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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Ramadhani, Azzah Nazhifa Wina
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Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences

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Peningkatan Kompetensi Guru dalam Analisis Data Hasil Pembelajaran dengan Metode Statistika Menggunakan Microsoft Excel di SD Muhammadiyah 1 Trenggalek: Improving Teachers’ Competence in Analyzing Learning Outcomes Data Using Statistical Methods with Microsoft Excel at SD Muhammadiyah 1 Trenggalek Rifada, Marisa; Saifudin, Toha; Kurniawan, Ardi; Ramadhani, Azzah Nazhifa Wina; Maharani, Prima; Sentosa, Martha Ayu
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 11 No. 1 (2026): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v11i1.10675

Abstract

Muhammadiyah 1 Trenggalek Elementary School is one of the leading private elementary schools in Trenggalek Regency that implements an innovative system in its learning and has a strong commitment to improving the quality of education. However, most of the teachers in this elementary school still lack understanding of the concept of statistics along with the application of technology to analyze student learning data, so that the evaluation of student learning outcomes becomes less than optimal. Therefore, this community service program is carried out to provide intensive training on basic understanding of statistics, the use of Microsoft Excel to process data, and the application of analysis results to improve teaching methods. This program was carried out through several stages, consisting of providing offline training and assistance to groups of teachers in applying the training results to student learning data. The evaluation results show an increase in teacher understanding of the training material provided, with evidence of an increase in the average pre-test score of 64.46 to 71.08 in the post-test. Thus, this community service activity can be said to have succeeded in increasing teacher competence in using Microsoft Excel as a means of analyzing student learning data, which is also a start to improving the quality of Muhammadiyah 1 Trenggalek Elementary School teachers.
Comparing MARS and Binary Logistic Regression to Modelling Hepatitis C Cases using the SMOTE Balancing Method Chamidah, Nur; Ramadhanti, Aulia; Ramadhani, Azzah Nazhifa Wina; Syahputra, Bimo Okta; Ariyawan, Jovansha; Kurniawan, Ardi
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.33196

Abstract

Hepatitis is an inflammatory liver disease caused by viral infection and remains a major global public health concern, responsible for approximately 1.4 million deaths annually. Egypt is among the countries with the highest prevalence of Hepatitis C. To address this issue and support Goal 3 of the Sustainable Development Goals (SDGs), this study applies a quantitative approach using secondary data to analyze factors influencing Hepatitis C infection in Egypt. Two statistical models Binary Logistic Regression and Multivariate Adaptive Regression Splines (MARS) were compared, with the SMOTE method implemented to correct class imbalance. The dataset consisted of 608 patient observations, initially imbalanced at a ratio of 86.5:13.5, and were balanced to 52.6:47.4 after SMOTE application. The results revealed that the MARS model demonstrated superior predictive performance compared to binary logistic regression. All independent variables were found statistically significant (p < 0.05), except sex. Additionally, all odds ratios were less than 1, indicating a lower probability of Hepatitis C infection relative to non-infection. These findings highlight the relevance of statistical modeling and data-driven strategies in supporting preventive health measures. 
Co-Authors Ardi Kurniawan Ariyawan, Jovansha Maharani, Prima Marisa Rifada Nur Chamidah Ramadhanti, Aulia Sentosa, Martha Ayu Syahputra, Bimo Okta Toha Saifudin