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All Journal International Journal of Islamic Business and Economics (IJIBEC) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Litbang Kota Pekalongan IC Tech: Majalah Ilmiah International Journal of Islamic Business and Economics (IJIBEC) Computing and Information System Journal
Sattriedi Wahyu Binabar
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Religion Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mechanical Engineering Public Health

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Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm Karomi, Ivandari; Tria Titiani Chasanah; Sattriedi Wahyu Binabar; Muhammad Adib Al Karomi
International Journal of Islamic Business and Economics (IJIBEC) Vol 1 No 1 (2017): Volume 1 Nomor 1 Tahun 2017
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/ijibec.v1i1.882

Abstract

Credit is one of the modern economic behaviors. In practice, credit can be either borrowing a certain amount of money or purchasing goods with a gradual payment process and within an agreed timeframe. Economic conditions that are less supportive and high community needs make people choose to buy goods with this credit process. Unfortunately the high needs sometimes are not in line with the ability to make payments in accordance with the initial agreement. Such condition causes the payment process to be disrupted or also called the term ”bad credit”. This research uses public data of credit card dataset from UCI repository and private data that is dataset of credit approval from local banking. The information gain algorithm is used to calculate the weights of each of the attributes. From the calculation results note that all attributes have different weights. This study resulted in the conclusion that not all data attributes inCuence the classification result. Suppose attribute A1 to UCI dataset as well as loan type attribute on local dataset that has information gain weight 0 (zero). The result of classification using K−Nearest Neighbors algorithm shows that there is an increase of 7.53% for UCI dataset and 3.26% for local dataset after feature selection on both datasets.
Integration of Spatial and Demographic Data into a Web-Based Platform for the Family Planning Program Using the Closed Polygon Method (A Case Study in Pekalongan City) Agus Ilyas; Sattriedi Wahyu Binabar; Tri Agus Setiawan; Hari Agung Budijanto
Computing and Information System Journal Vol. 1 No. 2 (2025): Integration of Automation and Information Systems in Enhancing Organizational C
Publisher : IndoCompt Publisher

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Abstract

The Family Planning (FP) program requires a data-driven approach to enhance service effectiveness. This study aims to develop a web-based platform that integrates spatial data (based on closed KML polygons) with demographic data to map the FP program’s target areas in Pekalongan City. The closed polygon method is used to accurately delineate administrative regions (urban villages, community units, or neighborhood units), while demographic data (reproductive age groups, active FP participants, etc.) are dynamically displayed in an interactive interface. The platform is built using Leaflet.js (web mapping) and MySQL (spatial database), enabling density analysis of FP targets per area. Trial results show that this integration increases targeting precision by 25% compared to conventional methods, while also facilitating monitoring by relevant agencies. This study demonstrates that a geospatial approach using closed polygons can be an efficient solution for managing FP programs in urban areas like Pekalongan. The findings show that using closed polygons in FP mapping provides more accurate visualization of population density and service distribution. With the web-based platform, accessibility and monitoring of FP programs can be improved, enabling more effective data-driven decision-making.
Co-Authors Agus Ilyas Agus, Ilyas Al Karomi, M. Adib Budidjanto, Hari Agung Budijanto, Hari Agung Chasanah, Tria Titiani Edi Purwanto Hari Agung Budijanto Ilyas, Agus Ivandari, Ivandari Ivandari, Ivandari Karomi, Ivandari M. Reza Maulana Maulana, M. Reza Mohammad Reza Maulana, Mohammad Reza Muhammad Adib Al Karomi Susanto, Eko Budi Tri Agus Setiawan Tri Agus Setiawan Tria Titiani Chasanah Utama, Dhiva Nanda Wahjuningsih, Tri Pudji Wijonarko, Rizqi Wiwik Handayani