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Herri Setiawan
Indo Global Mandiri University
Computer Science & IT

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Classification Of Eligibility For Assistance Recipients Program Indonesia Pintar Using The Naïve Bayes Method Via Kris Savitri; Herri Setiawan; Zaid Romegar Mair
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.v8i2.3002

Abstract

The manual process of determining student eligibility for the Indonesia Pintar Program (PIP) often results in inefficiencies and inaccuracies. Schools are required to evaluate large volumes of socioeconomic data, and errors in judgment may lead to misallocation, where eligible students are excluded and ineligible students are included. Such inefficiencies highlight the need for objective, data-driven approaches. This study aims to evaluate the performance of the Naïve Bayes classification algorithm in classifying PIP eligibility, with a particular focus on attribute selection and its effect on classification accuracy. Historical student data from a primary school (SDN 1 Sindang Marga), which has rarely been examined in previous works and the analysis of attribute selection strategies, showing that fewer but more relevant attributes can yield better results. A dataset of 172 students was pre-processed and divided into training (80%) and testing (20%) subsets. Model evaluation was conducted using confusion matrices to calculate accuracy, precision, recall, and F1-score. The results demonstrate that using four attributes parental occupation, parental income, KPS ownership, and KIP ownership achieved the highest performance, with 85.3% accuracy, 92.0% precision, 88.5% recall, and a 90.2% F1-score. By contrast, using all seven attributes resulted in slightly lower accuracy (82.4%). These findings highlight that selective attribute use improves model efficiency and accuracy. Beyond methodological contributions, this research provides practical implications by demonstrating how machine learning can enhance fairness, transparency, and objectivity in educational aid distribution.
Decision Support System For Selecting Smart Indonesia Card Candidates Using Preference Selection Index Method M. Reza Fhalepi; Herri Setiawan; Nazori Suhandi
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.v8i2.3068

Abstract

The Kartu Indonesia Pintar (KIP) program is a government initiative designed to ensure equal access to higher education for students from low-income families. However, the selection process remains challenging due to the large number of applicants, diverse evaluation criteria, and reliance on manual judgment, which can lead to inefficiency and bias. This study develops a decision support system (DSS) using the Preference Selection Index (PSI) method to improve transparency and objectivity in selecting KIP recipients at Universitas Indo Global Mandiri. Data were obtained through observation, structured interviews, documentation, and secondary records from the BKABK finance division. Five main criteria were used in the evaluation process: parents’ occupation, housing condition, number of siblings, academic achievements, and interview performance. The PSI method was implemented through data normalization, calculation of mean and deviation, automatic weight generation, and computation of each applicant’s final PSI score. A total of 270 valid applicants were processed, with most achieving scores between 0.80 and 0.90 (mean = 0.86; SD = 0.04), reflecting a high level of competition. The top five candidates scored between 0.8787 and 0.9179, led by Christopher Nathan Tanugraha and Kiagus Deru Cahyadi. These results demonstrate that PSI can reduce subjectivity in weight assignment, increase efficiency, and minimize human error, while ensuring fair scholarship distribution. More broadly, the proposed PSI-based DSS can be applied in other universities and scholarship programs, offering a scalable solution for equitable and data-driven decision-making in higher education.
Co-Authors M. Reza Fhalepi Nazori Suhandi Via Kris Savitri Zaid Romegar Mair