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Journal : Journal of Data Insights

Klasifikasi Dataset Diabetes menggunakan Algoritma K-Nearest Neighbors Musa, Fitri Diana; M. Al Haris; Purwanto, Dannu; Amri, Saeful; Fadlurohman, Alwan; Fitriyana Ningrum, Ariska
Journal of Data Insights Vol 2 No 1 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i1.201

Abstract

Data mining merupakan suatu metode yang baik untuk menangani data skala besar. Performasi menjadi penting dalam metode data mining. Salah satu metode yang memiliki performasi terbaik adalah K-Nearest Neighbor (KNN). Artikel ini membahas terkait performasi K-NN. Data yang digunakan pada penelitian ini adalah Diabetes. Data dibagi menjadi 80% data trainingdan 20% data testing. Dengan menggunakan 11 tetangga terdekat, model menghasilkan akurasi sebesar 0.765625. Angka ini mencerminkan kinerja yang baik. Metrik kritis termasuk akurasi sebesar 0.77, presisi sebesar 0.80, dan recall sebesar 0.85. Hasil ini menunjukkan bahwa model KNN memiliki potensi untuk mengklasifikasikan pasien diabetes dengan akurasi yang baik.
Fuzzy Gustafson Kessel for Infrastructure Development Strategy in South Sumatra Province: Fuzzy Gustafson Kessel Untuk Strategi Pembangunan Infrastruktur Di Provinsi Sumatera Selatan Ningrum, Ariska Fitriyana; Rahma Dhani, Oktaviana; Anggun Lestari, Febi; Aura Hisani, Zahra; Fadlurohman , Alwan
Journal of Data Insights Vol 2 No 2 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i2.650

Abstract

Infrastructure development is a strategic element in improving public services and economic growth. South Sumatra Province, with its large economic potential, faces challenges in managing efficient and sustainable infrastructure development. This research aims to apply the Fuzzy Gustafson Kessel (FGK) method in decision making related to infrastructure development in South Sumatra Province. FGK combines fuzzy logic with Gustafson Kessel clustering algorithm to handle uncertainty and data variation from various stakeholders. The data used in this study includes population and geographic census data from the Central Bureau of Statistics of South Sumatra Province in 2023, with five indicators: population, area, population growth rate, population density, and poverty rate. The results show that South Sumatra is divided into three main clusters based on its infrastructure and demographic characteristics. This clustering is expected to improve the effectiveness and efficiency of infrastructure development decision-making, provide more appropriate policy recommendations, and potentially be applied in other regions with similar challenges.
Panel Data Regression Approach to Identify Factors Affecting Unemployment in East Java Province: Pendekatan Regresi Data Panel untuk Mengidentifikasi Faktor-Faktor yang Mempengaruhi Pengangguran di Provinsi Jawa Timur Amalia Putri, Rizka; Fadlurohman, Alwan; Mughni, Mardiyah
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.722

Abstract

The Open Unemployment Rate (OOP) in East Java Province is a multidimensional problem influenced by economic and social factors, with significant disparities between districts/cities. This study analyses the effect of Poverty Percentage, Labour Force Participation Rate (TPAK), and Economic Growth on the open unemployment rate using a panel data regression approach to accommodate spatial and temporal heterogeneity. Cross-section (38 districts/cities) and time series (2019-2021) data were analysed through three models: Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). The results of statistical tests (Chow, Hausman, and Lagrange Multiplier) show the FEM as the best model with a coefficient of determination of 0.555, explaining 55.5% of the variation in the unemployment rate. The FEM estimation reveals that the Poverty Percentage has a significant positive effect on increasing the unemployment rate, while Economic Growth has a negative impact on reducing the unemployment rate. This finding confirms the need for policies focused on poverty alleviation and increasing economic growth based on regional leading sectors. This study enriches the methodological literature through the application of FEM that controls for region-specific heterogeneity, while providing practical recommendations for policy makers in designing precise unemployment reduction interventions, such as skills training based on industry needs and strengthening labour-intensive programmes.