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All Journal Anterior Jurnal Jurnal Sistem Cerdas Journal of Community Empowerment and Innovation
Iskandar, Muhaimin
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Medicine & Pharmacology Public Health Social Sciences

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CAR VEHICLE IMAGE OBJECT DETECTION USING YOU ONLY LIVE ONCE (YOLO) Azizah, Nur; Sahria, Yoga; Sahwari, Sahwari; Iskandar, Muhaimin
Anterior Jurnal Vol. 22 No. 3 (2023): Anterior Jurnal
Publisher : ​Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/anterior.v22i3.5577

Abstract

This research aims to analyze the performance of image object detection methods using You Only Live Once (YOLO) specifically in the context of car detection. YOLO-based object detection methods have gained great attention in the artificial intelligence community due to their ability to perform real-time object detection. In this research, we focus on using YOLO to detect car objects in images. The YOLO method will be tested for performance using a dataset of car images that have been collected from various sources. This dataset includes various lighting conditions, backgrounds, and car positions. The training process will be performed using the YOLO architecture that has been pre-trained with an extensive dataset. In the testing phase, the performance of the YOLO method in car object detection will be evaluated using standard evaluation metrics such as precision, detection speed, and recall. The results of this study will show the success rate of YOLO in car detection in images and provide a better understanding of the limitations and advantages of this method. The conclusion of this research is expected to provide valuable insight into the use of the YOLO method in car object detection. This information can be used as a basis for the development and improvement of YOLO-based object detection methods, and can be applied in various applications such as automated vehicle security systems and traffic analysis.
IMPLEMENTASI GAME EDUKASI ULAR TANGGA UNTUK MENINGKATKAN LITERASI DAN NUMERASI ANAK PMI DI SANGGAR BIMBINGAN KUBU GAJAH SELANGOR MALAYSIA Iskandar, Muhaimin; Arrosyid, Aditya Harun; Anton, M; Seituni, Siti
Jurnal Pemberdayaan Masyarakat dan Inovasi Vol 4 No 2 (2025)
Publisher : STKIP PGRI SITUBONDO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47668/join.v4i2.1727

Abstract

Game edukasi ular tangga pada anak-anak buruh migran di Sanggar Bimbingan Kubu Gajah merupakan salah satu upaya dalam meningkatkan literasi dan numerasi. Penerus bangsa buruh migran menghadapi tantangan dalam dunia pendidikan, yang berdampak pada perkembangan kemampuan literasi dan numerasi. Dalam kegiatan ini dibutuhkan metode pembelajaran berbasis permainan (game-based learning) digunakan sebagai pendekatan inovatif untuk mengatasi masalah tersebut. Implementasi game edukasi Ular Tangga dirancang khusus untuk meningkatkan keterampilan membaca, menulis, dan berhitung anak-anak usia sekolah dasar (6-14 tahun). Penelitian ini menggunakan metode partisipatori dalam berkolaborasi meningkatkan literasi dan numerasi. Hasil penelitian menghasilkan pembelajaran yang inovativ dan dapat memberikan wawasan tentang efektivitas penggunaan game edukasi dalam konteks pendidikan informal, serta memberikan rekomendasi bagi pengembangan program serupa untuk mendukung hak pendidikan anak-anak buruh migran. Luaran dari program ini meliputi game edukasi Ular Tangga yang diimplementasikan, peningkatan keterampilan literasi dan numerasi anak-anak buruh migran.
Real-Time Face Age Detection System Based on Deep Neural Networks with MediaPipe Optimization for Enhanced Accuracy iskandar, muhaimin; Azizah, Nur; Jaya, Firman
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025): In progress (December)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.593

Abstract

The transformation of machine learning and computer vision technology enables computers to automatically learn complex visual patterns, forming the foundation for biometric applications such as identity authentication, face detection, and demographic analytics. Face age estimation predicts age based on facial characteristics in digital images with high accuracy. Handcrafted feature-based approaches such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are less stable against variations in lighting, camera orientation, and facial expressions. Deep learning, particularly Deep Neural Networks (DNN), improves accuracy through automatic hierarchical feature extraction. However, raw image-based methods have high computational loads and require large GPUs, which are less than ideal for real-time use on limited devices. This research proposes a DNN-based age estimation system optimized through MediaPipe Face Mesh geometric features. The system consists of five layers: input, feature extraction (468 facial landmarks), optimization with Principal Component Analysis (PCA) for 64 features, DNN regression (three hidden layers), and output. A custom dataset of 1,235 facial images (ages 3–40 years) was divided into 80% training and 20% testing. The model was trained with the Adam optimizer (learning rate 0.001, epochs 500, loss MAE). Evaluation results: MAE 0.56 years, RMSE 1.94 years, R² 0.9726. Tolerance accuracy: 91% (±1 year), 96.7% (±2 years), 97.5% (±3 years), 99.2% (±5 years). An efficient system for real-time use on low-computing devices, supporting biometric applications such as security, content filtering, personalization, and health. This research contributes to accurate, lightweight, and adaptive age estimation systems.
Co-Authors Anton, M Arrosyid, Aditya Harun Firman Jaya Nur Azizah Sahria, Yoga sahwari, Sahwari Seituni, Siti