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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK Transmisi: Jurnal Ilmiah Teknik Elektro JURNAL SISTEM INFORMASI BISNIS Jurnal Penelitian dan Pengembangan Pendidikan TRANSIENT: Jurnal Ilmiah Teknik Elektro Journal of Information System Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Scientific Journal of Informatics
Wahyul Amien Syafei
Departemen Teknik Elektro, Universitas Diponegoro Semarang Jl. Prof Sudharto, SH, Kampus UNDIP Tembalang, Semarang 50275, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering

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Identification of Grouper Fish Types using Convolutional Neural Network Resnet-50 Algorithm Nuraini, Rini; Syafei, Wahyul Amien; Wibowo, Adi; Jaya, Indra
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp173-178

Abstract

Grouper is a type of fish that is popular with the public. It is necessary to identify the type of grouper fish based on color patterns with increase the epoch value to get the best accuracy. The purpose of the research is to predict the type of grouper. This research use CNN Resnet-50 algorithm. 30 data used. The accuracy of prediction is 75 % to predict the image groupers. In the grouper prediction process, the more we increase the epoch value, we will get the best accuracy value. Epoch is a factor that affects the time of training an AI model and affects the accuracy value of the AI model.
Boosting real-time vehicle detection in urban traffic using a novel multi-augmentation Ashari, Imam Ahmad; Syafei, Wahyul Amien; Wibowo, Adi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp656-668

Abstract

Real-time vehicle object detection in urban traffic is crucial for modern traffic management systems. This study focuses on improving the accuracy of vehicle identification and classification in heavy traffic during peak hours, with particular emphasis on challenges such as small object sizes and interference from light reflections. The use of multi-label images enables the simultaneous detection of various vehicle types within a single frame, providing more detailed information about traffic conditions. You only look once (YOLO) was chosen for its capability to perform real-time object detection with high accuracy. Multi-augmentation techniques were applied to enrich the training data, making the model more robust to varying lighting conditions, viewpoints, object occlusions, and issues related to small objects. YOLOv8n and YOLOv9t were selected for their speed and efficiency. Models without augmentation, 10 single-augmentation techniques, and 5 multi-augmentation techniques were tested. The results show that YOLOv8n with multiaugmentation (scaling, zoom in, brightness adjustment, color jitter, and noise injection) achieved the highest mAP50-95 score of 0.536, surpassing YOLOv8n with single-augmentation Blur, which had an mAP50-95 of 0.465, as well as YOLOv8n without augmentation, which scored 0.390. Multiaugmentation proved to significantly enhance YOLO’s performance.
Traffic flow prediction using long short-term memory-Komodo Mlipir algorithm: metaheuristic optimization to multi-target vehicle detection Ashari, Imam Ahmad; Syafei, Wahyul Amien; Wibowo, Adi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3343-3353

Abstract

Multi-target vehicle detection in urban traffic faces challenges such as poor lighting, small object sizes, and diverse vehicle types, impacting traffic flow prediction accuracy. This study introduces an optimized long short-term memory (LSTM) model using the Komodo Mlipir algorithm (KMA) to enhance prediction accuracy. Traffic video data are processed with YOLO for vehicle classification and object counting. The LSTM model, trained to capture traffic patterns, employs parameters optimized by KMA, including learning rate, neuron count, and epochs. KMA integrates mutation and crossover strategies to enable adaptive selection in global and local searches. The model's performance was evaluated on an urban traffic dataset with uniform configurations for population size and key LSTM parameters, ensuring consistent evaluation. Results showed LSTM-KMA achieved a root mean square error (RMSE) of 14.5319, outperforming LSTM (16.6827), LSTM-improved dung beetle optimization (IDBO) (15.0946), and LSTM-particle swarm optimization (PSO) (15.0368). Its mean absolute error (MAE), at 8.7041, also surpassed LSTM (9.9903), LSTM-IDBO (9.0328), and LSTM-PSO (9.0015). LSTM-KMA effectively tackles multi-target detection challenges, improving prediction accuracy and transportation system efficiency. This reliable solution supports real-time urban traffic management, addressing the demands of dynamic urban environments.
A Hybrid YOLOv8-ResNet50 Architecture for Enhanced Cardiomegaly Prediction from Chest X-rays Arif Nur Faudin; Farikhin Farikhin; Wahyul Amien Syafei
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35225

Abstract

Abstract. Objective: This study aims to develop a reliable deep learning architecture for predicting cardiomegaly by integrating the ResNet-50 backbone into the YOLOv8 object detection framework, overcoming the challenges of detecting subtle anatomical variations and low-contrast features often found in chest radiographs. Methods: This study used a publicly available chest X-ray dataset, with rigorous data annotation to establish ground truth for the heart and thoracic cavity regions. Preprocessing included resizing input images to 640×640 pixels, automatic orientation correction, and an 80:20 data split between training and testing. Real-time data augmentation was applied to the training set. The ResNet-YOLOv8 hybrid model was trained for 150 epochs with optimized hyperparameters (learning rate, momentum, weight decay, loss weight), and performance was evaluated using metrics such as mAP, precision, recall, and confusion matrix results. Results: The experimental results show that the proposed architecture achieves high accuracy in detecting cardiomegaly, with mAP50-95 of 0.7578, precision of 0.9955, recall of 0.9962, F1 score of 0.9959, and inference latency of only 4.5 ms/img. This model is more optimal than the standard YOLOv8 variant in both accuracy and computational efficiency. Innovation: The integration of ResNet-50 into YOLOv8 significantly improves feature extraction capabilities for chest X-ray images, enabling the recognition of fine anatomical details with high precision. This innovative hybrid approach advances automated cardiomegaly detection, offering potential for large-scale, real-time implementation in clinical settings and contributing to the development of advanced AI-powered diagnostic tools.
Enhancement of YOLOv9 Model for Traffic Vehicle Detection using Augmentation Techniques Ashari, Imam Ahmad; Syafei, Wahyul Amien; Wibowo, Adi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5196

