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All Journal Jurnal Masyarakat Informatika Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa 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 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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Perbandingan Performa SIFT dan ORB dalam Pengolahan Dataset Wajah nist_2 Amron, Azmi Jalaluddin; Azizi, Husin Fadhil; Arofi, Muhammad Labib Zaenal; Paramita, Cinantya; Naufal, Muhammad
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i2.2738

Abstract

The problem of detecting and matching facial features in digital images is becoming increasingly crucial with the development of biometrics and human computer interaction applications, especially under varying lighting, orientation, and expression conditions. In this study, two feature detection and matching algorithms Scale Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) are compared on grayscale images processed using adaptive local contrast and Gaussian filtering. The performance of both algorithms is quantitatively evaluated based on the number of keypoints, matching precision, execution time, and visual accuracy. Experimental results show that ORB has an execution time of about 4.7-fold faster than SIFT, indicating ORB’s suitability for real time applications. In contrast, SIFT produces a higher matching rate and shows better robustness to lighting variations and facial deformation. These findings provide practical guidelines for selecting algorithms based on priority: speed in real time applications or accuracy in challenging environmental conditions.Kata kunci: Feature matching; Facial image processing; Adaptive local contrast; Scale Invariant Feature Transform; Oriented FAST and Rotated BRIEF.   AbstrakPermasalahan deteksi dan pencocokan fitur wajah pada citra digital menjadi semakin krusial seiring berkembangnya aplikasi biometrik dan interaksi manusia komputer, terutama dalam kondisi pencahayaan, orientasi, dan ekspresi yang bervariasi. Dalam studi ini, dua algoritma deteksi dan pencocokan fitur Scale Invariant Feature Transform (SIFT) dan Oriented FAST and Rotated BRIEF (ORB) dibandingkan pada citra grayscale yang telah diproses menggunakan adaptive local contrast dan Gaussian filtering. Kinerja kedua algoritma dievaluasi secara kuantitatif berdasarkan jumlah titik kunci, presisi pencocokan, waktu eksekusi, dan akurasi visual. Hasil eksperimen menunjukkan bahwa ORB memiliki waktu eksekusi sekitar 4,7 lipat lebih cepat daripada SIFT, menandakan kecocokan ORB untuk aplikasi real time. Sebaliknya, SIFT menghasilkan tingkat kecocokan yang lebih tinggi dan menunjukkan ketahanan yang lebih baik terhadap variasi pencahayaan dan deformasi wajah. Temuan ini memberikan pedoman praktis dalam memilih algoritma sesuai prioritas: kecepatan pada aplikasi real time atau ketelitian dalam kondisi lingkungan yang menantang. 
Fairer Public Complaint Classification on LaporGub: Integrating XLM-RoBERTa with Focal Loss for Imbalance Data Zahro, Azzula Cerliana; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Fahmi, Amiq; Megantara, Rama Aria; Naufal, Muhammad; Azies, Harun Al; Iswahyudi, Iswahyudi
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15260

Abstract

The advancement of digital technology has provided opportunities for governments to improve the quality of public services through citizen complaint channels. One example of this implementation in Indonesia is Lapor Gub, managed by the Dinas Komunikasi dan Informasi Provinsi Jawa Tengah (Communication and Information Agency of Central Java Province). This platform receives thousands of complaints daily, ranging from infrastructure, social issues, to illegal levies. However, the large volume of data and the imbalanced distribution of categories pose significant challenges for both manual and automated processing. This study aims to classify citizen complaint texts using XLM-RoBERTa combined with Focal Loss as an approach to handle data imbalance. The dataset consists of 53,774 complaints after data cleaning and text preprocessing. The training process applied a stratified split (78% training, 18% validation, 10% testing) and fine-tuning for 10 epochs. Model performance was evaluated using accuracy, precision, recall, and macro F1-score. The results show that the model without Focal Loss achieved 78.1% accuracy with a macro F1-score of 0.606, while the model with Focal Loss improved the macro F1-score to 0.625 with 78.5% accuracy. These findings demonstrate that the application of Focal Loss enhances the model’s ability to recognize minority categories without reducing performance on majority classes. Therefore, the combination of RoBERTa and Focal Loss offers an effective solution to support faster, fairer, and more transparent public complaint management.
Implementasi Grid Search CV KNN dengan Preprocessing Z-Score Outlier Removal untuk Sistem Prediksi Risiko Kehamilan Anggita, Ivan Maulana; Naufal, Muhammad; Zami, Farrikh Al
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8206

