IMPLEMENTATION OF NAIVE BAYES METHOD FOR GRANTING FISHERMAN BUSINESS CREDIT

I Gede Totok Suryawan; I Kadek Surya Arimbawa; I Gede Iwan Sudipa

Authors

I Gede Totok Suryawan Fakultas Teknologi dan Informatika , Program Studi Teknik Informatika, Institut Bisnis dan Teknologi Indonesia
I Kadek Surya Arimbawa Fakultas Teknologi dan Informatika , Program Studi Teknik Informatika, Institut Bisnis dan Teknologi Indonesia
I Gede Iwan Sudipa Fakultas Teknologi dan Informatika , Program Studi Teknik Informatika, Institut Bisnis dan Teknologi Indonesia

Keywords:

Decision Support System, Fisherman Business Credit, Naive Bayes, Village Credit Institution

Abstract

The Lembaga Perkreditan Desa (LPD) is a village financial institution engaged in the savings and loan industry, provides credit as one of its services. Midway through 2021, the Jimbaran Traditional Village LPD issued a new credit product, the Fisherman Business Credit (KUN), to assist the Jimbaran villagers who are experiencing economic hardships due to Covid-19. The rapid increase in credit applications at the Jimbaran Traditional Village LPD, particularly for fisherman business loans, necessitates a more comprehensive analysis of incoming fisherman business loan application data to prevent a repeat of the poor credit decisions from the previous year. On the other hand, the community, especially those whose credit applications are denied, mandates transparency in the selection process for credit assessment. Current credit evaluation procedures are rife with subjectivity, necessitating software that can provide transparency by generating scores from each existing credit application using scientific methods. In this study, a credit granting decision support system was developed that evaluates each application for a business credit line from a fisherman at LPD Desa Adat Jimbaran using the nave bayes method. Using 340 data on prospective credit recipients including loan amount, collateral value, income, expenses, time period, other obligations, and credit history, it is determined that more prospective creditors are eligible than ineligible, with 321 declared eligible and 19 declared ineligible. The average accuracy result was 94.31%, with the first experiment yielding the highest accuracy at 95.30% and the third experiment yielding the lowest accuracy at 94.67%.

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