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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Indonesian Journal of Education And Computer Science The Journal of Information Technology, Computer Science, and Electrical Engineering
Sugeng Pranoto
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 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.
Data Mining: Building The Automatic Pipeline System for Clustering the Compliance Level of PBB-P2 Taxpayers Sugeng Pranoto; Sri Wahyuni
Journal of Information Technology, computer science and Electrical Engineering Vol. 2 No. 1 (2025): February-May 2025
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v2i1.190

Abstract

Taxpayer compliance analysis is crucial in regional revenue management, especially for Rural and Urban Land and Building Tax (PBB-P2). The large volume of taxpayer data poses a significant challenge in manual processing, so an automated and efficient approach is needed. This research develops a data mining pipeline to cluster the level of taxpayer compliance in the Badan Pengelola Keuangan dan Pendapatan Daerah (BPKPD) of Tebing Tinggi City. The proposed pipeline is implemented using the Java programming language and the SMILE library and includes three main procedures, namely Extraction, which is in charge of retrieving receivables and payment data from the SISMIOP database; Transformation, which is in charge of processing the extracted data to generate new insights through K-Means clustering, and Load is in charge of storing the transformation results into the MySQL database for further analysis and reporting. This pipeline is run every hour to ensure that data processing is carried out in real-time. By utilizing this automated system, this study aims to increase understanding of taxpayer compliance patterns and assist local governments in designing more effective policies to increase tax revenues and taxpayer compliance levels.
Business Intelligence Menggunakan Apache Superset untuk Sistem Pendukung Keputusan Kebijakan Penagihan Pajak Bumi dan Bangunan : Studi Kasus BPKPD Kota Tebing Tinggi Sugeng Pranoto; Darmeli Nasution
Indonesian Journal of Education And Computer Science Vol. 2 No. 3 (2024): INDOTECH - December 2024
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v2i3.922

Abstract

Pajak Bumi dan Bangunan (PBB) merupakan salah satu sumber pendapatan daerah yang signifikan dalam mendukung pembangunan berkelanjutan. Namun, pengelolaan dan penagihan PBB sering kali menghadapi berbagai tantangan, termasuk keterbatasan dalam analisis data dan pengambilan keputusan yang efektif. Penelitian ini bertujuan untuk mengimplementasikan solusi Business Intelligence (BI) menggunakan Apache Superset untuk mendukung sistem pendukung keputusan terkait kebijakan penagihan PBB di Kota Tebing Tinggi. Metode penelitian ini melibatkan analisis data PBB yang mencakup target, realisasi, ketetapan, dan piutang pada berbagai tingkatan, mulai dari kecamatan hingga jenis bumi. Data tersebut diolah dan divisualisasikan menggunakan Apache Superset untuk memberikan wawasan yang lebih mendalam dan berbasis data. Hasil penelitian menunjukkan bahwa teknologi BI mampu meningkatkan efisiensi, transparansi, dan akuntabilitas dalam pengelolaan PBB. Realisasi penerimaan PBB pada tahun 2024 melampaui target dengan capaian 100,2%, yang mengindikasikan keberhasilan pendekatan berbasis data dalam mendukung kebijakan penagihan. Penelitian ini memberikan kontribusi signifikan terhadap pengembangan literatur terkait peran BI dalam sektor publik dan menawarkan solusi praktis untuk meningkatkan pengelolaan pajak di tingkat daerah. Namun, penelitian ini menyadari keterbatasan dalam kualitas data yang digunakan, sehingga penelitian lanjutan diperlukan untuk mengembangkan sistem yang lebih terintegrasi dan adaptif
INTEGRASI DATA MINING DAN BUSINESS INTELLIGENCE MONITORING PIUTANG PBB-P2 BPKPD KOTA TEBING TINGGI Sugeng Pranoto; Sri Wahyuni
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.2571

Abstract

Abstract: The biggest challenge in managing the Rural and Urban Land and Building Tax (PBB-P2) lies in the large amount of taxpayer data that must be managed and analyzed as the basis for making accurate policies and decisions. Data mining and Business Intelligence (BI) provide a strong foundation for organizations to manage and analyze data effectively in support of decision-making. The integration of Data Mining in clustering taxpayer compliance levels using the K-Means algorithm and Business Intelligence (BI) through Apache Superset for monitoring the realization of PBB-P2 receivables can be implemented using the Research & Development (R&D) method. The stages of this method include problem identification, literature study, data collection, system integration design and implementation, system integration evaluation, analysis of integration results, and the final stage consisting of conclusions and recommendations from the research. The research results show that the K-Means Clustering algorithm in Data Mining is effective in clustering taxpayers based on their compliance level in paying the PBB-P2. Furthermore, the integration of Data Mining applications based on Java and Business Intelligence (BI) using Apache Superset can optimize the monitoring of PBB-P2 receivables realization in BPKPD Tebing Tinggi. Keywords: Data Mining, Business Intelligence, K-Means, Java, Apache Superset,                 PBB-P2 Abstrak: Tantangan terbesar dalam pengelolaan Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) terletak pada besarnya jumlah data wajib pajak yang dikelola dan akan dianalisa sebagai dasar pengambilan kebijakan dan keputusan yang tepat. Data maining dan Business Intelligence (BI) memberikan landasan yang kuat bagi organisasi dalam mengelola dan menganalisis data secara efektif untuk mendukung pengambilan keputusan. Penerapan integrasi Data Mining dalam pengelompokan tingkat kepatuhan wajib pajak dengan menggunakan algoritma K-Means dan Business Intelligence (BI) dengan menggunakan apache superset pada monitoring realisasi piutang Pajak Bumi dan Bangunan Perdesaan dan Perkotaan (PBB-P2) dapat dilakukan dengan menggunakan metode Research & Development (R&D). Tahapan metode tersebut meliputi identifikasi masalah, studi literatur, pengumpulan data, peracangan dan implementasi sistem integrasi, evaluasi sistem integrasi, analisa hasil integrasi dan tahapan akhir adalah kesimpulan dan rekomendasi dari penelitian. Hasil dari penelitian menunjukan algoritma K-Means Clustering dalam Data Mining efektif untuk mengelompokkan wajib pajak berdasarkan tingkat kepatuhan pembayaran Pajak PBB-P2 dan dengan integrasi aplikasi Data Mining berbasis Java dan Business Intelligence (BI) menggunakan Apache Superset dapat mengoptimalkan monitoring realisasi piutang PBB-P2 di BPKPD Tebing Tinggi. Kata kunci: Promosi, Brosur, Multimedia Development Life Cycle, Augmented Reality
Co-Authors Ami Abdul Jabar Andysah Putera Utama Siahaan Darmeli Nasution Desy Ramatika Sarifuddin Sitorus, Zulham Sri Wahyuni Sulis Sutiono