Abstract

This study aims to optimize the K-Nearest Neighbors (KNN) algorithm in predicting pregnancy risk levels using the “maternal health risk” dataset from the UCI Machine Learning Repository. The methodology includes data preprocessing through outlier detection and removal using Z-score, normalization with Standard Scaling, and categorical encoding on the target labels. Hyperparameter tuning is performed using GridSearchCV to identify the optimal combination of KNN parameters (number of neighbors, distance weight, and distance metric). The results show that the unoptimized KNN model achieved an accuracy of only 69.46%, whereas the optimized model reached an accuracy of 82.00%, with macro average precision of 81.91%, recall of 82.89%, and F1-score of 82.23%. Evaluation using a confusion matrix also revealed significant performance improvement, especially in the high-risk category. The optimized model was deployed as a web application using the Flask framework and Docker via Hugging Face Spaces, enabling real-time and efficient online pregnancy prediction. These findings indicate that combining KNN with GridSearchCV and data normalization significantly enhances prediction performance and offers practical application in healthcare decision support systems.
Optimalisasi Arsitektur LSTM dengan Pendekatan Bidirectional untuk Deteksi Kantuk Pengemudi Berbasis Fitur Wajah Hartono, Andhika Rhaifahrizal; Naufal, Muhammad; Alzami, Farrikh
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8219

Abstract

Traffic accidents caused by driver fatigue and drowsiness remain a serious safety concern in many countries, including Indonesia. Various image-based drowsiness detection systems have been developed, yet many still rely on single-frame analysis and lack the ability to capture complete temporal context. To address this issue, a system capable of accurately and real-time detecting signs of drowsiness is required. This study aims to evaluate and compare the performance of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) algorithms for a facial-feature-based drowsiness detection system. The dataset used is YawDD, which consists of videos of drivers yawning and in neutral conditions. Each video was decomposed into frames and analyzed using MediaPipe to extract facial landmarks. Two main features, Eye Aspect Ratio (EAR) and Mouth Opening Ratio (MOR), were utilized. Due to class imbalance, the SMOTE technique was applied to the minority class in the training data. Both LSTM and BiLSTM models were compared under similar architecture configurations. The results show that BiLSTM outperformed LSTM with an accuracy of 94,74% and an F1- score 94,82%, compared to 92,98% accuracy and 93,22% F1-score achieved by LSTM. These findings demonstrate that bidirectional sequential processing in BiLSTM is more effective in capturing the temporal patterns of drowsiness symptoms. This study contributes to the development of accurate and efficient computer vision-based drowsiness detection systems.
Pengaruh Penambahan Arsitektur Model dalam Klasifikasi Citra Bencana Alam Menggunakan Ensemble Learning Amanda Cahyadewi, Felicia; Richo Kurniawan, Ibnu; Umar Fakhrizal, Irsyad; Denta Saputra, Fahrizal; Achmad, Achmad; Naufal, Muhammad; Anggi Pramunendar, Ricardus
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9103

Abstract

Penelitian ini berfokus pada peningkatan akurasi klasifikasi citra bencana alam dengan mengintegrasikan Convolutional Neural Network (CNN), InceptionV3, dan InceptionResNetV2 dalam pendekatan ensemble learning. Model ini dilatih pada dataset multikelas yang terdiri dari citra empat kategori bencana: gempa bumi, banjir, kebakaran hutan, dan siklon. Pendekatan ensemble menghasilkan akurasi klasifikasi sebesar 96,02%, lebih tinggi dibandingkan model tunggal seperti CNN dengan 88,3%, InceptionV3 dengan 94,1%, dan InceptionResNetV2 dengan 92,4%. Penggunaan ensemble learning, khususnya soft voting, memungkinkan model untuk menggabungkan keunggulan dari masing-masing arsitektur, yang secara signifikan meningkatkan performa pada semua kategori bencana. Model ensemble menunjukkan kemampuan generalisasi yang lebih baik, terutama untuk kategori yang lebih sulit seperti Flood dan Earthquake, yang mana model tunggal kesulitan. Hasil juga menunjukkan peningkatan precision, recall, dan F1-score, dengan pendekatan ensemble mengurangi kesalahan klasifikasi sebesar 7,5% dibandingkan model terbaik tunggal, InceptionV3. Penelitian ini menunjukkan potensi ensemble learning untuk sistem deteksi bencana waktu nyata, khususnya dalam situasi kritis yang memerlukan akurasi tinggi dan kecepatan klasifikasi. Penelitian ini juga menekankan pentingnya kombinasi model deep learning yang beragam untuk meningkatkan kemampuan sistem dalam menangani berbagai skenario bencana sambil memastikan ketahanan dalam aplikasi dunia nyata.
The Effect of LAB Color Space with NASNetMobile Fine-tuning on Model Performance for Crowd Detection Rafid, Muhammad; Luthfiarta, Ardytha; Naufal, Muhammad; Al Fahreza, Muhammad Daffa; Indrawan, Michael
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17821

