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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) ILKOM Jurnal Ilmiah Teknomekanik Jurnal Ekonomika Dan Bisnis Internet of Things and Artificial Intelligence Journal
Rahmatullah, Rizky
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering

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Design and Implementation of a Real-Time Monitoring System for a 150 kV Substation with Multi-Platform Notification and Visualization: English Kartika, Eka Anggara Yuda; Muwardi, Rachmat; Rahmatullah, Rizky; Yunita, Mirna; Yuliza, Yuliza; Dani, Akhmad Wahyu
Internet of Things and Artificial Intelligence Journal Vol. 5 No. 2 (2025): Volume 5 Issue 2, 2025 [May]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v5i2.942

Abstract

This paper presents the development and implementation of an innovative real-time monitoring and notification system for a 150 kV electrical substation, leveraging Raspberry Pi 3, Node-RED, MySQL, and Firebase. The system measures key electrical parameters such as voltage, current, power, and frequency using sensors connected to a Programmable Logic Controller (PLC). The data is processed and displayed through a single-line diagram on both a web-based dashboard and an Android application. Color-coded indicators, controlled by JavaScript, reflect real-time equipment status, with normal conditions marked in red and fault conditions indicated in black. The novelty of this system lies in its integration of real-time data processing, dynamic visualization, and multi-channel notification mechanisms, combining web, mobile app, and messaging services like WhatsApp and email for operator alerts. This multi-layered approach improves operator response time and enhances monitoring accuracy, especially in remote or field environments. Experimental tests, including high-voltage and low-voltage fault simulations, demonstrated the system’s ability to accurately detect faults and communicate them through the notifications in real-time, with an average measurement error of just 1.56%. The system not only provides enhanced situational awareness but also offers an efficient, cost-effective solution for remote substation monitoring, ensuring continuous supervision and immediate response to power system anomalies.
Pengaruh Brand Experience dan Brand Image Terhadap Keputusan Pembelian Produk Semen Baturaja (Studi Kasus pada TB. Ridho Jaya, Banyuasin): (Studi Kasus pada TB. Ridho Jaya, Banyuasin) Rahmatullah, Rizky; Aliya, Sabeli
Jurnal Ekonomika Dan Bisnis (JEBS) Vol. 5 No. 4 (2025): Juli-Agustus
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jebs.v5i4.3066

Abstract

The purpose of this study is to examine the influence of brand experience and brand image on purchasing decisions of Baturaja cement products (a case study at TB. Ridho Jaya). This research employs a quantitative method with an associative-causal explanatory approach. The sample consists of 115 customers of TB. Ridho Jaya who purchased and used Baturaja cement products. Data was collected through the distribution of questionnaires using a Likert scale as the measurement instrument. Data analysis includes tests of data quality, classical assumption tests, multiple linear regression analysis, coefficient of determination, and hypothesis testing. The results reveal that both brand experience and brand image have a positive and significant influence, both partially and simultaneously, on the purchasing decisions of Baturaja cement among TB. Ridho Jaya’s customers. Abstrak Tujuan dari penelitian ini adalah untuk mengetahui pengaruh brand experience dan brand image terhadap keputusan pembelian produk semen baturaja (studi kasus TB. Ridho Jaya). Penelitian ini menggunakan metode kuantitatif dengan tingkat penjelasan asosiatif kausal. Adapun yang menjadi sampel dalam penelitian ini adalah pelanggan dari TB.Ridho Jaya yang membeli dan menggunakan produk semen baturaja, dengan jumlah 115 responden. Pengumpulan data dilakukan melalui distribusi kuesioner dengan skala Likert sebagai alat pengukur. Sedangkan Analisis data yang dilakukan meliputi uji kualitas data, uji asumsi klasik, analisis regresi linear berganda, uji koefisien determinasi, dan uji hipotesis. Hasil penelitian menunjukkan adanya pengaruh positif dan signifikan dari variabel Brand Experience dan Brand Image baik secara parsial maupun simultan, terhadap keputusan pembelian produk semen baturaja pada pelanggan TB. Ridho Jaya.
Raspberry Pi 4 and Ultrasonic Sensor for Real-Time Waste Classification and Monitoring with Capacity Alert System Yuliza, Yuliza; Muwardi, Rachmat; Kusuma, Prima Wijaya; Lenni, Lenni; Rahmatullah, Rizky; Yunita, Mirna; Dani, Akhmad Wahyu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30036

