Garuda - Garba Rujukan Digital

Implementation of Machine Learning Using Decision Tree Method for Social Assistance Recipient Classification Akbar Ilham Perhan; Indra Yustiana; Imam Sanjaya
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

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 Imam Sanjaya; Alun Sujjada; Yudistira Pratama
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

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.

Genre-Based Anime Recommendation System Using KNN with Fanbase Bias Detection Muhamad Rizky Fauzi; Imam Sanjaya; Ivana Lucia Kharisma
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2917

The rapid growth of the anime industry presents a challenge for users, especially newcomers, in finding content that matches their personal preferences. To address this issue, this study proposes a genre-based anime recommendation system using a Content-Based Filtering approach, incorporating Term Frequency-Inverse Document Frequency (TF-IDF), the K-Nearest Neighbor (KNN) algorithm, and fanbase bias detection. This system transforms genre information into numerical vectors using TF-IDF, allowing for precise similarity calculations between anime titles based on genre relevance. KNN is used with cosine similarity to identify the top five most similar anime to a given input. A key novelty of this study is the implementation of a fanbase bias detection mechanism that filters out anime with high ratings but very low member counts, which often distort overall ratings due to a small but passionate fanbase. This filtering process ensures that the recommendation output better reflects general audience preferences. The dataset, sourced from MyAnimeList via Kaggle, includes 12,294 entries and underwent extensive preprocessing, including missing value removal, duplicate elimination, and statistical thresholding for bias detection. Evaluation of the system was performed using accuracy, precision, recall, and F1-score, with results showing strong performance (F1-score of 91.94%). Additionally, 5-fold cross-validation confirmed the consistency of the model. Designed for general anime viewers, the system is implemented using the Streamlit framework to provide an accessible and interactive web-based interface. This study demonstrates that the combination of content-based techniques and fanbase bias filtering significantly enhances recommendation quality, offering a novel and practical solution for anime discovery

Interactive Webgis for Mapping and Monitoring Urban Drainage Systems Muhamad Adam; Somantri Somantri; Imam Sanjaya
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2918

The development of infrastructure and changes in land use in urban areas increase surface runoff that cannot be properly managed, posing a flood risk if the drainage channel capacity is inadequate. To address the lack of an integrated information system for drainage monitoring, this study developed a WebGIS system based on Laravel, QGIS, and LeafletJS using the Rapid Application Development (RAD) approach. The system is designed to support the Department of Public Works and Spatial Planning (DPUTR) of Sukabumi City in monitoring and mapping the distribution and condition of drainage channels interactively and in real-time. The WebGIS integrates spatial data (Linestring geometry) and non-spatial data (condition attributes, length, and road location), and provides features such as search, condition filters, and elevation contour layers. System testing was conducted using the Black-box method and Lighthouse tools to assess functionality and performance. The results showed scores of 91 for accessibility, 79 for performance (mobile), 72 for best practices, and 92 for SEO, indicating a user-friendly interface that complies with web development standards. The system is considered effective in improving drainage infrastructure management, supporting spatial-based decision-making, and enhancing public information transparency. Therefore, this system serves as a replicable WebGIS model for other cities facing similar drainage management challenges.

Title

Found 4 Documents
Search

Abstract

Abstract

Abstract

Abstract

Title Search

Found 4 Documents Search

Abstract

Abstract

Abstract

Abstract

Title

Found 4 Documents
Search