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All Journal Jurnal Statistika dan Aplikasinya Scientific Journal of Informatics
Anisa Nurizki
IPB University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences Other

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Analisis Karakteristik Keberadaan Perbankan di Nusa Tenggara Barat Terhadap Kondisi Perekonomian Daerah Menggunakan K-Means Clustering Anisa Nurizki; Muhammad Irfan Hanifiandi Kurnia; Anwar Fitrianto; Bagus Sartono; Sachnaz Desta Oktarina; Dian Handayani
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06211

Abstract

In certain areas, there are still many people who have to travel long distances to access some banks. Difficult mobility is considered to hinder business activities. The West Nusa Tenggara (NTB) Province is one of the favorite travel destinations for some foreigner tourists as well as domestic tourists because of its natural beauty and cultural diversity. the existence of some banks in the NTB Province , is important to facilitate the circulation of money. For this reason, this study aims to analyze the existence of some banks in the NTB Province and the condition of mobility in accessing themto regional economic conditions by applying K-Means clustering. Our results show that there are two clusters, , where the cluster 2 is an urban area and a tourist area. It has charactersitic has a GDP greater than cluster 1.
Performance of Ensemble Learning in Diabetic Retinopathy Disease Classification Anisa Nurizki; Anwar Fitrianto; Agus Mohamad Soleh
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4725

Abstract

Purpose: This study explores diabetic retinopathy (DR), a complication of diabetes leading to blindness, emphasizing early diagnostic interventions. Leveraging Macular OCT scan data, it aims to optimize prevention strategies through tree-based ensemble learning. Methods: Data from RSKM Eye Center Padang (October-December 2022) were categorized into four scenarios based on physician certificates: Negative & non-diagnostic DR versus Positive DR, Negative versus Positive DR, Non-Diagnosis versus Positive DR, and Negative DR versus non-Diagnosis versus Positive DR. The suitability of each scenario for ensemble learning was assessed. Class imbalance was addressed with SMOTE, while potential underfitting in random forest models was investigated. Models (RF, ET, XGBoost, DRF) were compared based on accuracy, precision, recall, and speed. Results: Tree-based ensemble learning effectively classifies DR, with RF performing exceptionally well (80% recall, 78.15% precision). ET demonstrates superior speed. Scenario III, encompassing positive and undiagnosed DR, emerges as optimal, with the highest recall and precision values. These findings underscore the practical utility of tree-based ensemble learning in DR classification, notably in Scenario III. Novelty: This research distinguishes itself with its unique approach to validating tree-based ensemble learning for DR classification. This validation was accomplished using Macular OCT data and physician certificates, with ETDRS scores demonstrating promising classification capabilities.
Co-Authors Agus Mohamad Soleh Bagus Sartono Dian Handayani Fitrianto, Anwar Muhammad Irfan Hanifiandi Kurnia