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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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DETERMINANTS OF POVERTY IN PAPUA PROVINCE: A PANEL DATA REGRESSION APPROACH (2021–2023) Indrasari, Nurvida; Yulianto, Safa'at
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.09112

Abstract

Papua Province, as one of the most underdeveloped regions in Indonesia, faces the problem of chronic poverty that occurs continuously. The high percentage of poverty and the low index in the aspects of shaping human development make the analysis of the factors that cause poverty urgent and worth researching. The purpose of this study is to identify the variables that affect the poverty rate in Papua Province in 2021-2023 comprehensively and systematically to identify vulnerable factors so that they can be used as a reference for mapping poverty alleviation policies that are more effective. The method used in this study is panel data regression, considering its advantages in providing a larger amount of data so that it can provide greater information that is not only produced by cross-sectional or time series data only. The data used is a publication from Badan Pusat Statistik (BPS) for 2021-2023 with the number of poor people, average length of schooling, labor force participation rate, per capita expenditure, life expectancy, human development index, gross regional domestic product, and length of schooling as predictor variables. The approach with fixed random effect was chosen to be the most suitable model. According to the results obtained from this analysis, the factor that has a significant effect on the condition of economic insufficiency in Papua Province in 2021-2023 is the number of poor people. These findings emphasize the importance of affirming that poverty alleviation policies need to be focused on reducing the number of poor people through programs that target increasing income and access to basic needs of the community.
MIXING DISTRIBUTION ANALYSIS OF MIXTURE POISSON DISTRIBUTION FOR THIRD PARTY LIABILITY INSURANCE CLAIM FREQUENCY DATA IN INDONESIA Aceng Komarudin Mutaqin; Syahla Anisah
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.09101

Abstract

The Indonesian government plans to mandate Third Party Liability (TPL) insurance for all vehicle owners in 2025. However, statistical modeling of TPL claim frequency data in Indonesia has received limited attention in academic research. The mixture Poisson distribution can be considered as a distribution for Third Party Liability claim frequency data in Indonesia. This is because claim frequency data often experiences overdispersion. In this study, the mixing distribution of the mixture Poisson distribution for TPL claim frequency data in Indonesia will be analyzed using a bootstrap approach. The data used in this study is policyholder claim frequency data for comprehensive coverage of TPL for underwriting years 2015-2019 for vehicle categories 1, 2, 3 and 6 of PT. X in Indonesia. The results generally show that most distributions with more parameters have a larger p-value (more suitable for use as a mixing distribution for mixture Poisson distribution) than distributions with fewer parameters.
THE APPLICATION OF SUPPORT VECTOR MACHINES IN FORECASTING INDONESIA'S EXPORT VALUES Muhammad Jimmy Saputra; Yeni Rahkmawati
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.09114

Abstract

Exports play a crucial role in Indonesia's economic growth, but fluctuations in export values can impact national economic stability. While there is existing research on export forecasting, the application of advanced machine learning methods such as Support Vector Machine (SVM) is limited. This study aims to forecast Indonesia’s export values using SVM based on monthly data from January 2017 to February 2025. The data were split into 80:20 proportions for training and testing, with input variables optimized using Partial Autocorrelation Function (PACF) analysis. Fifteen input schemes were tested, and the combination of lag 1 and lag 2 produced the lowest Mean Absolute Percentage Error (MAPE) of 5.04% on the test data, indicating very high accuracy. The forecasted results show a declining trend in export values from 21.87 billion USD in March 2025 to 20.66 billion USD in December 2025, driven by external factors such as global economic slowdown and commodity price fluctuations. Despite the decline, Indonesia’s export values remain relatively high compared to pre-2021 periods. This research highlights the effectiveness of SVM for export forecasting and suggests that this method could be used to inform policy decisions to mitigate global trade risks. Future research could explore the inclusion of additional external variables and other machine learning techniques to further improve forecast accuracy. The novelty of this study lies in the application of SVM for forecasting Indonesia’s export values, filling a gap in the literature on export forecasting models.
ANALYSIS OF INDONESIAN STUDENTS' READING LITERACY USING THE SMOOTHLY CLIPPED ABSOLUTE DEVIATION (SCAD) PENALTY Santi, Vera Maya; Riyantobi, Ariq Muammar; Widyanti Rahayu
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.09111

Abstract

Reading literacy significantly impacts a country's educational level, making it crucial to further investigate into this issue. Identifying factors that influence students' reading literacy, particularly in Indonesia, is a key area of exploration. PISA survey data, conducted every three years, is relevant for researching student proficiency. Each survey period focuses on one of three main topics: science literacy, mathematics literacy, and reading literacy. The 2018 PISA survey data is suitable for studying students’ reading literacy, as the main topic that year was reading literacy. However, PISA survey data includes many strongly correlated independent variables, potentially violating the multicollinearity assumption. To address this, regression analysis with a penalty function is used for variable selection. The SCAD (Smoothly Clipped Absolute Deviation) penalty function has proven effective in previous studies on PISA data. The model using the SCAD penalty function yielded excellent results, indicated by an Adjusted R2 value of 0.967. Based on this model, three main factors influence students' reading literacy in Indonesia: learning facilities, general knowledge, and students' self-confidence.
IDENTIFICATION OF PRIMARY SCHOOL LITERACY ACHIEVEMENT FACTORS IN PROVINCE X USING ORDINAL STEPWISE LOGISTIC Azizah, Siti Nur; Gustiara, Dela; Fitrianto, Anwar; Erfiani; Silvianti, Pika
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.09103