Abstract

Traffic vehicle detection is a crucial component in developing intelligent transportation systems, with object detection models like YOLO (You Only Look Once) often preferred for their speed and accuracy. However, challenges remain in detecting vehicles under diverse lighting conditions and small object scales, even with advanced models such as YOLOv9. To address these limitations, image augmentation techniques are employed to enhance model robustness by providing broader data variation. This study investigates the impact of multiple image augmentation methods on the YOLOv9t model for traffic vehicle detection. The techniques evaluated include Blur, Brightness Adjustment, Contrast Adjustment, Color Jitter, Cropping, Flipping, Noise Injection, Rotation, Scaling, and Zoom-In. Results reveal that Scaling and Brightness Adjustment significantly improve detection accuracy, achieving mAP50-95 values of 0.450 and 0.449, respectively. Conversely, methods such as Contrast Adjustment, Rotation, and Cropping produced unsatisfactory outcomes, with Contrast Adjustment performing the worst at only 0.167. Without augmentation, the baseline mAP50-95 was 0.378, emphasizing the vital role of augmentation in improving detection performance, especially under challenging conditions. These findings highlight the importance of selecting appropriate augmentation techniques to optimize YOLOv9t performance, with further improvements possible through combining multiple methods. Compared to approaches that solely focus on enhancing model architecture, the proposed augmentation-based strategy proves more effective in addressing real-world challenges, strengthening resilience against lighting variations and small object detection. This contribution supports the development of more accurate and reliable multilabel vehicle detection systems, advancing safer and more efficient intelligent transportation solutions.
Enhancing Model Trust in Load Forecasting using Counterfactual Explanations Muhammad Syarif Hidayatullah; Wahyul Amien Syafei; Tarno
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 4, November 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

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

Abstract

This study investigates the effectiveness of Counterfactual Explanations (CE) in improving the interpretability of short-term electricity load forecasting models. The forecasting model was developed using Random Forest Regression (RFR) due to its ability to capture nonlinear multivariate patterns in electricity load data and to provide global, descriptive interpretability through feature-importance analysis. However, feature importance provides static and potentially biased explanations, making it insufficient for instance-level interpretability. To address this limitation, the Diverse Counterfactual Explanations (DiCE) framework was integrated to generate multiple “what-if” scenarios that illustrate model-consistent input adjustments capable of shifting forecast outcomes. The study uses hourly electricity load data from Panama collected between 2015 and 2020, with data from 2020 excluded during preprocessing due to atypical demand patterns caused by COVID-19. The model was trained using time-series cross-validation and optimized through grid search. Forecasting performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Counterfactual Explanations were assessed using validity, proximity, and compactness metrics, indicating that most generated scenarios are goal-aligned and remain moderately close to the original instances, while the compactness results reflect a feasibility trade-off when large prediction shifts are required. Overall, combining RFR with counterfactual analysis enhances model transparency by bridging global and local interpretability perspectives and provides decision-relevant, scenario-based insights to support transparent and data-driven planning in the energy sector.
Co-Authors Achmad Hidayatno Adi Wibowo Adi Wibowo Adian Fatchur Rochim Afiq, Raihan Aghus Sofwan Ahmad Siddik Prayogi, Ahmad Siddik Ajub Ajulian Zahra Macrina Amelia Syarfina Qisthi Anky Setyadewa Ardea Satya Hasnanta Ardian Wijaya Arif Nur Faudin Aulia Iqbal M Baharuddin, Yusuf Bayu Surarso Bayu Surarso Budi Warsito Darjat Darjat Dinar Mutiara Kusumo Nugraheni Dionis Balan Pratama Durio Etgar Endriawan Endriawan Farich Novriana Sandy Farid Arifiyanto Fatkhurrahman, Muhammad Arief Fuad Rajab Nugroho Galoh Randicha Hayu Pratista Herlinah Baharuddin, Herlinah Hiroshi Ochi Imam Ahmad Ashari, Imam Ahmad Imam Santoso Indra Jaya Indra Jaya Jack Zijlmans Joanna Francisca Socaningrum Juwanda, Farikhin Kodrat Imam Satoto Kusnadi Kusnadi Laksono Trisnantoro M. Solehuddin Maman Somantri Muhammad Alfian, Muhammad Muhammad Satriya Utama Muhammad Syarif Hidayatullah Nugraheni, Dinar Oky Dwi Nurhayati R. Edith Indera Bagaskara R. Rizal Isnanto Rahmat Gernowo Raymond Octhario Retno Kusumaningrum Rini Indah Sulistiyawati Rini Nuraini Rini Nuraini, Rini Santoso, Imam Sri Mulyono Subchan Ajie Ari Bowo Sulistiyo, Budi Tarno Tri Murwanto Valentina Samaya Sari Dewi Yama Aryadanangjaya Yudhitya Sorrenti Yuli Chritiyono Zhaorui Yuan Zuhrotul Maulida