Abstract

In the COVID-19 pandemic, computer vision plays a crucial role in crowd detection, supporting crowd restriction policies to mitigate virus spread. This research focuses on analyzing the impact of using the RGB LAB color space on the performance of NASNetMobile for crowd detection. The fine-tuning process, involving freezing layers in various NASNetMobile base model variations, is considered. Results reveal that the model with LAB color space outperforms model with RGB color space, with an average accuracy of 94.68% compared to 94.15%. From all the test iterations, it was found that the highest performance for the NASNetMobile model occurred when freezing 10% of the layers from the back for both model LAB and RGB color spaces, with the LAB color space achieving an accuracy of 95.4% and the RGB color space achieving an accuracy of 95.1%.
Menumbuhkan Literasi Teknologi Melalui Pengenalan Aplikasi Computer Vision Di Kalangan Pelajar Muhammad Naufal; Harun Al Azies
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 1 No. 3 (2024): Agustus : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v1i3.356

Abstract

The purpose of this community service project is to provide education to enhance technological literacy by introducing the basics of Computer Vision applications among students. The activities included a seminar attended by 170 students. An analysis using the Wilcoxon statistical test on pre-post test results showed a significant improvement in participants' understanding of Computer Vision applications. The test results indicated a significant difference before and after the activity with a value of 0.011. Through this community service, participants have successfully grasped the material presented to enhance literacy as change agents in the digital era, positively impacting societal progress through improved understanding of technology in the field of Computer Vision.
Enhanced Brain Tumor Classification through Gamma Correction in Deep Learning Naufal, Muhammad; Al Azies, Harun; Brilianto, Rivaldo Mersis
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (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.v13i6.4474

Abstract

Classification of brain tumors is a problem in computer-aided diagnosis (CAD). This study classifies three classes of brain tumors: gliomas, meningiomas, and pituitary tumors. Image enhancement is useful for improving the quality of images to be recognized by Computer-Aided Diagnosis (CAD) systems. Gamma correction is one spatial method aimed at manipulating contrast. This method operates with a spatial approach and has relatively low computational time but yields satisfactory results in certain cases. This research compares Gamma Correction with Convolutional Neural Network (CNN) in the classification of brain tumor types. The CNN method without Gamma Correction achieves an accuracy of 86.52%, precision of 83.63%, sensitivity of 86.11%, and specificity of 93.27%. The application of Gamma Correction at 1.5 results in improved performance with an accuracy of 88.80%, precision of 86.49%, sensitivity of 88.06%, and specificity of 94.50%. Meanwhile, Gamma Correction at 0.5 shows an accuracy of 88.59%, precision of 87.59%, sensitivity of 86.68%, and specificity of 94.17%. Overall, the implementation of Gamma Correction in the classification of brain tumor types successfully enhances the CNN classification performance in terms of precision, sensitivity, and specificity compared to without its use.
A Comparative Study of MobileNet Architecture Optimizer for Crowd Prediction putra, Permana langgeng wicaksono ellwid; Naufal, Muhammad; Hidayat, Erwin Yudi
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5703

