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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
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Pemanfaatan Artificial Intelligence untuk Meningkatkan Efisiensi Layanan Birokrasi pada Organisasi Perangkat Daerah Pemerintah Provinsi Jawa Tengah: Utilization of Artificial Intelligence to Improve the Efficiency of Bureaucratic Services in Regional Government Organizations of Central Java Province Farrikh Alzami; Muhammad Naufal; Dewi Agustini Santoso; Dewi Pergiwati; Heni Indrayani; Karis Widyatmoko; Rama Aria Megantara
JAMU : Jurnal Abdi Masyarakat UMUS Vol. 6 No. 02 (2026): Februari
Publisher : LPPM Universitas Muhadi Setiabudi

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

Abstract

Transformasi digital birokrasi menuntut pemerintah daerah untuk meningkatkan efisiensi dan kualitas layanan publik. Artificial intelligence (AI) merupakan salah satu teknologi yang memiliki potensi besar dalam mendukung otomasi administrasi, pengolahan data, serta peningkatan responsivitas layanan pemerintahan. Namun, tingkat pemahaman dan kesiapan aparatur sipil negara (ASN) dalam memanfaatkan AI masih belum merata, terutama terkait aspek etika dan pelindungan data. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pemahaman dan kapasitas ASN Organisasi Perangkat Daerah (OPD) Pemerintah Provinsi Jawa Tengah dalam memanfaatkan AI secara tepat, aman, dan bertanggung jawab guna mendukung efisiensi layanan birokrasi. Metode pelaksanaan kegiatan berupa workshop tatap muka yang meliputi penyampaian materi konseptual, studi kasus pemanfaatan AI di sektor publik, diskusi interaktif, serta praktik penggunaan AI dalam konteks administrasi pemerintahan. Hasil kegiatan menunjukkan peningkatan pemahaman peserta terhadap konsep AI, kemampuan mengidentifikasi potensi penerapan AI dalam tugas birokrasi, serta meningkatnya kesadaran terhadap aspek etika dan keamanan data. Kegiatan ini menunjukkan bahwa pendampingan akademik melalui workshop praktis mampu memberikan kontribusi nyata dalam mendukung transformasi digital birokrasi di tingkat pemerintah daerah.
OPTIMASI KLASIFIKASI CITRA ALFABET SISTEM ISYARAT BAHASA INDONESIA (SIBI) MENGGUNAKAN AUGMENTASI DATA DAN FINE-TUNING MOBILENETV2 Amelia Safrida; Edy Mulyanto; Muhammad Naufal
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8340

Abstract

The Indonesian Sign Language System (SIBI) is a communication medium used by the deaf community in Indonesia. However, classifying SIBI alphabet images presents challenges due to visual similarities between signs for different letters and limited training data variation. This study aims to optimize SIBI alphabet image classification by applying data augmentation and fine-tuning to the MobileNetV2 model. The dataset consists of 7,582 SIBI alphabet images sourced from Kaggle, covering 24 classes (letters A through Y, excluding J and Z). The research process involved preprocessing, data augmentation, dataset splitting into training, validation, and testing sets, model development using MobileNetV2 transfer learning, and performance evaluation based on accuracy, precision, recall, and F1-score. The results show that the model without augmentation achieved 96.75% accuracy, whereas the model with augmentation achieved 97.10% accuracy, accompanied by improvements in precision, recall, and F1-score. These results indicate that data augmentation enhances the model's generalization capabilities, resulting in more accurate and consistent classification. Thus, the combination of data augmentation and MobileNetV2 fine-tuning is effective for SIBI alphabet image classification. McNemar's test revealed a statistically significant difference in model performance following the application of data augmentation (p = 9.6517 × 10⁻¹²).
EVALUASI MULTI-SEED L1 DAN L2 REGULARIZATION PADA RESNET-50 UNTUK DETEKSI DEEPFAKE WAJAH Nurayuni Kirana Muti; MUHAMMAD NAUFAL; Novita Kurnia Ningrum
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8401

Abstract

Facial biometric authentication systems are increasingly vulnerable to sophisticated digital manipulations, particularly deepfakes. Conventional single Convolutional Neural Network (CNN) models often encounter overfitting issues, especially when trained on high-resolution datasets containing subtle manipulation artifacts. To address this issue, this study evaluates and compares the effectiveness of applying L1 Regularization and L2 Regularization parameters to the final classification layer of a fine-tuned ResNet-50 architecture. The models were comparatively evaluated using the public CIPLAB Real and Fake Face Detection dataset. To ensure an objective stability measurement, a multi-seed evaluation method was applied using three random initializations. Experimental results indicate that the baseline model without regularization exhibited a high level of prediction fluctuation, with an accuracy standard deviation of 0.0387. The application of L1 Regularization achieved an average testing accuracy of 68.78%, but failed to resolve the stability issue, as it still recorded a high standard deviation of 0.0380. Conversely, L2 Regularization was proven to significantly mitigate the risk of overfitting, emerging as the most superior approach. This is evidenced by a sharp decrease in the accuracy standard deviation to 0.0105, while securing a consistent average testing accuracy of 68.78%. These findings emphasize that applying an L2 Regularization penalty to a single CNN model offers a far more stable, effective, and consistent solution for detecting facial image manipulations compared to both L1 Regularization and conventional models.
A Stacking Approach to Enhance K-Nearest Neighbors Performance for Autism Screening Harun Al Azies; Muhammad Naufal
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.432

