Garuda - Garba Rujukan Digital

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

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

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.

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

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

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

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

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

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

An Explainable Artificial Intelligence Framework for Breast Cancer Detection Ridha, Jamalur; Saddami, Khairun; Riswan, Muhammad; Roslidar, Roslidar
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.78

Breast cancer remains a leading cause of mortality among women worldwide, primarily due to delayed detection and a lack of early awareness. To address this issue, this study develops an advanced, thermal image-based breast cancer detection system that is non-invasive, radiation-free, and cost-effective, enhanced through the integration of artificial intelligence (AI) techniques. The proposed framework incorporates Attention U-Net for accurate segmentation of thermal breast images, K-Means Clustering to localize and isolate high-temperature regions suspected to be cancerous, and an EfficientNet-B7-based Convolutional Neural Network (CNN) for classification. To increase clinical reliability and transparency, the system employs Explainable AI (XAI) techniques using Local Interpretable Model-Agnostic Explanations (LIME), which provide visual interpretations of the model’s decision-making process. The dataset used in this research was obtained from the Database for Mastology Research (DMR) and consists of 2010 thermal images, including both healthy and abnormal cases. Preprocessing and segmentation effectively remove irrelevant areas and focus on the breast region, enhancing detection accuracy. Experimental evaluation indicates the proposed model achieves a training accuracy of 96.48% and a validation accuracy of 91.67%, with a recall of 91.95%, specificity of 91.43%, precision of 89.89%, and F1-score of 90.91%. These results highlight the system’s robust performance and generalizability. The LIME-generated superpixel visualizations help medical professionals better understand and validate the model's predictions, contributing to increased trust in AI-driven diagnostics. Overall, this research presents a reliable, explainable, and ethically grounded solution for early-stage breast cancer detection, demonstrating its strong potential for supporting clinical decision-making and future deployment in real-world healthcare settings.

Development of a self-driving RC car with lane-keeping system using a pure pursuit controller Rahman, Aulia; Alhamdi, Muhammad Jurej; Muchtar, Kahlil; Nurdin, Yudha; Roslidar, Roslidar; Razali, Safrizal; Effendi, Riki
Jurnal Polimesin Vol 23, No 4 (2025): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i4.6664

The development of autonomous vehicles is crucial for enhancing driving safety, comfort, and efficiency. This research presents the design of a self-driving Remote Controlled (RC) car at a 1:10 scale, equipped with a lane-keeping system and a pure pursuit controller. The primary objective is to evaluate the effectiveness of integrating computer vision techniques with trajectory tracking control to maintain lane stability. Lane detection was achieved using a sliding windows algorithm, while polynomial fitting estimated the lane centerline. A stereo camera provided spatial perception, capturing images that were processed to determine the steering angle needed to minimize deviation between the lookahead point and the viewpoint of the vehicle. Experimental results show that the system-maintained lane position with minimal deviation, achieving an average steering angle of 90.44° on straight paths, 65.4° on right turns, and 113.1° on left turns. These results demonstrate the feasibility of combining vision-based lane detection with a pure pursuit controller to improve path-tracking accuracy and stability in autonomous vehicles.

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 : Universitas Islam Negeri Ar-Raniry Banda Aceh

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

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.

Title

Found 21 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 21 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 21 Documents
Search