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All Journal The Journal of Information Technology, Computer Science, and Electrical Engineering
Risca Sri Mentari
Universitas Pembangunan Panca Budi, Indonesia

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Analysis of Property Tax Payment Compliance Classification in Tebing Tinggi City Using the C4.5 Decision Tree Algorithm Andysah Putera Utama Siahaan; Sulis Sutiono; Sugeng Pranoto; Sarifudin; Risca Sri Mentari
Journal of Information Technology, computer science and Electrical Engineering Vol. 1 No. 2 (2024): June-September 2024
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v1i2.59

Abstract

This research analyzes property tax payment compliance in Tebing Tinggi City using the C4.5 Decision Tree algorithm. With the rapid advancement of data mining technology, this analysis utilizes classification techniques to identify compliance patterns based on property tax payment data. The research methodology involves data collection, preprocessing, and building the Decision Tree model using RapidMiner. The results indicate that the Decision Tree model can effectively predict compliance levels based on attributes such as Total_Payment and Total_Bill. Individuals with higher payment and bill values tend to be compliant, while those with lower values show less compliance. These findings provide insights for authorities to design more effective strategies to improve tax compliance and identify areas that require special attention in Tebing Tinggi City.
Student Data Analysis for Decision Making Using Decision Tree Risca Sri Mentari; Muhammad Iqbal
Journal of Information Technology, computer science and Electrical Engineering Vol. 1 No. 3 (2024): October 2024
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v1i3.149

Abstract

In today's digital era, data has become a valuable asset that supports effective decision-making in various fields, including education. In Indonesia, the education sector needs a technology-based strategy to utilize data optimally in improving the quality of education services. Data mining, as an information technology approach, plays an important role in extracting valuable information from big and complex data. Classification algorithms such as the Decision Tree, specifically the C4.5 algorithm, are widely used in data mining to build accurate decision models. This study aims to apply the C4.5 algorithm to student data to support evidence-based decision-making in education. Using RapidMiner software, this research focuses on classifying and analyzing student data to build a model that can simplify the decision-making process, making it easier to understand and implement. The results of this study show that there is a pattern of gender distribution in various classes, with some classes dominated by female students and others dominated by male students. The preprocessing stage successfully simplifies the data, so that relevant information can be analyzed more easily. These results underscore the importance of data mining technology in education data analysis for better decision-making, as well as provide new insights in designing data-driven education policies.
Distribution Analysis of Student Numbers by Gender Using Decision Tree and Data Visualization Risca Sri Mentari; Sri Wahyuni
Journal of Information Technology, computer science and Electrical Engineering Vol. 1 No. 3 (2024): October 2024
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v1i3.151

Abstract

Rapid technological developments have brought significant changes in various sectors, including education. In the context of education, data management and analysis are important elements in supporting data-driven decision-making. Data mining, specifically the Decision Tree method, provides valuable insights into analyzing patterns from large data sets. This study uses Decision Tree modeling and data visualization through RapidMiner to analyze the distribution of the number of students based on gender in various classes at SMK Negeri 1 Stabat in the 2023-2024 school year. This research includes data collection, preprocessing, and decision tree modeling to uncover gender-based trends in various skill programs. Visualization using Scatter Plot makes it easier to present data for clearer analysis. The results of the study show that administrative and fashion skills programs are dominated by women, while engineering skills programs, such as TKR and TITL, are dominated by men. Some classes showed a more balanced gender composition. This research provides useful insights for classroom management and decision-making in the educational environment, as well as provides a basis for designing more inclusive learning programs and addressing gender imbalances in certain areas.
Co-Authors Andysah Putera Utama Siahaan Muhammad Iqbal Sarifudin Sri Wahyuni Sugeng Pranoto Sulis Sutiono