Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Student Research Journal of Vocational, Informatics and Computer Education
M. Iman Nur Hakim
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Education Public Health Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Pendekatan Machine Learning untuk Analisis dan Visualisasi Data Jembatan Timbang Siti Shofiah; Faris Humami; M. Iman Nur Hakim; Azimatun Lissyifa; Agus Siswono
Journal of Student Research Vol. 2 No. 1 (2024): Januari: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v2i1.2666

Abstract

In this research, a machine learning approach, especially a decision tree model, is implemented to improve the analysis and visualization of weighbridge data in Indonesia. The evaluation results show that the decision tree model provides better insight in predicting the carrying capacity, dimensions and loading procedures of vehicles. The advantage of this model lies in its combination of low Mean Squared Error (MSE) and high R-squared, indicating its effectiveness in capturing data variance and providing accurate predictions. The use of decision tree models can be a valuable tool in improving the visualization of bridge weighing data, allowing users to gain additional insights and understand the complex dynamics within the data. In addition, the model's adaptability to various types of data makes it a versatile analysis tool. The positive implications of using this model open up opportunities to understand more deeply the logic of predictions and make more informed decisions. As a suggestion, increasing the number and quality of weighing equipment, wider application of information and communication technology, human resource training, and cross-sector collaboration can further strengthen weighbridge management in Indonesia.
Real-Time Intelligent IoT-Based Drum Brake Temperature Monitoring System Maulana Yusuf Alkahfi; Raka Pratindy; M. Iman Nur Hakim; Nanang Okta Widiandaru
Journal of Vocational, Informatics and Computer Education Vol 4, No 1 (2026): March 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i1.601

Abstract

Purpose – This study addresses brake system failures in heavy vehicles caused by excessive thermal buildup in drum brakes. Existing monitoring systems rely on single-parameter sensing and lack early warning capabilities, thereby increasing the risk of brake fade and accidents. This study aims to develop a real-time monitoring system to improve safety. Methods: A Research and Development (R&D) approach was applied, including system design, implementation, and testing. The proposed system integrates a Raspberry Pi 4 Model B, Type K thermocouple, ESP32-C3 Super Mini, and GPS NEO-6M module. The data were transmitted via the Thingspeak IoT platform and displayed on a 7-inch TFT touchscreen. Experimental validation includes thermocouple calibration, GPS speed testing, and IoT latency measurement Findings – The thermocouple achieved a mean absolute error of 7.2°C and a percentage error of 3.4% (96.6% accuracy). The GPS speed measurement showed a 2.6% error (97.4% accuracy). IoT latency ranged from 1.2–2.0 s, with 100% data transmission success. The system reliably triggered alerts when the temperature exceeded 360°C, confirming effective real-time monitoring. Research implications: Limitations include dependence on Internet connectivity, environmental effects on sensors, and scalability challenges. Future work should focus on improving robustness and integrating predictive features. Originality – The developed system demonstrates reliable performance at the prototype level. However, the validation was conducted under controlled conditions using a single sensor and without vehicle load. Therefore, further validation under varying load conditions, road gradients, and multipoint brake measurements is required before practical large-scale deployment.
Co-Authors Agus Siswono Azimatun Lissyifa Faris Humami Maulana Yusuf Alkahfi Raka Pratindy Siti Shofiah Widiandaru, Nanang Okta