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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 316 Documents
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Implementation of PPCA Imputation, SMOTE-N Class Balancing in Hepatitis Classification Using Naïve Bayes Fathmah, Siti; Kartini, Dwi; Abadi, Friska; Budiman, Irwan; Mazdadi, Muhammad Itqan
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21528

Abstract

The availability of complete data in research is crucial, especially in the initial stages. The Hepatitis data used in this study encountered issues such as missing data and class imbalance, which hindered its optimal utilization. The method employed to address missing data was the PPCA imputation method. After filling in the missing data, the data was balanced using the SMOTE-N class balancing method and classified using Gaussian Naïve Bayes. The aim of this research was to compare the classification evaluation of hepatitis disease using Naive Bayes with the PPCA imputation approach and SMOTE-N class balancing. The best results from each scenario yielded an AUC value of 0.833 in the first scenario with an 80:20 data split for training and testing, and 0.875 in the second scenario with a 90:10 data split. The highest AUC value was obtained in the application of PPCA imputation with SMOTE-N class balancing using Naive Bayes classification. This demonstrates that the implementation of PPCA imputation with SMOTE-N class balancing has a better impact on the performance of Naïve Bayes classification.
ScreenMy: a Lightweight Architecture of Tuberculosis-Diabetes Mellitus Screening System Integrating with EMRs Suryanto, Farid; Arini, Merita; Riadi, Imam
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21541

Abstract

Background: Early detection of diseases like tuberculosis (TB) and diabetes mellitus (DM) is critical for preventive healthcare. However, integrating effective screening programs within existing workflows can be challenging. Objective: This study explores the feasibility and impact of integrating an electronic screening system (ESS) within electronic medical records (EMRs) in private primary care settings. The pilot study focuses on ScreenMy, an ESS engine designed for bi-directional TB-DM screening. Methods: A pilot study investigated the integration process of ScreenMy into an EMR system. Interviews with developers assessed factors like installation ease, flexibility, and impact on the EMR's functionality. Findings: The findings revealed a smooth integration process due to ScreenMy's external design (requiring only plugin injection) and clear documentation.  The integration maintained EMR performance and efficiency, enhanced the developer experience, and offered flexibility for customization.  Developers, unfamiliar with prior integrated screening systems, found ScreenMy user-friendly and expressed interest in further system flexibility concerning data privacy. Conclusion: This investigation highlights the potential for seamless integration of screening systems like ScreenMy within EMRs. This paves the way for improved preventive healthcare delivery in primary care settings.
Handling Noise Data with PCA Method and Optimization Using Hybrid Fuzzy C-Means and Genetic Algorithm Widianti, Risa; Surono, Sugiyarto; Ibraheem, Kais Ismail
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21765

Abstract

The significance of machine learning (ML) and data mining techniques particularly clustering is examined in this research, in managing large data sets for customer segmentation in the retail sector. The research emphasizes the challenges posed by data noise and proposes a solution using Principal Component Analysis (PCA) to improve accuracy. This study introduces a hybrid approach that combines Fuzzy C-Means (FCM) with genetic algorithms for optimization in customer segmentation, and suggests further research on the optimal number of clusters and data noise elimination. By addressing data noise, the proposed PCA-based method achieved a higher accuracy rate of 98% compared to 93% without PCA. This finding underscores the effectiveness of PCA in noise reduction, improving clustering accuracy. This research contributes to the advancement of customer-focused business strategies through better data analysis and interpretation. The proposed approach has potential applications in areas including data analysis, pattern recognition, and image processing, highlighting its relevance in the contemporary business environment.
Improved SVM Classification Using Particle Swarm Optimization for Student Completion Prediction System Asana, I Made Dwi Putra; Oka, I Dewa Gede Ari; Widyantara, I Made Oka; Sandhiyasa, I Made Subrata
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.21990

