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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : https://doi.org/10.15294/sji.vxxix.xxxx
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Scientific Journal of Informatics (p-ISSN 2407-7658 | e-ISSN 2460-0040) published by the Department of Computer Science, Universitas Negeri Semarang, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. The SJI publishes 4 issues in a calendar year (February, May, August, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 25 Documents
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Search results for , issue "Vol. 11 No. 4: November 2024" : 25 Documents clear
A Performance Comparison of Data Balancing Model to Improve Credit Risk Prediction in P2P Lending Pertiwi, Dwika Ananda Agustina; Ahmad, Kamilah; Unjung, Jumanto; Muslim, Much Aziz
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14018

Abstract

Purpose: The problem of imbalanced datasets often affects the performance of classification models for prediction, one of which is credit risk prediction in P2P lending. To overcome this problem, several data balancing models have been applied in the existing literature. However, existing research only evaluates performance based on classification model performance. Thus, in addition to measuring the performance of classification models, this study involves the contribution of the performance of data balancing models including Random Oversampling (ROS), Random Undersampling (RUS), and Synthetic Minority Oversampling (SMOTE). Methods: This research uses the Lending Club dataset with an imbalanced ratio (IR) of 4.098, and 2 classifiers such as LightGBM and XGBoost, as well as 10 cross-validation to assess the performance of the data balancing model including Random Oversampling (ROS), Random Undersampling (RUS), and Synthetic Minority Oversampling (SMOTE). Then the model is evaluated using the metrics of accuracy, recall, precision, and F1-score. Result: The research results show that SMOTE has superior performance as a data balancing model in P2P lending, with an accuracy of the LightGBM+SMOTE model of 92.56% and the XGBoost+SMOTE model of 92.32%, where this performance is better than other models. Novelty: This research concludes that SMOTE as a data balancing model to improve credit risk prediction in P2P lending has superior performance. Apart from that, in this case, we find that the larger the data size used as a model training sample, the superior performance obtained by the classification model in predicting credit risk in P2P lending.
Heart Disease Clustering Modeling Using a Combination of the K-Means Clustering Algorithm and the Elbow Method Wala, Jihan; Herman, Herman; Umar, Rusydi; Suwanti, Suwanti
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14096

Abstract

Purpose: Heart disease is the leading cause of death throughout the world, especially in developing countries like Indonesia. Modern approaches for diagnosing and managing heart disease rely on machine learning due to the complexity of medical data. Among the biggest challenges in using machine learning is clustering heart disease patients. This study aims to develop a machine-learning model using K-means clustering to determine the severity and level of patient emergencies. The specific objective of the model is to find the optimal number of clusters using the Elbow Method. Methods/Study design/approach: Model development using a dataset from the Kaggle Repository consisting of 303 patient data. Each data point consists of the attributes age, gender, type of chest pain, blood pressure, serum cholesterol level, blood sugar, electrocardiography results, maximum heart rate, angina, ST depression, and segment slope ST. The combination of the K-means clustering algorithm and the Elbow Method is expected to find the optimal number of clusters in the developed model. Result/Findings: The results of building a machine learning model show that k-means clustering is quite effective in clustering heart disease patients. For the model built with 303 data points, the elbow method successfully identified the optimal number of clusters, resulting in two clusters (k=2), where the elbow point on the graph shows a significant decrease in the Sum of Squared Errors (SSE). Novelty/Originality/Value: This study combines the k-means clustering algorithm and the elbow method to determine the severity and level of patient emergencies. The clustering model produces specific risk clusters that help doctors determine more appropriate interventions.
Evaluation of Integrated Business Licensing System in Indonesia Using HOT-fit Model Masbudi, Handika; Husain, Husain; Lusa, Sofian; Sensuse, Dana Indra; Indriasari, Sofianti; Putro, Prasetyo Adi Wibowo
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.14431

