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Dania Siregar
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INDONESIA
Jurnal Statistika dan Aplikasinya
Published by Universitas Negeri Jakarta
ISSN : -     EISSN : 26208369     DOI : https://doi.org/10.21009/JSA.041
Core Subject : Economy, Science, Education, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Social Sciences Other
Jurnal Statistika dan Aplikasinya JSA is dedicated to all statisticians who wants to publishing their articles about statistics and its application. The coverage of JSA includes every subject that using or related to statistics.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 169 Documents
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DETERMINATION OF IMPORTANT VARIABLES IN DIVORCE TYPE CLASSIFICATION USING THE RANDOM FOREST METHOD WITH SMOTE Siregar, Dania; Bintang Mahesa Wardana; Ahmad Syauqi Baihaqy; Liswatun Naimah; Almira Nindya Putri; Qory Meidianingsih; Dini Safitri
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08209

Abstract

Central Jakarta is highly strategic area situated at the heart of the Indonesian capital. It serves as the central hub for the government, history, tourism, and elite shopping sectors with convenient access to various buffer areas surrounding the capital. However, the availability of these facilities does not necessarily ensure the continuity of domestic life within the community. This can be observed from the increasing divorce rate in the region since 2017. Notably, a higher proportion of divorce suits are filed by wives than by husbands. There are various factors that can trigger divorce lawsuits such as continuous disputes and arguments, economic factors, and domestic violence. And these factors certainly cannot be separated from the individual profiles of married couples such as age, occupation, education level, and duration of marriage. The purpose of this study is to determine the level of importance of the variables used in the classification of wife-initiated divorce and husband-initiated divorce of married couples in the Central Jakarta area through the Random Forest method. Random Forest is a development of the CART (Classification and Regression Tree) method obtained through the application of bootstrap aggregating and random feature selection methods to the standard CART method. The number of wife-initiated divorce cases exceeds that of husband-initiated divorce cases, necessitating the use of SMOTE technique to address the imbalance in the data set. The results showed that the most important variable used to classify divorce cases was the plaintiff's age followed by the defendant's occupation, the defendant's age, and the plaintiff's occupation.
Front Matter Jurnal Statistika dan Aplikasinya Vol. 8 No.2, December 2024 JSA, Journal Editor
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08200

Abstract

Back Matter Jurnal Statistika dan Aplikasinya Vol. 8 No.2, December 2024 JSA, Journal Editor
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08299

Abstract

CLASSIFICATION ANALYSIS USING MINIMUM SPANNING TREE AND PREDICTIONS USING ARIMA ON THE MOST INFLUENTIAL STOCKS ON THE LQ45 INDEX Theo Markus; Ayu Sofia; Dwi Mahrani
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09113

Abstract

The 2023 recession, largely driven by high inflation, highlights the importance of investing. The stock market, with its regulated framework and potential for significant returns, presents a viable investment option. The LQ45, an index tracking the top 45 Indonesian stocks by market capitalization, provides a benchmark. To identify promising investments, this study employed the Minimum Spanning Tree (MST) method to pinpoint the most influential stocks within the LQ45 network, followed by Auto-Regressive Integrated Moving Average (ARIMA) for price prediction. The MST analysis, utilizing degree centrality, closeness, and betweenness measures, identified BBNI as the most influential, followed by BBTN and BMRI. Price predictions for BBNI and BBTN exhibited close alignment with actual market prices, while BMRI showed a larger deviation. For BBNI shares, the ARIMA(1,0,0) model is used with a MAPE of 1.78%; for BBTN shares, the ARIMA(0,2,2) model is used with a MAPE of 2.65%; and for BMRI shares, the ARIMA(2,2,1) model is used with a MAPE of 1.84%. This research contributes to the field of stock market analysis by demonstrating the effectiveness of combining network analysis techniques, specifically the MST method, with time series forecasting models like ARIMA for stock selection. The findings provide valuable insights for investors seeking to navigate market volatility and make informed investment decisions. The findings of this research can serve as a valuable guide for investors considering BBNI, BBTN, and BMRI shares.
ANALYSIS OF DIABETES MELITES DISEASE USING BINARY LOGISTIC REGRESSION Anistya, Mery; Putroue Keumala Intan; Ahmad Hanif Asyhar; Wika Dianita Utami
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09102

