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All Journal Jurnal Masyarakat Informatika Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi INTERACTION: Jurnal Pendidikan Bahasa Jurnal Teknologi Informasi dan Terapan (J-TIT) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Mahasiswa Ilmu Komputer JAMU : Jurnal Abdi Masyarakat UMUS SmartComp Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika: Jurnal Pengembangan IT Jurnal Sains dan Teknologi Informasi Jurnal Ilmu Komputer, Teknologi Dan Informasi SWAGATI: Journal of Community Service Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Muhammad Naufal
Dian Nuswantoro University, Semarang
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Journal : journal of applied informatics and computing

 1 
Improving YOLO Performance with Advanced Data Augmentation for Soccer Object Detection Puspita, Rahayuning Febriyanti; Naufal, Muhammad; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11256

Abstract

This study developed an object detection system for soccer games using the YOLOv8m algorithm with four main classes: player, goalkeeper, referee, and ball. The dataset, consisting of 372 annotated images, exhibited class imbalance, with significantly fewer ball instances compared to players. The basic YOLOv8m architecture was used without internal modifications, but adjustments were made to the output layer and fine-tuning of the pre-trained weights to adapt to the new dataset. Two models were compared: one without and one with advanced augmentation techniques (mosaic, mixup, cutmix). The experimental results showed an increase in mAP@50 from 74.9% to 81.4% in the augmented model, with a statistically significant difference (p < 0.01). However, model performance still decreased under extreme conditions such as high occlusion, rapid movement, and uneven lighting. The combination of data augmentation, output layer adaptation, and fine-tuning proved effective in improving object detection accuracy and provided the basis for the development of a real-time artificial intelligence-based soccer match analysis system.
Addressing Extreme Class Imbalance in Multilingual Complaint Classification Using XLM-RoBERTa Ariyanto, Muhammad; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Gamayanto, Indra; Naufal, Muhammad; Winarno, Sri; Iswahyudi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11606

Abstract

Government complaint management systems often suffer from extreme class imbalance, where a few public service categories accumulate most reports while many others remain under-represented. This research examines whether simple class weighting can improve fairness in multilingual transformer models for automatic routing of Indonesian citizen complaints on the LaporGub Central Java e-governance platform. The dataset comprises 53,877 Indonesian-language complaints spanning 18 service categories with an imbalance ratio of about 227:1 between the largest and smallest classes. After cleaning and deduplication, we stratify the data into training, validation, and test sets. We compare three approaches: (i) a linear support vector machine (SVM) with term frequency inverse document frequency (TF-IDF) unigram and bigram and class-balanced weights, (ii) a cross-lingual RoBERTa (XLM-RoBERTa-base) model without class weighting, and (iii) an XLM-RoBERTa-base model with a class-weighted cross-entropy loss. Fairness is operationalised as equal importance for categories and quantified primarily using the macro-averaged F1-score (Macro-F1), complemented by per-class F1, weighted F1, and accuracy. The unweighted XLM-RoBERTa model outperforms the SVM baseline in Macro-F1 (0.610 vs 0.561). The class-weighted variant attains similar Macro-F1 (0.608) while redistributing performance towards minority categories. Analysis shows that class weighting is most beneficial for categories with a few hundred to several thousand samples, whereas extremely rare categories with fewer than 200 complaints remain difficult for all models and require additional data-centric interventions. These findings demonstrate that multilingual transformer architectures combined with simple class weighting can provide a more balanced backbone for automated complaint routing in Indonesian e-government, particularly for low- and medium-frequency service categories.
Evaluation of Histogram-Based Image Enhancement Methods for Facial Images in Drowsy Driver Using No-Reference Metrics Naufal, Muhammad; Al Azies, Harun; Alzami, Farrikh; Brilianto, Rivaldo Mersis
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12055

