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All Journal Journal of Science and Applicative Technology Indonesian Journal of Applied Mathematics
Mika Alvionita S
Institut Teknologi Sumatera
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Engineering Materials Science & Nanotechnology Mechanical Engineering Physics

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Penerapan Metode Small Area Estimation dengan Pendekatan Hierarchical Bayesian (Studi Kasus: Tingkat Pengangguran Terbuka Di Provinsi Kepulauan Riau) Mika Alvionita S
Indonesian Journal of Applied Mathematics Vol. 5 No. 2 (2025): Indonesian Journal of Applied Mathematics Vol. 5 No. 2 October Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v5i2.2436

Abstract

The Open Unemployment Rate is an important indicator in describing the labor market conditions of a region. The Riau Islands Province recorded the highest open unemployment rate in Sumatra in 2024. However, direct estimation based on the survey is less accurate for small areas due to limited sample sizes. This study aims to estimate the OUR using the Small Area Estimation (SAE) approach based on a Hierarchical Bayesian (HB) Log-Normal–Normal model. The analysis results show that among several predictor variables used, only the Gross Regional Domestic Product (GRDP) has a significant effect on the OUR, with a regression coefficient of −1.2600. This indicates that an increase in GRDP is correlated with a decrease in the unemployment rate. The best model obtained is log(θi) = 1,5875 − 1,2600 x1i + vi with a Mean Squared Error of 0.309. For further development, it is recommended to conduct estimation at smaller area levels (sub-districts or villages) and to consider adding more predictor variables to improve model precision.
Statistical Pattern Recognition of Lithosphere Anomalous Activity Along the Indonesian Ring of Fire Mika Alvionita S; Ardika Satria; Triyana Muliawati; Fuji Lestari; Danni Gathot Harbowo
Journal of Science and Applicative Technology Vol. 9 No. 1 (2025): Journal of Science and Applicative Technology June Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/jsat.v9i1.1850

Abstract

The introduction of statistical pattern recognition becomes highly important for assessing disaster threats such as earthquakes. This approach is significantly more comprehensive and suitable for long-term event forecasting. Therefore, in the future, efforts can be promptly made to reduce the risk of disasters resulting from anomalies in lithospheric activity, especially frequent earthquakes in the Sumatra Island region, Indonesia. Statistical pattern analysis of lithospheric activity anomalies can be categorized through classification. Earthquake classification is performed based on magnitude scale and mathematical calculations of earthquake parameter unit conversion. The classification method employed in this research includes machine learning methods like k-nearest neighbor and support vector machine. The evaluation metrics used for machine learning models are model accuracy and confusion matrix tables.
Prediksi Terkena Diabetes menggunakan Metode K-Nearest Neighbor (KNN) pada Dataset UCI Machine Learning Diabetes Mika Alvionita S
Indonesian Journal of Applied Mathematics Vol. 3 No. 2 (2023): Indonesian Journal of Applied Mathematics Vol. 3 No. 2 October Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v3i2.1577

Abstract

Penelitian ini menggunakan algoritma K- Nearest Neighbor (KNN) untuk memprediksi resiko seseorang terkena diabetes. Variabel yang digunakan dalam prediksi adalah pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, dan age. Analisis menunjukkan bahwa Glucose, BMI, dan Age memiliki korelasi tinggi dengan diagnosis diabetes, menjadikannya indikator yang kuat untuk prediksi. Melalui metode KNN dengan k=1, dilakukan evaluasi model menggunakan Confusion Matrix. Hasil menunjukkan akurasi sebesar 96%, precision sebesar 91,6%, sensitivitas sebesar 88,7%, dan MSE sebesar 0,1376. Temuan ini menunjukkan bahwa KNN dengan k=1 efektif dalam memprediksi diabetes berdasarkan variabel klinis. Informasi ini dapat memberikan manfaat dalam pencegahan dan pengobatan diabetes secara lebih efektif.
Co-Authors Ardika Satria Danni Gathot Harbowo Fuji Lestari Triyana Muliawati