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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Kitektro Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Cyberspace: Jurnal Pendidikan Teknologi Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Green Intelligent Systems and Applications Jurnal Rekayasa elektrika Jurnal Polimesin Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Rekayasa dan Wirausaha Jurnal Komputer, Informasi Teknologi, dan Elektro (KITEKTRO) Jurnal Pengabdian Rekayasa dan Wirausaha Jurnal Rekayasa elektrika
. Roslidar
Departemen Teknik Elektro Dan Komputer, Fakultas Teknik, Universitas Syiah Kuala
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Other

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IoT based System for Air Pollution Monitoring in Banda Aceh Roslidar Roslidar; Karnaini Karnaini; Teuku Yuliar Arif
Jurnal Rekayasa Elektrika Vol 19, No 3 (2023)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v19i3.28686

Abstract

Air pollution is a factor that affects the clear skies and breathable air of the city. Humans cannot directly quantify the changes in air quality; hence we need a technological tool to detect the changes in air quality around them. This study proposed a prototype to monitor air quality using embedded system hardware of Arduino Uno-R4 and ESP8266. A Thingspeak database is used as a platform for data communication between smartphones and sensors in real time. The data is retrieved once every 15 seconds. In this prototype, the Arduino Uno-R3 is used as the main brain of the system to connect to WiFi communication via ESP8266 and to four (4) sensors, namely CO (MQ-7), CO2 (MQ-9), dust (PM10), and DHT22 (temperature and humidity). The developed prototype is portable and has low power consumption. Several testing locations have been identified to monitor the air pollution; (1) Simpang Lima Intersection and (2) Jeulingke Bus Stop in Banda Aceh. The system performance shows the connectivity between devices has only a delay of ± 1.1 seconds; therefore, the system is suitable for real-time usage.
Perancangan Robot Light Follower untuk Kursi Otomatis dengan Menggunakan Mikrokontroler ATmega 328P Roslidar Roslidar; Alfatirta Mufti; Haris Akbarsyah
Jurnal Rekayasa Elektrika Vol 13, No 2 (2017)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v13i2.8093

Abstract

This article discusses the design of light follower chair prototype with speed adjustment of DC motor according to light intensity using microcontroller ATmega328p. This prototype provides a solution for a chair to be back on the position under the table automatically by using a light follower robot principle. There are many possible positions of a chair after being used: perpendicular or sideways to the table. As the positions after being used are varied, the light is used to direct the chair toward under the table since the light can reach the area around the chair except for the back area. This prototype functions well if the chair is heading to the table and is not designed to function in the backward position. LDR (Light Dependent Resistor) sensors are used to detect the light. As the source of light, 1 W high power LED is put under the table. A microcontroller ATmega328p is used to execute the input and output of this system. Two DC motor are used as actuators to control the movement of the chair toward the light under the table. Ultrasonic sensors HC-SR04 are used to measure the distance between the table and the chair so that the chair can stop at the desired position.
Adaptasi Model CNN Terlatih pada Aplikasi Bergerak untuk Klasifikasi Citra Termal Payudara Roslidar Roslidar; Muhammad Rizky Syahputra; Rusdha Muharar; Fitri Arnia
Jurnal Rekayasa Elektrika Vol 18, No 3 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i3.8754

Abstract

The model development for breast thermal image classification can be done using deep learning methods, especially the convolutional neural network (CNN) architecture. This article focuses on adapting a trained CNN (trained model) on a mobile application for binary classification of breast thermal images into normal and abnormal classes. The CNN model applied in this study was based on ShuffleNet, called BreaCNet, with a learning weight of 1028 filters generated from training on images downloaded from the Database for Mastology Research (DMR) and a model size of 22 MB. The model must be converted into a mobile application to enable a trained model to be adapted into a mobile platform. The BreaCNet model was built using MatLab; thus, the stages in the adaptation process consisted of converting the model into ONNX file format, converting ONNX files into Tensorflow files, and Tensorflow files into Tensorflow Lite format. However, not all nodes are fully supported by MATLAB. The shuffle node on ShuffleNet cannot be fully exported using ExportToOnnx, so it needs to be re-defined with a placeholder named “MATLAB PLACEHOLDER”. In addition to the model conversion process, this article describes the user interaction process with the application using UML diagrams and application feature menu designs. The application was also tested on 20 thermal images of the breast. The testing results show that the application can perform the image classification process on mobile devices in less than 1 second with an accuracy rate of 85%. Finally, the breast thermal image screening application has been successfully built by directly interpreting the thermal image of the breast on a mobile device to keep the user data private.
Improved Lung Sound Classification Model Using Combined Residual Attention Network and Vision Transformer for Limited Dataset Jurej, Muhammad; Roslidar, Roslidar; Yunida, Yunida
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 4: December 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i4.5530

