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All Journal Katalogis Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Scientific Journal of Informatics
Affandy Affandy
Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Sentiment Analysis Of Public Comments On Licensing Services Seeks To Use The Naive Bayes Algorithm With Genetic Selection Algorithm Patue, Abdulatif; Sidik, Guruh Fajar; Affandy, Affandy; Ismail, Abdul Rahman
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3550

Abstract

The Investment and One-Stop Integrated Service Office is the Licensing Service service to support an area in terms of business potential and investment value. Presenting MSME Business Actors and Young Entrepreneurs. As time goes by, Licensing Services in the Region, especially PTSP, must know what are the constraints and problems faced by business actors in terms of business services and products issued by the Service. Naïve Bayes is the most common algorithm that we encounter in several libraries. Therefore, this research will discuss the level of accuracy of this algorithm. Then additional selection of Genetic Algorithm features was carried out to increase the accuracy of the Naïve Bayes method. The Naive Bayes Algorithm method with Genetic Algorithm selection is superior compared to only using the Naive Bayes method. This is proven by the acquisition of 83.17% accuracy, 86.38% Precision, and 83.05% recall
Smart Rupiah Recognition: A Mobile Machine Learning Approach for Visually Impaired Users Fadlurrahman, Hanan Nadhif; Affandy, Affandy; Cahyadi, Dede Faiz
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.v12i4.28930

Abstract

Purpose: Despite advances in assistive technology, low-connectivity areas lack reliable solutions for visually impaired individuals, prompting this study to enhance financial autonomy in cash-based economies. This research addresses high fraud risks and the limitations of online tools like Be My Eyes, which fail in areas with only 40% internet access, by developing a 3MB MobileNetV2 model for offline Rupiah denomination recognition on low-end Android devices. Methods: A MobileNetV2-based Convolutional Neural Network, optimized to 3MB via TensorFlow Lite quantization, was trained on 10,855 augmented images (rotation ±30°, flipping, Gaussian noise, σ=0.1). The Kotlin-based application integrates CameraX for 720p video and Bahasa Indonesia text-to-speech, with a “no object” class. The model was tested on 4–8GB RAM devices, validated through usability evaluations with diverse stakeholders. Result: The model achieves 90% accuracy (F1-score 0.90) at 1000 lux, 85% at <50 lux, 80% at >60° angles, and 88% for “no object,” with 10ms latency. Self-supervised learning (SimCLR) on 2,000 worn notes improves accuracy by 3% (p < 0.05). Usability evaluations yield 95% session success, with TTS and UI Likert scores of 4.2 and 4.0.. Novelty: The 3MB MobileNetV2 model, with 10ms latency and 15% false positive reduction, outperforms YOLOv5 (500MB, 50ms), Vision Transformer (1GB, 200ms), and YOLOv8 (200MB, 30ms). This model shows potential for cross-currency detection throught preliminary exploration (e.g., USD and euro), which may advance edge AI and financial inclusion in developing nations.
Optimizing YOLO11 for Dense Crowd Counting under Severe Occlusion via Head-Detection Fine-Tuning Sutrisno, Joko; Winarno , Sri; Affandy, Affandy
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.2026.7.2.5699

Abstract

Accurate and real-time people counting is essential for crowd management and public safety, yet achieving precision in high-density environments remains a challenge due to severe visual occlusion. While the recently released YOLO11 architecture introduces advanced features such as C3k2 and C2PSA modules, its performance as a pre-trained model for people counting tasks has not been fully explored. This study evaluates the efficacy of a head-detection-based fine-tuning strategy using the YOLO11 model, compared against the default pre-trained baseline. The fine-tuning performance is analyzed across three distinct scenarios: S1 (full fine-tuning at 960 pixels), S2 (partial backbone freezing at 960 pixels), and S3 (partial freezing at 640 pixels). The fine-tuning process was conducted using the CC_Mach_1 dataset from Roboflow Universe, which consists of high-density images annotated for head detection. The results demonstrate that the baseline pre-trained YOLO11, which relies on full-body features, exhibits extremely limited performance with an mAP@0.5 of 0.017 and a Mean Absolute Error (MAE) of 100.3. In contrast, the fine-tuned scenarios achieved substantial improvements, led by S1 which reached the highest accuracy with an mAP@0.5 of 0.682 and reduced the MAE by 62% to 37.8. While S2 remained highly competitive with an MAE of 39.6, the performance in S3 declined to 46.9, confirming that lower input resolutions limit the model's ability to identify small-scale head features. These findings provide empirical evidence that domain-specific fine-tuning for head detection substantially improves the robustness of YOLO11 against occlusion. Beyond technical accuracy, this detection-based approach offers a more computationally efficient alternative to traditional density-map-based methods, making it highly suitable for deployment in real-time surveillance systems for large-scale public monitoring.
Co-Authors Abdul Syukur Ahmad Zainul Fanani Althoff, Mohammad Noval Ardytha Luthfiarta Arie Nugroho, Arie Ayu Pertiwi Basiron, Halizah Cahyadi, Dede Faiz Dliyauddin, Muhammad Erwin Yudi Hidayat Etika Kartikadarma Fadlurrahman, Hanan Nadhif Guruh Fajar Shidik Hafidhoh, Nisa'ul Hardiansyah, Raindy Wicaksana Ismail, Abdul Rahman Joko Sutrisno Marjuni, Aris Mega Fatimah Rosana Mochammad Ilham Aziz Nurhindarto, Aris Patue, Abdulatif Pujiono Pujiono Ricardus Anggi Pramunendar Sari Ayu Wulandari Satya, Mohammad Wahyu Bagus Dwi Sidik, Guruh Fajar Soeleman, M. Arief Sukamto, Titien S. Winarno , Sri Yulianto, Satria Pradana Rizki