Abstract

Timely completion of a study program is crucial for evaluating the quality of universities. To achieve timely completion, student’s progress needs to be monitored early in order to ensure that they can complete the given task on time. This process is particularly important because universities often enroll thousands of students, thereby making individual supervision impractical. An effective solution to this problem is leveraging machine learning to develop a system that predicts whether student will complete the study without delay. Therefore, this study used Support Vector Machine (SVM) method for classification, with RBF kernel. Optimization of SVM classification was achieved by ensuring the values for Soft Margin C parameter and kernel parameter were correct. In addition, Particle Swarm Optimization (PSO) method was used to determine the optimal SVM parameter values. Consequently, the resulting model was evaluated using Cross Fold Validation. The optimized SVM parameter identified through PSO were gamma of 0.0085 and C of 0.4196. The average training accuracy recorded is 82.58%, with 81.22% validation, these results can be categorized into Good Classification. Finally, the application of PSO in optimization resulted in SVM models that avoided overfitting, as shown by the closeness of training and validation values.
Development of Staff Evaluation Software Based on Association Matrix Methods and Data Mining Using the Streamlit Framework Susetyo, Yosia Adi; Parhusip, Hanna Arini; Trihandaru, Suryasatriya
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23300

Abstract

This study discusses evaluating employee performance in microbiology laboratories using an association matrix implemented in web-based software with the Streamlit framework. The purpose of the research is to improve the employee performance evaluation process, which previously used conventional methods. This software is built from a sample receipt recording history data stored in a MySQL database. The initially unstructured data was processed using Python libraries such as NumPy, Matplotlib, Pandas, and Difflib to generate personnel evaluation information such as specialization, task duration, workload, and individual competencies. This software can provide a fast and accurate performance assessment according to the evaluation period. In a test with the System Usability Scale (SUS), the software scored 75.83, which was rated "good.". These results show that the software is easy to use and can improve the efficiency of employee performance evaluation. Follow-up tests with questionnaires given to 18 users showed that this system was preferable to previous conventional methods. This software helps laboratory managers evaluate employee performance effectively and efficiently.
Characteristics of Machine Learning-based Univariate Time Series Imputation Method Ramadhani, Dini; Soleh, Agus Mohamad; Erfiani, Erfiani
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23453

Abstract

Handling missing values in univariate time series analysis poses a challenge, potentially leading to inaccurate conclusions, especially with frequently occurring consecutive missing values. Machine Learning-based Univariate Time Series Imputation (MLBUI) methods, utilizing Random Forest Regression (RFR) and Support Vector Regression (SVR), aim to address this challenge. Considering factors such as time series patterns, missing data patterns, and volume, this study explores the performance of MLBUI in simulated Autoregressive Integrated Moving Average (ARIMA) datasets. Various missing data scenarios (6%, 10%, and 14%) and model scenarios (Autoregressive (AR) models: AR(1) and AR(2); Moving Average (MA) models: MA(1) and MA(2); Autoregressive Moving Average (ARMA) models: ARMA(1,1) and ARMA(2,2); and Autoregressive Integrated Moving Average (ARIMA) models: ARIMA(1,1,1) and ARIMA(1,2,1)) with different standard deviations (0.5, 1, and 2) were examined. Five comparative methods were also used in this research, including Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The research findings indicate that MLBUI performs exceptionally well in imputing successive missing values. The results of this study indicate that the performance of MLBUI in imputing consecutive missing values, based on MAPE, yielded values of less than 10% across all scenarios used.
Mobile Forensics in Human Trafficking Investigation Services using Mobile Laboratory Muammar, Muammar; Riadi, Imam; Umar, Rusydi
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24060

