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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM Variance : Journal of Statistics and Its Applications Jurnal Varian Jurnal Statistika dan Aplikasinya Parameter: Journal of Statistics Eksponensial STATISTIKA

M. Fathurahman

Universitas Mulawarman

Author-ID : 1288306

Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Health Professions Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Pemodelan Fungsi Transfer Multi Input M Fathurahman
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 4, No 2 (2009): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Model fungsi transfer merupakan salah satu teknik analisis data dalam analisis deret waktu (time series). Salah satu keunikan dan kelebihan dari model ini adalah ada unsur regresi dalam modelnya. Sehingga model fungsi transfer dapat dikatakan sebagai model yang melibatkan analisis regresi dan analisis time series. Oleh karena itu peneliti tertarik mengkaji pemodelan fungsi transfer. Hasil yang diperoleh diaplikasikan pada peramalan data curah hujan.

Pemilihan Model Regresi Terbaik Menggunakan Metode Akaike’s Information Criterion dan Schwarz Information Criterion M Fathurahman
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 4, No 3 (2009): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Analisis regresi seringkali digunakan untuk mengkaji hubungan antara beberapa variabel dan meramal suatu variabel. Agar diperoleh hasil analisis yang optimal, maka diperlukan model regresi terbaik. Beberapa metode dapat digunakan untuk memilih model regresi terbaik, diantaranya adalah dengan metode Akaike’s Information Criterion (AIC) dan Schwarz Information Criterion (SIC). Penelitian ini bertujuan mengkaji pemilihan model regresi terbaik menggunakan metode AIC dan SIC pada kasus faktor-faktor yang mempengaruhi nilai ujian nasional (UNAS) siswa Sekolah Menengah Kejuruan Negeri (SMKN) 1 Samarinda. Berdasarkan metode AIC model regresi terbaik yang dapat digunakan untuk mengetahui hubungan antara rata-rata nilai UNAS siswa SMKN 1 Samarinda dengan rata-rata nilai tryout (X1), nilai kompetensi (X2) dan rata-rata nilai ujian sekolah (X3) adalah Y = -0,0094 + 0,4541 X1 + 0,2178 X2 + 0,3291 X3. Adapun model regresi terbaik menurut metode SIC adalah Y = 0,4749 + -2,6174 + + 0,5322 X1 + + 0,2636 X3

Pemodelan Indeks Pembangunan Kesehatan Masyarakat Kabupaten/Kota di Pulau Kalimantan Menggunakan Pendekatan Regresi Probit M. Fathurahman
Jurnal Varian Vol 2 No 2 (2019)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v2i2.382

The Public Health Development Index (PHDI) is a composite indicator that describes the progress of health development and is useful for ranking provinces and districts/cities in achieving successful community health development. In addition, the PHDIM can also be used to determine regional priorities that require assistance in improving health development. Based on the publication of the PHDI ranking by the Health Research and Development Agency of the Ministry of Health in 2013, the PHDI ranking for 55 districts/cities in Kalimantan Island varied greatly. So it needs to be studied, examined the factors that influence it. The purpose of this study was to examine the modeling of the factors that influence the PHDI of districts/cities in Kalimantan Island in 2013 using the probit regression approach. The results of this study indicate that the factors that significantly influence the PHDI of districts/cities in Kalimantan Island in 2013 are the human development index and the labor force participation rate.

Regresi Logistik Multinomial untuk Memodelkan Kombinasi antara Status IPKM dan Status IPM Kabupaten/Kota di Pulau Kalimantan Yusrian Paliling; M. Fathurahman; Sri Wahyuningsih
Jurnal Matematika, Statistika dan Komputasi Vol. 19 No. 3 (2023): MAY, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v19i3.22299

Multinomial Logistics Regression (MLR) is a regression model developed from the Binary Logistics Regression (BLR) model. The response variable of the RLM model has three or more categories and has a multinomial distribution, with the data scale being nominal. The response variable in this study is a combination of the Public Health Development Index (PHDI) status and the Human Development Index (HDI) status of districts/cities in Kalimantan Island, 2018, divided into four categories with category one as a comparison. The predictor variables used were the number of the public health center, the percentage of poor people, economic growth, the pure junior high school participation rate, and the percentage of the population with a minimum of junior high school education. The MLR parameter model was estimated using the Maximum Likelihood Estimation (MLE) method and Newton-Raphson iteration. The hypothesis testing of the MLR model was used by the Likelihood Ratio Test (LRT) method and the Wald test. The best model selection in this study uses the backward method, and the interpretation of the best MLR model uses the odds ratio value. The results showed that the best MLR model is a model that has three predictor variables. The factors that significantly influenced the combination of PHDI status and the HDI status of districts/cities in Kalimantan Island in 2018 were the percentage of poor people, economic growth, and the percentage of people with the minimum level of education in junior high school.

