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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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Klasifikasi Otomatis PCOS pada Citra Ultrasound Ovarium Menggunakan MobileNetV2 Berbasis Transfer Learning Fadhila Putri Sani; Syafri Arlis
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 2 (2025): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i2.3025

Abstract

This study aims to develop an automatic classification model for Polycystic Ovary Syndrome (PCOS) using deep learning with transfer learning based on the MobileNetV2 architecture. The dataset consists of 1,987 ovarian ultrasound images that underwent preprocessing and augmentation. The model was initialized with pre-trained ImageNet weights, trained using binary cross-entropy loss and the Adam optimizer, and evaluated using accuracy, precision, recall, and F1-score metrics. The training and testing were conducted on a cloud computing platform with resource-efficient settings. The results demonstrate that the model can classify normal and PCOS ovarian images with 99% accuracy, 0.99 precision, 0.99 recall, and 0.99 F1-score. The confusion matrix indicates very few misclassifications, with four normal images incorrectly predicted as PCOS and seven PCOS images misclassified as normal. These findings confirm that MobileNetV2 is effective, efficient, and stable for classifying low-resolution medical images. The proposed model has the potential to serve as a practical automatic diagnostic tool based on ultrasound imaging, which can be implemented on resource-constrained devices as well as cloud platforms to support medical decision-making.
Decision Support System for Selecting Casual Daily Workers to Become Permanent Employees Using the Profile Matching Method Eggy Febyanti Edwar; Yuhandri; Syafri Arlis
Journal of Computer Scine and Information Technology Volume 10 Issue 4 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i4.109

Abstract

Information is the result of processing data from one or more sources, which is then processed to provide value, meaning and benefits. In modern times, the use of technology plays a very important role as a means of information and promotion, especially in the field of websites in delivering information. Technological advances in the field of computers are very helpful in the current decision-making process. One method of decision support systems is profile matching. This method is used to determine the assessment in selecting daily employees to become employees. Profile matching is broadly a process of comparing individual competition in job competition so that the difference in competition (also called gap) can be known, the smaller the gap produced, the greater the weight of the value which means that there is a greater chance for employees to occupy the position. After the calculation using the Profile Matching method, the ranking value that meets the requirements is in the alternative with the name of the worker, namely Bakhtiar with a score of 4.535 and is recommended to become a permanent employee. By applying this method, it is very helpful in determining the selection of casual laborers to become permanent employees.
Exploration of Data Augmentation in Xception for Waste Classification Ariza Ikhlas; Syafri Arlis
SISTEMASI Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.5709

Abstract

The increasing volume of waste worldwide has led to significant challenges related to pollution, waste management, and recycling. These issues require innovative solutions to enhance the waste management ecosystem, such as the implementation of Smart Waste Management, which leverages information technology and artificial intelligence. This study aims to implement the Xception Convolutional Neural Network (CNN) model for waste classification, explore various data augmentation techniques, and identify optimal model configurations for this task. The research methodology consists of several stages, including data preparation, model building and training, model adaptation for classification tasks, model evaluation, iterative experimentation, and saving and reloading the trained model. The dataset used in this study is the TrashNet dataset obtained from Kaggle, consisting of 2,527 images across several classes: cardboard, glass, metal, paper, plastic, and trash. Based on the optimization process, the selected hyperparameters include a batch size of 32, 64 convolutional filters, the Adam optimizer (learning rate = 0.0001), and a dropout rate of 0.25. After training for 100 epochs, the model achieved a training accuracy of 99% with a loss of 0.7%, and a validation accuracy of 87% with a validation loss of 52%. Evaluation on the test dataset yielded an accuracy of 76%, precision of 79%, recall of 75%, and an F1-score of 75%. The application of data augmentation techniques—such as scaling, translation, and color space transformation—resulted in performance improvements, increasing accuracy by 13%, precision by 11%, recall by 13%, and F1-score by 12%. This study contributes by implementing the Xception model on the TrashNet dataset for waste classification and proposing several data augmentation methods that provide empirical evidence to support or challenge existing approaches. The findings offer practical insights for the development of Smart Waste Management systems, enrich the literature through experimental results, and provide a comparative analysis of data augmentation techniques suitable for the TrashNet dataset.
Intelligent System for Diagnosing Infectious Diseases in  Children Using the Certainty Factor and Naive Bayes Methods Based on Android Ahmad Khomsi; Syafri Arlis; S Sumijan
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2743

