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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Teknomekanik Jurnal Ekonomika Dan Bisnis Internet of Things and Artificial Intelligence Journal Jurnal Riset Akuntansi Soedirman
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.
Design and Implementation of IoT-Based Monitoring Battery and Solar Panel Temperature in Hydroponic System Rahmatullah, Rizky; Kadarina, Trie Maya; Irawan, Bagus Bhakti; Septiawan, Reza; Rufiyanto, Arief; Sulistya, Budi; Santiko, Arief Budi; Adi, Puput Dani Prasetyo; Plamonia, Nicco; Shabajee, Ravindra Kumar; Atmoko, Suhardi; Mahabror, Dendy; Prastiyono, Yudi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26729

Abstract

Hydroponics is currently widely used for the effectiveness of farming in narrow areas and increasing the supply of food, especially vegetables. This hydroponic technology grew until it collaborated with the internet of things technology, allowing users to monitor hydroponic conditions such as temperature and humidity in the surrounding environment. This technology requires electronic systems to obtain cost-effective power coverage and have independent charging systems, such as power systems using solar panels, where the power received by solar panels from the sun is stored in batteries. It must ensure that the condition of the battery and solar panels are in good condition. The research contribution is to create a solar panel temperature monitoring system and battery power using Grafana and Android Application. Apart from several studies, solar panels are greatly affected by temperature, which can cause damage to the panels. If the temperature is too high, the battery and panel temperature monitoring system can help monitor the condition of the device at Grafana and Android application with sensor data such as voltage, current, temperature and humidity that have been tested for accuracy. Accuracy test by comparing AM2302 sensor with Thermohygrometer and INA219 sensor with multimeter and clampmeter, both of which have been calibrated. The sensor data gets good accuracy results up to 98% and the Quality-of-Service value on the internet of things network is categorized as both conform to ITU G.1010 QOS data based on network readings on the wireshark application. QOS results are 0% Packet loss with very good category, 14ms delay with very good category and Throughput 71.85 bytes/s.  With the results of sensor accuracy and QOS, the system can be relied upon with a high level of sensor accuracy so that environmental conditions are monitored accurately and good QOS values so data transmission to the server runs smoothly.
Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53 Muwardi, Rachmat; Faizin, Ahmad; Adi, Puput Dani Prasetyo; Rahmatullah, Rizky; Wang, Yanxi; Yunita, Mirna; Mahabror, Dendi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27402

Abstract

Currently, many object detection systems still use devices with large sizes, such as using PCs, as supporting devices, for object detection. This makes these devices challenging to use as a security system in public facilities based on human object detection. In contrast, many Mini PCs currently use ARM processors with high specifications. In this research, to detect human objects will use the Mini PC Nanopi M4V2 device that has a speed in processing with the support of CPU Dual-Core Cortex-A72 (up to 2.0 GHz) + Cortex A53 (Up to 2.0 GHz) and 4 Gb DDR4 Ram. In addition, for the human object detection system, the author uses the You Only Look Once (YOLO) method with the YoloV4-Tiny type, With these specifications and methods, the detection rate and FPS score are seen which are the feasibility values for use in detecting human objects. The simulation for human object recognition was carried out using recorded video, simulation obtained a detection rate of 0,9845 or 98% with FPS score of 3.81-5.55.  These results are the best when compared with the YOLOV4 and YOLOV5 models. With these results, it can be applied in various human detection applications and of course robustness testing is needed.
Analysis of IoT-LoRa to Improve LoRa Performance for Vaname Shrimp Farming Monitoring System Adi, Puput Dani Prasetyo; Ardi, Idil; Plamonia, Nicco; Wahyu, Yuyu; Mariana L, Angela; Novita, Hessy; Mahabror, Dendy; Zulkarnain, Riza; Wirawan, Adi; Prastiyono, Yudi; Waryanto, Waryanto; Susilo, Suhardi Atmoko Budi; Rahmatullah, Rizky; Kitagawa, Akio
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.27598