MODELING COUNT DATA WITH OVER-DISPERSION USING GENERALIZED POISSON REGRESSION: A CASE STUDY OF LOW BIRTH WEIGHT IN INDONESIA M. Fathurahman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 1 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.1.2023.45-60

Poisson regression is commonly used in modeling count data in various research fields. An essential assumption must be met when using Poisson regression, which is that the count data of the response has the mean and variance must be equal, namely equi-dispersion. This assumption is often unmet because many data for the response that the variance is greater than the mean, called over-dispersion. If the Poisson regression model contains the over-dispersion, then will be produced an invalid model can under-estimate standard errors and misleading inference for regression parameters. Therefore, an approach is needed to overcome the over-dispersion problem in Poisson regression. The generalized Poisson regression can handle the over-dispersion in Poisson regression. This study aims to obtain the generalized Poisson regression model and the factors affecting the low birth weight in Indonesia in 2021. The result shows that the factors affecting the low birth weight in Indonesia based on the generalized Poisson regression model were: poverty rate, percentage of households with access to appropriate sanitation, percentage of pregnant women at risk of chronic energy deficiency receiving additional food, percentage of pregnant women who received blood-boosting tablets, and percentage of antenatal care.

Pemilihan Model Terbaik pada Generalized Poisson Regression Menggunakan Akaike Information Criterion Rut Esra; Darnah Andi Nohe; M Fathurahman
Statistika Vol. 23 No. 1 (2023): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v23i1.1925

ABSTRAK Poisson regression merupakan salah satu model regresi yang dapat digunakan untuk menganalisis hubungan antara variabel respon yang berupa data count dengan variabel prediktor berupa data count, kontinu, kategorik atau campuran dengan syarat terjadi equidispersion yaitu nilai variansi dari variabel respon harus sama dengan nilai rata-ratanya. Namun yang sering terjadi adalah pelanggaran terhadap equidispersion. Generalized Poisson Regression (GPR) adalah suatu model regresi yang digunakan untuk menganalisis hubungan antara variabel respon yang berupa data count dengan satu atau lebih variabel prediktor dan mengalami underdispersion, equidispersion, atau overdispersion. Data tuberkulosis paru (TB paru) di Indonesia tahun 2020 mengalami overdispersion, sehingga GPR adalah metode yang cocok untuk memodelkan data tersebut. Tujuan penelitian ini adalah untuk mendapatkan model GPR terbaik pada data jumlah kasus TB paru di Indonesia tahun 2020 dan mengetahui faktor-faktor yang memengaruhinya. Hasil analisis menunjukkan bahwa terdapat lima belas model GPR yang terbentuk dari empat variabel prediktor yang berpengaruh terhadap jumlah kasus TB paru di Indonesia tahun 2020. Model GPR terbaik berdasarkan nilai Akaike Information Criterion (AIC) terkecil adalah model dengan empat variabel yang berpengaruh yaitu kepadatan penduduk, jumlah penduduk miskin, persentase lantai rumah tidak kedap air, dan persentase tempat pengelolaan pangan yang memenuhi syarat. ABSTRACT Poisson regression is a regression model that can be used to analyze the relationship between response variables in the form of count data and predictor variables in the form of count, continuous, categorical or mixed data with the condition that equidispersion occurs, namely the variance value of the response variable must be equal to the average value. However, what often happens is that the variance value is greater than the average value or is called overdispersion. Generalized Poisson Regression (GPR) is a regression model used to analyze the relationship between response variables in the form of count data and one or more predictor variables and occure underdispersion, equidispersion or overdispersion. Data for pulmonary tuberculosis in Indonesia in 2020 occured overdispersion, so GPR is a suitable method to model the data. The purpose of this study was to obtain the best GPR model and to obtain the factors that significantly influence the number of pulmonary tuberculosis cases in Indonesia in 2020. The results of the analysis show that there are fifteen GPR models formed from four predictor variables that affect the number of pulmonary tuberculosis cases in Indonesia in 2020. The best GPR model based on the smallest Akaike Information Criterion (AIC) value is a model with four influential variables, namely population density, number of poor people, percentage of house floors that are not waterproof, and percentage of food management places that meet the requirements.