Abstract

The problem of waste management creates daily rubbish buildup due to thorough sorting. garbage sometimes accumulates in public garbage receptacles due to officials' ignorance of bin capacity and collectors' schedules, causing unclean conditions and the development of deadly diseases. Internet of Things technology was used to create a smart waste classification system with a notification mechanism in this study. This system classifies waste into plastic, metal, B3, and organic using a Raspberry Pi 4, camera module, and deep learning model. The classification uses a Convolutional Neural Network to speed up waste processing and separation. This research can be linked with research on separating trash types in one container and then allocated to garbage bins by type. Ultrasonic sensors and Raspberry Pi 4 can continuously monitor waste levels by sending data to the Ubidots IoT platform over HTTP. Based on experimental device data, system analysis shows 90% classification accuracy for all four waste categories. A Wireshark network analysis showed 61,098 bytes/s of throughput, 16 ms of delay, and zero data loss, demonstrating the system's ability for real-time monitoring and alerting. This research provides a realistic, cost-effective, and minimal solution to improve garbage classification and reduce collection costs to promote sustainability.
Multiclass gas pipeline leak detection using multi-domain signals and genetic algorithm-optimized classification models Suprihatiningsih, Wiwit; Romahadi, Dedik; Pranoto, Hadi; Youlia, Rikko Putra; Anggara, Fajar; Rahmatullah, Rizky
Teknomekanik Vol. 9 No. 1 (2026): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v9i1.38372

Abstract

Pipeline networks are critical infrastructure for oil and gas transport because the occurrence of leaks can rapidly escalate into safety, economic, and environmental crises. Operators are practically required to identify the presence and type of leaks; however, applying multiclass recognition is challenging when labeled data and computing power are limited. Therefore, this study proposes a three-stage pipeline which consists of: (1) adopting the GPLA-12 dataset of acoustic or vibration signals spanning 12 leak types; (2) extracting multi-domain features by combining time-domain descriptors with Power Spectral Density (PSD)-based spectral features; and (3) applying a genetic algorithm (GA) as a wrapper for feature selection to enhance discriminability and reduce dimensionality, which was followed by benchmarking seven conventional classifiers and GA-based refinement of the top model with a focus on the feature subset and hyperparameters. A maximum accuracy of 96.35% was achieved on the GPLA-12 dataset with low computation time and a simple model architecture. The proposed pipeline also attained similar or better accuracy at substantially lower complexity and data requirements compared with prior deep CNN approaches. These results support timely multiclass decision-making in resource-constrained industrial settings. A key observation was that the focus was on supervised leak-type classification from acoustic or vibration signals, while localization, severity estimation, and multi-sensor fusion were beyond the scope of this study.
An Exploration of the Work Performance of Educators in Transformative Schools: Leveraging Machine Learning for Performance Insights Maulidi, Rakhmad; Palandi, Jozua Ferjanus; Kristanto, Bagus Kristomoyo; Isyriyah, Laila; Rahmatullah, Rizky; Adi, Puput Dani Prasetyo; Kitagawa, Akio
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2358.109-125

Abstract

Education has gone through various phases, and entered the transformative school mode which can be said to change the existing order of the previous schooling system or procedures, because many modes can be done in the transformative school, students can learn in school buildings or classes, or in the field or real industry or the real world of work, with the introduction of a wider and more complex world, this is one of them. This research tries to create and analyze transformative schools in 3 algorithms, namely regression algorithms, classification algorithms, and clustering algorithms that provide a detailed analysis of the results of the analysis of transformative schools currently promoted by the government. from the results of the analysis raises performance conclusions, and in this phase a conclusion can be drawn whether the Transformative school is able to provide answers about the performance of teachers, students, teacher education levels, school locations, number of students, learning methods, or any paramaters that can provide detailed and detailed answers to get performance analysis from Machine Learning, and Work Performance of teachers in Transformative schools with precision. Quantitatively, the value of performance is determined by innovation by 43.2%, followed by technological capabilities and collaboration, 27.9% and 17.2% respectively. and based on cluster level, cluster 3 is the best with 118 educators, cluster 0, 127 educators with high innovators, and cluster 2, 126 educators, and cluster 1 with 129 educators. and from the paradox of transformative practices 30.6% are high Adopters
Co-Authors Adi, Puput Dani Prasetyo Akhmad Wahyu Dani Anggara, Fajar Bagus Kristomoyo Kristanto Dedik Romahadi Hadi Pranoto Jozua Ferjanus Palandi Kartika, Eka Anggara Yuda Kitagawa, Akio Kusuma, Prima Wijaya Laila Isyriyah Lenni Lenni, Lenni Mirna Yunita Rachmat Muwardi Rakhmad Maulidi Rikko Putra Youlia Sabeli Aliya Wiwit Suprihatiningsih Yuliza Yuliza