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All Journal Jurnal Gaussian
Masithoh Yessi Rochayani
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
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PEMODELAN CLUSTERWISE LINEAR REGRESSION UNTUK IDENTIFIKASI FAKTOR YANG MEMENGARUHI PREVALENSI STUNTING DI JAWA TENGAH Berliana Ercha Pratiwi; Anton Saputro; Afifa Nur Mila; Moch. Abdul Mukid; Masithoh Yessi Rochayani
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.67-76

Abstract

Sustainable development in the Sustainable Development Goals (SDGs) emphasizes health as a key pillar, including in overcoming malnutrition that causes stunting. Central Java Province recorded a stunting prevalence rate of 20.7% in 2023, so it is necessary to analyze the factors that influence this condition. This study uses the Clusterwise Linear Regression (CLR) method to identify factors that contribute to the prevalence of stunting based on regional characteristics. The variables analyzed include the percentage of low birth weight babies (LBW), mothers who exclusively breastfeed less than six months, women who marry at an early age, households with proper sanitation, households with clean water sources, and households that have a Prosperous Family Card (KKS). The results showed that there were 3 optimal clusters. The coefficient of determination for each cluster was 99.52% for cluster 1, 99.76% for cluster 2, and 98.26% for cluster 3.
OPTIMALISASI PORTOFOLIO MENGGUNAKAN METODE MEAN-SEMIVARIANCE PADA SAHAM IDX30 Tsara Firda Nabila; Sudarno sudarno; Masithoh Yessi Rochayani
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.366-377

Abstract

Investment is one way to maximize income. Through investment, investors who invest funds will benefit. However, investment is inseparable from risk. Investors can reduce investment risk by forming an optimal portfolio. Mean-semivariance is one method of portfolio formation invented by Markowitz in 1959. Mean-semivariance is a mean-variance development method, but this method is free from all assumptions and this method measures portfolio risk by using semivariance and semideviation. The investment that many investors choose is a stock investment. This research uses stocks that have consistently joined the IDX30 for five years (2018-2022). IDX30 is composed of 30 stocks with relatively large market capitalization, high liquidity, and good fundamentals. The optimal portfolio is formed by calculating the weight of each stock using a function, so as to get the smallest risk. Based on the four optimal portfolios that are formed through the process, it is known that the optimal portfolio with the best performance is Portfolio 2. The Sharpe index belonging to Portfolio 2 is 0.083507. The investment weight for each share that makes up Portfolio 2 is 16.1039% for shares of PT Adaro Energy Indonesia Tbk; 57.5554% for shares of PT Indofood CBP Sukses Makmur Tbk; and 26.3407% for shares of PT Perusahaan Gas Negara Tbk.
BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) UNTUK KLASIFIKASI POTENSI KARYAWAN RESIGN BERDASARKAN KENYAMANAN BEKERJA Muhammad Fajar Syabana; Tatik Widiharih; Masithoh Yessi Rochayani
Jurnal Gaussian Vol 14, No 2 (2025): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.14.2.512-523

Abstract

Phenomenon of employee resignation is a significant challenge for companies because it affect the productivity and stability of the company's operations. Every companies supposed to analyze the potential of employee resignation. This research aims to classify the potential of employee resignation based on working comfort and applies the classification modeling method from Decision Tree: Classification And Regression Trees (CART) and the ensemble Bootstrap Aggregating (Bagging) method. CART is a non-parametric method that is effective in building classification and prediction models based on decision trees, while Bagging is an ensemble method that combines several CART models to improve the accuracy and stability of predictions. The CART model provides an accuracy of 73% and f1-score of 62%, while the Bagging CART model provides an accuracy of 87% and f1-score of 88%. This research shows an increase in accuracy when using Bagging CART model of 14%. The most important variable to build the model and make predictions is the age. Age is also used as the root node in building CART model.
Co-Authors Afifa Nur Mila Anton Saputro Berliana Ercha Pratiwi Moch. Abdul Mukid Muhammad Fajar Syabana Sudarno Sudarno Tatik Widiharih Tsara Firda Nabila