Abstract

According to WHO data, the prevalence of respiratory disorders is increasing, exacerbated by a shortage of skilled medical professionals. Consequently, there is an urgent need for an automated lung sound classification system. Current methods rely on deep learning, but limited lung sound data resulted in low model accuracy. The widely used ICBHI 2017 dataset has an imbalanced class distribution, with a normal class at 52.8%, wheezing at 27.0%, crackles at 12.8%, and combined wheeze and crackles at 7.3%. The imbalance of the dataset may affect the model's efficiency and performance in classifying lung sounds. Given these data limitations, we propose a hybrid model, combining residual attention network (RAN) and vision transformer (ViT), to construct an effective respiratory sound classification model with a small dataset. We employ feature fusion techniques between convolutional neural network (CNN) feature maps and image patches to enrich lung sound features. Additionally, our preprocessing involves bandpass filtering, resampling sounds to 16 kHz, and normalizing volume to 15 dB. Our model achieves impressive ICBHI scores with 97.28% specificity, 92.83% sensitivity, and an average score of 95.05%, marking a 10% improvement over state-of-the-art models in previous research.
Literature Review: Biomedical Information of Animal Treadmill Speed Control Using Proportional Integral Derivative Controller Nurbadriani, Cut Nanda; Melinda, Melinda; Roslidar, Roslidar
Green Intelligent Systems and Applications Volume 4 - Issue 2 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i2.526

Abstract

The use of treadmill exercise in cardiovascular research played a vital role in assessing heart health and determining appropriate exercise regimens for patients. Before applying these regimens to humans, experiments on animals, such as white rats or mice, were conducted to simulate human cardiovascular responses. A specialized treadmill designed for experimental animals was required to determine exercise doses based on individual abilities. This process involved controlling the treadmill speed, which was generated by a conveyor driven by a DC motor. The motor speed was regulated through PID (Proportional Integral Derivative) control, while encoder sensors monitored the motor’s rotation speed, and limit switch sensors determined the exercise duration. This article reviewed the design and implementation of treadmill systems used for animal-based cardiovascular research, focusing on the control of DC motor speed using PID controllers. Previous studies that contributed to the development of such systems were discussed, with an emphasis on the precise control mechanisms required to simulate exercise conditions tailored to the subject's abilities. The treadmill system also incorporated sensors to accurately adjust motor speed and track exercise duration, ensuring alignment with the physiological capabilities of the test subjects. Furthermore, this review explored the potential for advancing research on treadmill control systems, offering insights into how this technology could support medical experts in determining optimal exercise regimens for white rats. These developments helped bridge the gap between animal-based studies and human applications, facilitating improved cardiovascular research outcomes.
Intelligent Tuberculosis Detection System with Continuous Learning on X-ray Images A'yuni, Qurrata; Nasaruddin, Nasaruddin; Irhamsyah, Muhammad; Azhary, Mulkan; Roslidar, Roslidar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.572

Abstract

Tuberculosis (TB) has become a global health threat with millions of cases each year. Therefore, rapid and accurate detection is needed to control its spread. The application of artificial intelligence, especially Deep Learning (DL), has shown great potential in improving the accuracy of TB detection through DL-based X-ray image analysis. Although many studies have developed X-ray image classification models, very few have integrated them into web or mobile platforms. In addition, the models integrated into these platforms generally do not apply continuous learning methods so that model performance cannot be updated. Thus, it is necessary to build an intelligent system based on a web application that integrates the ResNet-101 model for TB detection in X-ray images. This system utilizes continuous learning methods, allowing the model to automatically update itself with new data, thereby improving detection performance over time. The results showed that before continuous learning, the model successfully classified all TB images correctly, but was only able to classify two normal images correctly, resulting in an accuracy of 62.5%. After manual continuous learning, the model showed an increase in accuracy to 71.4%, with better ability to recognize normal images, although there was a slight decrease in performance in detecting TB.
PENINGKATAN HASIL BUDIDAYA IKAN LELE MELALUI PENGENDALIAN KUALITAS AIR DENGAN MICROBUBBLE DAN SISTEM MONITORING IOT Islamy, Fajrul; Fauzan, Muhammad; Sakti, Indra; Roslidar, Roslidar
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 9, No 1 (2025)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v9i1.29464

