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All Journal Jurnal Teknologi Informasi dan Multimedia
Muhammad Azwar
Program Studi Ilmu Komputer, Universitas Bumigora
Computer Science & IT

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Pengenalan Bahasa Isyarat Hijaiyah: Augmentasi Data dengan EfficientnetB7 Tanwir Tanwir; Husain Husain; Rifqi Hammad; Andi Sofyan Anas; Muhammad Azwar
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.728

Abstract

Sign language plays an important role as the primary means of communication for individuals with hearing impairments. This study aims to improve the accuracy of hijaiyah sign language detection through the application of the EfficientNetB7 architecture and data augmentation tech-niques. The method used, namely the EfficientNetB7 algorithm, was chosen as the base model be-cause of its ability to balance high accuracy with optimal resource utilization by performing data augmentation with rescale, shear, zoom, rotation, and flip horizontal techniques applied to enrich the variation of the original dataset of 6,811 images to 105,615 images. The experimental results show that the combination of EfficientNetB7 and data augmentation produces 99% accuracy on the test data, with consistent performance seen from the confusion matrix and accuracy loss graph for 50 epochs. This study proves that this approach not only improves model generalization but also reduces the risk of overfitting, thus potentially supporting social inclusion through efficient and reliable technology.
Pemetaan Spasial Jemaat GPPS Betlehem Lombok Menggunakan Algortima K-Means Clustering dan Leaflet.js Ahmat Adil; Bambang Krimono Triwijoyo; Heroe Santoso; Muhammad Azwar; Raymond Putra Suntana
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.992

Abstract

The church as a place for people to gather requires accurate information to store congregational data. With the Geographic Information System (GIS) it will be easier for church officials to map congregations that number more than hundreds of people and share locations of scheduled activities. The purpose of this study is to implement the K-Means algorithm to map the distribution of the GPPS Bethlehem congregation so that it can provide informative and easy-to-understand spatial visualization.  The clustering algorithm method groups multiple data sets by explaining how data within a group has similar characteristics and how they differ from other groups.  The data in the form of geographic coordinates (latitude and longitude) of the congregation's location was then processed using the K-Means algorithm with a predetermined number of clusters. The processing results showed that the data was successfully grouped into several clusters based on location proximity, each cluster having a centroid as the center point of the group, the centroid value changing at each iteration until it reached a convergent condition. Based on the research results, it can be concluded that: The clustering process produces several groups (clusters) that represent the distribution pattern of congregations in a particular area with a clear cluster center (centroid) and visualization using Leaflet.js is able to display the clustering results in the form of an interactive map that is informative and easy for users to understand.
Co-Authors Ahmat Adil Andi Sofyan Anas Bambang Krimono Triwijoyo Heroe Santoso Husain Husain Raymond Putra Suntana Rifqi Hammad Tanwir Tanwir