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Pemodelan Stunting dan Gizi Kurang di Kabupaten Bone Bolango menggunakan Regresi Poisson Generalized Zubedi, Fahrezal; Oroh, Franky Alfrits; Aliu, Muftih Alwi
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 6, No 2 (2021): September 2021 - Februari 2022
Publisher : Universitas Pesantren Tinggi Darul Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/jmpm.v6i2.2507

Tujuan penelitian ini adalah untuk menentukan model kasus Stunting dan Gizi Kurang dengan Regresi Poisson Generalized dan faktor-faktor yang berpengaruh terhadap kejadian tersebut. Analisis Data menggunakan Regresi Poisson Generalized karena untuk menangani masalah overdispersi pada data. Hasil yang diperoleh yaitu variabel yang berpengaruh signifikan terhadap kejadian Stunting 2018 adalah Jumlah penduduk miskin dan untuk kejadian Stunting 2019 adalah Persentase balita diberi ASI eksklusif dan Jumlah penduduk miskin. Variabel yang berpengaruh signifikan terhadap kejadian Gizi Kurang 2018 adalah Persentase balita diberi ASI eksklusif dan Jumlah bayi mendapatkan vitamin A dan untuk Gizi Kurang tahun 2019 adalah variabel Persentase balita diberi ASI eksklusif dan Persentase berat badan lahir rendah.

KLASIFIKASI TINGKAT PENGANGGURAN TERBUKA DI PULAU JAWA MENGGUNAKAN REGRESI LOGISTIK ORDINAL Indah, Yunna Mentari; Fitrianto, Anwar; Erfiani, Erfiani; Indahwati, Indahwati; Aliu, Muftih Alwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.629

Unemployment is one of the indicators for measuring the economic conditions of a region. It is also a social and economic problem in many countries, including Indonesia, especially in areas with a density of economic activity, such as Java Island. The purpose of this study was to classify and analyze the factors that affect the open unemployment rate in cities and regions on Java Island, which are categorized as low, medium, and high. The research method used in this study was ordinal logistic regression analysis. The data source comes from the BPS website in 2023 with four predictor variables: population size, labor force participation rate, average years of schooling, and gross regional domestic product at constant prices. The research results show that the variables population size and labor force participation rate had a significant effect on the open unemployment rate, while the variables average years of schooling and gross regional domestic product at constant prices did not have a significant effect on the open unemployment rate with the accuracy of the ordinal logistic model is 77.27%.

MODEL KLASIFIKASI REGRESI LOGISTIK BINER UNTUK LAPORAN MASYARAKAT DI OMBUDSMAN REPUBLIK INDONESIA Daswati, Oktaviyani; Indahwati, Indahwati; Erfiani, Erfiani; Fitrianto, Anwar; Aliu, Muftih Alwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.702

A classification model is needed to predict data into the right class according to the pattern of previous data. Binary Logistic Regression can be used in building classification models, even though the independent variables are categorical scale data. Through binary logistic regression, it can also be seen which category of independent variables influences the response variable. Public complaint reports at the Ombudsman of the Republic of Indonesia are classified into reports that found maladministration and not. The Binary Logistic Regression model with several categorical independent variables related to the public complaint reports data applied resulted in a classification model with an overall classification accuracy of 66.08% and a sensitivity of 75.31% in estimating the presence of maladministration findings in the submitted public complaint reports. Based on the 95% confidence level of the model, it is known that the factors that influence the occurrence of maladministration are the Group of Reportees, the Substance of the Report, the Method of Submission, the Request for Confidentiality, and the Location of the Inspection Office. This model can be used as a reference to reduce the incidence of maladministration cases in public service providers by focusing socialization and education on categories that have a real influence on each of these factors

Pemodelan Tingkat Kecanduan Games Online Menggunakan Regresi Logistik Ordinal Hidayah, Nur; Indahwati; Fitrianto, Anwar; Erfiani; Aliu, Muftih Alwi
MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika Vol. 5 No. 1 (2024): MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika
Publisher : Universitas Tidar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31002/mathlocus.v5i1.4335

