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All Journal Jurnal Riset Informatika
Ninuk Wiliani
Cyber University
Computer Science & IT

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Ultra-Micro Lending Eligibility Support System With Exponential Comparison Method (MPE) Ninuk Wiliani; Mulyana Adi Supatra; Herry Wahyono
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.240

Abstract

The process of providing credit can now be done quickly and closely through the presence of BRILink agents with additional facilities in addition to payment points, namely as partners of ultra-micro loans, which are now popularly called UMi Partners, where BRILink agents can distribute microloans with a loan range of 1 to 5 million. This is done by management as a financial inclusion program and as a revitalization of work in all operational work units (UKO). This research uses the Exponential Comparison Method (MPE) to determine credit granting decisions to optimize all existing information systems by implementing a system that can be used and run by UMi partners to improve the process of providing creditworthiness to their partners. The results of the calculations carried out by the system are manual calculations that have been carried out so that the results of this study can be applied correctly to produce creditworthiness that helps the credit-granting process.
Identifying Skin Cancer Disease Types With You Only Look Once (YOLO) Algorithm Ninuk Wiliani; Anita Putri Valeria Dhiu Lusi; Nur Hikmah
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1013.677 KB) | DOI: 10.34288/jri.v5i3.241

Abstract

The skin is the outermost vital organ and is susceptible to various diseases, including skin cancer. The number of cases of skin cancer around the world continues to increase every year, including in Indonesia. Proper handling is critical to cure skin cancer, and one of the solutions that can be used is the Deep Learning method. This study aims to apply the Deep Learning method, specifically an object detection algorithm called You Only Look Once (YOLO), for early skin cancer detection. The YOLOv5s algorithm is the model for this study because it is accurate and can detect objects in real-time. The research method involved collecting data on skin cancer cases and training the YOLOv5s model. After training, model testing is used to evaluate the ability to detect skin cancer. The test results show that the YOLOv5s model has an accuracy of 89.1% in detecting skin cancer types. This research has important implications in the health sector, especially in early skin cancer detection.
Co-Authors Anita Putri Valeria Dhiu Lusi Herry Wahyono Mulyana Adi Supatra Nur Hikmah