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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Soft Computing Exploration
Ramadhani, Uri Arta
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Penerima Gelombang ELF berbasis Op-Amp untuk Pengolahan Akuisisi Data Gempa Bumi ASTHAN, RHEYUNIARTO SAHLENDAR; CORIO, DEAN; ULFAH, MIA MARIA; RAMADHANI, URI ARTA; MUNIR, ACHMAD
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 3: Published July 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i3.592

Abstract

ABSTRAKPenelitian ini membahas mengenai penerima gelombang extremely low frequency (ELF) untuk pengolahan akuisisi data gempa bumi. Penerima ELF dirancang menggunakan operational amplifier (Op-Amp) dengan masukan takmembalik. Sinyal yang diterima oleh antena diteruskan ke penerima ELF yang terdiri dari preamplifier dan amplifier untuk proses penguatan, serta filter aktif orde 2 untuk menekan sinyal di atas frekuensi cut-off sebesar 50Hz. Karakterisasi penerima ELF dilakukan dengan mengamati perbandingan level tegangan sinyal keluaran terhadap level tegangan sinyal masukan, sensitivitas, serta bentuk sinyal keluaran dari penerima ELF dalam domain waktu. Hasil simulasi menunjukkan bahwa penerima ELF menghasilkan penguatan sebesar 60,8dB dengan sensitifitas tinggi untuk level sinyal masukan di bawah -30dB yang mampu memenuhi level sinyal untuk pengolahan akuisisi data.Kata kunci: extremely low frequency, penerima ELF, operational amplifier, filter aktif, gempa bumi ABSTRACTThis research presents extremely low frequency (ELF) receiver for earthquake data acquisition processing. The ELF receiver is designed based on non-inverting operational amplifier (Op-Amp). The signal received by the antenna is fed into ELF receiver which consists of preamplifier and amplifier for amplification, and second order active filter to suppress unwanted signal above the cut-off frequency of 50Hz. Characterization of ELF receiver is performed by observing the comparison of the level output signal to level input signal, sensitivity, and ELF receiver signal output in time domain. The simulation results show that the ELF receiver has gain of 60.8dB with high sensitivity for low level input signals below -30dB that is able to meet signal level for data acquisition processing.Keywords: extremely low frequency, ELF receiver, operational amplifier, active filter, earthquake
Improved human image density detection with comparison of YOLOv8 depth level architecture and drop-out implementation Yulita, Winda; Ramadhani, Uri Arta; Mufidah, Zunanik; Atmajaya, Gde KM; Bagaskara, Radhinka; Kesuma, Rahman Indra; Aprilianda, Mohamad Meazza
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.556

Abstract

Energy inefficiency due to Air Conditioners (AC) running in empty rooms contribute to unnecessary energy consumption and increased CO₂ emissions. This study explores how different depth levels of the YOLOv8 architecture and dropout regularization can enhance human density detection for smarter AC control systems. By evaluating model accuracy through Mean Average Precision (mAP50-95), we provide quantitative insights into how these modifications improve detection performance. Our dataset consists of 1363 images taken in an office environment at ITERA under varying lighting conditions and different human presence densities. The results show that the YOLOv8m model performs best, achieving an mAP50-95 score of 0.814 in training and 0.813 in validation, outperforming other YOLOv8 variants. Furthermore, applying dropout regularization improves model generalization, increasing mAP50-95 from 0.552 to 0.6 and effectively reducing overfitting. This study highlights the balance between architectural depth and dropout regularization in YOLOv8, demonstrating its effectiveness in energy-efficient smart buildings. The findings support the potential of deep learning-based human density detection in improving energy conservation strategies, making it a valuable solution for intelligent automation systems.
Automatic License Plate Recognition (ALPRON) Using Optical Character Recognition Method Prasetyawan, Purwono; Aulia, Muhammad Athallah Cahya; Utami, Nia Saputri; Ramadhani, Uri Arta
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9903

Abstract

Manual parking systems are prone to inefficiencies and human error, especially with increasing vehicle density. This study proposes ALPRON, an automatic license plate recognition system using Optical Character Recognition (OCR) to automate motorcycle parking management. The system integrates Raspberry Pi 4, USB cameras, and Tesseract OCR to detect and recognize license plates in real-time. Performance testing was conducted under varying distances, lighting intensities, and camera angles. The results show that the system achieves a peak recognition accuracy of 98.75% at 70 cm, in bright lighting, and a 0° camera angle. These findings suggest that ALPRON is a potentially cost-effective and efficient solution for smart parking applications, particularly in controlled campus environments. While current limitations include daylight dependency and difficulty recognizing skewed angles plates, future improvements will address these through infrared support and deep learning enhancements.
Ideal Temperature Classification of Meeting Rooms Using You Only Look Once Architecture Version 8 and Multilayer Perceptron Based on Human Density Image Data Ridwan, Naufal Taufiq; Yulita, Winda; Kesuma, Rahman Indra; Ramadhani, Uri Arta; Bagaskara, Radhinka
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.34230

Abstract

Indonesia, located along the equator, experiences a tropical climate that results in high indoor temperatures. Elevated temperatures can affect health, making air conditioning (AC) necessary to regulate indoor environments. However, improper use of AC systems, such as leaving them on even when a room is unoccupied, can lead to significant energy waste. This research focuses on the efficient use of AC systems through the integration of sensors and cameras, combining two distinct technologies. The first technology is object detection using You Only Look Once (YOLOv8), which was chosen for its superior performance in terms of speed, accuracy, and computational efficiency. The second is the classification of optimal AC temperatures using the Multilayer Perceptron (MLP) algorithm, selected for its high performance in accuracy, sensitivity, and speed. In addition, the study takes into account human density in the room to optimize temperature regulation. The integration of object detection and temperature classification technologies enables the system to operate in real time and automatically adjust temperature settings based on dynamic room conditions. The research successfully implemented YOLOv8 for object detection and Multilayer Perceptron for optimal room temperature classification. Test results showed precision, recall, and F1-score values of 0.82, 0.92, and 0.86, respectively.
Co-Authors Achmad Munir Aprilianda, Mohamad Meazza Atmajaya, Gde KM Aulia, Muhammad Athallah Cahya Bagaskara, Radhinka Dean Corio Mufidah, Zunanik Nia Saputri Utami Prasetyawan, Purwono Rahman Indra Kesuma Rheyuniarto Sahlendar Asthan Ridwan, Naufal Taufiq ULFAH, MIA MARIA Yulita, Winda