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All Journal Media Statistika JTAM (Jurnal Teori dan Aplikasi Matematika) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Syam, Ummul Auliyah
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Evaluasi Kinerja Model Random Forest dan LightGBM untuk Klasifikasi Status Imunisasi Hepatitis B (HB-0) pada Balita Syam, Ummul Auliyah; Irdayanti, Irdayanti; Magfirrah, Indah; Sartono, Bagus; Firdawanti, Aulia Rizki
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.29762

Abstract

Hepatitis B (HB-0) immunization in infants is an important step in preventing the transmission of hepatitis B from an early age and improving public health. This study aims to classify the HB-0 immunization status of infants in West Java Province. The method used is the Random Forest and LightGBM algorithms. The research results showed that the Random Forest model had a balanced accuracy of 0.8443, which was slightly higher than LightGBM (0.8357). This indicated that Random Forest performed better in classifying the HB-0 immunization status of infants in West Java Province, accurately distinguishing between those who received and did not receive the immunization without bias toward either class. The global analysis using the Random Forest model identified six feature importance that contributed the most to the model’s performance: BCG immunization status, ownership of the KIA/KMS book, mother’s age, household head’s age, age at first pregnancy, and regency or city classification of residence. The feature importance analysis using SHAP for the first observation showed that BCG immunization status, ownership of the KIA/KMS book, and regency or city classification of residence increased the likelihood of infants receiving immunization. Conversely, the number of children (4), mother’s age (37 years), and household head’s age (40 years) increased the likelihood of infants not receiving immunization. This study is expected to provide data-driven insights for the government to design more effective interventions to improve immunization coverage and child health in Indonesia while also supporting the achievement of global health targets.
Spatial Clustering Regression in Identifying Local Factors in Stunting Cases in Indonesia Syam, Ummul Auliyah; Djuraidah, Anik; Syafitri, Utami Dyah
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.v9i2.29803

Abstract

Stunting is a significant health problem in Indonesia with high spatial disparities between regions. This study applies the Spatial Clustering Regression (SCR) method to analyze spatial patterns and identify local factors influencing stunting. SCR is a method that combines spatial regression and clustering analysis simultaneously using a k-means clustering-based formulation and a penalty likelihood function motivated by the Potts model to encourage similar clustering in adjacent locations with regression parameter estimation done locally in areas that have similar characteristics. This quantitative study uses secondary data from the Central Bureau of Statistics in 2022 covering 510 districts/cities, with one response variable (percentage of stunting) and seven explanatory variables reflecting socioeconomic, health, and infrastructure conditions. The results show that SCR divides the region into four spatial clusters characterized by different local factors. Cluster 1 has the lowest percentage of stunting that is influenced by access to clean water, sanitation, and education, Cluster 2 by poverty rate, number of public health centers, access to clean water, and education, Cluster 3 by poverty and nutrition of pregnant women, and Cluster 4 is the most vulnerable area with the highest stunting rate with a significant influential factor which is access to sanitation. The SCR approach allows for easier and more in-depth interpretation of results than other spatial methods such as GWR, as it can capture complex spatial patterns in the form of regional clusterings. These results provide a strong basis for formulating region-specific intervention policies, such as poverty alleviation and sanitation improvement in Cluster 4, strengthening health services in Cluster 2, developing education and nutrition programs in Cluster 3, and maintaining and improving nutrition consumption in Cluster 1.
COMPARISON OF SARIMA AND HIGH-ORDER FUZZY TIME SERIES CHEN TO PREDICT KALLA KARS MOTORBIKE SALES Syam, Ummul Auliyah; Irdayanti, Irdayanti; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.197-208

Abstract

Forecasting sales time series data is essential for companies to support effective planning and decision-making processes. This study evaluates the strengths of the Seasonal Autoregressive Integrated Moving Average (SARIMA) and High-Order Fuzzy Time Series Chen (FTS Chen) models in predicting motorbike sales at Kalla Kars Company, a prominent automotive dealer in Sulawesi, Indonesia. SARIMA is renowned for accurately capturing seasonal patterns, while the FTS Chen model excels in handling data uncertainties and incorporating complex relationships through high-order fuzzy logic. Weekly sales data from January 2020 to February 2024 were analyzed, with 205 weeks used for training and 13 weeks for testing. The results indicate that the third-order FTS Chen model outperforms SARIMA, achieving a Root Mean Square Error (RMSE) of 1.88 and a Mean Absolute Percentage Error (MAPE) of 4.64%. Forecasts for the next eight weeks using the third-order FTS Chen model suggest a decline in sales, contrasting with the SARIMA model, which predicts a slight increase. These results show that Chen's FTS model is more accurate and reliable, making it an effective choice for forecasting Kalla Kars motorbike sales.
Co-Authors Anik Djuraidah Aulia Rizki Firdawanti Bagus Sartono Irdayanti Irdayanti Magfirrah, Indah Mualifah, Laily Nissa Atul Utami Dyah Syafitri Yenni Angraini