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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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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.
Technological Literacy Improvement Program for Students Through Introduction to the Basics of Computer Vision: Program Peningkatan Literasi Teknologi untuk Mahasiswa Melalui Pengenalan Dasar-Dasar Computer Vision Harun Al Azies; Muhammad Naufal; Danar Cahyo Prakoso; Novianto Nur Hidayat
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 8 No. 1 (2024): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

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

Abstract

This activity fills the knowledge gap in students' fundamental grasp of computer vision by emphasizing direct engagement with daily technologies. The major objective is to raise students' technology literacy and equip them to become future leaders with contextually applicable knowledge of computer vision. This program, which involves 170 students, consists of seminars, workshops, and on-the-job training. Statistical tests, such as the Wilcoxon test, are used in the analysis process to compare understanding levels before and after the activity. The Wilcoxon test regularly confirmed significant differences and statistical data demonstrated a considerable gain in computer vision literacy among participants. A comprehensive picture of how student comprehension has changed is provided by descriptive analysis. Analyzing statistical data confirms how well the activities alter participants' perceptions, to have a long-term effect on the advancement of technology and society development.
Multivariate LSTM-Based Intraday Gold Price Prediction with Rolling Time Series Validation Mohammad Arif; Farrikh Alzami; Amiq Fahmi; Erika Devi Udayanti; Muhammad Naufal; Sri Winarno; Nurul Hashimah Ahmad Hassain Malim; Marcelinus Yosep Teguh Sulistyono
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.78091

Abstract

Projecting XAUUSD (gold vs. US dollar) prices on a one-hour interval is particularly challenging due to the market's dynamic and nuanced character. To address short-term financial forecasting, an advanced deep learning methodology utilizing Long Short-Term Memory (LSTM) models was employed. Historical XAUUSD data for 2024 was resampled to hourly intervals and supplemented with SMA, RSI, MACD, and Bollinger Bands to understand the market structure better. An LSTM model was developed using open, high, low, and close prices as inputs, with the close price designated as the output target. Data normalization was performed via MinMaxScaler. The model was validated using Time Series Cross-Validation (TSCV) with a rolling origin expanding window over five splits—a sophisticated method for evaluating performance. The results demonstrated the LSTM model's capability, showcasing a mean RMSE of 9.9574, a mean MAE of 7.4411, an R² score of 0.9535, and a remarkably low MAPE of 0.3009%. These findings indicate the advanced model effectively predicts intraday prices, even while grappling with complex and nonlinear patterns, offering a powerful instrument for trading professionals and researchers to cut through market noise.
Komparasi Algoritma Fitur Matching SIFT Dan AKAZE Untuk Pencocokan Fitur Wajah Berbasis Citra Galih Putra Pratama; Husin Fadhil Azizi; Tira Karel Agata; Muhammad Naufal
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 1 (2026): Januari 2026
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i1.264

Abstract

The problem of matching facial features is an important challenge in biometric systems, especially due to variations in lighting, texture and facial details that affect the stability of keypoint detection. This research aims to compare the performance of the Scale-Invariant Feature Transform (SIFT) and Accelerated-KAZE (AKAZE) algorithms in the facial feature extraction and matching process to determine the trade-off between accuracy and computational efficiency. The dataset used comes from NIST with 393 training images and 341 validation images. Evaluation is carried out using the number of detected keypoints, number of matching keypoints, number of inliers and outliers, feature extraction time, as well as error metrics such as MSE, MAE, RMSE, and R². Experimental results show that SIFT produces better matching performance with a total of 934,763 keypoints detected, an average matching keypoint of 121.14, and the number of inliers of 116.95. In addition, SIFT produces lower MSE, MAE, and RMSE values ​​than AKAZE, indicating better feature matching consistency in facial images. However, AKAZE has higher computational efficiency with an average feature extraction time of 0.1699 seconds, faster than SIFT of 0.2928 seconds. The contribution of this research lies in the comparative analysis of the performance of SIFT and AKAZE in keypoint-based facial feature matching, so that it can be a reference in selecting algorithms according to application needs, both oriented towards accuracy and computational efficiency.
Utilization of Alkane Hydrocarbons in Waste Management to Support a Green Economy Movement for Students GUSTINA ALFA TRISNAPRADIKA; NOOR AGENG SETIYANTO; SUPRIADI RUSTAD; MULJONO; MUHAMMAD NAUFAL
SWAGATI : Journal of Community Service Vol. 4 No. 1 (2026): March
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/swagati.2026v4i1.2645