Abstract

This study aims to identify risk factors that affect the incidence of diabetes mellitus and evaluate the accuracy of the prediction model using binary logistic regression. The research method used secondary data from 140 patients at UPT Puskesmas Teja, Pamekasan, consisting of 60 diabetes negative patients and 80 diabetes positive patients. The variables analyzed included age, gender, heredity, smoking habit, body mass index (BMI), blood glucose level, cholesterol, and blood pressure. The results showed that the variables of gender and glucose levels had a significant influence on the incidence of diabetes, with significance values of 0.022 and 0.001, respectively. The gender variable has an Odds Ratio (OR) value of 0.135, indicating that female patients tend to have a lower risk of developing diabetes than men. Meanwhile, glucose levels showed a positive association with the incidence of diabetes, with each unit increase in glucose levels increasing the risk of diabetes by 1.016 times. The binary logistic regression model developed has an accuracy of 87.1% based on the Area Under Curve (AUC) value, which falls into the category of strong classification ability. This study provides important implications in supporting the development of more effective diabetes prevention and management strategies through an in-depth understanding of risk factors, so that it can be used as a basis for decision-making in public health services.
MULTIDIMENSIONAL POVERTY OF OLDER ADULTS IN JAVA ISLAND: A MULTILEVEL BINARY LOGISTIC REGRESSION ANALYSIS Putra, Dwima Agus Nurcahya; Istiana, Nofita
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09105

Abstract

Population aging is a global phenomenon, including in Indonesia, which poses socio-economic challenges. Many older adults still belong to the lowest 40% of household expenditure groups, indicating poor quality of life. Previous studies have generally used monetary measurements, while poverty in older adults is multidimensional, involving health, education, and living standards. This study addresses this gap by analyzing multidimensional poverty among older adults in Java in 2022 using multilevel binary logistic regression with a hierarchical data structure (individuals at level 1 and districts at level 2). The data sources include SUSENAS March 2022 and Province in Figures 2023. The results show that individual factors such as gender, marital status, type of occupation, functional impairment, savings ownership, and residential area, as well as regional factors like GRDP per capita and healthcare facilities ratio, significantly affect multidimensional poverty status among adults. The Intraclass Correlation Coefficient (ICC) is 0.383, confirming substantial variation at the district level, highlighting the importance of multilevel analysis. Furthermore, the model’s goodness-of-fit test concluded that the model is appropriate for explaining the multidimensional poverty status among older adults in Java in 2022. The findings provide comprehensive insights into targeted policy interventions to improve older adults' welfare.
GEOGRAPHICALLY WEIGHTED LASSO (GWL) MODELING TO IDENTIFY FACTORS INFLUENCE STUNTING INCIDENTS IN SOUTH SULAWESI Novianti, Andi Rima; Aswi, Aswi; Mar'ah, Zakiyah
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09109

Abstract

The Geographically Weighted Lasso (GWL) method is a technique that employs the Lasso approach within the Geographically Weighted Regression (GWR) model, which can also simultaneously select non-significant variables by shrinking the regression coefficient values to zero. Consequently, any variable assigned to a zero coefficient is considered statistically insignificant. In 2022, stunting remained a significant public health issue in South Sulawesi, ranking 10th nationwide with a prevalence of 27.2%. This underscores the urgent need for spatially sensitive analytical methods that can address regional heterogeneity and reveal key determinants at the district level. Notably, the application of GWL to analyze stunting in South Sulawesi using data from the Indonesian Nutrition Status Survey (SSGI 2022) represents a significant contribution that addresses an important research gap. This study aims to model stunting prevalence and identify its influential factors using GWL. The analysis yielded a tuning parameter λ = 0.04, achieving a model goodness of fit of R² = 0.957, demonstrating GWL’s effectiveness in mitigating multicollinearity. Four primary predictors of stunting emerged: low birth weight (LBW), access to safe drinking water, the human development index (HDI), and the average length of parental schooling.
AUTOREGRESSIVE DISTRIBUTED LAG MODELING FOR RICE PRICE PREDICTOR ANALYSIS IN BOJONEGORO REGENCY Khoirina, Jami’atul; Nurdiansyah, Denny; Kartini, Alif Yuanita
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09108

