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All Journal Jurnal Riset Informatika bit-Tech Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Media Computer Science Jurnal Pengabdian Masyarakat Bangsa
Sanjaya, Imam
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Journal : bit-Tech

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Implementation of Machine Learning Using Decision Tree Method for Social Assistance Recipient Classification Perhan, Akbar Ilham; Yustiana, Indra; Sanjaya, Imam
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2755

Abstract

The distribution of social assistance in Indonesia often faces challenges in accuracy, where individuals who are financially capable still receive aid, while those truly in need are excluded. To address this issue, this study applies a Machine Learning approach using the C4.5 Decision Tree algorithm to classify the eligibility of recipients in Bojonggenteng Village. This algorithm was chosen because it is easy to interpret, performs well, and is suitable for categorical data. The main objective of the study is to develop a classification model that enhances the objectivity and accuracy in determining aid recipients, ensuring that assistance is directed to those who truly need it. The research process involves several stages, including problem identification, literature review, data collection, preprocessing, classification, and model evaluation. A total of 904 records from the 2023 BPNT and PBI-JK programs were obtained in collaboration with the local village authorities. The classification process was conducted using RapidMiner, which allows for visual data processing and model building without requiring programming. The model evaluation was carried out using a confusion matrix, yielding an accuracy of 98.90%, precision of 100%, recall of 97.60%, and an AUC score of 0.988. These results indicate that the C4.5 algorithm is effective for prediction tasks and can be a valuable tool in supporting fair and data-driven decision-making in social assistance programs. This study concludes that the application of Machine Learning in this context improves the fairness and transparency of aid distribution and recommends future research to involve larger datasets for broader implementation.
Implementation of Content-Based Filtering in a Novel Recommendation System to Enhance User Experience Sanjaya, Imam; Sujjada, Alun; Pratama, Yudistira
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2833

Abstract

This study addresses a critical challenge in digital novel platforms: the difficulty of delivering personalized and accurate recommendations due to limited user interaction data. This limitation often leads to irrelevant or generic suggestions, which can diminish user engagement and hinder content discovery. The significance of solving this issue lies in enhancing user experience by ensuring that readers are presented with novels that truly align with their interests, even in the absence of extensive behavioral data. To overcome this problem, the study proposes an innovative hybrid recommendation system that integrates Content-Based Filtering (CBF) with the Random Forest algorithm. The system generates personalized recommendations by analyzing novel attributes such as title, genre, score, and popularity. The methodology involves extracting features from textual data using Term Frequency-Inverse Document Frequency (TF-IDF), followed by the calculation of cosine similarity to assess title relevance. These similarity scores are then combined with popularity predictions derived from the Random Forest model to produce final recommendations that reflect both content similarity and statistical relevance. The proposed system demonstrates strong performance, achieving an accuracy of 94.0%, precision of 81.4%, recall of 80.3%, and an F1-score of 80.8%. These results underscore the system’s capability to deliver accurate and diverse suggestions. By enhancing personalization and addressing the limitations of conventional CBF systems, this hybrid approach offers practical value for digital novel platforms. It serves as an effective tool for improving content discovery, increasing reader satisfaction, and supporting user retention in content-rich environments.
Co-Authors Alawiyyah, Siti Alfiyyatuz Zakiyyah Alun Sujjada Arul, Sahrul Ismail Usman Lelita, Tiara Muhamad Fadiah Nurjaman Parwati, Lusiana Sani Perhan, Akbar Ilham Pratama, Yudistira Purnama Insany, Gina Wahyuni, Yulinar Sri Yustiana, Indra