Abstract

Household waste management, particularly used cooking oil (waste cooking oil), remains an environmental issue due to low public awareness and limited understanding of sustainable waste management practices. Improper disposal of used cooking oil can lead to environmental pollution and pose health risks. This community service activity aims to improve students’ knowledge and skills in processing used cooking oil into value-added aromatherapy candles, while fostering environmental awareness and an entrepreneurial spirit based on the green economy. The method applied is Community Based Participatory Research (CBPR), conducted in two stages: delivery of theoretical material on the 3R concept (Reduce, Reuse, Recycle) and hands-on practice in processing used cooking oil. The activity was carried out at SMK Negeri 9 Semarang involving 23 students. The results indicate an increase in participants’ understanding, as shown by improvements in scores on 3R education (from 59.5 to 83.3) and used cooking oil processing (from 50 to 85). In addition, students were able to produce aesthetically pleasing aromatherapy candles with economic value potential.
EVALUASI SISTEM ALPR BERBASIS YOLOV10 PADDLEOCR UNTUK PENGENALAN PLAT NOMOR KENDARAAN INDONESIA Esadhipa Raif Syihabuddin; 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.v11i2.8073

Abstract

Automatic License Plate Recognition (ALPR) is an important component in intelligent transportation systems, utilized for traffic surveillance, automated parking, and law enforcement. This research develops an ALPR system based on YOLOv10l integrated with fine-tuned PaddleOCR to detect and recognize characters on Indonesian vehicle license plates. The dataset used consists of 532 Indonesian license plate images from Roboflow Universe, divided into 426 training images and 106 validation images. The YOLOv10l model was trained for 50 epochs using COCO pretrained weights, while PaddleOCR PP-OCRv4 was fine-tuned for 100 epochs on license plate crops from the dataset. Evaluation was conducted by comparing three OCR engines: fine-tuned PaddleOCR, EasyOCR, and Tesseract. Results show that the YOLOv10l model achieved an mAP@0.5 of 0.981, Precision of 0.920, and Recall of 0.943, with a Detection Rate of 97.17%. Fine-tuned PaddleOCR outperformed the other engines with a Readable OCR rate of 47.57% and Correct Recognition rate of 36.89%, followed by EasyOCR at 13.59% and Tesseract at 0.00%. This research confirms that fine-tuning PaddleOCR on a domain-specific dataset contributes positively to the accuracy of Indonesian license plate character recognition.
Optimizing XGBoost Performance through Recursive Feature Elimination for Methanol Conversion Prediction Ibnu Richo Kurniawan; Muhamad Febrian Akrom; Novianto Nur Hidayat; Muhammad Naufal
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33509

Abstract

The strong nonlinear interaction between catalytic properties and operating conditions complicates accurate space time yield modeling in thermocatalytic carbon dioxide hydrogenation, especially when redundant descriptors are included. Although XGBoost is widely used for predictive tasks, the influence of feature redundancy on generalization and interpretability in carbon dioxide to methanol systems remains insufficiently examined. This study investigates the integration of Recursive Feature Elimination with XGBoost using 639 experimental observations derived from copper based catalysts. Reducing the feature set from fifteen to eight variables improves generalization performance, as indicated by lower prediction error and higher explained variance. The retained variables correspond to key catalytic and operational parameters, including reaction temperature, pressure, and copper content, aligning with established kinetic and mechanistic principles. These results show that eliminating redundant descriptors stabilizes cross validated performance and reduces training complexity without sacrificing predictive accuracy. The reduced model concentrates predictive weight on kinetically relevant variables, providing a clearer quantitative representation of the parameters that govern space time yield in carbon dioxide hydrogenation.
Optimizing XGBoost Performance through Recursive Feature Elimination for Methanol Conversion Prediction Ibnu Richo Kurniawan; Muhamad Febrian Akrom; Novianto Nur Hidayat; Muhammad Naufal
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.33509

