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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JTAM (Jurnal Teori dan Aplikasi Matematika) Indonesian Community Journal
Naura, Sheila Sevira Asteriska
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Environmental Science Mathematics Public Health Social Sciences Other

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Peningkatan Kompetensi dan Profesionalitas Warga Desa Tambaksawah Sidoarjo dalam Mengoperasikan Microsoft Excel Untuk Menuju Desa Yang Unggul dan Produktif Jannah, Sa'idah Zahrotul; Pusporani, Elly; Ana, Elly; Syahzaqi, Idrus; Makkiyah, Afifah Nur; Ramadhanti, Aulia; Ariyani, Azizah Dewi; Ramadhani, Azzah Nazhifa Wina; Trisa, Nadya Lovita Hana; Carista, Nashwa; Naura, Sheila Sevira Asteriska
I-Com: Indonesian Community Journal Vol 5 No 1 (2025): I-Com: Indonesian Community Journal (Maret 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v5i1.6272

Abstract

Tambaksawah Village, Sidoarjo, is an industrial village that has the potential to improve the quality of life. However, this village faces obstacles, such as the limited skills of village officials using Microsoft Excel and the minimal application of technology in village institutions. Community service programs using Microsoft Excel are useful in supporting village administration efficiency and encouraging productivity. The methods include on-site training, group mentoring, international certification, and certification exams. Training followed by mentoring is carried out for one month. The evaluation results show an increase in the average score from 50.4 to 60.16, which shows an increase the understanding of participants. Apart from 7 participants, only 5 participants succeeded in obtaining official certificates. This activity succeeded in improving participants' skills in operating Microsoft Excel, so it is hoped that this can be the first step to advancing technological capacity and can help in developing Tambaksawah Village.
Modeling Prevalence of Hypertension in Indonesia with Multivariate Adaptive Regression Splines Method Suliyanto, Suliyanto; Saifudin, Toha; Naura, Sheila Sevira Asteriska; Dewanty, Sanda Insania; Wulandari, Indana Zulfa; Aflaha, Nabila Shafa; Aulia, Niswa Faizah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28392

Abstract

Hypertension is one of the important public health problems in Indonesia, which contributes to the high prevalence of non-communicable diseases. This study aims to model the prevalence of hypertension in Indonesia using the Multivariate Adaptive Regression Splines (MARS) method to identify significant predictors and their interactions. The data used was secondary data from the 2023 Indonesian Health Survey, including variables such as smoking prevalence, physical inactivity, dietary habits (consumption of fatty and sweet foods), lack of fruit and vegetable consumption, and obesity prevalence. The MARS method was used to analyse the nonlinear relationships and interactions between these predictors. After a trial-and-error process to determine the optimal number of basis functions (BF), maximum interactions (MI), and minimum observations (MO), the best model was achieved with BF = 18, MI = 3, and MO = 1. This model produced a Generalised Cross Validation (GCV) value of 13.428 and R-Square of 0.278. This fairly low R-Square value indicates that the factors analysed have contributed to the variation in hypertension prevalence, but there are still other aspects that can be taken into account to improve the predictive power of the model. The significant predictor variables were consumption of fatty foods (X3), lack of physical activity (X2), and consumption of sweets (X4), with the highest importance on X3 (100%). The findings reveal that interactions between variables, such as dietary habits and physical inactivity, significantly influence the prevalence of hypertension. For example, higher consumption of fatty and sweet foods combined with low physical activity increases the risk of hypertension. These results demonstrate the effectiveness of the MARS method in capturing complex and nonlinear relationships and serve as findings that highlight the need for health policies that focus on healthy diets and increased physical activity, in line with Goal 3 of the SDGs, “Good Health and Well-Being,” which aims to reduce premature mortality from noncommunicable diseases. Recommended interventions include nutrition education campaigns and community-based exercise programs to reduce the prevalence of hypertension in Indonesia.
Comparing Multivariate Adaptive Regression Splines and Machine Learning Methods for Classifying Pneumonia in Indonesian Toddlers Kurniawan, Ardi; Azizah, Nur; Naura, Sheila Sevira Asteriska
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.32418

Abstract

Pneumonia is a type of infectious and contagious respiratory disease that causes death in toddlers. According to the Indonesian Ministry of Health (2024), the coverage of pneumonia among toddlers in 2023 was 36.95% with a total of 416,435 cases. This study aims to model and classify the pneumonia status of toddlers in Indonesia using the Multivariate Adaptive Regression Splines (MARS) method and several machine learning methods, such as logistic regression, K-NN, random forest, and SVM. This study uses secondary data from the Survei Kesehatan Indonesia in 2023 and the Profil Kesehatan Indonesia in 2023, including variables such as the percentage of toddlers health service coverage, low birth weight babies, population density, percentage of malnutrition in toddlers, prevalence of smoking in the population aged ≥10 years in the last 1 month, percentage of toddlers who are exclusively breastfed, and percentage of toddlers who have incomplete basic immunization. The best model obtained using the MARS method is with BF = 14, MI = 2, and MO = 3. This model produces a GCV value of 0.122 and R-Square of 82.9%, which shows good prediction performance. The classification results show that the MARS method is superior to the logistic regression, K-NN, random forest, and SVM methods with an accuracy rate of 97.06%.
Co-Authors Aflaha, Nabila Shafa Ardi Kurniawan Ariyani, Azizah Dewi Aulia, Niswa Faizah Carista, Nashwa Dewanty, Sanda Insania Elly Ana Elly Pusporani Idrus Syahzaqi Jannah, Sa'idah Zahrotul Makkiyah, Afifah Nur Nur Azizah Ramadhani, Azzah Nazhifa Wina Ramadhanti, Aulia Suliyanto Suliyanto Toha Saifudin Trisa, Nadya Lovita Hana Wulandari, Indana Zulfa