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All Journal Indonesian Journal of Applied Statistics Jurnal Pendidikan Matematika
Mufid, Nadiya Azhar
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Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Mathematics Other

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Clustering Kabupaten/Kota di Jawa Tengah Tahun 2022 berdasarkan Jumlah Kasus Kemunculan Penyakit dengan Algoritma K-Means Mufid, Nadiya Azhar; Hanum, Derra Risqi Nurulita; Sidiq, Aldy Hasbiya
Jurnal Pendidikan Matematika Vol. 1 No. 1 (2023): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v1i1.107

Abstract

This research aims to conduct clustering or grouping of Regencies/Cities in Central Java Province based on the number of occurrences of specific diseases in 2022 using the K-Means algorithm. The research results obtained 3 clusters, namely high, medium, and low for 29 Regencies and 6 Cities. The percentage for cluster 1 is 34.29%, consisting of 10 regencies and 2 cities, cluster 2 is 40.00%, consisting of 11 regencies and 3 cities, and cluster 3 is 25.71%, consisting of 8 regencies and 1 city. These clustering results can be used as a basis for making effective strategic decisions in the development of prevention and control efforts for diseases in each region.
Klasifikasi Besar Potensi Kemunculan Batu Ginjal Menggunakan Fuzzy Inference System (FIS) Metode Mamdani Mufid, Nadiya Azhar
Jurnal Pendidikan Matematika Vol. 1 No. 1 (2023): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v1i1.110

Abstract

The phenomenon of modernization that arises in the lives of society has an impact on unhealthy lifestyles. This has led to an increased prevalence of kidney stone disease in recent decades. In this study, a fuzzy inference system will be developed using the Mamdani method for the classification of the large potential occurrence of kidney stones based on urine conditions. The input variables used are urine pH, urea content in urine, and calcium content in urine, while the output variable used is the magnitude of the potential occurrence of kidney stones. The variables included in the fuzzy set are formed by combinations of each variable with its respective linguistic terms. These fuzzy sets are then processed with the help of Matlab software to produce a classification result indicating small or large potential. The classification results from the developed fuzzy inference system provide accurate classifications for users with an accuracy level of 94%. Thus, in general, this fuzzy system can be used to assist in classifying the large potential occurrence of kidney stones, thereby minimizing the number of individuals suffering from this disease as much as possible.
Implementasi Matriks Augmented dan Metode Eliminasi Gauss-Jordan untuk Menyelesaikan Masalah GPS (Studi Kasus: Menentukan Posisi Pejalan Kaki yang Tersesat) Mufid, Nadiya Azhar
Jurnal Pendidikan Matematika Vol. 1 No. 1 (2023): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ppm.v1i1.111

Abstract

The Global Positioning System (GPS) is a satellite-based navigation system that enables users to determine their position and time anywhere near the Earth's surface. GPS calculates distance by knowing how long it takes for a radio signal to move from one point to another. Linear algebra, especially augmented matrices and the Gauss-Jordan elimination method, can be applied to solve GPS problems in the case study of a pedestrian lost in the desert. The position of a lost pedestrian in a desert will be determined based on satellite position and time data. The calculation results indicate that the position of the lost pedestrian is in the Bilma desert area, Agadez district, Niger, with coordinates at a longitude of 11.41199 and a latitude of 18.549.
Analisis Kualitas Tidur Penduduk Usia Produktif di Indonesia dengan Model Regresi Logistik Ordinal Mufid, Nadiya Azhar; Kismiantini, Kismiantini
Indonesian Journal of Applied Statistics Vol 7, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i2.86659

Abstract

The prevalence of poor sleep quality in the productive age population in Indonesia is quite high. This study aims to analyze factors that influence the sleep quality level of the productive age population in Indonesia by an ordinal logistic regression model based on the Fifth Indonesian Family Life Survey (IFLS5). In this study, the response variable used is sleep quality with an ordinal scale of 5 categories and 12 predictor variables with 1 continuous predictor variable that is age and 11 categorical predictors including education, job status, smoking habit, health, gender, marital status, physical activity, religious, depression level, life satisfaction, and economic level with data of 28.743 respondents. The results of this study indicated that the ordinal logistic regression model with proportional odds model was more suitable to be used to analyze the sleep quality level of productive age population in Indonesia than non-proportional odds model. Based on the analysis result, it was found that among 12 predictor variables, variables that had a significant effect on sleep quality level were education, job status, smoking habit, health, age, depression level, life satisfaction, and economic.Keywords: IFLS, sleep quality, ordinal regression, productive age population 
Co-Authors Hanum, Derra Risqi Nurulita Kismiantini Kismiantini Sidiq, Aldy Hasbiya