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All Journal Jurnal Algoritma JUITA : Jurnal Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Diksatrasia : Jurnal Ilmiah Pendidikan Bahasa dan Sastra Indonesia Jurnal Algoritma
Leni Fitriani
Institut Teknologi Garut
Computer Science & IT Education Languange, Linguistic, Communication & Media

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Journal : juita jurnal informatika

 1 
Image Classification On Garutan Batik Using Convolutional Neural Network with Data Augmentation Leni Fitriani; Dewi Tresnawati; Muhammad Bagja Sukriyansah
JUITA: Jurnal Informatika JUITA Vol. 11 No. 1, May 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i1.16166

Abstract

In Indonesia, Batik is one of the cultural assets in the field of textiles with various styles. There are many types of batik in Indonesia, one of which is Batik Garutan. Batik Garutan has different motifs that show the characteristics of Batik Garutan itself. Therefore, to distinguish the features of Batik Garutan from another batik, a system is needed to classify the types of batik patterns. Classification of batik patterns can be done using image classification. In image classification, there are methods to increase the size and quality of the limited training dataset by performing data augmentation. This study aims to obtain an image classification model by applying data augmentation. The image classification process is carried out using the Deep Learning method with the Convolutional Neural Network algorithm, which is expected to be helpful as a reference for research and can be applied to software development related to image classification. This study generated models from several experiments with different epoch parameters and dataset proportions. A system obtained the investigation with the best performance with a data proportion of 9:1, resulting in an accuracy value of 91 percent.
Multiplayer Game Guessing Sunda’s Proverb Using Socket.Io And Node.Js Leni Fitriani; Dewi Tresnawati; M. Iqbal Ismail Safei Pamungkas
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.16828

Abstract

Game development is currently quite rapid. Now games can be played by various groups, because many games now contain not just games, but there are also games with educational content. The educational game that will be made in this study is a website-based Sundanese proverb game, this type of game will be multiplayer so that players can compete with other players. The purpose of this research is to make a Sundanese proverb educational multiplayer game that can be played simultaneously with many players, so that it can introduce the regional language, namely Sundanese, to the wider community. The technology used in making this game is Socket.IO and Node.JS, using these technologies can make end users interact in real time. In making this game using the Game Development Life Cycle (GDLC) methodology with the stages of initialization, pre-production, production, testing, beta and release. The results obtained in this research are website-based Sundanese proverb educational games that can be used without taking up much space on the device.
Transformer-Based Detection Model for Number Recognition on Electric kWh Meters Leni Fitriani; Ahmad Sanusi; Rita Rismala; Dewi Tresnawati
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26161

Abstract

Manual recording of analog kWh meters frequently results in user complaints due to discrepancies between recorded and actual electricity usage. These issues stem from the continued reliance on manual data collection. This study proposes a model that automatically detects and extracts numerical values from kWh electricity meters using the Detection Transformer (DETR) for object detection and EasyOCR for optical character recognition (OCR). The model was developed using the Machine Learning Life Cycle (MLLC) methodology, comprising data acquisition, preprocessing, modeling, evaluation, and deployment. Evaluation using the Mean Average Precision (mAP) metric yielded a score of 96.83%, demonstrating high object detection accuracy. The trained model was integrated into a simple web application built with the Flask framework. While the model performed well on high-quality images, its effectiveness declined on low-quality images, such as blurry or distant captures. This study highlights the potential of DETR for object detection and OCR-based text extraction in analog meter reading, while also identifying challenges in handling suboptimal image conditions for future improvements
Co-Authors A.A. Ketut Agung Cahyawan W Abdurohman Iqbal Abi Abdurahim Ade Sutedi Agas Moch Fauzan Ahmad Ilham Maulana Ahmad Sanusi Aji Pangestu Aldy Rialdy Atmadja Annisa Ridha Ramadhani Ayu Latifah Azmi Inda Roby Bunyamin . Cecep Iqbal Hambali Dede Kurniadi Dewi Tresnawati Dini Destiani Siti Fatimah Fakhrul Hawariyan Fassa Febriansyah Adli Ramadhan Fikri Zakaria Rahman Gema Semesta Sholihat Husni Mubarok Ikbal Lukmanul Hakim Ilpad Mutaqin Intania Sintiani Isan Priana Ivan Muhammad Hanif M. Iqbal Ismail Safei Pamungkas Mohamad Fikri Haekal Mohamad Ramdan Nurul Falah Muhammad Bagja Sukriyansah Muhammad Dzulfi Muwaffaq Muhammad Rois Dien Nenden Zakiatinnufus Popi Maspupah Raden Erwin Gunadhi Rahayu Raden Erwin Gunadhi Rahayu Raden Nazar Labani Rendi Nurdian Ridwan Setiawan Rifan Miptah Purnama Rina Kurniawati Rina Kurniawati Rinda Cahyana Risma Liyana Ulfa Rita Rismala Rival Firmansyah Sendra Ilham Mandala Sherine Valentin Margaretha Siti Hapsah Azizah ST Dini Nur Ismiyanti Taofik Faturochman Titin Rostini Tova Nanda Pratama Yoga Handoko Agustin Yosep Septiana