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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi Jurnal Teknologi Elektro InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Abdi Masyarakat (JAM) Indonesian Journal of Electrical Engineering and Computer Science Journal of Integrated and Advanced Engineering (JIAE) Internet of Things and Artificial Intelligence Journal
Rachmat Muwardi
Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Fiber Optic Attenuation Analysis Based on Mamdani Fuzzy Logic in Gambir Area, Central Jakarta Yuliza Yuliza; Ninda Sari; Rachmat Muwardi; Lenni Lenni; Yosy Rahmawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.24549

Abstract

In this study, the authors conducted an analysis of the quality of fiber optic network maintenance based on attenuation value and maintenance time using fuzzy Mamdani logic and simulated using Matlab software, to improve accuracy in drawing conclusions on maintaining quality. This study uses a quantitative method, in which the author obtains a summary of customer data from PT. Telkom Indonesia in a period of 4 months of observation from August to November 2021. In August there were 776 customers, in September there were 362 customers, in October there were 359 customers, and in November 445 customers who underwent Indihome fiber optic cable maintenance. The test results with the centroid method with an input Handling Time of 1.5 hours and an Attenuation of 15 dB, then the output Repair Quality is 5.5 or categorized as Good. The greater the attenuation value generated, the more time it takes to maintain the IndiHome internet network disturbance. This is due to the many technical maintenance of fiber optic cables carried out by technicians to adjust for damage/trouble in the field. It is expected that maintenance can be carried out routinely in order to avoid fatal internet disturbances on the customer's side, and maximize maintenance time according to the dosage determined by the company, which is less than 3 hours, taking into account the work performance of technicians and also the quality of maintenance.
Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family Mirna Yunita; Rachmat Muwardi; Zendi Iklima
SINERGI Vol 27, No 1 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2023..1.004

Abstract

Dipterocarpaceae is one of the most prominent plant families, with more than 500 members of species. This family mostly used timber plants for housing, making ships, decking, and primary materials for making furniture. In Indonesia, many Dipterocarpaceae species have morphological similarities and are challenging to recognize in the field. As a result, the classification process becomes difficult and even results are inconsistent when viewed only from the morphology. This research will analyze the phylogenetic tree of Dipterocarpaceae based on the chloroplast matK gene. The aim of the research is to classify the phylogenetics tree of Dipterocarpaceae family using Bayesian inference algorithm. This research used the chloroplast gene instead of morphological characters which has more accurate. The analysis steps are collecting data, modifying the structure sequence name, sequence alignment, constructing tree by using Markov Chain Monte Carlo (MCMC) from Bayesian Inference, and evaluating and analyzing the phylogenetic tree. The results showed that the tree constructed based on the gene is different from the tree based on morphology. Based on the morphological, Dipterocarpus should be in the Dipterocarpeae tribe but based on the similarity of its genes, Dipterocarpus is more similar to the Shoreae tribe.   
Human Object Detection for Real-Time Camera using Mobilenet-SSD Rachmat Muwardi; Joe Mada Ranseda Permana; Hongmin Gao; Mirna Yunita
Journal of Integrated and Advanced Engineering (JIAE) Vol 3, No 2 (2023)
Publisher : Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51662/jiae.v3i2.108

Abstract

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can benefit from this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is safety, namely in searching for people, which is still done manually. Searching for teams to find people is often challenging because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the MobilenetV2-SSD, where this algorithm has high detection and accuracy. Using the mobilenetV2-SSD simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.
Fast Human Recognition System on Real-Time Camera Yuliza Yuliza; Rachmat Muwardi; Mustain Rhozaly; Lenni Lenni; Mirna Yunita; Galatia Erica Yehezkiel
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.v9i4.27009

Abstract

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can get many benefits by implementing this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is in the area of safety, namely in the field of searching for people, which is still done manually. Often search teams find it challenging to find people because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the You only look once (YOLO) method with the YoloV4-Tiny type, where this algorithm has high detection speed and accuracy. Using the YOLOV4-Tiny simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.
Design of Equipment for Detecting and Ensuring Reliability of The Substation Ihsan, Hafid; Muwardi, Rachmat; Yunita, Mirna; Yuliza, Yuliza; Dani, Akhmad Wahyu
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.774

Abstract

Substations are vital elements of electrical infrastructure that necessitate continuous monitoring and maintenance to ensure optimal performance. This research advocates for the deployment and design of devices based on the Raspberry Pi 3 Model B to enhance substation reliability. The project involves developing hardware and software capable of real-time monitoring of substation conditions, utilizing sensors to measure critical parameters such as temperature, current, voltage, and humidity. The monitoring software is designed to collect, analyze, and report data, employing detection algorithms, including the Fuzzy Mamdani method, to ensure accurate sensor and frequency measurements and to identify potential disturbances or anomalies. Additionally, the system integrates automatic mechanisms for maintaining substation conditions, encompassing preventive measures and rapid responses to emergency situations. Testing under various fault scenarios and operational conditions demonstrated the device's effectiveness in detecting issues and providing swift responses, thereby enhancing substation performance. The results show an average error of 0.14% for voltage measurements, 0.31% for current measurements, and 0.02% for data transmission frequency. This implementation is expected to positively impact substation management and maintenance, reduce the risk of system failures, and improve overall operational efficiency. Leveraging Raspberry Pi technology ensures a cost-effective solution that can be seamlessly integrated with existing substation monitoring systems.
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.
Remove glasses diffusion model an innovative conditioned of eye glasses removal with image diffusion model Yuliza, Yuliza; Muwardi, Rachmat; Yehezkiel, Galatia Erica; Yunita, Mirna; Lenni, Lenni
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1503-1516

