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All Journal Jurnal Gizi dan Pangan Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Journal of Mathematics: Theory and Applications
Nur Aidi, Muhammad
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Education Health Professions Mathematics

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Multilevel Regression Analysis on Graduate Student Grade Point Average Riswan, Riswan; Dyah Syafitri, Utami; Nur Aidi, Muhammad
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 12 No. 1 (2024): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v12i1.3969

Abstract

Abstract:Multilevel regression is one of the methods used to analyze hierarchical data structures. One case of data with a hierarchical structure is the cumulative grade point average (GPA) data for students each semester (level one) which is nested within students (level two), and nested within faculties (level three). This study produced the three best three-level regression models: the multilevel regression model, the multilevel regression model with natural logarithmic transformation, and the multilevel binary logistic regression model. The multilevel regression model and the multilevel regression model with natural logarithmic transformation at a significant level of 5%, have the same variables that affect student GPA scores, including semesters, credits, gender, scholarships, and marital status with the same interaction effect, namely semester interactions with scholarships. In addition, the ICC values by the two models are also the same which explains that 91% of the total diversity of student GPA comes from the student level and 8% comes from the faculty level. For the multilevel binary logistic regression model, all explanatory variables affect GPA without involving interaction between levels. Abstrak:Regresi multilevel merupakan salah satu metode yang digunakan untuk menganalisis struktur data hirarkhi. Salah satu kasus data dengan struktur hirarki adalah data indeks prestasi kumulatif (IPK) mahasiswa tiap semester (level satu) yang tersarang dalam mahasiswa (level dua), tersarang dalam fakultas (level tiga). Dalam penelitian ini menghasilkan tiga model regresi tiga level terbaik yaitu model regresi multilevel, model regresi multilevel dengan transformasi logaritma natural, dan model regresi logistik biner multlevel. Model regresi multilevel dan model regresi multilevel dengan transformasi logaritma natural pada taraf nyata 5%, memiliki peubah sama yang berpengaruh terhadap nilai IPK mahasiswa antara lain semester, SKS, jenis kelamin, beasiswa, dan status nikah dengan pengaruh interaksi yang sama yaitu interaksi semester dengan beasiswa. Selain itu, nilai ICC oleh kedua model tersebut juga sama yang menjelaskan bahwa 91% total keragaman IPK mahasiswa berasal dari level mahasiswa dan 8% berasal dari level fakultas.  Untuk model regresi logistik biner multilevel semua peubah penjelas berpengaruh terhadap IPK tetapi tanpa melibatkan interaksi antar level.
Pemodelan Geographically Weighted Regression (GWR) pada Prevalensi Severely Stunting di Indonesia Tahun 2023 Idris, Muh Akbar; Nur Aidi, Muhammad
Journal of Mathematics: Theory and Applications Vol 7 No 1 (2025): Volume 7, Nomor 1, 2025
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jomta.v7i1.4873

Abstract

Stunting masih menjadi tantangan kesehatan global yang kritis, terutama di negara berkembang seperti Indonesia, di mana tingkat prevalensi nasional mencapai 20,8% pada tahun 2023. Penelitian ini menggunakan Geographically Weighted Regression (GWR) untuk menganalisis faktor-faktor yang bervariasi secara spasial yang memengaruhi prevalensi stunting parah di berbagai provinsi di Indonesia. Dengan memanfaatkan data dari Survei Kesehatan Dasar Nasional (RISKESDAS) tahun 2013 dan 2018, penelitian ini mencakup variabel seperti pemberian ASI eksklusif, sanitasi rumah tangga, pernikahan dini, imunisasi, dan status sosial ekonomi. Hasil penelitian menunjukkan adanya heterogenitas spasial yang signifikan, dengan determinan utama seperti pemberian ASI eksklusif (X1), sanitasi yang memadai (X3), dan pernikahan di bawah umur (X6) menunjukkan dampak yang bervariasi di berbagai wilayah. Provinsi di Indonesia bagian timur, seperti Papua dan Maluku, menunjukkan prevalensi stunting yang lebih tinggi terkait dengan faktor sosial ekonomi dan lingkungan yang bersifat lokal. Model GWR menunjukkan performa yang lebih baik dibandingkan regresi global, menangkap ketergantungan spasial (Moran’s I = 0.303, p < 0,001) dan menekankan perlunya intervensi yang spesifik untuk setiap wilayah. Rekomendasi kebijakan menekankan perbaikan yang terarah dalam bidang gizi, sanitasi, dan pendidikan untuk mengatasi disparitas dan mencapai target nasional penurunan stunting sebesar 14% pada tahun 2024.Stunting remains a critical global health challenge, particularly in developing countries like Indonesia, where the national prevalence rate was 20.8% in 2023. This study employs Geographically Weighted Regression (GWR) to analyze spatially varying factors influencing severe stunting prevalence across Indonesian provinces. Utilizing data from the 2013 and 2018 National Basic Health Surveys (RISKESDAS), the research incorporates variables such as exclusive breastfeeding, household sanitation, early marriage, immunization, and socioeconomic status. Results reveal significant spatial heterogeneity, with key determinants like exclusive breastfeeding (X1), adequate sanitation (X3), and underage marriage (X6) showing varying impacts across regions. Provinces in eastern Indonesia, such as Papua and Maluku, exhibited higher stunting prevalence linked to localized socioeconomic and environmental factors. The GWR model outperformed global regression, capturing spatial dependencies (Moran’s I = 0.303, p < 0.001) and highlighting the need for region-specific interventions. Policy recommendations emphasize targeted improvements in nutrition, sanitation, and education to address disparities and achieve Indonesia’s national stunting reduction target of 14% by 2024.
Multilevel Regression Analysis on Graduate Student Grade Point Average Riswan, Riswan; Dyah Syafitri, Utami; Nur Aidi, Muhammad
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 12 No. 1 (2024): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v12i1.3969