Abstract

Infectious diseases in children remain a serious health problem due to their high vulnerability resulting from an immune system that is not yet fully developed. Limited access to medical personnel and delays in early detection often result in ineffective treatment. Therefore, this study aims to design and implement an Android-based intelligent system application capable of detecting infectious diseases in children early on by utilizing the Certainty Factor and Naïve Bayes methods. This system is designed as an expert system that mimics the way pediatricians analyze symptoms and determine preliminary diagnoses. The research methods used include collecting disease and symptom data based on the knowledge of pediatric health experts, data analysis, rule base formation, and the design and implementation of an Android-based system. The Certainty Factor method is used to handle the uncertainty of the level of confidence in the symptoms selected by the user, while the Naïve Bayes method is used to calculate the probability of disease based on historical data. The combination of these two methods aims to improve the accuracy and reliability of diagnostic results. The results of the study show that the developed expert system application is capable of providing initial diagnostic information on infectious diseases in children quickly and easily accessible to parents and health workers. This system is expected to be an effective early detection tool, support initial medical decision-making, and contribute to the development of artificial intelligence-based health technology in Indonesia.
Teacher Performance Evaluation Analysis Using K-Means Clustering Algorithm and Random Forest Classification Dito Jurinaldo; Musli Yanto; Syafri Arlis
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2744

Abstract

Teacher performance assessment is a primary parameter in determining the quality of educational institutions. Evaluation systems in many elementary schools still rely on descriptive qualitative approaches. Abundant school administrative data often remain as unprocessed archival records without further analytical utilization. This condition results in school management decision-making that lacks a strong empirical foundation. This study applies data mining technology to transform administrative data into strategic information. The research focuses on SD Negeri 12 Padang Besi and involves all active teaching staff during the current academic year. The research dataset is entirely derived from internal school records. This study excludes the use of questionnaire instruments, and in-depth interview methods are not employed in the data collection process. The analysis is strictly limited to administrative aspects, without including assessments of in-class pedagogical competence. The technical implementation utilizes the K-Means Clustering algorithm to automatically identify patterns in teacher performance grouping. This process is followed by the application of the Random Forest algorithm to measure classification accuracy based on the available administrative features. The combination of these methods produces a performance mapping that is free from human subjectivity. The analytical results provide clear performance labels for each individual teacher. This study contributes to the development of a data-driven digital evaluation model. School management can use the outputs of this research as a basis for reward allocation or targeted professional development programs. This approach ensures transparency in human resource governance within the educational environment.
PADANG FOOD IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) Nabilah Putri Permana; Syafri Arlis
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.7388

Abstract

The recognition of Padang traditional foods presents a challenge because of their high visual similarity, which makes manual classification difficult. This study aims to develop an automatic image classification model for Padang foods using the Convolutional Neural Network (CNN) algorithm. The dataset consisted of 1350 images across nine classes of Padang dishes including omelet, chili egg, cow tendon curry, stuffed intestine curry, fish curry, dendeng batokok, rendang, ayam pop, and fried chicken. The CNN architecture was trained for twenty epochs and evaluated using accuracy, loss, confusion matrix, and testing with new images. The results show that the model reached a final training accuracy of 70.2 percent and a validation accuracy of 65 percent, while testing with unseen images produced correct predictions with moderate confidence levels. These findings suggest that CNN is effective for classifying Padang traditional foods and can be applied in culinary promotion, digital food catalogs, and technology based ordering platforms.
Deteksi Perokok Menggunakan Algoritma You Only Look Once (YOLO) dan Convolutional Neural Network (CNN) Aprilian Gevindo; Syafri Arlis
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2783

Abstract

Image processing technology continues to advance and is widely used for visual identification of human activities, including monitoring smoking behavior in no-smoking areas. This study develops an automated smoking activity detection and recognition system based on digital image processing, combining YOLO (You Only Look Once) for object detection and a CNN (Convolutional Neural Network) as an image classifier. YOLO detects and crops human objects, while the CNN classifies smoking and non-smoking activities based on visual features. The preprocessed dataset contains 560 valid images per class (smoking and not smoking). Training results show 96.09% accuracy on the training set and 94.44% on the validation set, with stable loss, while model evaluation yields 94.44% accuracy, 92.55% precision, 96.67% recall, 94.57% F1-score, and Average Precision (AP), indicating excellent classification performance. The model can also detect smoking activities in real-time images and camera feeds, demonstrating the effectiveness of combining YOLO and a CNN for automated detection, with potential applications in no-smoking areas.
Data-Based Health Insurance Premium Modeling at KUD Tirta Kencana Using a Machine Learning Approach Sukardi Sukardi; Syafri Arlis
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.9354

