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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Komputer, Informasi dan Teknologi
Rizka Tri Alinse
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Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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A Decision Support System for Giving Used Car Loans Using the AHP Method at PT.Batavia Prosperindo Finance Tbk Anggit Permana; Siswanto Siswanto; Rizka Tri Alinse
Jurnal Komputer, Informasi dan Teknologi Vol. 2 No. 2 (2022): Desember
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v2i2.1018

Abstract

Batavia Prosperindo Finance Tbk is one of the companies engaged in the financing of four-wheeled vehicles. The large number of requests for used car loans requires credit financing companies to make decisions quickly and accurately, previously the process of determining creditworthiness by the company was still using the manual method, so it did not rule out the possibility of miscalculations and errors in reading credit granting data. With these problems, a special application is needed that can assist companies in making decisions using the AHP method to determine creditworthiness quickly and accurately. The AHP (Analytical Hierarchy Process) method is one method to help develop a priority from various options using several criteria (multi criteria). Due to its multi-criteria nature, AHP is quite widely used in setting priorities. With this assessment pattern, the cases found at PT. BPF Bengkulu is very suitable when using this method in solving the problem of providing used car loans. From the results of research conducted by the AHP method, it is able to solve problems in the feasibility of giving used car loans using predetermined criteria and sub-criteria, resulting in a final score with the highest score of 0,458273 and the lowest score of 0,133065 and those eligible for used car loans. there are 3 people and 1 people don't deserve to be accepted.
OPTIMASI PENJUALAN ROTI MENGGUNAKAN  MODEL ENSEMBLE MACHINE LEARNING PADA TOKO ROTI SYAKILA Diggo Efrimansyah; Lena Elfianty; Rizka Tri Alinse
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 5 (2026): October (2026)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i5.7276

Abstract

Improving the effectiveness of bread sales requires a data-driven approach capable of identifying historical sales patterns and accurately estimating sales volumes. This study applied the Extreme Gradient Boosting (XGBoost) algorithm as an ensemble machine learning model to predict bread sales at Syakila Bakery. The data consisted of six months of historical sales data, from July to December 2025, covering sweet bread, white bread, snack bread, and sponge cake products. The research process used the CRISP-DM methodology, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The system was developed using PHP as the application interface, Python for the modeling process, and MySQL as the database. Model evaluation was conducted using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE). The evaluation results showed a MAPE of 1.3% and an MSE of 0.21. These results indicate a low prediction error rate, suggesting that the XGBoost model can support more measurable sales planning and facilitate decision-making.
Co-Authors Anggit Permana Diggo Efrimansyah Lena Elfianty Siswanto Siswanto Siswanto Siswanto