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Hindayati Mustafidah
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Gedung Fakultas Teknik dan Sains Universitas Muhammadiyah Purwokerto Jl. K.H. Ahmad Dahlan, Dukuh Waluh, Kembaran, Banyumas, Central Java, Indonesia
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JUITA : Jurnal Informatika
Published by Universitas Muhammadiyah Purwokerto
ISSN : 20869398     EISSN : 25798901     DOI : 10.30595/JUITA
Core Subject : Science,
Computer Science & IT
UITA: Jurnal Informatika is a science journal and informatics field application that presents articles on thoughts and research of the latest developments. JUITA is a journal peer reviewed and open access. JUITA is published by the Informatics Engineering Study Program, Universitas Muhammadiyah Purwokerto. JUITA invites researchers, lecturers, and practitioners worldwide to exchange and advance knowledge in the field of Informatics. Documents submitted must be in Ms format. Word and written according to author guideline. JUITA is published twice a year in May and November. Currently, JUITA has been indexed by Google Scholar, IPI, DOAJ, and has been accredited by SINTA rank 2 through the Decree of the Director-General of Research and Development Strengthening of the Ministry of Research, Technology and Higher Education No. 36/E/KPT/2019. JUITA is intended as a media for informatics research among academics, practitioners, and society in general. JUITA covers the following topics of informatics research: Software engineering Artificial Intelligence Data Mining Computer network Multimedia Management Information System Digital forensics Game
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 16 Documents
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Search results for , issue "JUITA Vol. 13 Issue 2, July 2025" : 16 Documents clear
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
Performance Evaluation of ARIMA and GRU Models for Forecasting Chili Price in East Jawa Windi Pangesti; Nabila Syukri; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
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.v13i2.26445

Abstract

Time series forecasting plays a crucial role in predicting future conditions based on historical data, particularly in the food sector, which is highly susceptible to price fluctuations. This study compares two approaches: the conventional ARIMA method and the deep learning method GRU, to forecast the price of red chillies in East Java. East Java was chosen because it is the largest national producer of chilies, thus the stability of its prices has a broad impact. The research results indicate that the GRU model outperforms the ARIMA model with a MAPE value of 19.80% compared to a MAPE of 27.63% for the latter. The benefit of this research is to contribute to the literature on developing agricultural commodity price forecasting models as a basis for enhancing food security policies and stabilizing commodity prices, particularly in East Java Province, Indonesia
Kajian Simulasi untuk Identifikasi Faktor yang Memengaruhi Kinerja LSTM dan XGBoost untuk Deteksi Anomali pada Data Deret Waktu yang Dilabelkan Muhammad Rizky Nurhambali; Yenni Angraini; Anwar Fitrianto
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.v13i2.26604

Abstract

Time series analysis has evolved to include forecasting and anomaly detection, which can be applied in various fields. Machine learning methods, such as long short-term memory (LSTM) and extreme gradient boosting (XGBoost), are widely developed because they are considered superior to conventional methods. Both use a forecasting approach for anomaly detection. However, the limitations of both methods on anomalies, such as data length, labeling method, and number of anomalies have not been explored. Therefore, this study aims to identify factors that affect the performance of LSTM and XGBoost in forecasting and anomaly detection through various scenarios and compare their metrics evaluation. The study utilizes Jakarta's air quality index data for 2018–2023, which was preprocessed and augmented for simulation purposes. The study shows that the LSTM method is superior to XGBoost, as shown by the lower MAPE (14.7024%), lower RMSE (13.9909), and higher balanced accuracy (0.9935). These results are reinforced by the significant Mann-Whitney test between the two methods, indicating a difference in the method's accuracy. In addition, the Kruskal-Wallis test for each combination of method and treatment showed significant results. These results indicate that data length, labeling method, and number of anomalies affect the method's accuracy
Deteksi CO di Kabin Kendaraan Berjarak Jauh dan Analisis Kepadatan Lalu Lintas Menggunakan Logika Fuzzy: Deteksi CO di Kabin Kendaraan Berjarak Jauh dan Analisis Kepadatan Lalu Lintas Menggunakan Logika Fuzzy Suzuki Syofian; Aji Setiawan; Muhamad Fathan; Rolan Siregar
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.v13i2.26854

Abstract

Carbon monoxide (CO) inside vehicle cabins poses a significant health risk to passengers and can even lead to fatalities. This danger primarily arises from inadequate ventilation, which allows exhaust fumes to seep into the cabin and be gradually inhaled. CO is a gas that lacks color, odor, taste, and does not cause irritation, making it difficult to detect without proper tools. It is commonly encountered in industrial environments and is produced by the incomplete combustion of fuel in motor vehicles, heating systems, devices that burn carbon-based materials, wood stoves, train emissions, gas burning, and even tobacco smoke. However, the primary contributor is the residual combustion from vehicle engines. Given these concerns, this study aims to develop a system to monitor and control carbon monoxide concentrations within vehicle cabins using fuzzy logic. The system achieved an average error rate of 2.9% in reducing CO concentrations, with responsive fan control latency below 5 seconds. A microcontroller will serve as the core component for processing and control. The implementation of this system is expected to enable real-time detection of CO levels in the cabin and alert the driver accordingly. Ultimately, this can help reduce incidents of CO poisoning among vehicle occupants
Image-Based Classification of Freshwater Fish Species to Support Feed Recommendation Using Random Forest Hindayati Mustafidah; Suwarsito Suwarsito; Rahmat Setiawan; Abdul Karim
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.v13i2.27358

Abstract

Accurate identification of freshwater fish species plays a vital role in aquaculture, particularly in determining appropriate feed strategies to optimize fish growth. Visual similarities among species—such as color, shape, and surface texture—often hinder novice farmers from correctly recognizing fish types. This study proposes an image-based classification system using the Random Forest algorithm to identify six freshwater fish species: pomfret (bawal), gourami (gurame), catfish (lele), barb (melem), tilapia (nila), and Java barb (tawes) and provide automated feed recommendations. A total of 120 fish images were used as the dataset, collected from various sources, including online repositories and field documentation. Feature extraction was applied to capture color characteristics (HSV), texture patterns (GLCM), and morphological features (regionprops). The model was trained on 70% of the dataset and tested on the remaining 30%. Evaluation results show that the system achieved a classification accuracy of 83.33%, with a precision of 83.53%, recall of 83.33%, and an F1-score of 82.86%. Notably, catfish, barb, and tilapia classes achieved perfect classification, while pomfret and gourami showed room for improvement due to overlapping visual features. The findings indicate that the integration of Random Forest with multi-domain image features offers an effective, affordable, and practical solution to support the digital transformation of small and medium scale aquaculture systems through intelligent species recognition and feed guidance
Editor Preface and Table of Content JUITA: Jurnal Informatika
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

Abstract

Editor Preface and Table of Content JUITA Vol. 13 Issue 2, July 2025

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