Abstract

Keberhasilan budidaya perairan bergantung pada kondisi air yang optimal, termasuk kualitas dan kuantitas oksigen terlarut dalam air yang merupakan unsur penting dalam kehidupan akuatik. Tingkat oksigen yang rendah menjadi faktor pembatas serius dalam pertumbuhan dan kesehatan organisme akuatik. Artikel ini bertujuan untuk mengimplementasikan penggunaan teknologi microbubble secara IoT dalam aplikasi akuakultur dengan memastikan kondisi yang optimal bagi organisme akuatik. Penerapan microbubble dalam akuakultur menjanjikan peningkatan signifikan dalam ketersediaan oksigen bagi ikan lele, yang berdampak positif pada pertumbuhan, kesehatan, dan produktivitasnya. Teknologi Internet of Things (IoT) memungkinkan pengawasan kondisi lingkungan secara real-time dari jarak jauh, memungkinkan pengambilan keputusan yang cepat dan tepat dalam respons terhadap perubahan kondisi lingkungan. Metode yang digunakan pada penelitian ini adalah pengujian dari 3 sensor yaitu DS18B20, pH, dan DO yang masing-masing mengukur suhu, pH, dan kadar oksigen dalam air. Selanjutnya data dikirim ke aplikasi blynk dan diprogram pada Raspberry Pi. Hasil yang didapat menunjukkan bahwa pertumbuhan lele selama 10 hari meningkat sebanyak 30% dibandingkan dengan akuarium tanpa sistem microbubble.
Comparative Study of BiLSTM and GRU for Sentiment Analysis on Indonesian E-Commerce Product Reviews Using Deep Sequential Modeling Nasution, Khairunnisa; Saddami, Khairun; Roslidar, Roslidar; Akhyar, Akhyar; Fathurrahman, Fathurrahman; Aulia, Niza
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4878

Abstract

Sentiment analysis plays a crucial role in understanding customer perspectives, especially within Indonesian e-commerce platforms. Despite the success of deep learning in high-resource languages, its application to Indonesian sentiment data remains underexplored. Previous studies using models like BERT-CNN or fine-tuned IndoBERT achieved modest results, highlighting the need for more effective architectures for Indonesian language. This study aims to investigate the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) models in classifying buyers’ sentiment from Indonesian product reviews on the PREDECT-ID dataset comprising 5,400 annotated product reviews. Standard NLP preprocessing techniques—including text normalization, tokenization, stopword removal, and stemming—were applied. Both models were trained using Adam and Stochastic Gradient Descent (SGD) optimizers, and their performance was evaluated using accuracy, precision, recall, and F1-score metrics. The GRU model trained with SGD achieved the highest performance, with an accuracy of 94.07%, precision of 93.84%, recall of 94.53%, and F1-score of 94.18%. Notably, the BiLSTM model combined with SGD resulted in competitive results, achieving 93.61% accuracy and 93.84% F1-score. The results confirm that GRU with SGD optimizer, are highly effective for sentiment classification in Indonesian language datasets. By leveraging deep sequential modeling for a low-resource language, this study contributes to the advancement of scalable sentiment analysis systems in underrepresented linguistic domains. The results contribute to the advancement of NLP systems for Indonesian by providing a benchmark for the future development of sentiment analysis tools in low-resource languages.
Pembangkit Listrik Dengan Sistem Multihybrid dari Tenaga Fotovoltaik dan Mikrohidro Berbasis Fingerprint dan Internet of Thing (IoT) Roslidar, Roslidar; Irhamsyah, Muhammad; Sara, Ira Devi; Syukriyadin, Syukriyadin
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 1, No 1 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v1i1.36541

