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All Journal Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Journal of Applied Smart Electrical Network and System (JASENS) The Indonesian Journal of Computer Science
Mustika Kurnia Mayangsari
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Rancang Bangun Sistem Pelacak Posisi Human Indoor Location menggunakan ESP-Now Rachmawan, Ega Adi; Lilik Subiyanto; Adianto; Mat Syaiin; Aulia Rahma Annisa; Mustika Kurnia Mayangsari
Journal of Applied Smart Electrical Network and Systems Vol 6 No 01 (2025): Vol 06, No. 01 June 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jasens.v6i01.1150

Abstract

This research aims to develop an Internet of Things (IoT)-based employee attendance and position tracking system using the ESP-NOW protocol integrated with a web database. This system is designed to automatically record attendance and monitor the position of employees in real-time using the Trilateration method with the Exponential Path Loss model. Data from the ESP32 device is sent to the MySQL database via the MQTT protocol and displayed in an easily accessible website interface. The results show that the system can efficiently recap daily, weekly, and monthly attendance data, as well as display the position history of employees and guests with sufficient accuracy. With the implementation of this system, the efficiency and accuracy of attendance management and employee monitoring at Sutami Hydroelectric Power Plant has increased significantly.
Sistem Input Output Inventaris Tools Menggunakan Long Range RFID Study Case di PLTA Sutami Fa'iz, Muhammad; Joko Endrasmono; Sholahuddin Muhammad Irsyad; Lilik Subiyanto; Anggar Juna Puncak Pujiputra; Mustika Kurnia Mayangsari
Journal of Applied Smart Electrical Network and Systems Vol 6 No 01 (2025): Vol 06, No. 01 June 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jasens.v6i01.1152

Abstract

The problem faced by Sutami Hydroelectric Power Plant in the management and data collection of tools is that the data collection is still done manually, and some tools are not returned to their place due to negligence in use. To support the achievement of the 5S culture (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) at Sutami Hydroelectric Power Plant, an automated tools management system is needed. In the application of the goods input and output system, many are still using low frequency RFID with a reading range of >5cm so that tapping must be done with an RFID Reader. This still has the potential for an unrecorded loan process if the item is not tapped. This research optimizes the tools management system with long range RFID. With the designed update, it is expected that the tools management system at Sutami Hydroelectric Power Plant can provide increased productivity and also achieve the 5S culture at Sutami Hydroelectric Power Plant.
Penerapan Deteksi Titik Api Pada Area graving dock Menggunakan YOLO dan GRAD-CAM Fadlol, Muhammad Thoriq; Khumaidi, Agus; Subiyanto, Lilik; Joko Endrasmono; Mustika Kurnia Mayangsari; Anggarjuna Puncak Pujiputra
Jurnal Elektronika dan Otomasi Industri Vol. 12 No. 1 (2025): Vol 12 No 1 (Mei 2025): Jurnal Elkolind Vol 12 No 1 (Mei 2025)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v12i1.7253

Abstract

Fire spot detection in the graving dock area is crucial to prevent potentially harmful fires. This study employs the YOLOs method as a deep learning-based object detection technique to detect fire and sparks in real-time. Despite its high accuracy, visual interpretation of detection results remains challenging. Therefore, the Grad-CAM technique is utilized to generate a heatmap on the detection area of YOLO. The heatmap is calculated using the alpha blending method with a specific transparency factor, resulting in clearer visualization of detected objects. The test results show that the combination of YOLO and Grad-CAM can detect fire with an accuracy of 73%. The heatmap visualization validates the critical areas that contribute to the model's decision, making it suitable for fire monitoring systems in high-risk areas.
Penerapan Harris Corner Detection dan YOLOv5 pada Kamera Stereo Vision untuk Estimasi Jarak Robot Sepak Bola Beroda KRSBI-B Adi Rahmad Ramadhan; Khumaidi, Agus; Mustika Kurnia Mayangsari; Mat Syai’in; Imam Sutrisno; Aulia Rahma Annisa
Jurnal Elektronika dan Otomasi Industri Vol. 12 No. 1 (2025): Vol 12 No 1 (Mei 2025): Jurnal Elkolind Vol 12 No 1 (Mei 2025)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v12i1.7254

Abstract

This research enhances the performance of a wheeled soccer robot in the RoboCup Middle Size League (KRSBI-B) by integrating Stereo Vision cameras, YOLOv5, and Harris Corner Detection for precise distance estimation and object detection. The objective is to improve the robot's ability to accurately recognize and measure the distance of objects, particularly the ball and opposing robots. Using image processing, the system significantly enhances real-time object detection, improving decision-making during the match. The YOLOv5 algorithm, trained with 4,000 labeled images, achieved impressive accuracy with confidence levels up to 0.99 for ball detection at 250 cm. Results show strong correlations between high-confidence detections and accurate distance estimations, enabling effective responses to dynamic match situations. This system provides a competitive edge, improving responsiveness, adaptability, and gameplay strategies, supporting its application in real-world robotic competitions.
Development of an LSTM-Based Power Monitoring and Prediction System for Campus Electrical Facilities Using ESP32 and PM2120 Sholikhah, Evi Nafiatus; Oktavia Rizqi Kurniawan; Dimas Pristovani Riananda; Mustika Kurnia Mayangsari; Rohmad Hadi Handayani
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5030

Abstract

This study develops a data acquisition system for monitoring, detecting, and forecasting electrical energy consumption to support efficient energy management. Electrical parameters such as voltage, current, and power are measured using a PM2120 power meter via Modbus RTU RS485 and processed by an ESP32 microcontroller. The data are displayed in real-time through a Nextion Human-Machine Interface (HMI) and utilized as input for a Long Short-Term Memory (LSTM) model trained on historical consumption data. Safety features include LED indicators that activate when current reaches 80% of maximum capacity and a buzzer that signals threshold violations. Experimental results demonstrate high prediction accuracy, with RMSE values of 0.38 kW (5.32%) for phase R, 0.47 kW (7.55%) for phase S, and 0.28 kW (5.39%) for phase T. Transmission latency averages two to three seconds, while prediction computation is under 10 seconds. The system effectively reflects consumption trends, making it a reliable decision-support tool for enhancing energy efficiency in small- to medium-scale installations.
Co-Authors Adi Rahmad Ramadhan Adianto Agus Khumaidi Anggar Juna Puncak Pujiputra Anggarjuna Puncak Pujiputra Aulia Rahma Annisa Dimas Pristovani Riananda Fa'iz, Muhammad Fadlol, Muhammad Thoriq Imam Sutrisno Joko Endrasmono Lilik Subiyanto Mat Syaiin Mat Syai’in Oktavia Rizqi Kurniawan Rachmawan, Ega Adi Rohmad Hadi Handayani Sholahuddin Muhammad Irsyad Sholikhah, Evi Nafiatus