Abstract

The strong nonlinear interaction between catalytic properties and operating conditions complicates accurate space time yield modeling in thermocatalytic carbon dioxide hydrogenation, especially when redundant descriptors are included. Although XGBoost is widely used for predictive tasks, the influence of feature redundancy on generalization and interpretability in carbon dioxide to methanol systems remains insufficiently examined. This study investigates the integration of Recursive Feature Elimination with XGBoost using 639 experimental observations derived from copper based catalysts. Reducing the feature set from fifteen to eight variables improves generalization performance, as indicated by lower prediction error and higher explained variance. The retained variables correspond to key catalytic and operational parameters, including reaction temperature, pressure, and copper content, aligning with established kinetic and mechanistic principles. These results show that eliminating redundant descriptors stabilizes cross validated performance and reduces training complexity without sacrificing predictive accuracy. The reduced model concentrates predictive weight on kinetically relevant variables, providing a clearer quantitative representation of the parameters that govern space time yield in carbon dioxide hydrogenation.
Optimizing Driver Drowsiness Detection: Evaluating CLAHE and AHE Enhancement Techniques Muhammad Naufal; Harun Al Azies; Farrikh Al Zami; Rivaldo Mersis Brilianto
SISTEMASI Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.5206

Abstract

Driver drowsiness is a critical factor in road safety, and early detection can be key to preventing accidents. This research focuses on improving the accuracy of drowsiness detection by enhancing the contrast of driver facial images using image processing techniques. Specifically, the study explores the effectiveness of Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) in this context. The research utilizes the Drowsy Driver Detection (DDD) dataset, which includes facial images categorized into Drowsy and Non-Drowsy classes. AHE and CLAHE techniques are applied to preprocess these images, aiming to improve contrast and subsequently enhance drowsiness detection accuracy. Evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Signal-to-Noise Ratio (SNR) are employed to assess the quality of the processed images. The findings indicate that CLAHE performs better than AHE in terms of image enhancement. CLAHE achieves significantly lower MSE (93.90) compared to AHE (103.92), along with higher PSNR (28.41 for CLAHE vs. 27.97 for AHE) and SNR (0.49 for CLAHE vs. 0.04 for AHE) values. These results suggest that CLAHE effectively enhances contrast and improves image clarity. The success of CLAHE as a contrast enhancement technique highlights its potential application in real-time driver monitoring systems. In conclusion, this research underscores the importance of image preprocessing techniques like CLAHE in advancing driver safety technologies, emphasizing their potential to enhance the performance of drowsiness detection systems in practical driving scenarios.
Analisis Performa Class Weight Dan Focal Loss Pada Model Indobert Untuk Klasifikasi Teks Depresi Berbahasa Indonesia Rafi Jonathan Siger; Muhammad Naufal; Farrikh Alzami
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9620

Abstract

The development of depression detection can be done using exploration of social media content. However, the classification of depression indicative texts faces a major challenge in the form of class distribution imbalances, which can degrade the model's generalization capabilities. This study aims to analyze how the method of overcoming class imbalance affects the performance of the IndoBERT model in the classification of Indonesian depression indication texts by emphasizing the analysis of training stability based on the dynamics of training loss and validation loss. The dataset used consists of 3,863 data, data that has gone through the process of cleaning, removing duplicate data, tokenization, encoding, and dividing data into stratification into training data, validation data, and test data. The IndoBERT-base-p1 model was fine-tuned using three training scenarios, namely baseline, class weight, and focal loss with an early stopping mechanism based on validation loss. The test results showed that the baseline IndoBERT scenario produced an accuracy of 77.52%, a weighted precision of 0.7752, a weighted recall of 0.7752, a weighted F1-score of 0.7737, and a ROC-AUC of 0.8528 with a relatively stable training pattern. The class weight method produced an accuracy of 74.68%, a weighted F1-score of 0.7467, and a ROC-AUC of 0.8342 which showed an increase in class discrimination ability but accompanied by a decrease in overall accuracy. Meanwhile, the focal loss method produced an accuracy of 72.87%, a weighted F1-score of 0.7291, and a ROC-AUC of 0.8188 with more balanced training characteristics than the weight class. The findings suggest that handling classroom imbalances does not necessarily improve global performance, so model evaluations need to consider a balance between accuracy, sensitivity, and stability of training.
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