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All Journal Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population)
Kuntoro Kuntoro
Faculty of Public Health, Universitas Airlangga, 60115 Surabaya, East Java, Indonesia

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APPLICATION OF THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD ON THE AIR POLLUTION STANDARD INDEX IN SURABAYA Silmi Muna; Kuntoro Kuntoro
Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population) Vol. 10 No. 1 (2021): JURNAL BIOMETRIKA DAN KEPENDUDUKAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jbk.v10i1.2021.53-60

Abstract

The Air Pollution Standards Index (APSI) is an indicator that shows how clean or polluted the air is in a city. It also portrays the health impacts towards the people who breathe it in. Based on the Indonesian Ministry of Environment monitoring through the Air Quality Monitoring Station (AQMS), the city of Surabaya only had 22 up to 62 days of air categorized as good in a year. The purpose of this study was to forecast APSI as a scientific-based reference for making decisions and policies that were appropriate in tackling the effects of air pollution on health. This study was non-obstructive or non-reactive research. The research method used was time series to identify the time relationship. The data used were secondary data taken from the APSI documents from 2014 to 2019 at the Surabaya City Environment Agency. The results of this study obtained the best model through α (0.8), γ (0.5), and δ (0.6) with the values of MAPE (0.104355), MAD (0.00842), and MSD (0.001050) calculated with the Holt-Winters exponential smoothing method. The highest produced forecast value of APSI was in September 2020, and the smallest was in January 2020. This study suggests the government of Surabaya to create policies and programs to suppress the number within APSI.
DIFFERENCE OF POWER TEST AND TYPE II ERROR (β) ON MARDIA MVN TEST, HENZE ZIKLER'S MVN TEST, AND ROYSTON'S MVN TEST USING MULTIVARIATE DATA ANALYSIS Wahyul Anis; Kuntoro Kuntoro; Soenarnatalina Melaniani
Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population) Vol. 10 No. 2 (2021): JURNAL BIOMETRIKA DAN KEPENDUDUKAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jbk.v10i2.2021.153-161

Abstract

The Mardia MVN test, Henze Zikler's MVN test, and Royston's MVN test are the most widely used tests to analyze multivariate normal (MVN) data, but there have not been many studies explaining the advantages and disadvantages of these tests. The research objective was to analyze the difference in test strength and type II (β) error in the Mardia MVN test, Henze Zikler's MVN test, and Royston's MVN test. The research data were analyzed using three MVN tests, namely the Mardia MVN test, Henze Zikler's MVN test, and Royston's MVN test. The results of the analysis in the form of test strength and type II error (β) would be compared at alpha (α) 1%, 5%, 10%, 15%, 20%, and 25%. The comparison results explained that the Mardia test had the greatest test strength and the smallest type II (β) error. The study concluded that the Mardia MVN test was a multivariate normal test better than Henze Zikler's MVN test and Royston's MVN test.
MODEL FOR PREDICTING PREVENTIVE BEHAVIOR AGAINST HYPERTENSION AMONG ISLAMIC STUDENTS USING MACHINE LEARNING APPROACH Ida Srisurani Wiji Astuti; Kuntoro Kuntoro; Mochammad Bagus Qomaruddin; Krish Naufal Anugrah Robby
Jurnal Biometrika dan Kependudukan Vol. 15 No. 1 (2026): JURNAL BIOMETRIKA DAN KEPENDUDUKAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jbk.v15i1.2026.33-43

Abstract

The prevalence of hypertension is currently increasing among adolescents. Despite numerous efforts to improve hypertension prevention, there are still limited approaches capable of accurately predicting hypertension prevention behaviors. Machine learning is needed to develop predictive models that can identify key predictors. This study aimed to develop and evaluate a machine learning model for predicting hypertension prevention behaviors among students in Islamic boarding schools. A cross-sectional design was employed in this study. Primary quantitative data were collected through validated questionnaires from 378 students, aged 15–18 years at three Islamic boarding schools in Jember, Indonesia. The data were analyzed using a machine learning approach involving data preprocessing, selection of indicator variables, and division of the dataset into training and testing datasets to develop and evaluate a predictive model of hypertension prevention behavior. The results showed that the machine learning–based predictive model of hypertension prevention behavior performed well, achieving an area under the curve (AUC) of 0.72, an accuracy of 95%, and a precision of 70%. The model identified competence, autonomy, and subjective norms as the main predictors and adequately distinguished between students with good and poor hypertension prevention behaviors. The machine learning approach performs better by providing a preprocessing phase, comprehensive model performance evaluation metrics, and new or previously unseen data to assess the model's generalizability. Future studies should extend the study to various settings and populations to improve generalizability. The predictive model can be used to predict hypertension prevention behavior using a number of independent variables.
Co-Authors Ida Srisurani Wiji Astuti Krish Naufal Anugrah Robby Mochammad Bagus Qomaruddin Silmi Muna Soenarnatalina Melaniani Wahyul Anis