Abstract

The presence of eyeglasses in facial images poses challenges for image processing, particularly in facial recognition. This paper introduces the remove glasses diffusion model (RGDM), a conditioned denoising diffusion probabilistic model (DDPM) designed for precise glasses removal. RGDM employs conditional modeling to focus on the glasses region while seamlessly restoring facial features. An eyes position accuracy mechanism, leveraging facial landmarks, ensures accurate eye restoration post-removal. Comprehensive evaluations on the CelebA dataset demonstrate RGDM’s superior performance, achieving the lowest Fréchet inception distance (FID) of 27.09 and learned perceptual image patch similarity (LPIPS) of 0.299, outperforming state-of-the-art methods such as 3D synthetic, cycleconsistent generative adversarial network (CycleGAN), and eyeglasses removal generative adversarial network (ERGAN). These results highlight the model’s effectiveness in producing natural and high-fidelity facial reconstructions. This work advances glasses removal technology and underscores the significance of conditional models in image processing. The proposed approach has practical implications for facial recognition and image enhancement, paving the way for more accurate and robust real-world applications.
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.
Modification of Control Oil Feeding with PLC Using Simulation Visual Basic and Neural Network Analysis Yuliza Yuliza; Rachmat Muwardi; Danang Widya Pratama; Makmur Heri Santoso; Mirna Yunita
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 1 (2022): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i1.22336

Abstract

The oil feeding system is an oil distribution system used in engine lubrication by flowing it directly to the engine parts to be lubricated through pipes. In addition, it is also a raw material for the production process by collecting the oil first in the storage tank, then weighing it on the oil scale before use in the production process. The current control is still using the conventional model. The operating system is still manual, and the absence of identity and damage information makes it difficult for the engineer to troubleshoot. The research method is to modify the oil feeding system control using PLC (Programmable Logic Controller) and Visual Basic to display process information. This process uses the Neural Network (NN) method. The simulation results show that the PLC program and visual basic software can be connected properly. The speed of the data transfer test connection that can be obtained is 32 ms. The prediction process of the oil feeding system using the backpropagation algorithm Neural Network and the activation function, which uses the binary sigmoid function (logsig) with the 17-10-1 architecture having very good performance getting the MSE value below the error value of 0.001 maximum epoch 961 and hidden layer 10 with an MSE value of 0.00099915.
Optimize Image Processing Algorithm on ARM Cortex-A72 and A53 Rachmat Muwardi; Mirna Yunita; Harun Usman Ghifarsyam; Hendy Juliyanto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.24457

Abstract

This work presents a technique to optimize processing image algorithms. The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has the mandate to optimize them so as to meet real-time deadlines. A methodology to take advantage of the asymmetric dual-core processor, which includes an ARM supported by shared memory, is presented with implementation details. The target platform chosen is the NanoPi M4V2. It has a dual-core and quad-core architecture with an ARM Cortex-A72 and Cortex-A53. The basic image correlation algorithm is chosen for benchmarking as it finds widespread application for various template-matching tasks such as face recognition. The basic algorithm prototypes conform to OpenCV, a popular computer vision library. OpenCV algorithms can be easily ported to the ARM core, which runs a popular operating system such as Linux. The algorithms are tested on a variety of images, and performance results are presented, measuring the speedup obtained due to dual-core and quad-core implementation. A major advantage of this approach is that it allows the ARM processor to perform important real-time tasks.
Co-Authors Ahmad Faizin Akhmad Wahyu Dani Alvin Sepbrian Andika Arrizki Asif Khan Bugi Nur Rohman Danang Widya Pratama Dendi Mahabror Diah Septiyana Firman Andika Freddy Artadima Silaban Galang P. N. Hakim Galang Persada Nurani Hakim Galatia Erica Yehezkiel Gao Hongmin Gregorius Justin Chung Harun Usman Ghifarsyam Harun Usman Ghifarsyam Hendy Juliyanto Heri Santoso, Makmur Hongmin Gao Hongmin Gao Ihsan, Hafid Ivan Prasetyo Nugroho Joe Mada Ranseda Permana Julpri Andika Kartika, Eka Anggara Yuda Ketty Siti Salamah Kusuma, Prima Wijaya Lenni Lenni Lenni Lenni Lenni Lenni, Lenni Lukman Medriavin Silalahi Mhd Adanan Purba Mirna Yunita Mirna Yunita Mirna Yunita Mirna Yunita Muhammad Hafizd Ibnu Hajar Mustain Rhozaly Ninda Sari Puput Dani Prasetyo Adi Rahmatullah, Rizky Reyhan Rahmat Adisaputro Rizky Rahmatullah Septiyana, Diah Setiyo Budiyanto Suwoyo, Heru Triyanto Pangaribowo Yanxi Wang Yehezkiel, Galatia Erica Yosy Rahmawati Yudhi Gunardi Yuliza Yuliza Yuliza Yuliza Zendi Iklima Zody Arifiansyah