Abstract

Rice price fluctuations in Bojonegoro Regency are driven by complex interactions of economic, social, and environmental elements. These dynamics have a direct impact on the welfare of low-income households, making it essential to understand the underlying factors to support effective price stabilization efforts. Addressing this issue requires a comprehensive econometric model capable of capturing both immediate and lagged effects of relevant variables. This study analyzes the main drivers of rice price changes in Bojonegoro Regency by applying the Autoregressive Distributed Lag (ARDL) model. It focuses on how variables such as dried corn prices, rice consumption, harvest area, rice production, and money exchange rates contribute to rice price volatility. The ARDL model is employed to explore both short-term and long-term relationships between selected variables and rice prices. Model selection is guided by performance indicators including the Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), R-Square, as well as results from stationarity, cointegration, and classical assumption tests. The study utilizes secondary data sourced from the Bojonegoro Regency Food Security and Agriculture Office and the Bojonegoro Statistics Agency. The optimal model, identified as ARDL (3,4,4,4,4,0), produces an R-Square of 97.13% and the lowest AIC among alternatives. The analysis reveals that dried corn prices, rice consumption, harvest area, and rice production significantly influence rice prices, each with distinct lag structures. The money exchange rate, however, is found to have no significant effect. This study does not account for policy-specific variables or broader external factors such as global climate change or international trade regulations, which may also impact rice prices. Additionally, the availability and quality of secondary data may affect the model’s predictive accuracy. By incorporating lag structures and localized economic factors, this research offers a robust predictive framework tailored to Bojonegoro Regency. It provides practical insights for policymakers aiming to enhance rice price stability and protect household purchasing power.
ANALYSIS OF THE INFLUENCE OF DEMOCRATIC PARENTING PATTERNS ON CHILDREN'S INTERPERSONAL COMMUNICATION USING SEM-PLS Sudrimo, Sella Nofriska; Mutmainnah, Annisa
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09107

Abstract

Humans are gregarious and lonely beings who must interact with one another to exchange thoughts, knowledge, or feelings. This can be done through written, oral, nonverbal, or gestural means. Interpersonal communication is the process of direct, in-depth communication between two people or a small group of people through distinctive touch and message exchange. Interpersonal communication is one of the communication skills that should be taught from an early age since effective communication is communication that other people can accept and understand. The process of direct, in-depth conversation between two or a small group of individuals via physical touch and message exchange is known as interpersonal communication. Particularly for kids between the ages of 10 and 12, when they are going through a phase of adjusting to their surroundings, including making friends, effective interpersonal communication is crucial. This study aimed to determine the effect of democratic parenting patterns on interpersonal communication in children aged 10-12 years. The research data were 59 children aged 10-12 years at SD Hidayatullah, Salak Village, Klawasi Village, Sorong City, Southwest Papua. This type of research is quantitative research with the PLS-SEM method. From the data analysis, it was obtained that the valid indicators were X.2, X.9, Y.2, Y.3, and Y.7. The results of the t-statistic value were 3.773, which was greater than the t-table value of 2.003 (3.773 > 2.003). This means that democratic parenting patterns of parents significantly influence interpersonal communication in children aged 10 to 12 years. The analysis results also obtained an f-square value of 0.237, which means that the democratic parenting pattern of parents has quite an effect on children's interpersonal communication.
FOOD AND BEVERAGE PRODUCT SEGMENTATION BASED ON NUTRITION FACTS USING THE DBSCAN METHOD Fadlilah, Dhika Nurul; Primandari, Arum Handini
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09106

Abstract

Type 2 diabetes mellitus is increasingly affecting not only teenagers and adults in Indonesia but also children. This serious issue is linked to high-sugar foods, particularly candy and chocolate products consumed by children. The aim of this research is to categorize these products based on their nutritional information, specifically total fat, saturated fat, sugar, and salt (SSF) content per serving, using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. By doing so, the study seeks to produce simplified product labels that offer clearer nutritional insights compared to conventional nutrition facts labels. Data was collected through purposive sampling from three retail stores. The clustering results, using parameters Eps 0.4 and MinPts 10, revealed two distinct clusters and 133 noise points. Cluster 1 consists of 215 products with low levels of total fat, saturated fat, sugar, and salt, while Cluster 2 includes 27 products that are high in these nutrients. The clustering quality is validated with a Silhouette Coefficient of 0.77 and a Davies-Bouldin Index of 0.345.

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