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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 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi 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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Journal : Jurnal Teknik Informatika (JUTIF)

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Enhancing Monkeypox Skin Lesion Classification With Resnet50v2: The Impact Of Pre-Trained Models From Medical And General Domains Azhar, Saifulloh; Syukur, Abdul; Soeleman, M. Arief; Affandy, Affandy; Marjuni, Aris
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4486

Abstract

The monkeypox outbreak has emerged as a pressing global health concern, as evidenced by the rising number of cases reported in various countries. This rare zoonotic disease, caused by the Monkeypox virus (MPXV) of the Poxviridae family, is commonly found in Africa. However, since 2022, cases have also spread to various countries, including Indonesia. The dermatological symptoms exhibited by affected individuals vary, with the potential for further transmission through contamination. Early and accurate detection of monkey pox disease is therefore essential for effective treatment. The present study aims to improve the classification of Monkey Pox using the modified Resnet50V2 model, trained using pre-training datasets namely ImageNet and HAM10000, where batch size and learning rate parameters were adjusted. The study achieved high accuracy in distinguishing monkeypox cases, with 98.43% accuracy for Resnet50V2 with pretrained ImageNet and 70.57% accuracy for Resnet50V2 with pretrained HAM10000. Future research will focus on refining these models, exploring hybrid approaches incorporating convolutional neural networks, this advancement contributes to the development of automated early diagnosis tools for monkeypox skin conditions, especially in resource-limited clinical settings where access to dermatology experts is limited.
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 Al zami, Farrikh Althoff, Mohammad Noval Ardytha Luthfiarta Arie Nugroho, Arie Assaqty, Mohammad Iqbal Saryuddin Ayu Pertiwi Basiron, Halizah Cahyadi, Dede Faiz Dliyauddin, Muhammad Erwin Yudi Hidayat Etika Kartikadarma Fadlurrahman, Hanan Nadhif Firdaus, Iqbal Guruh Fajar Shidik Hafidhoh, Nisa'ul Hardiansyah, Raindy Wicaksana Ismail, Abdul Rahman Joko Sutrisno Marjuni, Aris Mega Fatimah Rosana Mochammad Ilham Aziz Muhammad Yunan, Muhammad Nurhindarto, Aris Patue, Abdulatif Prasetya, Rudy Eko Pujiono Pujiono Ricardus Anggi Pramunendar Sari Ayu Wulandari Satya, Mohammad Wahyu Bagus Dwi Sidik, Guruh Fajar Soeleman, M. Arief Soeleman, Mochamad Arief Sukamto, Titien S. Winarno , Sri Yulianto, Satria Pradana Rizki