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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Informatics for Educators and Professional : Journal of Informatics Media Jurnal Informatika
Rully Pramudita
Bina Insani University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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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.
Perancangan Smart Parking Gate System Berbasis Internet of Things di Yayasan Arsyada Ari Nurul Alfian; Rita Wahyuni Arifin; Rully Pramudita; Reva Sabrina Nabila Ridwan
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol. 10 No. 2 (2025): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Desember
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v10i2.3854

Abstract

Comfort and safety are important aspects in institutional environments, including the management of parking access gates at the Arsyada Foundation, located at Jl. Pahlawan Gg. Mesjid Karang Asem Timur, Citeureup, Bogor. Currently, the foundation still uses a manual gate system, allowing vehicles to enter and exit freely. This system is often exploited by local residents who park their cars in the foundation's area, disrupting comfort and activities. This study designed a Smart Parking Gate System based on the Internet of Things (IoT) with Radio Frequency Identification (RFID) as valid vehicle identification and Blynk for parking capacity monitoring and gate control. This system is intended to limit the use of parking spaces only to those with access rights, such as foundation owners, teachers, staff, community members, and security officers, while also improving security through automatic identification integrated with IoT. Testing results showed a feasibility rate of 90.4% with a “Highly Feasible” rating, indicating the system is highly suitable for use. Thus, this smart parking gate is expected to be an effective solution for improving security, reducing unauthorized access risks, and enhancing parking management at the Arsyada Foundation.
Co-Authors Ari Nurul Alfian Medina Aprilia Putri Nining Rahaningsih Reva Sabrina Nabila Ridwan Rita Wahyuni Arifin Sekar Puspita Arum Sok Piseth Vina Zahrotun Nazah