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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH The Journal of Information Technology, Computer Science, and Electrical Engineering
Sulis Sutiono
Universitas Pembangunan Panca Budi
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Comparison of Accuracy between Naive Bayes and Decision Tree Methods for Property Tax (PBB-P2) Compliance in Tebing Tinggi City Zulham Sitorus; Sugeng Pranoto; Sulis Sutiono; Sarifuddin
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.v1i2.57

Abstract

This research aims to compare the accuracy of the Naïve Bayes and Decision Tree methods in predicting Land and Building Tax (PBB-P2) compliance in Tebing Tinggi city. The data used includes tax and payment determination for 2022 and 2023. The methods applied include data preprocessing, use of an inconvenience matrix for evaluation, as well as measuring accuracy with various data sharing ratios (80:20, 75:25, 70:30, 60:40, and 50:50). The research results show that the Decision Tree model consistently has much higher accuracy compared to the Naïve Bayes model, with accuracy reaching 99% at all data split ratios, while Naïve Bayes shows accuracy between 54% and 56%. The confusion matrix supports this finding by showing that the Decision Tree model has higher True Positives and True Negatives, and lower False Positives and False Negatives compared to Naïve Bayes. In conclusion, the Decision Tree method is more effective in classifying tax compliance compared to Naïve Bayes so that it is a more optimal choice for a tax compliance classification system based on the accuracy and performance obtained from this research.
Analysis of Student Graduation at SMK Negeri 1 Stabat Using the C4.5 Algorithm Sulis Sutiono; Darmeli Nasution
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.v1i3.96

Abstract

This research aims to analyze the graduation rates of students at SMK Negeri 1 Stabat in the general subjects group using the C4.5 algorithm. Secondary data covering 717 students was collected and underwent a preprocessing stage to ensure accuracy. The modeling results indicate that the average score threshold for graduation is 73.823; of the 717 students, 709 are declared graduated and 8 did not pass. The use of the C4.5 algorithm has proven effective in providing insights into student graduation as well as generating decision tree visualizations that clarify the decision-making process. This study emphasizes the importance of applying data mining technology in education to enhance understanding of student learning outcomes.
Analysis of Property Tax Bill Classification Using the C4.5 Algorithm Andysah Putera Utama Siahaan; Ami Abdul Jabar; Sugeng Pranoto; Sulis Sutiono; Desy Ramatika
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.v1i3.100

Abstract

This study analyzes the classification of Property Tax (Pajak Bumi dan Bangunan, PBB) bills in Tebing Tinggi City using the C4.5 algorithm to improve tax management efficiency. The secondary data used consists of 56,332 entries related to PBB for the 2022-2023 tax year. Using data mining methods and decision tree modeling, the C4.5 algorithm successfully classified taxpayers based on their total bill amount into five categories of tax books. The analysis results show that the majority of taxpayers are classified into categories with lower bills (Books I and II), while high-bill taxpayers (Book V) represent only a small portion of the data. These findings can help local governments design more efficient tax collection policies and adjust resource allocation. Although the study is limited to a single tax year and a specific region, these results contribute to data mining-based PBB management and can serve as a foundation for further research.
ANALISIS PENGELOMPOKAN JADWAL MENGAJAR GURU DI SMKN 1 STABAT MENGGUNAKAN METODE K-MEANS CLUSTERING Sulis Sutiono; Rian Farta Wijaya; Muhammad Syahputra Novelan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3550

Abstract

Abstract: This study aims to analyze the distribution of teaching load of teachers at SMKN 1 Stabat using the K-Means Clustering method. The main problem faced is the imbalance in the number of teaching hours between teachers which can affect the effectiveness of teaching and learning activities. The data used is the total weekly teaching hours of each teacher, which is taken from the document on the distribution of teaching tasks for the even semester of the 2024/2025 academic year. The K-Means method is used to group teachers into three categories of teaching load: light, medium, and heavy. The grouping process is carried out by determining the number of clusters (K = 3), initializing the centroid, calculating the distance of each data to the centroid, and updating the position of the centroid until the results are stable. The final results show that most teachers are included in the medium load cluster, while a small number are in the light and heavy categories. This shows that the distribution of the teaching load is not yet completely even. The application of K-Means has been proven to be able to provide an analytical picture of the distribution of teacher work, as well as support data-based decision making in education management. Keywords: K-Means Clustering, Teaching Load, Teacher Schedule, Data Clustering, SMKN 1 Stabat Abstrak: Penelitian ini bertujuan untuk menganalisis pembagian beban mengajar guru di SMKN 1 Stabat dengan menggunakan metode K-Means Clustering. Masalah utama yang dihadapi adalah ketidakseimbangan jumlah jam mengajar antar guru yang dapat memengaruhi efektivitas kegiatan belajar-mengajar. Data yang digunakan berupa total jam mengajar mingguan dari setiap guru, yang diambil dari dokumen pembagian tugas mengajar semester genap tahun ajaran 2024/2025. Metode K-Means digunakan untuk mengelompokkan guru ke dalam tiga kategori beban mengajar: ringan, sedang, dan berat. Proses pengelompokan dilakukan dengan menentukan jumlah klaster (K=3), menginisialisasi centroid, menghitung jarak masing-masing data ke centroid, serta memperbarui posisi centroid hingga hasil stabil. Hasil akhir menunjukkan bahwa sebagian besar guru tergolong dalam klaster beban sedang, sementara sebagian kecil masuk kategori ringan dan berat. Hal ini menunjukkan bahwa distribusi beban mengajar belum sepenuhnya merata. Penerapan K-Means terbukti mampu memberikan gambaran analitis terhadap distribusi kerja guru, serta mendukung pengambilan keputusan berbasis data dalam manajemen pendidikan. Kata kunci: K-Means Clustering, beban mengajar, jadwal guru, pengelompokan data, SMKN 1 Stabat
Co-Authors Ami Abdul Jabar Andysah Putera Utama Siahaan Darmeli Nasution Desy Ramatika Muhammad Syahputra Novelan Rian Farta Wijaya Sarifuddin Sitorus, Zulham Sugeng Pranoto