Abstract

Abstract:Multilevel regression is one of the methods used to analyze hierarchical data structures. One case of data with a hierarchical structure is the cumulative grade point average (GPA) data for students each semester (level one) which is nested within students (level two), and nested within faculties (level three). This study produced the three best three-level regression models: the multilevel regression model, the multilevel regression model with natural logarithmic transformation, and the multilevel binary logistic regression model. The multilevel regression model and the multilevel regression model with natural logarithmic transformation at a significant level of 5%, have the same variables that affect student GPA scores, including semesters, credits, gender, scholarships, and marital status with the same interaction effect, namely semester interactions with scholarships. In addition, the ICC values by the two models are also the same which explains that 91% of the total diversity of student GPA comes from the student level and 8% comes from the faculty level. For the multilevel binary logistic regression model, all explanatory variables affect GPA without involving interaction between levels. Abstrak:Regresi multilevel merupakan salah satu metode yang digunakan untuk menganalisis struktur data hirarkhi. Salah satu kasus data dengan struktur hirarki adalah data indeks prestasi kumulatif (IPK) mahasiswa tiap semester (level satu) yang tersarang dalam mahasiswa (level dua), tersarang dalam fakultas (level tiga). Dalam penelitian ini menghasilkan tiga model regresi tiga level terbaik yaitu model regresi multilevel, model regresi multilevel dengan transformasi logaritma natural, dan model regresi logistik biner multlevel. Model regresi multilevel dan model regresi multilevel dengan transformasi logaritma natural pada taraf nyata 5%, memiliki peubah sama yang berpengaruh terhadap nilai IPK mahasiswa antara lain semester, SKS, jenis kelamin, beasiswa, dan status nikah dengan pengaruh interaksi yang sama yaitu interaksi semester dengan beasiswa. Selain itu, nilai ICC oleh kedua model tersebut juga sama yang menjelaskan bahwa 91% total keragaman IPK mahasiswa berasal dari level mahasiswa dan 8% berasal dari level fakultas.  Untuk model regresi logistik biner multilevel semua peubah penjelas berpengaruh terhadap IPK tetapi tanpa melibatkan interaksi antar level.
Identifying the Characteristics of Pregnant Women with Inflammation/Infection in Indonesia Nur Aidi, Muhammad; Efriwati, Efriwati; Suryanty, Santy; Rahman, La Ode Abdul; Nurfadilah, Khalilah; Ernawati, Fitrah
Jurnal Gizi dan Pangan Vol. 17 No. 3 (2022)
Publisher : The Food and Nutrition Society of Indonesia in collaboration with the Department of Community Nutrition, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.816 KB) | DOI: 10.25182/jgp.2022.17.3.177-186

Abstract

Infection in pregnant women is common and one of the highest causes of death in Indonesia. Reducing infection conditions through early infection prevention needs to be done, one of which is by knowing the characteristics that contribute to the incidence of infection in pregnant women in Indonesia. This study used the Classification and Regression Tree (CART) method to determine the pregnant women with infections and not infections characteristics and classify them. The results of the CART analysis found that seven variables contributed to separating infected and not-infected status in pregnant women, they are nutritional status based on Body Mass Index (BMI), history of anemia, pregnancy distance, Chronic Energy Deficiency (CED) status, ages, socioeconomic and gestational age. Characteristics of the highest incidence of infection, namely 79%, occurred in the group of pregnant women with overweight – obese (BMI>25.0), anemia and pregnancy distance <3 years. The classification analysis of the CART method in this study resulted in the accuracy of identification performance which was still not good, with an accuracy value of 52.78%. It is necessary analysis with other classification methods such as the Chi-square Automatic Interaction Detection (CHAID) in the future.
Co-Authors Efriwati Efriwati Fitrah Ernawati Idris, Muh Akbar La Ode Abdul Rahman Nurfadilah, Khalilah Riswan Riswan Suryanty, Santy Utami Dyah Syafitri