Abstract

Artificial intelligence technology has grown quickly in recent years. Convolutional neural network (CNN) technology has also been developed as a result of these developments. However, because convolutional neural networks entail several calculations and the optimization of numerous matrices, their application necessitates the utilization of appropriate technology, such as GPUs or other accelerators. Applying transfer learning techniques is one way to get around this resource barrier. MobileNetV2 is an example of a lightweight convolutional neural network architecture that is appropriate for transfer learning. The objective of the research is to compare the performance of SGD and Adam using the MobileNetv2 convolutional neural network architecture. Model training uses a learning rate of 0.0001, batch size of 32, and binary cross-entropy as the loss function. The training process is carried out for 100 epochs with the application of early stop and patience for 10 epochs. Result of this research is both models using Adam's optimizer and SGD show good capability in crowd classification. However, the model with the SGD optimizer has a slightly superior performance even with less accuracy than model with Adam optimizer. Which is model with Adam has accuracy 96%, while the model with SGD has 95% accuracy. This is because in the graphical results model with the SGD optimizer shows better stability than the model with the Adam optimizer. The loss graph and accuracy graph of the SGD model are more consistent and tend to experience lower fluctuations than the Adam model.
Integrasi Convolutional Autoencoder dengan Support Vector Machine untuk Klasifikasi Varietas Almond Fadlullah, Rizal; Winarno, Sri; Naufal, Muhammad
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 1 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i1.9738

Abstract

This research aims to optimize almond variety classification by integrating Convolutional Autoencoder (CAE) as a feature extraction method and Support Vector Machine (SVM) for classification. The research process includes data collection from available datasets, preprocessing, and splitting data for training and testing. Features from almond images are extracted using CAE, which are then used in the SVM model for classification. Model evaluation shows a classification accuracy of 97% on the test data, a significant increase compared to the 48% accuracy of conventional SVM. The CAE-SVM approach offers more compact and informative feature representations, effectively improving almond variety recognition. This study highlights the potential of combining CAE and SVM advantages to enhance plant image analysis and encourages further advancements in machine learning applications in agriculture.
Co-Authors Achmad Achmad Akrom, Muhamad Akrom, Muhamad Febrian Al Fahreza, Muhammad Daffa Al zami, Farrikh Al-Azies, Harun Alzami, Farrikh Amanda Cahyadewi, Felicia 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 Dwi Puji Prabowo, Dwi Puji Eko Purnomo Bayu Aji Erika Devi Udayanti Erwin Yudi Hidayat Fadlullah, Rizal Fahmi Amiq Firmansyah, Gustian Angga Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hadi, Heru Pramono Handayani, Ni Made Kirei Kharisma Harisa, Ardiawan Bagus Hartono, Andhika Rhaifahrizal Harun Al Azies Harun Al Azies Heni Indrayani Hepatika Zidny Ilmadina Hidayat, Novianto Nur Ifan Rizqa Indra Gamayanto Indrawan, Michael Iswahyudi ISWAHYUDI ISWAHYUDI Kharisma, Ni Made Kirei Khoirunnisa, Emila Kurniawan Aji Saputra Kurniawan, Defri Kurniawan, Ibnu Richo Kusumawati, Yupie Liya Umaroh Liya Umaroh Liya Umaroh, Liya Malim, Nurul Hashimah Ahmad Hassain Maulana, Isa Iant Megantara, Rama Aria Moch Anjas Aprihartha Mohammad Arif Mukaromah Mukaromah MUKAROMAH MUKAROMAH Muslih Muslih Nazella, Desvita Dian Ningrum, Novita Kurnia Noor Ageng Setiyanto, Noor Ageng Novianto Nur Hidayat Nugraini, Siti Hadiati Paramita, Cinantya Pergiwati, Dewi Prabowo, Wahyu Aji Eko Puspita, Rahayuning Febriyanti Putra, Permana Langgeng Wicaksono Ellwid Rafid, Muhammad Ramadhan Rakhmat Sani Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Richo Kurniawan, Ibnu Ruri Suko Basuki Safitri, Aprilyani Nur Sofiani, Hilda Ayu Sri Winarno Sudibyo, Usman Suharnawi Suharnawi Trisnapradika, Gustina Alfa Umar Fakhrizal, Irsyad Very Kurnia Bakti, Very Kurnia Widyatmoko Karis Yosep Teguh Sulistyono, Marcelinus Zahro, Azzula Cerliana Zami, Farrikh Al