Analisis regresi yang digunakan untuk memodelkan hubungan antara variabel prediktordan variabel respon yang berskala ordinal disebut regresi logistik ordinal. Data ini diperoleh darisurvei yang dilakukan oleh peneliti sebelumnya untuk mengukur tingkat kecanduan games onlinedengan menggunakan pemodelan matematika PEAR. Kecanduan games online menjadifenomena yang semakin mengkhawatirkan di era digital ini, dengan dampak negatif yangsignifikan pada aspek sosial, psikologis, dan akademik. Penelitian ini bertujuan untukmemodelkan model prediktif dengan mengukur tingkat kecanduan games online melalui aplikasiregresi logistik ordinal. Model ini mempertimbangkan beberapa variabel prediktor, yaitu umur,durasi bermain games, durasi bermain per hari, dan jenis games. Regresi logistik ordinaldigunakan karena variabel responnya, yaitu tingkat kecanduan bermain games, bersifat ordinaldan terdiri dari lebih dari dua kategori yang berurutan. Model ini menunjukkan akurasi sebesar92,5%, yang mengindikasikan kemampuan model dalam mengklasifikasikan tingkat kecanduanbermain games online dengan keandalan yang tinggi.

Comparison of Ordinal Logistic Regression and Geographically Weighted Ordinal Logistic Regression (GWOLR) in Predicting Stunting Prevalence among Indonesian Toddlers Setyowati, Silfiana Lis; Indahwati; Fitrianto, Anwar; Erfiani; Aliu, Muftih Alwi
Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Vol. 21 No. 2 (2024): Sainmatika : Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam
Publisher : Universitas PGRI Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31851/sainmatika.v21i2.15416

Ordinal logistic regression is a type of logistic regression used for response variables with an ordinal scale, containing two or more categories with levels between them. This method is an extension of logistic regression where the observed response variable is ordinal with a clear order. It addresses spatial effects that can cause variance heterogeneity and improve parameter estimation accuracy compared to logistic regression. Geographically Weighted Regression (GWR) is a statistical analysis technique designed to account for spatial heterogeneity. GWOLR is an extension of OLS and GWR models that incorporates spatial elements into regression with categorical variables. This study compares the effectiveness of OLR and GWOLR in analyzing stunting prevalence in toddlers. Comparing OLR and GWOLR can help assess the spatial impact on stunting prevalence. This analysis could reveal that certain regions have a higher tendency for stunting prevalence, while others might have lower tendencies, thus helping in understanding regional disparities. Toddler height is a key indicator of health and nutrition in early growth. The prevalence of stunting for toddlers, according to WHO, is categorized into four levels: low, moderate, high, and very high. The Ordinal Logistic Regression model is better suited for modeling toddler stunting prevalence in Indonesia than the GWORL model. The Ordinal Logistic Regression model and the GWOLR both have a classification accuracy of 85.7%, but the OLR model has a lower AIC value. The GWOLR model is not suitable for analyzing stunting prevalence among Indonesian toddlers due to the lack of spatial variability in the data. The Breusch-Pagan test results indicate that there is no spatial heterogeneity in the data on stunting prevalence among Indonesian toddlers, as the p-value is less than the significance level of 0.05. The prevalence of undernourished toddlers is the main factor influencing stunting among Indonesian toddlers.

