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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Journal of Applied Data Sciences Journal Medical Informatics Technology
Muhammad Haris
Universitas Nusa Mandiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Engineering Medicine & Pharmacology Public Health

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ANALISIS PERFORMA DAN KECEPATAN KOMPUTASI ALGORITMA K-MEANS DAN K-MEDOIDS PADA TEXT CLUSTERING Karno Nur Cahyo; Agus Subekti; Muhammad Haris
Pixel :Jurnal Ilmiah Komputer Grafis Vol 15 No 2 (2022): Vol 15 No 2 (2022): Jurnal Ilmiah Komputer Grafis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v15i2.931

Abstract

The large number of theses will certainly make it difficult to find categories on thesis topics that have been written by students at a university. One of the uses of the Text Mining method is being able to group thesis objects into the number of clusters formed by the clustering algorithm. This study aims to compare 2 clustering algorithms, namely the K-Means and K-Medoids algorithms to obtain an accurate evaluation of the performance and computational time in the case of thesis clustering, so that relevant topics can be grouped and have better clustering accuracy. The evaluation parameter used is the Davies Bouldin Index (DBI) which is one of the testing techniques on clustering results, with the distribution of training data and testing data using cross validation using a repetition parameter of 10 folds iteration. From the results of the study with the Term Weighting condition used is Term Occurrences and using the N-Grams value is 2, it can be concluded that the K-Means algorithm has a better DBI value of -0.426. Meanwhile, the range of DBI values owned by K-Medoids with the same conditions has a DBI value of -1,631. However, from the visualization results using t-SNE with the same supporting parameters, there are options that can be used, namely the number of clusters is 6, and the DBI value is -1.110. For testing the computational time in the clustering process of 50 thesis documents, the K-Means algorithm has an average time of 2.5 seconds while the K-Medoids algorithm has an average time of 261.5 seconds. The computer specifications used are Asus ZenBook UX425EA.312 with the processor used is 11th Gen Intel® Core™ i5-1135G7 @ 2.40GHz @ 2.40GHz, the graphics card is Intel® Iris® Xe Graphics, the RAM used is 8GB, with storage of 512GB SSD.
Early Diabetes Detection Using Machine Learning Models: A Case Study from Indonesian Clinical Data Yasrizal Chairul; Muhammad Haris
Journal Medical Informatics Technology Volume 4 No. 1, March 2026
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v4i1.80

Abstract

Diabetes is a major health problem that can significantly reduce life expectancy and increase the risk of serious complications such as kidney failure, stroke, and cardiovascular disease. Therefore, early detection is essential to prevent the progression of the disease. This study proposes a machine learning-based approach for early diabetes detection using a private dataset obtained from RSUP Persahabatan General Hospital in Jakarta, Indonesia. The dataset consists of 501 patient records with clinical and laboratory features extracted from the hospital’s electronic medical record system. Several machine learning algorithms were implemented and compared, including Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Naïve Bayes, Extreme Gradient Boosting, Ensemble methods, and Artificial Neural Networks. Feature selection was performed using ANOVA, and hyperparameter optimization was applied using GridSearchCV to improve model performance. The experimental results show that the Artificial Neural Network model achieved the best performance with an accuracy of 0.86 (86%). Statistical analysis using logistic regression identified systolic blood pressure, diastolic blood pressure, age, HDL cholesterol, and leukocyte levels as the most significant risk factors associated with diabetes. These findings demonstrate the potential of machine learning techniques to support early diabetes detection using clinical data from Indonesian healthcare settings.
EagleEyes: An Artificial Intelligence-Based Approach for Automatic Traffic Violation Detection Using Deep Learning Windu Gata; Muhammad Haris; Maria Irmina Prasetiyowati; Sony Harianto
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1097

Abstract

Rapid urbanization and the advancement of smart city programs in Indonesia necessitate intelligent, automated solutions for traffic monitoring and law enforcement. This study introduces EagleEyes, an artificial intelligence–based framework designed for automatic detection of multiple traffic violations by integrating the YOLOv8 deep learning architecture with Optical Character Recognition (OCR) for vehicle license plate identification. YOLOv8 was selected due to its anchor-free design, decoupled detection head, and enhanced feature fusion modules, which collectively improve detection accuracy, convergence speed, and small-object recognition compared to YOLOv5 and YOLOv7, while maintaining lightweight computational efficiency suitable for real-time applications. The proposed system was trained on a multi-class dataset representing common Indonesian violations, including seat belt non-compliance, helmet absence, motorcycle overcapacity, and unreadable license plates. Experimental results demonstrate robust performance, achieving a precision of 0.91, recall of 0.92, and mean average precision (mAP@0.5) of 0.96 at the optimal epoch, with an average inference speed of 25 frames per second and total training time of approximately 15 minutes on an NVIDIA RTX GPU. The OCR module attained an average recognition accuracy of 98.7%, although its performance decreased for vehicles captured beyond a five-meter distance due to reduced clarity and illumination inconsistencies. Implemented as a web-based application using the Flask framework, EagleEyes enables flexible browser-based visualization, and can be seamlessly integrated into Indonesia’s Electronic Traffic Law Enforcement (ETLE) infrastructure. Overall, the system demonstrates high potential to enhance smart city traffic management through scalable, AI-driven, and ethically responsible automation.
Co-Authors Agus Subekti Karno Nur Cahyo Maria Irmina Prasetiyowati Sony Harianto Windu Gata Yasrizal Chairul