Abstract

Low-light facial images suffer significant quality degradation, leading to performance degradation in surveillance and face recognition systems, where conventional enhancement methods often produce over-enhancement or unnatural noise artifacts. This study compares three histogram equalization methods, namely HE, AHE, and CLAHE, for low-light facial image enhancement, with evaluation using no-reference quality assessment metrics, including NIQE, LOE, and Entropy, as well as visual analysis and histogram distribution. The results showed that AHE produced the lowest NIQE (4.96 ± 1.38) and the highest entropy (7.86 ± 0.11) but had significant noise artifacts, HE produced an overly even distribution with NIQE of 6.34 ± 1.41, while CLAHE showed the most balanced performance with the lowest LOE (0.07 ± 0.02) and the best visual quality when using the optimal clip limit in the range of 1.2-2.0, providing an optimal trade-off between contrast enhancement, naturalness preservation, and artifact minimization with computational efficiency below 1 ms.
Improving YOLO12 Performance Using Efficient Channel Attention For Ship Object Detection Richard Christoper Subianto; Muhammad Naufal; Farrikh Alzami
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13067

Abstract

Ship object detection in aerial imagery remains a critical challenge due to complex marine backgrounds, varying object scales, and occlusion, which often lead to unstable model performance. This research proposes integrating the Efficient Channel Attention (ECA) module into the YOLO12-L architecture to enhance feature selectivity and prediction robustness. The model was trained for 500 epochs on the Ship Detection from Aerial Images dataset, comprising 621 images and 1,951 annotated ship instances, and performance was evaluated across five distinct random seeds to ensure statistical reliability. Quantitative results demonstrate that the proposed YOLO12-L + ECA model achieved a median Average Precision (mAP@50) of 71.32% and a Precision of 92.5%, outperforming the baseline YOLO12-L model. To evaluate statistical validity, a Paired Bootstrap Median Test with 100 resamples confirmed a statistically significant improvement in median performance (Δ = +1.01%, p = 0.02). Furthermore, the standard deviation of mAP@50 decreased from 1.1% in the baseline to 0.3% in the ECA model, representing a 72.7% reduction in performance variance. Computational efficiency analysis revealed that the ECA module introduced negligible overhead, adding merely 5 parameters (totaling 26,389,880) and keeping FLOPs constant at 89.4, while maintaining a high inference speed of 10.7 FPS (a marginal 2.5% reduction). These findings confirm that ECA effectively suppresses background noise, stabilizes detection outputs, and provides statistically significant improvements without compromising architectural efficiency. The proposed architecture offers a lightweight and reliable solution for automated maritime monitoring systems, particularly in challenging visual environments.
Evaluating LSB and MSB Steganography in Retinal Fundus Images Through Image Quality Assessment and VGG19-Based Classification Gilang Faturrahman; Muhammad Naufal; Wahyu Aji Eko Prabowo; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13198