Abstract

Purpose: In an increasingly interconnected global economy, there is a growing recognition among governments of the importance of fostering an environment for businesses to thrive. Indonesia has introduced an integrated business licensing system that centralizes and automates the licensing and permitting processes, simplifying the journey for entrepreneurs, business owners, and government agencies. To multiply the benefits and effectiveness of the system, the government must assess the system to establish a firm basis for its ongoing advancement and enhancement. Methods: The evaluation of integrated business licensing system was conducted using the HOT-fit framework with the perspective of government agencies. Their involvement is crucial for effective licensing and supervision processes. Data was gathered through questionnaires and analyzed using the PLS-SEM method. Result: The findings revealed that system quality and service quality significantly influenced user satisfaction, whereas information quality, user satisfaction, organizational structure, and organizational environment positively impacted system usage. The results also showed that system usage and user satisfaction influenced net benefit. These results can be used to improve the system to match the government agencies’ expectations. Novelty: The novelty of this study lies in its research object which is an integrated system. This research provides insights for targeted improvements in technological, organizational, and human aspects of integrated system implementation.
Web Design and Consumer Repurchase Intention: The Roles of E-Satisfaction Pangestika, Devi Wahyu; Rayhan, Raditya Valeri Aurelia; Mawardi, Wisnu
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15367

Abstract

Objective: Repurchase Intention (RI) becomes one of the frame aspects of customer retention. This study aims to investigate the impact of Web Design (WD) on customers Repurchase Intention (RI) and to evaluate whether E-Satisfaction (ES) serves as a mediator in this relationship. Methods: The research employed a quantitative approach, utilizing purposive sampling to gather data sourced from 124 students from several colleges in Semarang. It conducted a Structural Equation Modeling (SEM) analysis using SmartPLS. Result: The findings of this research show that Web Design (WD) has a significant and positive effect on Repurchase Intention (RI). Additionally, E-Satisfaction (ES) positively influences Repurchase Intention (RI) and serves as a mediator for the beneficial impact of Web Design (WD) on Repurchase Intention (RI). Novelty: The findings of this study provide recommendations for Bukalapak to enhance its Web Design (WD) and ensure high levels of user E-Satisfaction (ES). These strategies can help boost user interest in making repeat purchases on the Bukalapak platform. The novelty in this study lies in the use of diverse range of objects and research methods that are different from previous studies.
Evaluating Ensemble Learning Techniques for Class Imbalance in Machine Learning: A Comparative Analysis of Balanced Random Forest, SMOTE-RF, SMOTEBoost, and RUSBoost Fulazzaky, Tahira; Saefuddin, Asep; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15937

Abstract

Purpose: This research aims to identify the optimal ensemble learning method for mitigating class imbalance in datasets utilizing various advanced techniques which include balanced random forest (BRF), SMOTE-random forest (SMOTE-RF), RUSBoost, and SMOTEBoost. The methods were systematically evaluated against conventional algorithms, including random forest and AdaBoost, across heterogeneous datasets with varying class imbalance ratios. Methods: This study utilized 13 secondary datasets from diverse sources, each with binary class outputs. The datasets exhibited varying degrees of class imbalance, offering scenarios to assess the effectiveness of ensemble learning techniques and traditional machine learning approaches in managing class imbalance issues. Study data were split into training (80%) and testing (20%), with stratified sampling applied to maintain consistent class proportions across both sets. Each method underwent hyperparameter optimization with distinct settings with repetition over 10 iterations. The optimal method was evaluated based on balanced accuracy, recall, and computation time. Result: Based on the evaluation, the BRF method exhibited the highest performance in balanced accuracy and recall when compared to SMOTE-RF, RUSBoost, SMOTEBoost, random forest, and AdaBoost. Conversely, the classical random forest method outperformed other techniques in terms of computational efficiency. Novelty: This study presents an innovative analysis of advanced ensemble learning techniques, including BRF, SMOTE-random forest, SMOTEBoost, and RUSBoost, which demonstrate significant effectiveness in addressing class imbalance across various datasets. By systematically optimizing hyperparameters and applying stratified sampling, this research produces findings that redefine the benchmarks of balanced accuracy, recall and computational efficiency in machine learning.

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