Abstract

The increasing prevalence of autism spectrum disorders necessitates improved early screening methods for children to ensure timely intervention and support. While existing screening techniques play a vital role, they often face challenges regarding accuracy, accessibility, and scalability. This research addresses these gaps by enhancing the K-Nearest Neighbors (K-NN) algorithm by implementing a stacking model that integrates multiple distance metrics—Manhattan and Minkowski—to improve predictive performance. Utilizing a public dataset, the study employed K-Fold Cross-Validation with K=5 to ensure a robust evaluation of the models. The results demonstrated that the stacking model achieved an average accuracy of 86.67%, significantly surpassing the traditional K-NN approaches, which reported accuracies of 82.67% for Manhattan and 81.33% for Minkowski. A user-friendly web interface was also developed to facilitate real-world application, allowing users to input data and receive immediate predictive outcomes regarding autism risk. These findings confirm the effectiveness of the stacking method in enhancing K-NN performance and highlight its potential for practical use in autism screening. Future research may explore alternative machine learning algorithms and additional features to refine the predictive capabilities and user experience further.
Addressing Class Imbalance in Android Backdoor Malware DetectionUsing Ensemble Models Rama Aria Megantara; Dewi Pergiwati; Farrikh Alzami; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Muhammad Naufal; Rivaldo Mersis Brilianto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i2.6198

Abstract

Backdoor malware represents one of the most critical threats in the Android ecosystem due to its capability to enable covert remote access, escalate privileges, and exfiltrate sensitive data without user awareness. Although the CCCS-CIC-AndMal-2020 dataset is publicly available, prior studies have not specifically formulated Backdoor detection as a binary classification problem under extreme class imbalance, nor systematically evaluated the impact of oversampling and cost-sensitive weighting using imbalance-aware performance metrics. This study proposes a comprehensive detection pipeline that integrates ensemble learning, class imbalance handling strategies, and explainability-based analysis to extract behavioral signatures of Backdoor malware. A two-stage feature selection process is employed to reduce the original 9,502-dimensional feature space to 500 informative features. Subsequently, five classification algorithms are evaluated under three imbalance-handling scenarios using a composite ranking criterion based on F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), Geometric Mean (G-Mean), and Matthews Correlation Coefficient (MCC). The experimental results demonstrate that the Random Forest model combined with Synthetic Minority Oversampling Technique (SMOTE) achieves the best performance, with an F1-score of 0.9043, AUC of 0.9909, G-Mean of 0.9422, and MCC of 0.8948. Furthermore, SHAP analysis identifies 39 Android permissions related to account access, covert communication, and privilege escalation as key behavioral signatures, with the permissions feature group contributing 2.31 times higher discriminative importance than nonpermission features. These findings indicate that interpretable ensemble learning not only improves detection performance but also provides actionable insights for static malware analysis.
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.
Klasifikasi Penyakit Mata Menggunakan Random Forest Dengan Optimasi Hyperparameter RandomSearchCV Muh. Fatkhi Alexander; Virgiafan Rido Taufik Adrian; Levi Renov Esprayenduo; Muhammad Naufal
Jurnal Sains dan Teknologi Informasi Vol 5 No 2 (2026): Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jussi.v5i2.9847

Abstract

Eye diseases such as cataracts, diabetic retinopathy and glaucoma are the leading causes of visual impairment and blindness worldwide, so early detection through medical image analysis is essential to prevent complications and permanent vision loss. The development of artificial intelligence and machine learning technology provides great opportunities to help medical personnel carry out diagnoses more quickly, accurately and efficiently. This research aims to develop an eye disease classification model using the Random Forest algorithm with hyperparameter optimization to differentiate four eye conditions, namely cataract, diabetic retinopathy, glaucoma, and normal. The dataset used is sourced from the public and consists of eye fundus images that have gone through preprocessing and feature extraction to improve data quality. The data was divided into training and testing, then the Random Forest model was trained with hyperparameter optimization using RandomizedSearchCV for 20 iterations and 5-fold cross-validation to obtain the best parameter combination. The best model achieved an accuracy of 80.92% on testing data with a macro ROC-AUC value of 0.9422, where the best performance was obtained in the classification of diabetic retinopathy with a precision of 99.54%, recall of 99.09%, and ROC-AUC of 1.0000. In addition, macro specificity reached 93.66%, indicating the model's good ability to identify negative cases correctly. The research results show that the Random Forest approach with hyperparameter optimization has excellent performance for eye disease classification and has the potential to be implemented as an artificial intelligence-based medical diagnosis support system in health care facilities.
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