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All Journal Edumatsains Jurnal Literasi Akuntansi
Syasya Qonita Azizah
Universitas Pertahanan Republik Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Economics, Econometrics & Finance Education Engineering Mathematics Physics Social Sciences

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Non-Linear Regression on the Number of Stunting Cases in Central Java in 2023 Syasya Qonita Azizah
EduMatSains : Jurnal Pendidikan, Matematika dan Sains Vol 9 No 2 (2025): January
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Kristen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33541/edumatsains.v9i2.6469

Abstract

Chronic nutritional problems, namely stunting, have a major impact on physical growth and development of children, especially in Central Java. Central Java Province is one of the regions with the highest number of stunting in Indonesia, reaching 20.7% in 2023. This study used data from the 2023 Central Java Health Profile. This study aims to analyze the relationship between factors that cause stunting, including access to proper sanitation, complete basic immunization, underweight, exclusive breastfeeding, maternal health services K4, maternal health services K6, completion of basic education, and Consumption of Blood Supplement Tablets to the number of stunting cases in Central Java. The analysis used the Generalized Linear Model (GLM) and Integrated Nested Laplace Approximation (INLA) approaches with three distributions, namely Poisson, Gaussian, and Negative Binomial distributions. Negative Binomial Model. The Negative Binomial distribution proved to be the best model in analyzing stunting data in Central Java based on the smallest values in AIC, BIC, dan DIC. Based on the analysis results obtained, the percentage of underweight in toddlers shows a significant influence on the number of stunting cases in Central Java.
Optimasi Portofolio Saham BUMN20 Menggunakan Particle Swarm Optimization dan Genetic Algorithm dengan Pendekatan Maximizing Sharpe Ratio Syasya Qonita Azizah; Lidya Ananda Talalu; Muhammad Haikal Ziaulhaq
Jurnal Literasi Akuntansi Vol 6 No 2 (2026): Juni 2026
Publisher : Yayasan Literasi Ilmiah Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55587/jla.v6i2.290

Abstract

Purpose: This study aims to construct and optimize a stock portfolio within the BUMN20 index by maximizing the Sharpe Ratio using the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) approaches under volatile market conditions. Method: This study uses Adjusted Close Price data from 20 BUMN20 member stocks for the period from August 1, 2024, to August 1, 2025, obtained from Yahoo Finance, with stages including pre-processing to calculate returns and excess returns, portfolio optimization using PSO and GA, and performance evaluation based on returns, risk, and the Sharpe Ratio. Findings: The results show that the PSO method yields a higher Sharpe Ratio of 0.112 compared to GA’s 0.101, with respective returns of 0.00322 and 0.00248 and risks of 0.02880 and 0.02440. The PSO portfolio tends to be concentrated in two main stocks, namely ANTM and PGEO, while GA produces a more diversified portfolio but with a lower risk-adjusted return. Novelty: This study provides a comparative analysis between PSO and GA in stock portfolio optimization on the BUMN20 index using the Maximizing Sharpe Ratio (MSR) approach, thereby contributing to the development of portfolio optimization methods for indices that are sensitive to policy dynamics and political conditions.
Co-Authors Lidya Ananda Talalu Muhammad Haikal Ziaulhaq