ONLINE LOAN GROUPING ANALYSIS OF FINANCIAL TECHNOLOGY (FINTECH) PLATFORM-BASED FOR MSMES IN INDUSTRY 4.0 WITH NAÏVE BAYES STATISTICAL METHOD - Istanti, Enny; Negoro, Bramastyo Kusumo; Adityo, R Dimas (LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta), 2020)
UMKM (Micro, Small and Medium Enterprises) is one of the Indonesian economy driving forces currently as the foundation of various sectors. Along with the growth of information technology that has increased sharply, many digital applications have been developed that offer convenience to the public, especially in terms of inclusion of funds as working capital, this has indirectly been broadly used by MSME players in seeking working capital in a short way. Peer to Peer Lending / P2PL (Fintech) or commonly referred to as an application online lending-based institutions, currently many have been present in the community either through licensed or unlicensed through the OJK Institution. As of October 14, 2020, OJK has released data on as many as 157 Legal Peer to Peer Lending Companies, while the number of Unlicensed P2PLending Institutions reported to OJK is around 2400, in the research conducted only 108 data were taken. From the data processing using the naïve Bayes method in determining the grouping / classification, it is found that 50% of P2PL companies carried out activities with the Very fraudulently Category, 33% are quite fraudulently and 17% not fraudulently. With the release of research results, it is hoped that MSME players in obtaining loans online can be more vigilant in determining which institutions to appoint in venture capital participation. Keywords: Peer to Peer Lending, Naïve Bayes, UMKM