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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Unnes Journal of Mathematics Journal of Mathematics, Computation and Statistics (JMATHCOS)
Wiwik Wiyanti
Statistics Department, Matana University
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Mathematics Nursing

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Stunting Risk Factor Analysis Using Seemingly Unrelated Regression (SUR) Integrated with Machine Learning Wiwik Wiyanti; Amanatullah Pandu Zenklinov; Jacob Stevy Seleki; Sausan Ramadhani; Valensius Jimy; Samsul Arifin
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9630

Abstract

Stunting is a disorder in children caused by chronic nutritional problems. Stunting is usually characterized by a child’s failure to grow and develop optimally. In Indonesia, the government is focusing on addressing of stunting case, as it aims to develop the superior of human resources. In Tangerang regency is currently holding a “Gebrak Tegas” program to address the problem of stunting. The causes of stunting required scientific study to help the government understand the scientific factors that causing stunting. Therefore, this study aims to analyze the factors causing stunting the children in the Tangerang regency. The data analysis method in this study use the Seemingly Unrelated Regression (SUR), which is integrated with a machine learning, namely Random Forest. The data used in this study were obtained from primary data through questionnaires. The subjects of this study were parents of stunting and non-stunting children who were at “Posyandu” under of “Kelapa dua” and “Binong” health centers. Sampling method in this study is purposive-random sampling. The results of the data analysis showed that the five variables from the factors measured had the most significant influence, namely nutritional, socio-economi, and pregnancy and childbirth history factors. Five variables that influence children stunting are animal protein, which has the highest probability of 87.5% when the children consumes protein once or twice a week. The children consume vitamin A twice has a 97% probability. The source of income for the parents, whether from the private or self-employment, has a probability of over 90%. Furthermore, the consumption of iron-boosting tablets by mother during pregnancy and the amount of income from the parents have a probability of 84%.
Integrated Force Field Analysis (FFA) and Inferential Statistics Method for Student Recruitment Strategy Evaluation Wiwik Wiyanti; Jacob Stevy Seleky; Rambu Marshanda Tamu Ina Paa
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/ebwk5a42

Abstract

This study applies an integrated approach combining inferential statistics, specifically inferences for two population means, with the Force Field Analysis (FFA) method. This study aims to analyze marketing strategies to increase new student enrollment at a university in Banten Province, Indonesia. The strategies analyzed in this study were classified into two categories: driving forces and restraining forces. The subjects of this study were employees from the Sales and Marketing Division of the university. The data analyzed in this study consisted of primary data collected through the administration of a structured questionnaire. The data analyzed in this study were primary data collected through a questionnaire. The questionnaire responses were categorized into two groups: driving forces and restraining forces. Data were analyzed using an integrated approach that combined FFA with inferential statistics, specifically the independent-samples t-test. Prior to its administration to the research subjects, the questionnaire underwent an expert validation process to ensure the relevance and appropriateness of its items. Descriptively, the mean score for the driving forces was 8.3, which was higher than that of the restraining forces was 5.4. The t-test yielded a calculated  ( ) of 11.4, which was greater than the critical t-value ( ) of 1.67. It’s means  that marketing strategies classified as driving forces had significantly higher mean scores than those classified as restraining forces in efforts to recruit prospective new students to the university over the past three years. Furthermore, the findings revealed two restraining forces with higher mean scores than the driving forces, specifically for Items 32 and 33, which concerned heavy workload and an inadequate compensation and incentive system, respectively.
Co-Authors Agatha, Dhela Asafiani Albert Agung Prayogo Alicia Arifin Amanatullah Pandu Zenklinov Arifin, Samsul Bakti Siregar Brigita Tiara Elgityana Melantika Clara Della Evania Dhela Asafiani Agatha Jacob Stevy Seleki Jacob Stevy Seleky Naftali Brigita Gunawan Naufal Fadlulloh Rambu Marshanda Tamu Ina Paa Sausan Ramadhani Valensius Jimy Widjaya, Ferdinand Nathaniel