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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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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

Abstract

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

Abstract

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

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.
Implementation of Convolutional Recurrent Neural Network for Vehicle Number Plate Identification in Raspberry Pi Based Parking System Muzammil, Rivaul; Oktiana, Maulisa; Roslidar, Roslidar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2320

Abstract

The rapid growth of vehicles in Indonesia has created significant challenges in managing parking facilities. To address this issue, this study proposes an intelligent parking system based on automatic license plate character recognition. The system employs YOLOv8 (You Only Look Once) for license plate region detection and CRNN (Convolutional Recurrent Neural Network) for alphanumeric character recognition. Its architecture integrates a Raspberry Pi, camera module, and servo motor to enable automated license plate detection and recognition during vehicle entry and exit. YOLOv8 generates bounding boxes to isolate license plate regions, which are then processed as input for CRNN. The CRNN extracts visual features through convolutional layers and captures sequential relationships among characters using recurrent layers. The entire pipeline is deployed on Raspberry Pi with TensorFlow Lite to ensure efficient computation in resource-constrained environments. Experimental results demonstrate that YOLOv8 achieved a detection accuracy of 94.69%, with a precision of 98.32%, recall of 96.25%, and F1-score of 97.27%, while CRNN reached a character recognition accuracy of 93.8% across 30 license plates. Although some recognition errors occurred, such as misclassifying ‘G’ as ‘C’, 'W' as 'H', and 'Q' as 'O', the proposed system proved effective and feasible for embedded smart parking applications.
Pemanfaatan Papan Pintar Multisensori PeuHaba sebagai Solusi Teknologi Inklusif bagi Siswa SLB Fajrul Islamy; Miftahul Rizki; Dini Anisa Futri; Cahaya Amonta; Roslidar Roslidar; Niza Aulia
Jurnal Pengabdian Rekayasa dan Wirausaha Vol. 3 No. 1 (2026): Mei
Publisher : Fakultas Teknik Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v3i1.1924

Abstract

Implementasi pendidikan inklusif di Banda Aceh masih menghadapi tantangan dalam penanganan siswa disleksia yang mengalami kesulitan membaca dan fokus belajar. Penelitian ini bertujuan mengembangkan papan pintar PeuHaba, sebuah teknologi bantu berbasis perangkat keras dengan pendekatan multisensori Visual, Auditory, Kinesthetic, Tactile (VAKT) untuk mempermudah pembelajaran bahasa Aceh. Perangkat ini mengintegrasikan stimulasi multi-indra melalui layar LCD, audio otomatis, tombol interaktif, dan modul latihan menulis langsung. Metode pelaksanaan melibatkan uji coba kelas, pelatihan guru pendamping, dan evaluasi berkala yang bertempat di SLB Negeri Banda Aceh. Hasil implementasi selama enam bulan menunjukkan peningkatan signifikan pada partisipasi aktif siswa serta kemudahan dalam memahami materi visual. Selain itu, kompetensi guru pendamping dalam mengoperasikan alat secara mandiri meningkat dengan waktu adaptasi kurang dari tujuh menit, serta mengubah metode mengajar dari konvensional menjadi visual interaktif. Kesimpulannya, papan pintar PeuHaba terbukti efektif sebagai solusi edukatif inklusif yang mempercepat literasi sekaligus mendukung pelestarian budaya daerah.
Real-Time PPE Detection for Utility Field Workers Using YOLOv11 on Raspberry Pi with Automated Safety Reporting Israk Faradila; Roslidar Roslidar; Fathurrahman Fathurrahman; Yudha Nurdin; Mohd Syaryadhi
Jurnal Komputer Informasi Teknologi dan Elektro Vol. 11 No. 1 (2026): April
Publisher : Departemen Teknik Elektro dan Komputer Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/kitektro.v11i1.1506

Abstract

The level of compliance with the use of Personal Protective Equipment (PPE) in the work environment, especially in the field, remains a serious challenge with a direct impact on worker safety. Manual supervision is considered ineffective because it is subjective and limited in scope, time, and space. This research aims to develop an automatic PPE detection system using deep learning that can recognize five main objects: helmets, vests, gloves, shoes, and people. The system was designed to automatically detect real-time PPE availability and record workers' occupational safety status in the field. This research used the Convolutional Neural Network (CNN) with the YOLOv11 nano variant architecture, due to its advantages in efficiency and inference speed. The dataset comprised 1,471 images collected from PT PLN Aceh field documentation and the Roboflow Universe platform, which were expanded to 7,310 images following data augmentation. The dataset was split into training (96%), validation (2%), and testing (2%) subsets. The model was trained for 150 epochs and deployed on a Raspberry Pi 4 B for real-time inference. Evaluation results show a mean Average Precision at IoU 0.5 (mAP@0.5) of 90%, precision of 91.8%, and recall of 82%. The deployed system operates at 5–8 frames per second (FPS) and automatically logs worker safety status to Excel reports, demonstrating its practicality for real-time occupational safety monitoring.
Sistem Pemantauan Kualitas Air Sungai Secara Real Time Berbasis Internet of Things Fadlurrahman Al Hafizh Redi; Roslidar Roslidar; Mohd Syaryadhi; Alfatirta Mufti; Alfisyahrin Alfisyahrin; Zulhelmi Zulhelmi
Jurnal Komputer Informasi Teknologi dan Elektro Vol. 11 No. 1 (2026): April
Publisher : Departemen Teknik Elektro dan Komputer Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/kitektro.v11i1.1653