Abstract

This research examines the application of a mobile digital forensic laboratory developed by the Digital Forensics Center (DFC) at UMP to handle digital evidence directly at crime scenes, specifically in human trafficking cases. Integrating the Association of Chief Police Officers' (ACPO) methods, this mobile lab facilitates the investigative process, from planning to evidence collection and analysis, without delaying transport to a central lab, thereby speeding response times and minimizing evidence degradation.  We employed Magnet Axiom and DF-Tools to analyze WhatsApp data. Each demonstrated varying performance in identifying key digital evidence such as text messages, media, and group chats. DF-Tools showed an advantage in identifying multimedia artifacts with a 69.48% data acquisition success rate, compared to Magnet Axiom at 68.57%. Additionally, police bolstered their efforts to uncover human trafficking networks by implementing Base Transceiver Station (BTS)-based location tracking techniques to pinpoint suspect and victim locations via phone data or identity numbers. The research findings demonstrate that mobile labs enable rapid on-site responses, offer flexibility in collecting and securing digital evidence, and enhance efficiency and effectiveness of digital forensic investigations.
A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) Putra, Muhammad Ricky Perdana; Utami, Ema
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24061

Abstract

The problem faced in the implementation of Massive Open Online Course (MOOC) is the high dropout rate (DO) reaching 90% which exceeds the formal school dropout rate. Preventive action needs to be taken to minimize the impact on MOOCs, instructors, and students. One solution is to do machine learning (ML) based prediction. The use of ML does not escape the problem of prediction performance that is still less accurate so it needs to be improved by blending ensemble learning (BEL). This research builds a BEL model consisting of two layers including base model with KNN, Decision Tree, and Naïve Bayes algorithms, then meta model with XGBoost. The dataset from KDD Cup 2015 contains clickstream from XuetangX website. The pre-processing stage includes selecting the course with the most participants, normalization, SMOTE, feature selection, and breaking it into three: ensemble, blender, and test data. The BEL model evaluation results obtained an accuracy value of 90.16%, precision of 85.64%, recall of 97.31%, F1-Score of 91.10%, and AUC of 92.83%.
Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach Biddinika, Muhammad Kunta; Masitha, Alya; Herman, Herman; Fatimah, Vita Arfiana Nurul
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.24153

Abstract

This analysis explores the efficiency of machine learning systems for heart disease identification through a multi-algorithm approach. The main objective is to identify the best performing algorithm for accurate disease prediction, improving clinical decision making. Using criteria including accuracy, precision, recall, F1 score, and recall, the study assessed four algorithms: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). The results show that Random Forest outperforms the others, achieving 86.23% precision, 93.76% recall, 89.84% F1 score, and 88.41% accuracy. Random Forest gets an AUC ROC result of 0.94, so Random Forest is considered a superior model in this scenario, especially because it has higher accuracy. The algorithms showed a strong balance between sensitivity and specificity. Decision Tree showed reasonable performance with a precision of 84.18% and a recall of 90.27%, while Naïve Bayes recorded a precision of 87.68% and a recall of 87.03%. SVM showed a precision of 87.40% and a recall of 84.78%, indicating some limitations in capturing positive cases. The novelty of this study lies in the comparative analysis of several algorithms to optimize the heart disease prediction model for clinical use. The random forest algorithm is one of the choices, but there is still a medical standard for classifying people as either indicating or not experiencing heart failure, according to the study.
Assessment of Retrieval and Generative Chatbots in Tourism Information Service Handayani, Sharfina Febbi; Dairoh, Dairoh; Af'idah, Dwi Intan
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24182

Abstract

Chatbots are essential for improving the customer experience on tourism websites, especially when it comes to arranging travel and offering precise information. The purpose of this study is to evaluate the effectiveness of generative and retrieval-based chatbots in the tourism information service. Two retrieval-based models are MLP-based single QA and multi QA and two generative-based models namely LLaMA 2 and GEMMA were evaluated using confusion matrix, BLUE score, correctness response and naturalness response. The study found that LLaMA 2 outperformed other models, with the highest response Accuracy of 0.89, naturalness of 0.75, and BLEU score of 0.33. GEMMA received the lowest score, suggesting that it has trouble coming up with precise and organic answers. The retrieval-based models showed strong accuracy but were less natural in their responses. The ease of dataset creation for generative models, which only requires narrative text, further positions LLaMA 2 as the most suitable option for improving user experience in Tegal tourism services.

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