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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 17 Documents
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Search results for , issue "JUITA Vol. 12 No. 2, November 2024" : 17 Documents clear
Implementation of PPCA Imputation, SMOTE-N Class Balancing in Hepatitis Classification Using Naïve Bayes Siti Fathmah; Dwi Kartini; Friska Abadi; Irwan Budiman; Muhammad Itqan Mazdadi
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 Farid Suryanto; Merita Arini; Imam Riadi
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 Risa Widianti; Sugiyarto Surono; Kais Ismail Ibraheem
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 I Made Dwi Putra Asana; I Dewa Gede Ari Oka; I Made Oka Widyantara; I Made Subrata Sandhiyasa
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 Yosia Adi Susetyo; Hanna Arini Parhusip; Suryasatriya Trihandaru
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 Dini Ramadhani; Agus Mohamad Soleh; 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.
Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach Muhammad Kunta Biddinika; Alya Masitha; Herman Herman; Vita Arfiana Nurul Fatimah
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.
Optimizing Attendance System: Integrating Liveness Detection and Deep Learning for Reliable Face Recognition Joseph Teguh Santoso; Eko Sediyono; Kristoko Dwi Hartomo; Irwan Sembiring
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.v12i2.21738

Abstract

The study focuses on using vitality detection and deep learning technologies in the context of facial recognition in an IT presence management project. The combination of deep learning with vitality detection provides a considerable advancement in security and effectiveness. This work integrated vitality-detecting technology with in-depth learning in facial recognition systems. Vitality detection technologies are used to verify the authenticity of persons by examining live indicators such as movements or facial expressions before face recognition. Meanwhile, deep learning is used to analyze and process facial photos correctly by learning from large amounts of data and recognizing facial features in depth. The study data set consists of 1300 photographs of professional school instructors taken with official authority. Model testing and training are carried out in the Google Colab environment, using Python and the Hardy package. The test findings showed an 87% accuracy in face recognition, proving the system's capacity to consistently identify persons and distinguish real from false ones. Furthermore, the performance of Liveness Detection achieves 92% accuracy, as does the integration of Live Detection technology with Deep Learning at 78%.
Improving Sentiment Analysis Accuracy Using CRNN on Imbalanced Data: a Case Study of Indonesian National Football Coach Slamet Riyadi; Muhammad Dzaki Mubarok; Cahya Damarjati; Asnor Juraiza Ishak
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.v12i2.21847

Abstract

Conducting sentiment research on the perception of the Indonesian people towards Shin Tae Yong's (STY) role as coach of the Indonesian National Football Team (PSSI) is crucial as it can assist PSSI in determining whether to extend STY's contract. Prior studies have demonstrated that Deep Learning achieves a high level of accuracy when applied to sentiment analysis in many domains. Nevertheless, no investigation has been conducted thus far utilizing deep learning techniques to examine emotion towards STY. This study employs modified Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Convolutional Recurrent Neural Networks (CRNN), and CRNN models with and without data oversampling. The research findings indicate that the CRNN model, when combined with data oversampling and a redesigned architecture, achieves the highest level of accuracy (1.00) and consistently performs well. This research provides significant contributions in three areas: firstly, it utilizes Deep Learning techniques for sentiment analysis on STY; secondly, it modifies the CRNN architecture; and thirdly, it applies data oversampling to address the issue of imbalanced data.
Development of ERP System for Outsourcing Company using Internet of Things and Blockchain Technology Muhammad Agus Zainuddin; Sritrusta Sukaridhoto; Oktafian Sultan Hakim; Agus Prayudi
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.v12i2.22059

Abstract

Enterprise Resource Planning (ERP) systems are pivotal in optimizing operations within outsourcing industries, providing a unified platform for managing business processes. However, the current generation of ERP platforms exhibits significant limitations in seamlessly integrating with emerging technologies such as the Internet of Things (IoT) and blockchain. the paper highlights the transformative potential of effective integration with IoT can offer real-time visibility and operational efficiency such as attendance of the worker, while blockchain can provide robust security and transparency of worker activity through smart contracts. The implementation of an ERP system compared to the traditional manual method to quantify the efficiency gained through this technological advancement. In this analysis, we collected attendance data for 20 employees, documenting the time taken for both manual and ERP-based attendance in seconds. The result of ERP system significantly reduces attendance time. Efficiency is calculated as the percentage reduction in time achieved by using the ERP system compared to the manual method. An average efficiency of 69.05% shows that, overall, the use of ERP is nearly 70% more efficient than the manual method with IoT data transfer using MQTT to blockchain result is 100% retention rate.

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