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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 3, November 2025" : 16 Documents clear
Blockchain untuk Penggalangan Dana Filantropi di Indonesia: Meningkatkan Transparansi, Akuntabilitas, dan Kepercayaan Publik Muhammad Rizky; Teduh Dirgahayu
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27342

Abstract

Indonesia’s philanthropic sector faces significant challenges related to transparency and accountability, which undermine public trust, especially in donation management through crowdfunding platforms. This paper aims to explore the potential of blockchain technology for enhancing transparency and accountability in philanthropic crowdfunding by adapting the Blockchain-Based Donation Traceability (BBDT) framework. Using the Design Science Research methodology, data were collected through interviews with two major philanthropic organisations (Baitul Maal Hidayatullah and Human Initiative) and a survey involving 180 respondents. Survey results showed that 72% of respondents consider donation tracking very important, and 81.9% believe that blockchain could enhance transparency. A prototype of a blockchain-based donation system for philanthropic organisations was designed and developed, featuring functionalities to meet user needs identified during interviews. The prototype was demonstrated to Baitul Maal Hidayatullah and Human Initiative, with all transactions executed on the Ethereum testnet and the local Ganache network. The demonstration showed that blockchain implementation can enhance transparency and accountability through real-time donation tracking and automated reporting. However, barriers to adoption, such as limited public familiarity with cryptocurrency and digital wallets, remain substantial, indicating that transactions should still be conducted in rupiah, with blockchain used solely for transparent transaction recording. In conclusion, blockchain demonstrates strong potential to support digital philanthropy, while ongoing education and system adaptation are essential.
Analisis Perbandingan Algoritma Machine Learning untuk Klasifikasi Potabilitas Air Tanah di Jakarta Diky Arianto Tarihoran; Hadi Santoso
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27348

Abstract

Groundwater quality is a fundamental aspect of fulfilling clean water needs, particularly in urban areas such as Jakarta, which faces significant supply limitations due to severe contamination from domestic waste, chemical pollutants, industrial activities, and septic tank leakage. This study aims to compare the performance of nine machine learning algorithms in developing a classification model for groundwater feasibility based on physical parameters. Real-time data were collected from three administrative regions in Jakarta using Internet of Things (IoT) sensors, which monitored pH, temperature, total dissolved solids (TDS), and turbidity. Model evaluation involved hyperparameter tuning, cross-validation, feature importance analysis, LIME interpretation, and performance metrics including AUC, accuracy, precision, recall, and F1-score. The results indicate that CatBoost achieved the highest overall performance (AUC: 0.9448, accuracy: 0.9318, F1-score: 0.9209). LightGBM demonstrated competitive results with an F1-score of 0.9211 and AUC of 0.9431, while XGBoost recorded the highest recall at 0.9359. Random Forest and AdaBoost also exhibited consistent performance, with precision of 0.9094 and recall of 0.9327, respectively. In contrast, Support Vector Machine (SVM) yielded the lowest performance (AUC: 0.8860, accuracy: 0.8499). Based on a comprehensive evaluation, CatBoost model is recommended as the most suitable model for IoT-based groundwater quality classification systems.
A Comprehensive Evaluation of CatBoost and LightGBM Algorithms for Honorarium Prediction on Categorical Datasets with Class Imbalance Slamet Widodo; Fandy Setyo Utomo; Berlilana
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27363

Abstract

Determining income, including honoraria in the academic environment, is often done manually and subjectively, necessitating a predictive model to objectively determine the honorarium amount. However, the development of the prediction model faces challenges due to the dataset's characteristics, which include categorical data and an imbalanced class distribution. This research aims to evaluate the predictive performance and computational resource efficiency of the CatBoost and LightGBM algorithms in predicting honorariums. The dataset used includes 58,332 actual honorarium data of employees from higher education institution "A" in Purwokerto for the period from January 2024 to February 2025. The methods used include data preprocessing, dataset splitting using Stratified Splitting, modeling with CatBoost, LightGBM, Random Forest, Neural Network, and Linear Regression, as well as evaluation using MSE, RMSE, MAE, R² metrics, and computational resources (execution time, memory, CPU time). LightGBM achieved an RMSE of 665.960 and an R² of 0.54, while recording the lowest memory usage at only 2.67 MB. CatBoost produced an RMSE of 667.395 and an R² of 0.53, excelling in processing categorical features without one-hot encoding. Meanwhile, Linear Regression showed the lowest accuracy and high memory usage. These results confirm that LightGBM is the most optimal choice for fast, efficient, and accurate honorarium predictions. However, this research is limited to testing in a laboratory environment. Further research is recommended to implement direct integration with an active database and the integration of information retrieval methods to enhance the effectiveness and security of real-time honorarium predictions, as well as to integrate interpretability methods such as SHAP to improve decision-making transparency.
Transfer Learning-Based Detection of Dysarthric Speech Using Lightweight Convolutional Neural Networks Henry Ardian Irianta; Abdul Fadlil; Rusydi Umar
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27695

Abstract

Automatic Speech Recognition (ASR) for a typical speech, such as dysarthria, presents a significant challenge due to high acoustic variability, which often leads to failures in standard models. This challenge is further compounded when implementation is targeted for edge devices with limited computational resources, memory, and power. The need for model architectures that are not only accurate but also highly efficient (lightweight) is crucial for realizing on-device ASR systems with low latency. This research focuses on exploring modern deep learning architectures to address these two primary challenges: accuracy in dysarthric speech and computational efficiency. The study aims to implement and evaluate three efficient models—MobileNetV3Small, EfficientNetB0, and NASNetMobile—on the UASpeech and TORGO datasets. The methodology involves extracting Mel-Frequency Cepstral Coefficients (MFCC) features, which are visualized as spectrograms and subsequently classified using a transfer learning approach. Experimental results show that the MobileNetV3Small model achieved the highest performance on the UASPEECH dataset, attaining a uniform score of 97,8 % for accuracy. This study concludes that lightweight CNN architectures like MobileNetV3Small are highly effective for dysarthric speech classification and demonstrate the feasibility of developing robust and practical ASR systems for resource-constrained environments.
Development and Evaluation of Stroke Disease Classification Models: Classical Machine Learning, Deep Learning, and Explainable AI Approaches Lianny Wydiastuty Kusuma; Andri Wijaya; Asahiro Nathanael Star Sitohang; Ceng Giap Yo
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27868

Abstract

This study evaluates the impact of the Synthetic Minority Oversampling Technique (SMOTE) on improving machine learning and deep learning performance in stroke risk classification using secondary, publicly available data from Kaggle’s Stroke Prediction Dataset (n = 5,110; 249 stroke cases, 4,861 non-stroke cases), for deep learning. Performance was measured using accuracy, precision, recall, and F1-score, while Explainable AI (XAI) methods (SHAP, LIME) were utilized for interpretability. The results show that applying SMOTE improves the model's sensitivity to the minority "Stroke" class, with Random Forest after SMOTE achieving 97% accuracy and a balanced precision–recall. These findings highlight the methodological potential of combining SMOTE with machine learning, deep learning, and XAI; however, they should not be interpreted as direct clinical validation. Future work with clinical and population-based datasets is necessary to assess the applicability in real-world healthcare settings.
Editor Preface and Table of Content JUITA: Jurnal Informatika
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 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 3, November 2025

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