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INDONESIA
Indonesian Journal of Statistics and Its Applications
Published by Institut Pertanian Bogor
ISSN : 25990802     EISSN : 25990802     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802) (formerly named Forum Statistika dan Komputasi), established since 2017, publishes scientific papers in the area of statistical science and the applications. The published papers should be research papers with, but not limited to, the following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education. All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 5 Documents
 1 
Search results for , issue "Vol 7 No 2 (2023)" : 5 Documents clear
Small Area Estimation Using Empirical Bayes Poisson Gamma on Adolescent Fertility Rate in Indonesia: Small Area Estimation Menggunakan Empirical Bayes Poisson Gamma pada Angka Fertilitas Remaja di Indonesia Septianingsih, Putri; Wulansari, Ika Yuni
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p114-129

Abstract

High population growth is one of the main population problems facing Indonesia. One of the government's efforts to overcome this is by preventing adolescent fertility. The Adolescence Fertility Rate (AFR) produced by the IDHS is designed until provincial level, whereas the availability of AFR at the district/city level is needed as an indicator of regional development measurement. The purpose of this research is to produce an estimation of AFR at the district/city level in Indonesia and find out which auxiliary variables significantly influence it and evaluate the performance of the model in estimating AFR. The analytical method used is descriptive analysis to explain the characteristics of adolescent fertility and auxiliary variables and also direct estimation and the indirect estimation method using Small Area Estimation Empirical Bayes Poisson Gamma. The results showed that the number of villages, school facilities, health facilities, health workers, telephone lines and operators significantly affected the fertility of adolescents and the results of the SAE EB Poisson Gamma estimation were better than the direct estimation method. Suggestions proposed are the government need to increase attention to districts/cities that have AFR that is higher than the average AFR or National AFR and increase the number of school facilities and the number of health workers.
Analyzing Low Birthweight in Java Based on Logistic Regression Model for Matched Pair Data: Analisis Berat Badan Lahir Rendah di Pulau Jawa Berdasarkan Model Regresi Logistik untuk Data Berpadanan Putri, Christiana Anggraeni; Irfani, Rini; Notodiputro, Khairil Anwar
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p75-85

Abstract

Low birthweight is one of the leading causes of neonatal death. Generally, the study of low birth weight is done by modeling logistic regression without considering the influence of confounding variables that can deviate the actual relationship between the explanatory variables and the response. This paper aims to identify low birth weight determinants in Java based on the logistic regression model for conditional study design, in which the analysis is based on matching the education level of the mother with one control. The results of the analysis showed that matched logistic regression can be used to correct bias due to the influence of a confounding variable. It reveals that based on the results of modeling, the frequency of pregnancy examinations and the parity of children are significantly affect the risk of low birth weight in Java Island.
Loopy Orthogonal Signal Correction Scatter Correction in Non-Invasive Blood Glucose: Koreksi Pencaran Loopy Orthogonal Signal Correction pada Glukosa Darah Non-Invasif Misrika, Dahlia; Erfiani, Erfiani; Wigena, Aji
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p105-113

Abstract

Spectroscopy is the study of matter based on light, sound, or particles emitted, absorbed, or reflected as well as the study of methods for generating and analyzing spectra. The spectrum has systematic diversity, namely the presence of light scattering and differences in the size of objects. The spectroscopic output allows for scattering shifts, because the same object measured several times does not exactly produce the same spectrum. Problems found in the spectrum can be overcome by pre-processing the data, namely the scatter correction method. Scatter correction is used to reduce the physical properties in the spectrum so that the information obtained is relatively the same for each spectrum, produces good estimates, and can be interpreted well. One of the spectroscopic tools that utilize infrared light is a non-invasive blood glucose level measuring device. The output of the tool is the time domain and intensity spectrum. Each object from the resulting spectrum still has noise, so scatter correction can be applied to this data. The purpose of this study was to perform a loopy Orthogonal Signal Correction (OSC) scatter correction method on time domain spectrum data on intensity on a non-invasive blood glucose level measuring device. The OSC method uses the concept of orthogonality to the mean by drawing the intensity value, weighting it, calculating the vector loading and then making corrections to the initial intensity. Based on the analysis, the loopy OSC method is better than OSC because the convergence is more accurate, the mean difference is smaller, the variance is smaller and the value converges on all the values tested. Based on exploration and the average difference, the loopy OSC method is better able to form the same pattern for each replication. This also shows that an object that is measured repeatedly has been able to be identified as the same object.
Determinants of Antenatal Care Visits in Indonesia with Synthetic Minority Over-Sampling Techniques for Imbalance Data: Determinan Kunjungan Antenatal Care di Indonesia dengan Teknik Synthetic Minority Over-Sampling untuk Imbalanced Data Thamrin, Nurafiza; Baktiar, Aditya Firman; Addawiyah, Firda Aini; Husna, Miftahul; Irwati, Tati
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p86-104

Abstract

Maternal mortality and infant mortality are two indicators that describe the degree of public health as well as indicators of sustainable development in Indonesia. The acceleration of reduction these two indicators must be supported by antenatal care services since pregnancy for the safety of mothers and babies. Based on the results of the IDHS 2017, antenatal care coverage in Indonesia (77.4%) is still far from the target in 2024 (95%.) This study used logistic regression analysis with Synthetic Minority Oversampling Technique (SMOTE) resampling method because of imbalance data to explore the determinants of complete antenatal care visits in Indonesia and descriptive analysis to find out an overview of complete antenatal care associated with factors that are considered influencing it. Data that was used in this study is the Indonesia Demographic and Health Survey (IDHS) 2017 with unit of analysis for women of childbearing age who are married or live together and gave birth to their last child in the period 2012-2017. The logistic regression results of the SMOTE method show that the variables of mother's education, husband's work status, knowledge of pregnancy danger signs, distance to health facilities, timing first antenatal check, mother's age, economic status, birth order, and number of problems during pregnancy significantly affect the completeness of antenatal care visits. The policy recommendations in this study are expected to be adopted by government to increase antenatal care visits in Indonesia as an effort to reduce maternal and infant mortality.
Identification of Prospective Subindustries Ahead of the 2024 Simultaneous General Elections with K-Medoids Clustering: Identifikasi Subindustri Prospektif Menjelang Pemilihan Umum Serentak 2024 dengan K-Medoids Clustering Amelia, Vera; Silvianti, Pika; Rahman, La Ode Abdul
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p64-74

Abstract

Indonesia Stock Exchange (IDX) Composite has grown in each general election year since 1998. This indicates that certain subindustries have benefited positively from the election year momentum. However, analyzing each subindustry was less efficient. This study aimed to identify prospective subindustries leading up to the 2024 Simultaneous Election based on the results of K-Medoids clustering on data from the lead-up to the 2019 Simultaneous Election. Research variables covered long-term price rate of change (indicating trends) and volatility (depicting fluctuations). These were derived from transforming historical stock price data for each issuer on a weekly basis in the two years before the 2019 Simultaneous Election. Four clusters emerged: high positive, low positive, high negative, and low negative. Positivity/negativity signify trends and high/low represent fluctuations. High fluctuations indicate higher risks. Prospective subindustries for the 2024 Simultaneous Election with low risk include household furniture manufacturers, basic chemical producers, construction materials, packaging, tires, household goods retail, life insurance, consumer finance, and financial holding companies. On the other hand, sub-industries with high risks for the 2024 Simultaneous Election include aluminum, paper, and textiles.

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