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All Journal Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri Jurnal Ekobistek Sebatik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Jurnal Informatika Terpadu Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Media Computer Science Jurnal Komtekinfo SATIN - Sains dan Teknologi Informasi Jurnal Teknik dan teknologi tepat guna The Indonesian Journal of Computer Science TECHNOMEDIA : Informatics and Computer Science Jurnal TAM (Technology Acceptance Model) Jurnal Komputer, Informasi dan Teknologi
Syafri Arlis
Universitas Putra Indonesia YPTK
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Implementasi Convolutional Neural Network untuk Klasifikasi Penyakit Daun pada Tanaman Jagung Ilsa hidayat; Syafri Arlis
Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Vol 16, No 2 (2025): Desember
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jsit.v16i2.4448

Abstract

This study aims to evaluate the performance of Convolutional Neural Network (CNN) models in detecting pests on caisim (Chinese mustard) plants using different architectural approaches, namely CNN from scratch, VGG16, and Xception. The dataset used consists of 1,000 images classified into several disease categories and a healthy class. Five experiments were conducted to compare the effectiveness of the models based on evaluation metrics such as accuracy, precision, recall, and F1-score.The results show that CNN models trained from scratch produced varying levels of performance. The first and second experiments experienced underfitting, with accuracies of 30.25% and 62.29%, respectively. A significant improvement was observed in the third experiment, achieving an accuracy of 83.73% and an F1-score of 0.82, indicating that the model began to better recognize data patterns. The best performance was achieved in the fourth (VGG16) and fifth (Xception) experiments, with accuracies of 91.41% and 92.12%, respectively, and balanced precision, recall, and F1-score values above 0.90.Factors contributing to model success include an optimal proportion of training data, appropriate architectural selection, hyperparameter tuning, and the use of callbacks such as early stopping and model checkpoint. This study demonstrates that selecting the appropriate CNN architecture can significantly improve the accuracy of image classification systems for pest detection in plants
Composite Loss-Enhanced DeepLabV3+ for Crack Segmentation in Photogrammetric Mining Imagery Yesri Elva; Iskandar Fitri; Syafri Arlis
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7807

Abstract

Crack detection in photogrammetric mining imagery remains challenging due to severe class imbalance, thin and irregular crack morphology, and complex soil textures that hinder accurate boundary delineation. This study proposes a DeepLabV3+ semantic segmentation model with a ResNet-50 backbone trained using a Composite Loss that integrates Focal Tversky Loss, Weighted Cross-Entropy Loss, and Boundary Loss into a unified multi-objective framework. The dataset comprised 425 photogrammetric crack images acquired from open-pit mining sites, divided into 380 training and 45 test images. Data augmentation, including horizontal flip, vertical flip, channel shuffle, and random brightness and contrast adjustment, was applied to improve model generalization under varying image conditions. The proposed configuration was evaluated against Default Loss, Binary Cross-Entropy Loss, and Weighted Cross-Entropy Loss. The model achieved a Global Accuracy of 0.9808, Mean Intersection over Union of 0.7890, and Mean Boundary F1-Score of 0.8642, outperforming all baseline configurations. Boundary fidelity showed the largest improvement, with a Mean BFScore gain of 27.3% over Default Loss, demonstrating the effectiveness of Boundary Loss for precise crack contour delineation. The trained model was also deployed in a GUI-based system supporting automated crack analysis and PDF report generation for practical field application.
Machine Learning Analysis Using the Hybrid K-Means and C4.5 Model in Determining Student Readiness for Educational Field Practice Nilam Agsera; Musli Yanto; Syafri Arlis
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12456