Literasi Dasar Melalui Numerasi dan Keuangan Rito Goejantoro; Ika Purnamasari; Memi Nor Hayati; Meiliyani Siringoringo; Darnah Andi Nohe; Muhammad Fathurahman; Surya Prangga; Khairun Nida; Sekar Nur Utami; Dini Elizabeth
Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) Vol 6, No 12 (2023): Volume 6 No 12 2023
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jkpm.v6i12.12705

ABSTRAK Gerakan Literasi Nasional (GLN) merupakan kegiatan yang saat ini diserukan sebagai bentuk penerapan dari peraturan KEMENDIKBUD untuk menumbuhkan budi pekerti masyarakat. Numerasi dan literasi keuangan merupakan dua jenis literasi yang saling terkait. Salah satu dimensi dari literasi keuangan yaitu keterampilan menghitung. Keterampilan ini terkait pemahaman numerik, lambang bilangan dan analisa kuantitatif yang berkenaan dengan statistika dasar dalam dimensi numerasi. Kegiatan ini memiliki tujuan yaitu memberikan informasi dan pengetahuan numerasi dan keuangan kepada peserta dengan cara sederhana, menyenangkan, dan mudah dipahami berdasarkan tema lingkungan sekitar. Hasil penilaian sebelum dan sesudah kegiatan, menunjukkan bahwa adanya peningkatan kemampuan dan pemahaman peserta terkait numerasi dan keuangan, yang terlihat dari kenaikan nilai rata-rata pada saat evaluasi. Untuk kegiatan literasi selanjutnya, materi yang disampaikan dapat ditingkatkan ke jenjang materi lanjutan, serta dapat mengkombinasikan antara numerasi, literasi keuangan, dan digital untuk lebih menarik. Kata Kunci: GLN, KEMENDIKBUD, Literasi, Numerasi, Literasi Keuangan ABSTRACT The National Literacy Movement (GLN) is an activity that is currently called for as a form of application of the regulation of KEMENDIKBUD to foster community ethics. Numeracy and financial literacy are two types of literacy that are interrelated. One dimension of financial literacy is counting skills. This skill is related to numerical understanding, number symbols and quantitative analysis related to basic statistics in the numeracy dimension. This activity has the following objectives is to provide numeracy and financial information and knowledge to participants in a simple, fun, and easy-to-understand way based on the theme of the surrounding environment. The results of the assessment before and after the activity showed an increase in the abilities of participants and understanding related to numeracy and finance, which can be seen through the increase in the average scores at the time of evaluation. For further literacy activities, the material delivered can be upgraded to an advanced level of material, and can combine numeracy, financial literacy, and digital to be more attractive. Keywords: GLN, KEMENDIKBUD, Literacy, Numerasi, Financial Literacy.

K-Means Algorithm for Grouping Provinces in Indonesia Based on Macroeconomic and Criminality Indicators Andrea Tri Rian Dani; Fachrian Bimantoro Putra; Meirinda Fauziyah; Sifriyani Sifriyani; Suyitno Suyitno; M Fathurahman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.12-21

Cluster analysis is a method in multivariate analysis to group n observations into K groups (K ≤ n) based on their characteristics. One of the well-known algorithms in cluster analysis is K-Means. K-Means uses the non-hierarchical principle where at the initial initiation, it is necessary to determine the number of groups in advance. The K-Means algorithm can be applied to classify provinces in Indonesia based on macroeconomic indicators (percentage of poor people, open unemployment rate, and Gini ratio) and crime rate (Crime rate). The ultimate goal of this research is of course to get optimal grouping results. The similarity measure used is Euclidean Distance. The number of groups tested K=2,3,4,…,10 and the optimal number of groups with the highest Silhouette value was selected. Based on the results of the analysis, the optimal number of clusters is four. These four clusters have characteristics that distinguish one cluster from another.