Abstract

Shrimp farming requires a touch that must be right on the side of water quality; water is a fundamental factor that must be met to achieve maximum yields. Many factors affect the quality of the water, but some things cause changes in water quality caused by external and internal factors causing death in shrimp. Disease conditions in shrimp can attack at any time, coupled with external factors such as extreme climate change, and cause changes in water components such as water pH, CaMg or hardness, and other factors that cause death in shrimp. Water turbidity oxygen demand (DO) in water determines the life of shrimp. It is coupled with microorganisms that must be maintained to maintain water quality for the growth of a Vaname shrimp. This research raises the Aquaculture System, specifically in the process of intelligent monitoring of water quality in shrimp nurseries to the shrimp harvest process, especially vaname shrimp from the results of observations use three sensors connected to LoRaWAN is able to provide real-time data from pond water and transmit it to LoRa Server or Internet Server, and the realtime data can be read through a Smartphone. This research analyzes in detail the ability of LoRaWAN to send multi-sensor data and Quality of Service LoRaWAN communication at different distances. This research also discusses how the LoRa antenna design can be developed to improve the performance of LoRa as transmitting devices or Radio Frequency 920-923 MHz for sending sensor data for Aquaculture.The contribution of this research is shown in the real-time monitoring system of the water environment, namely water pH, ammonia, turbidity, DO, salinity, water temperature, and nitrate in vaname shrimp ponds. The following contribution is the development of LoRaWAN with Tago IO servers capable of being used in Smart Aquaculture for contributions to The Things Network community or LoRaWAN Community.
Optimization of YOLOv4-Tiny Algorithm for Vehicle Detection and Vehicle Count Detection Embedded System Muwardi, Rachmat; Nugroho, Ivan Prasetyo; Salamah, Ketty Siti; Yunita, Mirna; Rahmatullah, Rizky; Chung, Gregorius Justin
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29693

Abstract

Currently, the implementation of object detection systems in the traffic sector is minimal. CCTV cameras on highways and toll roads are primarily used to monitor traffic conditions and document violations. However, the data recorded by these cameras can be further utilized to enhance traffic management systems. The author proposes a vehicle detection and counting system using YOLOv4-Tiny. The research aims to improve vehicle detection and counting accuracy by employing a median filter and grayscale processing, which simplify object detection. The proposed YOLOv4-Tiny algorithm has shown impressive results on various datasets, including MAVD, GRAM-RTM, and author dataset. The system achieved a detection accuracy of 98.95% on the MAVD dataset, 99.5% on the GRAM-RTM dataset (comparable to YOLOv4), and 99.1% on the author dataset. Furthermore, the system operates at 25 frames per second (FPS), a notably high rate compared to other methods. While the system demonstrates excellent accuracy in counting cars, it encounters some accuracy loss with other vehicle classifications. The author concludes that the system is highly suitable for real-world applications but notes that inaccurate labeling can lead to vehicle counting errors.
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.
PENGARUH GOOD CORPORATE GOVERNANCE, LIKUIDITAS DAN LEVERAGE TERHADAP KINERJA KEUANGAN PERUSAHAAN SEKTOR TEKNOLOGI TAHUN 2019-2023 Rahmatullah, Rizky; Rusmianto, Rusmanto; Pentiana, Destia
Jurnal Riset Akuntansi Soedirman Vol 4 No 2 (2025): JURNAL RISET AKUNTANSI SOEDIRMAN
Publisher : Jurusan Akuntansi Fakultas Ekonomi dan Bisnis, Universitas Jenderal Soedirman, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32424/1.jras.2025.4.2.18037

Abstract

This study aims to analyze the factors that influence financial performance. These factors include Good Corporate Governance (GCG), Liquidity, and Leverage. In this study, GCG is measured using (Independent Board of Commissioners, Audit Committee, Managerial Ownership), Liquidity is measured using the Current Ratio, and Leverage is measured using the Debt to Equity Ratio. The population used in this study were technology sector companies listed on the Indonesia Stock Exchange in 2019-2023, resulting in a population of 18 companies. Meanwhile, the sampling technique in this study used a purposive sampling method, namely using predetermined criteria, so that the data used as a sample of 11 companies. This research is a quantitative study using multiple linear regression analysis methods. The data used in this study are secondary data. The findings in this study indicate that an independent board of commissioners, audit committee, managerial ownership, and liquidity have no effect on financial performance. Meanwhile, leverage has a negative effect on financial performance.
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.
Co-Authors Adi Wirawan Adi, Puput Dani Prasetyo Ahmad Faizin, Ahmad Akhmad Wahyu Dani, Akhmad Wahyu Anggara, Fajar Ardi, Idil Arief Budi Santiko, Arief Budi Atmoko, Suhardi Chung, Gregorius Justin Hadi Pranoto Irawan, Bagus Bhakti Kartika, Eka Anggara Yuda Ketty Siti Salamah Kitagawa, Akio Kusuma, Prima Wijaya Lenni Lenni, Lenni Mahabror, Dendi Mahabror, Dendy Mariana L, Angela Mirna Yunita Novita, Hessy Nugroho, Ivan Prasetyo Pentiana, Destia Plamonia, Nicco Prastiyono, Yudi Rachmat Muwardi Rikko Putra Youlia Romahadi, Dedik Rufiyanto, Arief Rusmianto, Rusmanto Sabeli Aliya Septiawan, Reza Shabajee, Ravindra Kumar Sulistya, Budi Susilo, Suhardi Atmoko Budi Trie Maya Kadarina Wang, Yanxi Waryanto, Waryanto Wiwit Suprihatiningsih Yuliza Yuliza Yuyu Wahyu Zulkarnain, Riza