Abstract

The evaluation of student readiness for Educational Field Practice (PLK) has conventionally relied on the subjective assessments of supervising lecturers and mentor teachers, posing a risk of inaccurate outcomes. This traditional framework continues to depend on manual, non-standardized analytical approaches. To address these limitations, this study aims to develop a hybrid K-Means and C4.5 machine learning model to categorize the readiness levels of PLK students at Universitas Negeri Padang. K-Means clustering was initially applied to group evaluation data into three distinct readiness categories, with the resulting cluster labels serving as target variables. Subsequently, the C4.5 algorithm was utilized to extract decision rules and generate a predictive decision tree. The dataset comprises 3,578 student grade records obtained from the Center for Field Experience (Pusat PPL) at Universitas Negeri Padang for the July–December 2025 period, encompassing ten assessment indicator variables ranging from partner school observations to final practice reports. K-Means clustering classified 2,619 students (73.20%) into the "Ready" category, 936 students (26.16%) as "Sufficiently Ready," and 23 students (0.64%) as "Needs Mentoring". The C4.5 model selected the learning media indicator (X4) as the root node, achieving an overall accuracy of 92.32%, a weighted precision of 92.33%, a recall of 92.32%, and an F1-score of 92.33%. These findings demonstrate that the hybrid K-Means–C4.5 approach provides an objective, transparent, and data-driven instrument to support PLK administrators in final evaluations and in determining student readiness status upon completing field practice.
Analysis of Student Performance Determination Using K-Means Clustering and Perceptron at Private Madrasah Tsanawiyah Koto Anau Zakiya Hikmi; Musli Yanto; Syafri Arlis
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12458

Abstract

The way we normally evaluate how well students are doing at MTsS Koto Anau is not good enough. This is because it does not show us the hidden patterns of how students learn. This study wants to use a way to evaluate how well students are doing. It uses K-Means Clustering and Perceptron together to get an idea of what helps students achieve. We used a two step method. First K-Means Clustering puts students into three groups based on how similar they're in terms of school work. Then we use these groups to teach the Perceptron model. The information we used came from the office at MTsS Koto Anau. It had details about 113 students. Included things like their average grades how often they came to school and their final exam scores. When we tested the model it was very good at putting students into the group. It was 99 percent of the time. This shows that the model is very good, at figuring out which group a student belongs to. The main thing we found out is that using K-Means Clustering and Perceptron is a good way to understand how students learn and predict how well new students will do. This study helps because it gives madrasahs an useful way to analyze student data. This means they can make learning plans that're just right for each student. The MTsS Koto Anau can use this to make sure students get the help they need to do their best.
Co-Authors Agung Ramadhanu Ahmad Khomsi Andrian, Reski Aprilian Gevindo Ariza Ikhlas Aswandi, Nopan Bambang Supperianto Billy Hendrik Candra Putra Darma Syahrullah Ekajaya Deri Marse Putra Dito Jurinaldo Dodi Guswandi Doli Raharjo, Tio Eggy Febyanti Edwar Fachriqi Naldes Fadhila Putri Sani Fadhila Putri Sani Fajrul Islami Febri Hadi Febri Hadi Feri Pratama Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra Hanum, Luthfia Harkamsyah Andrianof Ilsa hidayat Imam Fakhri Muhammad Iskandar Fitri Johan Harlan Jufriadif Na`am, Jufriadif Larissa Navia Rani, Larissa Larissa Naviarani M.Iqbal, M.Iqbal Mardison Mardison Marfalino, Hari Muhammad Reza Putra Muhammad, Abulwafa Nabila Frisca Oktavia Nabilah Putri Permana Nilam Agsera Olivia, Ladyka Febby Permana, Nabilah Putri Purnama, Ayu Widya Putra, Muhammad Reza Rahma Yanti Ridwan, Ridwan Rofil M. Nur Rusydi, Rezki Sahari Sahari Sahari Sahari Sahari Saputra, Randy Sarjon Defit SIGIT SANJAYA, SIGIT Sofika Enggari Sovia, Rini Sukardi Sukardi Sumijan Sumijan Sumijan, Sumijan Sumijan Supperianto, Bambang Syahputra, Afriadi Wahyu, Fungki Waruwu, Kalfinus Winda Sutra Amanda Winda Sutra Amanda Yanto, Musli Yanto, Musli Yesri Elva Yuhandri Yuhandri, Yuhandri Yulasmi Zakiya Hikmi