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Asep Yudistira Saputra
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IMPLEMENTASI DECISION SUPPORT SYSTEM UNTUK PENENTUAN LOAN CREDIT SCORE DENGAN MENGGUNAKAN METODE TOPSIS (Studi Kasus: BFI Finance BSD Tanggerang) Asep Yudistira Saputra; Hadi Zakaria
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 11 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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The oldest lending company in Indonesia is PT BFI Finance Indonesia Tbk ("BFI" or the "Company"), which was established in 1982 under the name PT Manufacturer Hanover Leasing Indonesia. BFI Finance has established itself as a leader in this sector throughout the course of its lengthy history. Regarding the issues at PT. BFI Finance, calculating loan credit scores effectively and precisely is difficult since it takes into account a number of variables, including income, credit history, and other risk indicators. As a result, risk credit scores are accurately determined when evaluating BFI Finance's lending viability. By taking into account the relative proximity to the ideal solution and the ideal negative solution, the TOPSIS approach makes it possible to choose the optimal option among several options. This approach can more reliably and accurately calculate credit scores by utilizing past data and pertinent borrower attributes. It is anticipated that this implementation will lower credit risk, boost confidence in financial institutions, and improve the accuracy of determining borrower eligibility. In addition, a more effective decision-making process can lower operating expenses and boost productivity. Users can gather pertinent borrower data, including income, credit history, and collateral details, by creating the appropriate database structure. with the use of MySQL and PHP scripts. A borrower's credit score can be determined using the TOPSIS approach. By using the TOPSIS method, this study significantly enhances BFI Finance's operational effectiveness and service quality, which helps the organization become more accurate in its evaluations.
Rancangan Aplikasi Mobile Agribis Investasi Syari’ah Asep Yudistira Saputra; Safitri Ristanti; Sultan Rafly Sya’Ban; Sofyan Mufti Prasetiyo
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 2 No 08 (2023): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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With the advent of the Industrial Age 4.0, technological developments occurred in various fields, such as agriculture. Basically there are many programs that help rural workers such as iGrow, TaniFund and Tanijoy. However, farmers find it difficult to obtain the funds needed to cultivate their land, because many conditions are needed, such as: a minimum land area, joining a farmer group, or having a business entity. With that in mind, we hope to have an application that is easy to use and combines the functions of several applications into one. An example is the agribis Invest shari'ah mobile application, which is expected to be the answer. Mobile agribis invests Syari'ah can provide information about the land to be invested and other information about agriculture. With a sharia-based investment system. Sharia investment products that use sharia principles in their system use profit sharing contracts. Investments from an Islamic perspective can only be made on instruments that are in accordance with Islamic Shari'a and do not contain usury. Investments can also only be made on securities issued by parties (issuers) that do not conflict with Shari'ah. The methodology used in developing this application is research and development (R&D). The Islamic Investment agribis Mobile prototype was initially implemented in a pilot area with support. This is done so that the quality of the data entered by farmers meets the standards set.
Pembelajaran Machine Learning Agung Wijoyo; Asep Yudistira Saputra; Safitri Ristanti; Sultan Rafly Sya’Ban; Mila Amalia; Randi Febriansyah
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 3 No 02 (2024): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Machine learning is a system that can learn to make decisions on its own without having to be programmed repeatedly by humans so that computers can become smarter and learn from their experiences with data. Based on the learning technique, supervised learning can be distinguished by using labeled datasets (training data), while unsupervised learning draws conclusions based on datasets. Input in the form of a dataset is used by machine learning to produce the right analysis. The solution uses Python which provides the algorithms and libraries used to create machine learning. Artificial intelligence (AI) is now rising again after decades of ups and downs. Artificial intelligence is back in popularity where its application is carried out massively in today's business and social media applications such as Facebook, Twitter, Google, Amazon, and even various large applications from Indonesia such as Go-jek, Tokopedia, and so on. The structure of the discussion in this book includes 3 major sections, namely (1) Concept of Machine Learning and Artificial Intelligence (2) Fundamentals of Python Programming for Machine Learning and (3) Examples of Application of Machine Learning Using Python by implementing several algorithms, both Supervised Learning and Unsupervised Learning. Several case studies are discussed in full starting from understanding algorithms, dataset processing to training and testing as well as visualizing the results of the machine learning models developed.
Co-Authors Agung Wijoyo Mila Amalia Randi Febriansyah Safitri Ristanti Sofyan Mufti Prasetiyo Sultan Rafly Sya’Ban Zakaria, Hadi