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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Media Jurnal Informatika
Rully Pramudita
Bina Insani University
Computer Science & IT Decision Sciences, Operations Research & Management

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Cluster Analysis Using Principal Component Analysis Method and K-Means to Find Out the Compliance Group of Property Tax Rully Pramudita; Nining Rahaningsih; Sekar Puspita Arum; Medina Aprilia Putri; Sok Piseth
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5924

Abstract

Abstract The village of Kendal has experienced a decline in local income due to the high rate of property tax arrears, with 226 taxpayers (19% of residents) known to have outstanding payments. Additionally, with 1,159 separate residents residing in 10 block areas with varying tax amounts, it has become increasingly difficult for the Village Apparatus to profile taxpayers based on their characteristics. To overcome these problems, a data analysis model based on Machine Learning technology will be developed using the Principal Component Analysis (PCA) Method combined with the K-Means method. The objective of this study is to create a cluster analysis model that can accurately map the characteristics of taxpayers, making it easier for the Village Apparatus to identify and assist residents who need to pay their property tax. This proposed solution will also simplify the reporting process to the central government regarding the estimated regional revenue sourced from property tax.
Cluster Analysis Using Principal Component Analysis Method and K-Means to Find Out the Compliance Group of Property Tax Rully Pramudita; Nining Rahaningsih; Sekar Puspita Arum; Medina Aprilia Putri; Sok Piseth
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5924

Abstract

Abstract The village of Kendal has experienced a decline in local income due to the high rate of property tax arrears, with 226 taxpayers (19% of residents) known to have outstanding payments. Additionally, with 1,159 separate residents residing in 10 block areas with varying tax amounts, it has become increasingly difficult for the Village Apparatus to profile taxpayers based on their characteristics. To overcome these problems, a data analysis model based on Machine Learning technology will be developed using the Principal Component Analysis (PCA) Method combined with the K-Means method. The objective of this study is to create a cluster analysis model that can accurately map the characteristics of taxpayers, making it easier for the Village Apparatus to identify and assist residents who need to pay their property tax. This proposed solution will also simplify the reporting process to the central government regarding the estimated regional revenue sourced from property tax.
AI Persona-Based Student Counseling Chatbot Using Large Language Model, RAG, and Prompt Engineering Vina Zahrotun Nazah; Rully Pramudita
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6449

Abstract

Chatbots are increasingly used in student counseling services because they offer easy access, fast responses, and flexible availability. However, conventional chatbots often produce generic responses, have limited contextual understanding, and provide insufficient emotional support. This study aims to develop an AI Persona-based student counseling chatbot using a Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and prompt engineering to generate relevant, contextual, and empathetic responses. The study uses a Research and Development (R&D) approach with the CRISP-DM framework. The system uses Gemini 2.5 Flash as the generative model, multilingual-e5-small as the embedding model, and FAISS as the vector index. Four institutional documents and campus service data are processed through chunking, embedding, and semantic retrieval. Evaluation is conducted using LLM-as-a-Judge on 45 scenarios and User Acceptance Testing (UAT) with 20 students. The LLM-as-a-Judge evaluation produces an average score of 4.47 out of 5, with the highest score in Context Relevance at 4.70. UAT achieves 91% user acceptance in the very good category, with naturalness and empathy as the highest indicator at 95%. The results show that integrating LLM, RAG, and prompt engineering can improve chatbot response quality without fine-tuning, although further development is needed in multimodal document support, local model deployment, and retrieval mechanism improvement.
Co-Authors Medina Aprilia Putri Nining Rahaningsih Sekar Puspita Arum Sok Piseth Vina Zahrotun Nazah