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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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Front Matter Jurnal Statistika dan Aplikasinya Volume 8 Issue 1, June 2024 JSA, Journal Editor
Jurnal Statistika dan Aplikasinya Vol. 8 No. 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08100

Abstract

Back Matter Jurnal Statistika dan Aplikasinya Volume 8 Issue 1, June 2024 JSA, Journal Editor
Jurnal Statistika dan Aplikasinya Vol. 8 No. 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08199

Abstract

WEST KALIMANTAN FOREST FIRE PROBABILITY MAPPING USING BINARY LOGISTIC REGRESSION Imro'ah, Nurfitri; Dadan Kusnandar; Debatraja, Naomi Nessyana
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.08207

Abstract

Forest fires, which occur almost annually, are common in West Kalimantan during the dry season. There is little question that the rate of regional development will slow down as a result of the huge effects this condition has on the social, economic, and environmental domains. Naturally occurring factors are one of the many potential causes of forest fires. The goal of this research was to identify the factors that significantly influence forest fires and to produce a map showing the likelihood of forest fires occurring in various West Kalimantan cities and districts. The analytical technique that enabled us to achieve our goal was logistic regression. The existence or absence of forest fires is one of the dependent variables being used. The temperature, geography, vegetation, and human influences are the independent variables during this time. The bulk of forest fires that occurred in West Kalimantan were caused by human activity as opposed to natural causes, per the study's findings. There are several reasons why humans set off forest fires, whether on purpose or accidentally, but one of them is the distance that people can go to conduct activities inside the forest. Beyond the variables listed above, there are two other criteria that can start a forest fire: the distance from the point to the road and the distance from the point to the air. Using logistic regression, it was discovered that the variable distance between the site and the river contributed thirty percent to the likelihood of forest fires.
ANALYSIS OF BICLUSTERING ITERATIVE SIGNATURE ALGORITHM ON POVERTY DATA IN SULAWESI ISLAND IN 2022 Yekti Widyaningsih; Safitri, Nabila
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.08206

Abstract

Poverty in Indonesia is still a problem that must be addressed every year. According to the March 2022 Susenas report, Sulawesi Island ranks third among the six major islands in Indonesia in terms of the percentage of the population living in poverty. This shows that there are still many people living in poverty in Sulawesi. Therefore, the government needs to make the right policies to address this problem. One potential approach is to cluster districts or cities in Sulawesi based on poverty-related variables. The objective of this research is to group the data in two directions: first, by districts or cities and, second, by its variables simultaneously. The formation of these groupings will facilitate the development of the right government policies to address poverty. The appropriate method for these groupings is the biclustering method, which can group observations and characteristics simultaneously so that biclusters formed can be characterized differently. One of the biclustering algorithms is the Iterative Signature Algorithm (ISA), which requires an upper threshold value and a lower threshold value. The threshold value is the value used to determine whether a district or city and variables can be included in a bicluster. The best result is selected based on the average Mean Square Residue (MSR) per volume. Biclustering analysis of poverty data in Sulawesi in 2022 using ISA produced 2 biclusters. Based on these results, the government is expected to make the right policy to overcome poverty problems in Bicluster 1 and Bicluster 2.
IMPLEMENTATION OF MIXED GEOGRAPHICALLY WEIGHTED REGRESSION MODEL TO ANALYZE SOCIAL ASSISTANCE BUDGET IN EAST JAVA Utami, Putri; Nurdiansyah, Denny; Kartini, Alif Yuanita
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.08204

Abstract

Background - Social assistance (BANSOS) is aid provided by the government to low-income communities in the form of money, goods, or services. Understanding the allocation and influencing factors of social assistance in East Java is crucial for effective distribution. Mixed Geographically Weighted Regression (MGWR) combines global and local regression models to address spatial variability in the data. Purpose – This study aims to develop an MGWR model with a fixed kernel weighting function for the social assistance budget in East Java for 2022. The specific objectives are to identify factors affecting the budget and determine the best model that represents these global and local relationships. Methodology – The study employs the Mixed Geographically Weighted Regression (MGWR) method with a fixed Gaussian kernel to analyze social assistance budget data and economic factors in East Java for 2022. Models OLS, GWR, and MGWR are applied and evaluated using the Akaike Information Criterion (AIC) to identify the best-performing model. Findings – The MGWR model with a fixed Gaussian kernel is the best for the social assistance budget in East Java, yielding a lower AIC compared to OLS and GWR models. The globally influential factor in this model is economic growth (
FORECASTING HORTICULTURAL PRODUCTION BASED ON RAINFALL WITH VECTOR AUTOREGRESSIVE EXOGENOUS (VARX) METHOD Ayu Septiani; Hardianti Fatmi; Ritsu Haiban Hirzi; Muhammad Gazali
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.08203

Abstract

Changes in the production yield of three leading commodities in NTB Province continue to fluctuate and may harm society. To examine the condition of the production of cayenne pepper, red onion, and tomatoes based on rainfall in the coming period, a model is needed that can predict multivariate time sequence data. There are several models of multivariate time sequence analysis, one of which is the autoregressive exogenous vector (VARX). The VARX model is a multivariate time series model consisting of several endogenous variables (p) and supplemented with exogenous variables (q). The purpose of this study is to obtain a suitable VARX model and an estimation of cayenne pepper, onion, and tomato data. Using the VARX method, the optimum lag was obtained with the smallest Akaike Information Criterion (AIC) value, namely at lag 5 with a value of 66.5160. Based on the overfit carried out, the appropriate and best model to be estimated is VARX (1.1) with a total value of 66.42364, which meets the assumption of white noise and multivariate normal distribution that satisfies the optimum forecast amount.
DETERMINANTS ANALYSIS OF STUNTING PREVALENCE AMONG TODDLERS IN CENTRAL JAVA PROVINCE IN 2022 Prayogi, Dicki; Rini Rahani
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.08202

