Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)
Agastyar Priatdana, Gde Yoga
Unknown Affiliation
Computer Science & IT

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Decision Tree Model for Classifying University Students Eligible for UKT Waivers Agastyar Priatdana, Gde Yoga
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 7 No 1 (2024): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.245

Abstract

This paper develops a Decision Tree-based classification model to determine student eligibility for UKT (Single Tuition Fee) waivers using socio-economic factors such as parental income, household type, parental occupation, number of dependents, and vehicle ownership. The goal is to automate the identification of students qualifying for financial aid, improving efficiency and fairness in resource allocation. The model was trained on a dataset containing both categorical and numerical features, with the target variable being binary: "Eligible" (1) or "Not Eligible" (0). The model achieved an overall accuracy of 93.33%, with strong performance for the "Eligible" class, reflected by excellent precision, recall, and F1-score. However, the model performed poorly on the "Not Eligible" class, with low recall and F1-score, highlighting the issue of class imbalance. To address this, techniques like resampling and class weighting are recommended to improve classification of the minority class. Exploring alternative models like Random Forest or XGBoost could also provide more balanced results. This underscores the importance of addressing class imbalance and using evaluation metrics beyond accuracy when developing classification models for imbalanced datasets.
Classifying UKT Fee Relief Eligibility Using K Nearest Neighbors Algorithm Anggara Putra, I Wayan Kintara; Agastyar Priatdana, Gde Yoga
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 5 No 3 (2023): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.251

Abstract

This research develops a K-Nearest Neighbors (KNN)-based classification model to determine the eligibility of students for Tuition Assistance (UKT) based on socio-economic factors, including parental income, family size, parental occupation, number of dependents, and housing conditions. The goal is to automate the process of identifying students eligible for financial aid, enhancing both the efficiency and fairness in resource allocation. The model was trained using a dataset consisting of both categorical and numerical features, with the target variable being binary: "Eligible" (1) or "Not Eligible" (0) for UKT relief. The KNN model achieved an overall accuracy of 92%, with strong performance in predicting the "Eligible" class. However, the "Not Eligible" class showed lower performance, particularly in terms of recall and F1-score, suggesting the presence of class imbalance. To address this issue, techniques such as class balancing, resampling, or adjusting KNN parameters are suggested to improve the model's ability to correctly classify minority instances. Additionally, exploring ensemble methods like Random Forest or XGBoost may provide more robust results. This study highlights the importance of addressing class imbalance and using appropriate evaluation metrics beyond accuracy when building classification models for imbalanced datasets.
Implementation of the TOPSIS Method for a Decision Support System in Recommending Tourist Destinations in Tabanan Anggara Putra, I Wayan Kintara; Agastyar Priatdana, Gde Yoga
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.262

Abstract

Tourism development plays an important role in stimulating regional economic growth, particularly in areas with diverse natural and cultural attractions such as Tabanan Regency in Bali. However, visitors often experience difficulties in selecting destinations that match their preferences due to the presence of multiple decision factors and scattered informational resources, making destination decisions less systematic and potentially inconsistent. This situation highlights the need for a methodical decision support mechanism capable of evaluating tourist destinations based on multiple criteria. Motivated by this issue, this study implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method as part of a Decision Support System designed to recommend suitable tourist destinations in Tabanan. The system evaluates nine destinations based on eight criteria, which include accessibility, attractiveness, facility availability, cleanliness, cost, popularity, safety, and visitor density, and applies weight values determined through expert judgment. The evaluation results show that Jatiluwih Rice Terrace has the highest ranking with a closeness coefficient of 0.679126, followed by Ulun Danu Beratan and Tanah Lot, indicating that heritage value and environmental management strongly contribute to recommendation outcomes. The model provides transparent ranking reasoning and can support tourists, planners, and local tourism administrators in making informed decisions. Future development may involve expanding the destination dataset, integrating real-time visitor data, and deploying the system as a mobile application to improve personalization and accessibility.
Co-Authors Anggara Putra, I Wayan Kintara