Abstract

Abstrak Pembangkit listrik yang berkelanjutan dan dapat diakses oleh masyarakat luas menjadi kunci untuk memenuhi kebutuhan energi global dan meningkatkan kesejahteraan masyarakat. Pelaksanaan pengabdian kepada masyarakat ini mengusulkan dan mengimplementasikan pembangkit listrik tenaga multihybrid yang mengintegrasikan sistem tenaga surya fotovolatik dan sistem tenaga air mikrohidro. Penambahan sistem keamanan berbasis fingerprint dan teknologi Internet of Things (IoT) menjadikan pembangkit multihybrid ini lebih aman dan dapat diatur penggunaan energi listriknya. Langkah-langkah dalam pengembangan sistem kelistrikan ini melibatkan pemilihan lokasi di Desa Bung Pageu, Kecamatan Blang Bintang Kab. Aceh Besar, perancangan sistem kelistrikan dan instalasi fotovoltaik dan pembangkit mikrohidro sesuai potensi air setempat, serta integrasi sistem keamanan dengan pengelolaan energi menggunakan teknologi fingerprint. Selain itu, sensor IoT diterapkan untuk pemantauan real-time dan pengendalian jarak jauh. Dengan menggabungkan beberapa teknologi dan partisipasi aktif masyarakat setempat, kegiatan ini memberikan solusi terhadap permasalahan energi dan pengetahuan baru terkait dengan implementasi sistem energi terbarukan dengan pendekatan multihybrid. Keberhasilan kegiatan ini memberikan kontribusi positif terhadap pembangunan berkelanjutan dan memberdayakan masyarakat untuk turut serta dalam pemanfaatan energi terbarukan secara berkesinambungan.Kata kunci: Energi Terbarukan, Pembangkit Tenaga Multihybrid, Fotvoltaic, Microhydro
Improving Bi-LSTM for High Accuracy Protein Sequence Family Classifier Roslidar, Roslidar; Brilianty, Novia; Alhamdi, Muhammad Jurej; Nurbadriani, Cut Nanda; Harnelly, Essy; Zulkarnain, Zulkarnain
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 1: March 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/.v12i1.4732

Abstract

The primary nutrient that is crucial for identifying biochemical processes and biological norms in living cells is protein. Proteins are usually centered around one or a few functions which are defined by their family type. Hence, identification and classification are needed to separate the proteins according to their structure and families. In this work, we built a model to classify families of protein sequences. We used the protein sequences dataset consists of various macromolecules of biological significance. The classifier is built up using deep learning of Bi-LSTM. We began the research by collecting the dataset from the Protein Data Bank of the Research Collaboratory for Structural Bioinformatics, pre-processing the data using tokenizing, and modeling the classifier based on deep learning network of Bi-LSTM. As we get the best accuracy rate of the trained model, we figure out the model performance using the evaluation metrics of learning curve, accuracy rate, and loss. The results show that Deep Bi-LSTM provides excellent performance with fit learning curve, 99% accuracy rate, and 0.042 loss.
Co-Authors . Melinda . Nasaruddin A'yuni, Qurrata Abdul Karim Achmad Fauzi Akhyar Akhyar Alfatirta Mufti Alfisyahrin Alfisyahrin Alhamdi, Muhammad Jurej Alhamdi, Muhammad Jurej Aulia Rahman Aulia, Niza Brilianty, Novia Cahaya Amonta Damayanti, Elok Devi Sara, Ira Dini Anisa Futri Fadlurrahman Al Hafizh Redi Fajrul Islamy Fathurrahman Fathurrahman Fathurrahman Fathurrahman Fitri Arnia Haris Akbarsyah Harnelly, Essy Hendra Hidayat Indra Sakti Islamy, Fajrul Israk Faradila Jeperson Hutahaean Jurej, Muhammad Karnaini Karnaini Khairul Munadi Miftahul Rizki Mohd. Syaryadhi Muchtar, Kahlil Muhammad fauzan Muhammad Irhamsyah Muhammad Rizky Syahputra Mulkan Azhary Muzammil, Rivaul Nasaruddin Nasaruddin Nasaruddin Syafie Nasution, Khairunnisa Niza Aulia Nurbadriani, Cut Nanda Nurbadriani, Cut Nanda Oktiana, Maulisa Rauzatul Jannah Ridha, Jamalur Riki Effendi Riswan, Muhammad Rizal Munadi Rusdha Muharar Saddami, Khairun Safrizal Razali Safwan Syahrial, Syahrial Syukriyadin, Syukriyadin T. M. Rizki Aldiza Teuku Radhi Muhammad Fitrah Teuku Yuliar Arif Teuku Yuliar Arif William Ramdhan Yazid Yaskur Yessica Siagian Yudha Nurdin Yudha Nurdin Yudha Nurdin Yunida, Yunida Yunidar Yuwaldi Away Zulhelmi . Zulkarnain, Zulkarnain