Abstract

The security of medical image data within electronic medical record systems has become a critical issue due to the increasing threat of health data breaches. Steganography is a promising technique for protecting patient information by concealing secret data within medical images without significantly altering their visual appearance. However, the application of steganography to retinal fundus images, which carry high diagnostic value, has never been comprehensively evaluated in terms of image quality or its impact on artificial intelligence-based diagnostic model performance. This study compares Least Significant Bit (LSB) and Most Significant Bit (MSB) steganography methods applied to 3,200 retinal fundus images from the Retinal Fundus Multi-disease Image Dataset (RFMiD) dataset across four payload levels (0.1-0.4 bpp), evaluated using PSNR, SNR, SSIM, and FSIM for image quality, and VGG19 classification accuracy and AUC for diagnostic impact. Results show LSB achieves substantially superior image quality (PSNR: 59.97-65.93 dB; SNR: 49.34-55.30 dB; SSIM: 0.9981-0.9997; FSIM: 0.9999-1.0000) compared to MSB (PSNR: 12.98-18.99 dB; SNR: 2.35-8.37 dB; SSIM: 0.5979-0.9003; FSIM: 0.5342-0.7500), while VGG19 classification accuracy remains stable for both methods (LSB: 0.8938-0.9000; MSB: 0.8953-0.9031) with a maximum difference of 0.62% from baseline. This study demonstrates that LSB is the more appropriate steganography method for retinal fundus images, delivering superior visual quality while preserving VGG19 diagnostic capability.
Co-Authors Achmad Achmad Al Fahreza, Muhammad Daffa Al zami, Farrikh Al-Azies, Harun Alzami, Farrikh Amanda Cahyadewi, Felicia Amelia Safrida Amiq Fahmi Amron, Azmi Jalaluddin Andrean, Muhammad Niko Anggi Pramunendar, Ricardus Anggita, Ivan Maulana Ardytha Luthfiarta ARIYANTO, MUHAMMAD Arofi, Muhammad Labib Zaenal Ashari, Ayu Ayu Pertiwi Azizi, Husin Fadhil Brilianto, Rivaldo Mersis Dairoh Dairoh Danar Cahyo Prakoso Dega Surono Wibowo Denta Saputra, Fahrizal Dewi Agustini Santoso Dewi Agustini Santoso Dewi Pergiwati Dewi Pergiwati Dwi Puji Prabowo Edy Mulyanto Eko Purnomo Bayu Aji Erika Devi Udayanti Erwin Yudi Hidayat Esadhipa Raif Syihabuddin Fadlullah, Rizal Fahmi Amiq Farrikh Al Zami Farrikh Alzami Farrikh Alzami Farrikh Alzami Firmansyah, Gustian Angga Galih Putra Pratama Gilang Faturrahman Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Gustina Alfa Trisnapradika Hadi, Heru Pramono Handayani, Ni Made Kirei Kharisma Harisa, Ardiawan Bagus Hartono, Andhika Rhaifahrizal Harun Al Azies Harun Al Azies Harun Al Azies Heni Indrayani Hepatika Zidny Ilmadina Hidayat, Novianto Nur Husin Fadhil Azizi Ibnu Richo Kurniawan Ifan Rizqa Indra Gamayanto Indrawan, Michael Iswahyudi ISWAHYUDI ISWAHYUDI Kharisma, Ni Made Kirei Khoirunnisa, Emila Kurniawan Aji Saputra Kurniawan, Defri Kusumawati, Yupie Levi Renov Esprayenduo Liya Umaroh Liya Umaroh Liya Umaroh, Liya Marcelinus Yosep Teguh Sulistyono Maulana, Isa Iant Megantara, Rama Aria Moch Anjas Aprihartha Mohammad Arif Mohammad Arif Muh. Fatkhi Alexander Mukaromah Mukaromah MUKAROMAH MUKAROMAH Muljono, - Muslih Muslih Nazella, Desvita Dian Ningrum, Novita Kurnia Noor Ageng Setiyanto, Noor Ageng Novianto Nur Hidayat Novianto Nur Hidayat Novita Kurnia Ningrum Nugraini, Siti Hadiati Nurayuni Kirana Muti Nurul Hashimah Ahmad Hassain Malim Paramita, Cinantya Prabowo, Wahyu Aji Eko Puspita, Rahayuning Febriyanti Putra, Permana Langgeng Wicaksono Ellwid Rafi Jonathan Siger Rafid, Muhammad Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Ricardus Anggi Pramunendar Richard Christoper Subianto Richo Kurniawan, Ibnu Rivaldo Mersis Brilianto Rivaldo Mersis Brilianto Ruri Suko Basuki Safitri, Aprilyani Nur Sindhu Rakasiwi Sofiani, Hilda Ayu Sri Winarno Sri Winarno Sudibyo, Usman Suharnawi Suharnawi SUPRIADI RUSTAD Tira Karel Agata Trisnapradika, Gustina Alfa Umar Fakhrizal, Irsyad Very Kurnia Bakti, Very Kurnia Virgiafan Rido Taufik Adrian Wahyu Aji Eko Prabowo Widyatmoko Karis Zahro, Azzula Cerliana Zami, Farrikh Al