Abstract

Literacy is a foundational skill that underpins students’ academic success and lifelong opportunities. Low literacy skills can result in long-term disadvantages such as limited access to higher education, low productivity, and social inequality. Indonesia continues to face challenges in improving students' literacy achievement, particularly at the primary school level. According to the PISA 2022 results, Indonesia ranked 69th out of 81 countries, indicating that students’ literacy levels remain relatively low. This study aims to identify the factors that influence the literacy achievement of primary school students in Province X. The analytical method employed is ordinal logistic regression with a backward stepwise approach. The dependent variable is the level of literacy achievement (categorized as low, moderate, and good), while the independent variables include learning quality, teacher reflection and improvement, instructional leadership, school climate (including safety, diversity, and inclusiveness), and curriculum type. The results show that the final selected model follows the partial proportional odds assumption and includes only the significant predictors identified through backward stepwise elimination. Variables that positively influence literacy achievement include safety climate, diversity, inclusiveness, curriculum type, and teachers’ reflection and improvement of learning. Model evaluation using AIC, BIC, and accuracy measures indicates good predictive performance. These findings offer valuable insights for policymakers in designing strategies to enhance literacy through strengthening school climate and improving the quality of teaching and learning.
IDENTIFICATION OF LOCATION ALLOWANCE ZONE FOR BANK SYARIAH "X" OUTLETS USING ORDINAL LOGISTIC REGRESSION Aldinda Albanna; Yekti Widyaningsih
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.09110

Abstract

Companies need good human resources to achieve their goals, one of which is by providing rewards, such as location allowances. Bank Syariah "X" is one of the institutions that provides location allowance, which is an allowance based on the employee's work location. This policy was last established in 2021, therefore adjustments are needed. This study aims to analyze the factors that explain the determination of location allowance zoning and predict the zoning of new outlet location allowances. Location allowance zoning is determined based on the factors of cost, remoteness, and location access. Factors that are thought to represent these three factors and influence the determination of location allowance zoning are the consumer price index (CPI), human development index (HDI), construction cost index (CCI), infrastructure pillar index (IPI), outlet distance to the nearest health center (ODHC), and outlet distance to the nearest primary school (ODPS). The location allowance zoning consists of three categories with an ordered nature. Based on the research objectives and the type of dependent variable, the method used was ordinal logistic regression. This research produces factors that explain the determination of location allowance zoning, namely CCI, IPI, and ODHC with 70% accuracy and balanced accuracy for Zone 1, Zone 2, and Zone 3 & 4, respectively 81.2%, 70.8%, and 76.7%. Based on the initial policy data of Bank Syariah "X", the model misclassified 35.6% of outlets.
ITEM RESPONSE MODEL FOR ANALYZING ITEM RESPONSES IN THE INSTRUMENT OF CHANGE MANAGEMENT AND ORGANIZATIONAL CULTURE Dian Handayani; Muhammad Alief Ghifari; Vera Maya Santi; Rahfa Qur’aniyatin Dhuha
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.09104

Abstract

Item Response Theory (IRT) is an approach that can be used to analyze the responses/answers given by respondents to a measurement instrument. Unlike the classical test theory (CTT) approach that measures the latent traits of respondents based on the total score, IRT measures latent traits based on the responses given by respondents to each item. Another difference between CTT and IRT is that the CTT approach is theory-based while IRT is model-based. The purpose of this study is to apply Item Response Theory (IRT) to analyze the item responses of the employees of the Kementerian Desa, Pembangunan Daerah Tertinggal, dan Transmigrasi/KDPDTTT (Ministry of Village, Development of Disadvantaged Regions and Transmigration) on the items in the instrument/questionnaire which was administered to the employees, in order to understand their attitudes towards the changes management and organizational culture in the KDPDTT. We applied item response theory to analyze the answers provided by the respondents to the items. These responses were modelled based on the dichotomous IRT models, namely the 1PL, 2PL, and 3PL models. The IRT modeling in this study is based on the results of a survey conducted by KDPDTTT in 2020. Among the three models, the 2PL model is the most suitable for our item responses data because it has the smallest AIC, BIC, and G2. Based on the 2PL model, the probability for endorsing the items related to the change management ranges from 0.68 to 0.95. Meanwhile, the probability for endorsing items related to organizational culture ranges from 0.87 to 0.98. Although each item in the instrument has three response options, namely "disagree", "undecided (neutral)", and "agree", we will treat them as dichotomous. We classify the "undecided" answer as the "disagree" category. The reason is that many Indonesian people usually find it hard to say "disagree" for a question related to the evaluation of a policy. They tend to feel safer by choosing “undecided”. Therefore, the item responses that have been analyzed in our study are dichotomous, that is, "agree" or "disagree". The novelty of this research is utilizing a non-classical approach, namely IRT, which has several advantages over Classical Test Theory (CTT), including that item characteristics do not depend on respondent characteristics, and vice versa.
Front Matter Jurnal Statistika dan Aplikasinya Vol. 9 No.1, June2025 JSA, Journal Editor
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.09100

Abstract

Back Matter Jurnal Statistika dan Aplikasinya Vol. 9 No.1, June2025 JSA, Journal Editor
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.09199

Abstract

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