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All Journal Jurnal Pilar Nusa Mandiri Jurnal Sains dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Riset Informatika BRITech : Jurnal Ilmiah Ilmu Komputer, Sains dan Teknologi Terapan Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Teknomatika: Jurnal Informatika dan Komputer Jurnal Rekayasa Informasi Innotech Journal of Informatics and Advanced Computing Journal of Applied Research In Computer Science and Information Systems
Ninuk Wiliani
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Jurnal Riset Informatika

 1 
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.
Enhancing Ulos Batik Pattern Recognition through Machine Learning: A Study with KNN and SVM Chusna, Nuke L.; Wiliani, Ninuk; Abdillah, Achmad Feri
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.311

Abstract

This research aims to develop an automated classification system to accurately identify and classify Ulos batik patterns using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM) techniques. The method is based on computer vision technology and texture analysis using the Gray-Level Co-occurrence Matrix (GLCM). The dataset consists of 1,800 images of Ulos fabric categorized into six main motif classes. The preprocessing process involves converting images to grayscale and extracting features with GLCM. Two classification algorithms, K-NN and SVM, were used for modeling, with evaluation using confusion matrix metrics and Area Under Curve (AUC). Evaluation results show that the K-NN model has an accuracy of 82%, while SVM has an accuracy of 57%. The analysis also highlights the superiority of K-NN in distinguishing Ulos fabric patterns. This research contributes to cultural preservation and the development of the creative industry by introducing an effective automated classification system for Ulos fabric patterns.
Co-Authors Abdillah, Achmad Feri Abdul Rahman, Titik Khawa Adi Saputra, Mulyana Agus Trihandoyo Akbar Nugroho, Faathir Alfi Prabowo Anang Martoyo Andiani Andiani, Andiani Angel Tiarma Sipahutar Anita Putri Valeria Dhiu Lusi Arafat, Fadhilah Asrul Sani Aulia Fahmi Budiyantara, Agus Chusna, Nuke L Chusna, Nuke L. Eksa Manda Pramaswari Endang Fitria Gina Annisa Herry Wahyono Heru Abrianto Hesananda, Rizki Jane Arnecia, Zahra Khawa, Titik Latifah Maspiyanti, Febri Mulyana Adi Supatra Natasya, Desima Nawangtyas, Nur Novita Maharani Suparta Nur Hikmah Prasetyo, Dio Prianggara, Erdham Hestiadhi Rachmalia Feta, Neneng Rahmawati, Nidya Sari Ramli, Suzaimah Rizki Hesananda Sampoerno, Ahmad RIzqi Setiaji, Hari Shabrina Putri Simanjuntak, Yoel Tri Hartati, Tri Wahyono, Herry Yunita Achsanty