Abstract

Stunting is a chronic malnutrition condition that results in toddlers being too short for their age. The World Bank (2020) reported that 54 percent of Indonesia's workforce comprises stunting survivors. This can pose a serious threat to the achievement of the ‘Indonesia Emas 2045’ vision. Central Java had the highest prevalence of stunting among toddlers in Java Island in 2022 (20.8 percent), exceeding the WHO threshold (20 percent). This study aims to provide a general overview of the prevalence of stunting among toddlers and to identify the variables influencing it. The data used are secondary data from the Ministry of Health of the Republic of Indonesia, Statistics Indonesia (BPS), and the Health Office of Central Java, and were analyzed using multiple linear regression analysis. The results show that the variables of water and food management, food expenditure, and consumption of iron tablets have a significant negative effect on the prevalence of stunting among toddlers, while the percentage of teenage pregnancies and the percentage of toddlers with helminth infections have a significant positive effect on the prevalence of stunting among toddlers.
VISUALIZATION AND MAPPING OF HOUSEHOLD HOUSING CONDITIONS IN WEST JAVA USING MULTIDIMENSIONAL SCALING Hafsah, Siti; Rifda Nida’ul Labibah; Anwar Fitrianto; Erfiani; L.M. Risman Dwi Jumansyah
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.08201

Abstract

This study aims to map household housing conditions in West Java using the Multidimensional Scaling (MDS) approach. West Java, as the most populous province in Indonesia, faces significant challenges regarding housing inequalities, infrastructure access, and socio-economic disparities between urban and rural areas. These disparities necessitate a comprehensive and systematic approach to identify vulnerable regions and inform targeted policy interventions. Using data from the 2023 National Socio-Economic Survey (Susenas), this study analyzes five main groups of variables: basic needs, housing facilities and ownership, socio-economic status, access to services and infrastructure, and household demographics and welfare. The Multidimensional Scaling (MDS) technique is employed due to its capability to reduce complex, high-dimensional data into a two-dimensional representation, allowing clearer visualization of regional disparities and interrelationships among variables. MDS also facilitates robust model evaluation, ensuring high-quality mapping results. The MDS results reveal significant variations in household conditions, with urban areas such as Bekasi and Depok City showing better infrastructure access and welfare outcomes compared to rural areas like Cirebon and Sukabumi District. Evaluation of the MDS model indicates excellent performance, with STRESS values ranging from 0.042 to 0.083 and RSQ values between 0.993 and 0.999, demonstrating high accuracy. This study addresses a research gap where few studies have comprehensively mapped housing inequalities in large, diverse regions like West Java using advanced multidimensional techniques. The findings emphasize the importance of policies focusing on infrastructure development and equitable distribution of social assistance in underdeveloped regions to reduce regional disparities.
THE APPLICATION OF THE ARTIFICIAL NEURAL NETWORK (ANN) METHOD FOR FORECASTING THE SOUTHERN OSCILATION INDEX (SOI) Fathia Syahla Az Zahra; Bagus Sumargo; Siregar, Dania; Auria Yusrin Fathya
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.08205

Abstract

Indonesia's seasons are influenced by global phenomena such as ENSO. This phenomenon affects rainfall intensity in Indonesia through its two main phases: El Nino and La Nina. One method to detect these events is by analyzing the Southern Oscillation Index (SOI). A highly accurate SOI forecasting model is critical for both short-term and long-term development planning, particularly in anticipating future extreme seasons. One of the methods used for forecasting is the Artificial Neural Network (ANN). This study aims to develop an ANN model capable of predicting the SOI index. Based on forecasting using training data, the optimal model architecture identified is 12-7-1, which achieved the smallest MSE value of 0.0095 and a MAPE of 17.6851. With an error rate below 20%, the 12-7-1 architecture demonstrates strong forecasting capabilities. The study forecasts the SOI index for the next 12 months, indicating a trend from negative values at the beginning of the year to more positive values toward the year's end.
COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHMS IN GROUPING INCOME DISTRIBUTION INEQUALITIES IN JAVA AND BALI Qorinul Huda; Puput Budi Aji
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.08208

Abstract

Inequality is a growing issue in several countries, both developed and developing countries. The level of state income reflected in Gross Domestic Product (GDP) cannot yet describe whether income allocation is equitable or not. High GDP is the goal of a country, but welfare is much more important. Community welfare in a country can be interpreted as how much state income is enjoyed by the community. One benchmark for whether a country's income is equally enjoyed by its people or not is through the Gini index. As industry 4.0 progresses, economic growth continues to increase. The largest share of Indonesia's GDP is on the islands of Java and Bali. Behind the rapid economic growth on the two islands, there is also inequality in income distribution. This research aims to classify districts and cities on the islands of Java and Bali based on factors that influence inequality using a data mining classification algorithm. This research uses four algorithms, namely Decision Tree, Logistic Classification, Random Forest, and Support Vector Machine (SVM). These four methods will be compared (compared) based on model evaluation, so that they are able to predict testing data for the next period in order to produce the correct regional classification. This research also accommodates handling of imbalanced data, data imputation, and forecasting using Generalized Regression Neural Network (GRNN).

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