Pemodelan Stunting dan Gizi Kurang di Kabupaten Bone Bolango menggunakan Regresi Poisson Generalized Zubedi, Fahrezal; Oroh, Franky Alfrits; Aliu, Muftih Alwi
JMPM: Jurnal Matematika dan Pendidikan Matematika Vol 6 No 2 (2021): September 2021 - February 2022
Publisher : Prodi Pendidikan Matematika Universitas Pesantren Tinggi Darul Ulum Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/jmpm.v6i2.2507

Tujuan penelitian ini adalah untuk menentukan model kasus Stunting dan Gizi Kurang dengan Regresi Poisson Generalized dan faktor-faktor yang berpengaruh terhadap kejadian tersebut. Analisis Data menggunakan Regresi Poisson Generalized karena untuk menangani masalah overdispersi pada data. Hasil yang diperoleh yaitu variabel yang berpengaruh signifikan terhadap kejadian Stunting 2018 adalah Jumlah penduduk miskin dan untuk kejadian Stunting 2019 adalah Persentase balita diberi ASI eksklusif dan Jumlah penduduk miskin. Variabel yang berpengaruh signifikan terhadap kejadian Gizi Kurang 2018 adalah Persentase balita diberi ASI eksklusif dan Jumlah bayi mendapatkan vitamin A dan untuk Gizi Kurang tahun 2019 adalah variabel Persentase balita diberi ASI eksklusif dan Persentase berat badan lahir rendah.

The Comparison A-Optimal and I-Optimal Design in Non-Linear Models to Increase Purity Levels Silicon Dioxide Aliu, Muftih Alwi; Syafitri, Utami Dyah; Fitrianto, Anwar; Irzaman, Irzaman
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26253

One of the obstacles that arise in optimal design is the non-linear model. The relationship between temperature factors and the temperature increase rates with the purity of silicon dioxide (SiO2) forms a non-linear pattern. Determining the optimal design for a non-linear model is relatively more complex than a linear model because it requires additional information in its information matrix. Therefore, this issue necessitates further research on optimal design in non-linear models. This study uses the polynomial Taylor approach to approximate the non-linear equation through a linear equation using the appropriate optimal design methods, namely A-Optimal and I-Optimal criterion. The point search algorithm used was variable neighborhood search, this algorithm searches for design points by exploring several different neighborhood structures. These two methods were chosen to compare the characteristics and performance of the designs produced, aiming to obtain an optimal design to improve SiO2 purity (non-linear case) using the same algorithm, VNS. The research results showed that the design pattern produced by the A-Optimal design formed three temperature groups, namely the minimum temperature of 800°C - 820°C, the middle temperature of 850°C, 860°C, and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The design pattern produced by the I-Optimal design formed a full quadratic pattern, namely the minimum temperature of 800°C and the maximum temperature of 900°C, with varying temperature increase rates in the design area. The I-Optimal design demonstrated the best performance (most optimal) in the aspect of prediction variance compared to the A-Optimal design across all alternative points in this study to improve SiO2 purity.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI STUNTING PADA BALITA DI KOTA GORONTALO MENGGUNAKAN REGRESI BINOMIAL NEGATIF ZUBEDI, FAHREZAL; ALIU, MUFTIH ALWI; RAHIM, YOLANDA; OROH, FRANKY ALFRITS
Jambura Journal of Probability and Statistics Vol 2, No 1 (2021): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v2i1.10284

This study aims to model stunting cases in children under five in Gorontalo city in 2018. In this model, it can be seen that the significant factors that affect stunting cases in children under five in Gorontalo city in 2018. This study uses data on stunting cases in 9 (nine) districts in the city of Gorontalo and the factors that influence it. The research data were obtained from the Public Health in Gorontalo city. This study used one response variable, namely the number of cases of stunting and four predictor variables, namely number of toddlers who received exclusive breastfeeding, the percentage of low birth weight (LBW), the percentage toddlers who received complete basic immunization, and number of proper sanitation. The results obtained were the variables of number of toddlers who received exclusive breastfeeding and the percentage toddlers who received complete basic immunization which had a significant effect on stunting cases in children under five in the city of Gorontalo in 2018. This was indicated by the P-value of the variable for number of toddlers who received exclusive breastfeeding of 0.00283 and P-value of variable the percentage toddlers who get complete basic immunization is 0.06564.

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