Abstract

Pencemaran air sungai akibat aktivitas manusia berdampak negatif terhadap lingkungan dan kesehatan masyarakat. Pemantauan kualitas air secara manual dinilai kurang efektif karena tidak mampu menyediakan data secara cepat dan berkelanjutan. Penelitian ini bertujuan untuk merancang sistem pemantauan kualitas air sungai secara real-time berbasis Internet of Things (IoT). Sistem ini dikembangkan menggunakan NodeMCU ESP8266 yang terintegrasi dengan sensor pH, dissolved oxygen (DO), kekeruhan, suhu, dan debit aliran. Data sensor diambil setiap 30 menit dan ditampilkan melalui aplikasi seluler. Hasil pengujian menunjukkan bahwa sistem mampu melakukan pemantauan secara otomatis serta mengklasifikasikan kondisi air menjadi tiga kategori: clean (bersih), polluted (tercemar), dan offline. Selain itu, sistem ini dilengkapi dengan alarm sebagai mekanisme peringatan dini. Dengan demikian, sistem yang dikembangkan dapat mendukung upaya pemantauan kualitas air secara efektif dan berkelanjutan.
Comparison Of Machine Learning Algorithms For Rice Production Prediction Abdul Karim; Yuwaldi Away; Syahrial; Roslidar; Jeperson Hutahaean; William Ramdhan; Yessica Siagian
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7453

Abstract

Rice production forecasting plays an important role in supporting future agricultural planning, food supply management, and food security. Accurate yield prediction allows governments and farmers to estimate production outcomes and develop appropriate strategies to maintain stable food availability.This study addresses this gap by comparing four regression-based machine learning models: Random Forest, XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN). All models were trained and tested using the same dataset to ensure a fair evaluation. Model performance was measured using the coefficient of determination (R²). The results show that Random Forest achieved the best performance (R² = 0.963), followed by XGBoost (R² = 0.959). In contrast, SVR (R² = -0.064) and ANN (R² = -2.417) performed poorly, indicating limited predictive capability. Overall, these findings suggest that ensemble-based methods, particularly Random Forest and XGBoost, are more reliable and effective for rice production forecasting compared to SVR and ANN.
Peningkatkan Keamanan ElGamal Menggunakan CNN dan Rolling Hash untuk Generasi Kunci dalam Enkripsi Gambar Fauzi, Achmad; Arif, Teuku Yuliar; Away, Yuwaldi; Roslidar, Roslidar
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.4484

Abstract

The large scale exchange of digital images requires security mechanisms that are robust not only at the cryptographic algorithm level but also in the key generation process, which is often the weakest component of the system. In conventional ElGamal schemes, security may degrade due to static entropy sources and predictable key patterns. This study proposes an ElGamal key generation model based on a pipeline of Convolutional Neural Networks (CNNs) and a rolling hash function, utilizing visual image content as an adaptive entropy source. The CNN extracts latent features through a fully connected layer, while the rolling hash enhances diffusion and key sensitivity to minor image variations. The model was evaluated using the CIFAR-10 dataset in PNG, WEBP, and JPG formats. Experimental results show stable key generation times ranging from 0.426 to 0.444 ms, with high entropy values between 7.98 and 7.99 bits, indicating strong randomness and resistance to prediction. Strong diffusion characteristics were also observed (PSNR 5.94 dB, SSIM −0.24, MAE 0.43). During encryption, WEBP achieved the fastest processing time (0.48 ms), followed by PNG (1.01 ms) and JPG (15.39 ms), while PNG demonstrated the highest size efficiency with a reduction of up to 70.6%. Decryption remained highly reliable, with success rates exceeding 97% across all formats. Overall, the results confirm that integrating CNNs and rolling hash significantly enhances ElGamal key generation security without compromising decryption reliability or image quality.
Energy-Proportional Modelling of a Dual-Axis Sun Tracker Controller Based on ANFIS Rauzatul Jannah; Yuwaldi Away; Roslidar Roslidar
Jurnal Rekayasa Elektrika Vol. 22 No. 2 (2026): Vol. 22, No. 2, June 2026
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Sun tracker consists of two energy types: proportional energy and operational energy. Proportional energy refers to the clean energy stored in the battery, while operational energy is used to support the mechanical performance of the sun tracking system. One of the main challenges in such systems is the high operational energy consumption, which can reduce the overall system efficiency. This study aims to develop a model to predict the proportional energy resulting from a dual-axis sun-tracker controller by implementing the Adaptive Neuro-Fuzzy Inference System (ANFIS). The method comprises analyzing the performance of the dual-axis sun tracker system and modeling the ANFIS. The performance of the dual-axis was investigated by observing the limitation of servo motor movement based on the difference in LDR sensor readings using threshold values of 50, 100, and 150. The ANFIS modeling was conducted by testing 24 configurations of membership functions to determine the most optimal structure. The results of the threshold value analysis show that a threshold value of 100 provides the best efficiency in generating proportional movement and energy. While modeling the ANFIS, the highest proportional energy was obtained using the Generalized Bell (belief) membership function type with a 9×9 configuration, yielding the lowest error value of 0.011692. Model validation using external test data showed an RMSE of 0.1128 and an MSE of 0.0127, indicating high predictive accuracy and good generalization capability. Implementing ANFIS control on the analyzed dual-axis PV system demonstrated an average increase in the proportional energy efficiency of 3%, from 90% to 93%. The findings indicate the effectiveness of ANFIS in enhancing the performance of the sun tracking system by adaptively adjusting to variations in light intensity.
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