Abstract

The Village Unit Cooperative (KUD) plays a strategic role in improving the economic welfare of rural communities. However, attention to the health aspects of members is often not a priority. This study aims to analyze the health insurance costs required by members of the Tirta Kencana KUD in Kuantan Singingi District using a data-based approach and predictive modeling. The methods used include collecting membership and health claim data, processing the data using Principal Component Analysis (PCA), and applying predictive algorithms to estimate ideal and sustainable insurance costs. The analysis results indicate a significant correlation between age, membership status, and health history with the amount of premiums that should be covered. The predictive model successfully identified the optimal premium scheme with a predictive accuracy of 92%. These findings are expected to serve as a basis for policy-making in planning more efficient and equitable cooperative-based insurance. This research also opens opportunities for the application of data science in optimizing community-based microhealth systems.
Automated Medical Classification of Human Brain Tumors Leveraging the Xception Convolutional Neural Network Bambang Supperianto; Syafri Arlis
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.9356

Abstract

Brain tumors are among the most critical neurological disorders, marked by abnormal cell proliferation within the brain, either benign or malignant,adversely impacting cognitive, motor, and overall patient quality of life.Accurate and prompt diagnosis is pivotal for determining effective treatment and improving survival outcomes. While Magnetic Resonance Imaging (MIRI) remains the standard diagnostic tool due to its high soft-tissue contrast, manual interpretation is labor-intensive, expertise-dependent, and subject to observer bias. Consequently, deep learning approaches, particularly Convolutional Neural Networks (CNN), have garnered considerable attention for automating brain tumor classification with superior efficiency and accuracy. This study presents a medical classification model for human brain tumors based on the Xception CNN architecture. The model was developed using a publicly available MRI dataset comprising 2,875 images categorized into glioma, meningioma, and pituitary tumor classes. Preprocessing involved resizing, normalization, and data augmentation. The model was initialized with ImageNet weights and fine-tuned for the three-class classification task with softmax activation.The proposed model achieved robust performance, recording test accuracy of 98.4% and an average F1-score of 98.5%, indicating balanced precision and recall. Confusion matrix and error analysis revealed minimal and evenly distributed misclassifications, while training dynamics showed rapid convergence with no significant overfitting.These findings demonstrate the effectiveness and clinical feasibility of the Xception CNN for automated brain tumor diagnosis. Future research should validate the approach on larger, multi-institutional datasets and integrate interpretability techniques to strengthen clinical applicability.
Analysis of Strategies for Improving Learning Quality Based on Naive Bayes and Support Vector Machines Fadhila Putri Sani; Syafri Arlis; Agung Rahmadhanu
Jurnal KomtekInfo Vol. 13 No. 2 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i2.682

Abstract

Islamic educational institutions, particularly Islamic boarding schools, face increasing challenges in improving the quality of learning. The learning quality in Islamic boarding schools should be analyzed in depth to support effective improvement strategies. Based on this background, this study aims to classify strategies for enhancing learning quality using the Naïve Bayes and Support Vector Machine (SVM) algorithms. Naïve Bayes with a Gaussian distribution is widely recognized for its simplicity and accuracy in data classification. Meanwhile, Support Vector Machines (SVM) with a linear kernel are effective for linearly separable and high-dimensional data, enabling stable and efficient modeling in the context of data-driven analysis of learning quality in formal education. The data were collected through questionnaires distributed to 100 female students and 100 teachers. The variables examined include teacher competence, infrastructure, school management, student participation, and learning quality level. The analysis results indicate that the Naïve Bayes algorithm achieved superior performance with an accuracy of 90%, precision of 95.65%, recall of 83.33%, and an F1-score of 86.56%. In contrast, the Support Vector Machine (SVM) obtained an accuracy of 80%, precision of 58.97%, recall of 66.67%, and an F1-score of 62.32%.These findings demonstrate that Naïve Bayes provides more stable classification performance across all learning quality categories. Conversely, the Support Vector Machine (SVM) shows less optimal performance in the low-quality class due to the limited number of data samples. This study contributes effectively to the classification of learning quality levels in Islamic boarding schools
Co-Authors Agung Rahmadhanu 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 hidayat, Ilsa Imam Fakhri Muhammad 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 Olivia, Ladyka Febby Permana, Nabilah Putri Purnama, Ayu Widya Putra, Muhammad Reza Rahma Yanti Ramadhanu, Agung - 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 Yuhandri Yuhandri, Yuhandri Yulasmi