IMPLEMENTASI METODE NEIGHBOR WEIGHTED K-NEAREST NEIGHBOR PADA PENGKLASIFIKASIAN STATUS GIZI BALITA DI WILAYAH KERJA PUSKESMAS WONOREJO KOTA SAMARINDA Putri Aisha; M. Fathurahman; Surya Prangga
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss1page11-20

Neighbor Weighted K-Nearest Neighbor (NWKNN) merupakan salah satu metode klasifikasi dalam machine learning yang dikembangkan dari metode K-Nearest Neighbor (KNN). Metode NWKNN memiliki kelebihan dibanding KNN, terutama untuk menangani masalah jumlah kelas yang tidak seimbang dalam pengklasifikasian data mining. Konsep dari metode NWKNN adalah pemberian bobot yang besar untuk kelas minoritas dan bobot yang kecil untuk kelas mayoritas. Metode NWKNN dalam penelitian ini diterapkan pada pengklasifikasian status gizi balita di Kota Samarinda. Menurut World Health Organization (WHO), Indonesia merupakan salah satu negara dengan prevalensi stunting pada balita tertinggi di regional Asia Tenggara pada Tahun 2005 sampai dengan 2017, dengan rata-rata prevalensi stunting pada balita sebesar 36,4%. Status gizi adalah salah satu faktor penyebab terjadinya stunting pada balita. Penelitian ini bertujuan mendapatkan hasil pengklasifikasian status gizi balita menggunakan metode NWKNN dan nilai akurasinya. Data yang digunakan adalah data balita di wilayah kerja Puskesmas Wonorejo Kota Samarinda Tahun 2022. Berdasarkan hasil klasifikasi status gizi balita menggunakan metode NWKNN, terdapat 93 balita yang diklasifikasikan secara tepat dari 128 balita dengan nilai akurasi sebesar 72,65%. Nilai akurasi ini termasuk cukup baik dan menunjukkan bahwa metode NWKNN layak digunakan untuk memprediksi ketepatan klasifikasi status gizi balita di wilayah kerja Puskesmas Wonorejo, Kota Samarinda. .

Clustreing of Province in Indonesia Based on Education Indicators Using K-Medoids Annisa Zuhri Apridayanti; M Fathurahman; Surya Prangga
Jurnal Varian Vol 7 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3205

Data mining is searching for interesting patterns or information by selecting data using specific techniques or methods. One method that can be used in data mining is K-Medoids. K-Medoids is a method used to group objects into a cluster. This research aimed to obtain the optimal number of clusters using the K-Medoids method based on Davies-Bouldin Index (DBI) validity on education indicators data by province in Indonesia in 2021. The results showed that the optimal number of clusters using the K-Medoids method based on DBI validity is 5 clusters. Cluster 1 consists of 1 province with a higher average dropout rate, average length of schooling, and well-owned classrooms compared to other clusters. Cluster 2 consists of 15 provinces with an average proportion of school libraries lower than Clusters 3 and 4 and higher than Clusters 1 and 5. Cluster 3 consists of 9 provinces with an average proportion of school libraries, proportions of school laboratories, net enrollment rates, and higher school enrollment rates than other clusters. Cluster 4 consists of 8 provinces with a higher average enrollment rate than the other clusters. Cluster 5 consists of 1 province with a higher average repetition rate and student-per-teacher ratio than other clusters.

Co-Authors Abidin, Ahmad Aliful Andrea Tri Rian Dani Annisa Zuhri Apridayanti Arif, Zainul Astafira, Ilyas Dani, Andrea Tri Rian Darnah Darnah, Darnah Dini Elizabeth Fachrian Bimantoro Putra Hasanah, Siti Hadijah Ika Purnamasari Khairun Nida Khasanah, Lisa Dwi Nurul Meiliyani Siringoringo Meirinda Fauziyah Memi Nor Hayati Memi Nor Hayati Murdani, Endah Mulia Ni'matuzzahroh, Ludia Ningsih, Eva Lestari Nurmayanti, Wiwit Pura Orvanita Putra, Fachrian Bimantoro Putri Aisha Putri, Ditha Reginna Rasidia, Fikri Regita Putri Permata Rito Goejantoro, Rito Roejantoro, Rito Rut Esra Sekar Nur Utami Sifriyani Sifriyani Sinaga, Julia Oriana Siringoringo, Meiliyani Sri Wahyuningsih Sri Wahyuningsih Surya Prangga Suyitno Suyitno Suyitno Suyitno Suyono, Ari Krisna Yusrian Paliling

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