28:12+07:00

The Meteorology, Climatology, and Geophysics Agency (BMKG) provides information to the public through the Info BMKG mobile application. This app delivers data on earthquakes, early warnings, weather, climate, and other meteorological topics. Observations, interviews, and user reviews from the Google Play Store highlight several issues. These include an unstructured display, inaccurate location information, and delays in earthquake notifications. To address these, the application’s performance was evaluated using the ITIL V4 management practices. The assessment collected questionnaire data from internal users, stakeholders, and the public. Analysis showed an average maturity level of 4.56, with the largest gap (0.76) in service desk management. Recommendations for improvement were provided for each management activity. These aim to ensure best practices in the application. With these findings, the Info BMKG app is seen as applying continuous improvement practices based on ITIL V4, supporting IT integration, and enhancing organizational quality, efficiency, and adaptability.