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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal Ibriez : Jurnal Kependidikan Dasar Islam Berbasis Sains BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM Variance : Journal of Statistics and Its Applications Jurnal Varian Proceedings of Annual Conference for Muslim Scholars Jurnal Statistika dan Aplikasinya Parameter: Journal of Statistics WISDOM: Jurnal Pendidikan Anak Usia Dini Journal of Research Applications in Community Services Eksponensial STATISTIKA

M. Fathurahman

Universitas Mulawarman

Author-ID : 1288306

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

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Klasifikasi Penyakit Tuberkulosis Menggunakan Metode Naive Bayes (Studi Kasus: Data Pasien Di Puskesmas Petung Kabupaten Penajam Paser Utara) Abidin, Ahmad Aliful; Goejantoro, Rito; Fathurahman, M.
EKSPONENSIAL Vol. 14 No. 1 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The Naive Bayes method is one of the data mining methods used in classifying data and predicting future opportunities based on experience or previous data. This method was proposed by British scientist Thomas Bayes using a branch of mathematics known as probability theory. One of the diseases that can be detected using the classification using the Naive Bayes method is Tuberculous (TB). Tuberculous is an infectious respiratory disease caused by the bacterium Mycobacterium Tuberculosis. The purpose of this study was to determine the results and accuracy of the classification of Tuberculous disease using the Naive Bayes method in one of the health service units, namely Puskesmas Petung, Penajam Paser Utara. The results showed that data mining classification using the Naive Bayes method was appropriate in classifying Tuberculous. For training and testing data, divided into 90:10, the accuracy rate is 87.5%, categorized as Excellent Classification. As for the training and testing data divided into 70:30, the accuracy rate is 90.9%, classified as Excellent Classification.

Pemodelan Regresi Spasial Data Panel: Studi Kasus : Indeks Pembangunan Manusia di Provinsi Kalimantan Timur Menurut Kabupaten/Kota Tahun 2017-2020 Murdani, Endah Mulia; Fathurahman, M; Goejantoro, Rito
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Panel data is a combination of cross-section data and time-series data. The panel data regression can model the panel data. In its development, panel data regression has been developed to model spatial data, called panel data spatial regression. Spatial data is data that considers the empirical observations and considers the location factor of these observations. This study examines the spatial regression modeling of panel data and applies it to model the factors that influence the Human Development Index (HDI) of districts/cities in East Kalimantan Province from 2017 to 2020. HDI is a composite index that measures the average achievement in the three basic dimensions of human development that are considered very basic, namely life expectancy, knowledge, and a decent standard of living. HDI is one of the measuring tools considered to reflect the status of human development in a region and plays an essential role in improving the quality of human resources. The results show that the panel data spatial regression model suitable for modeling the HDI of districts/cities in East Kalimantan Province from 2017 to 2020 is the Spatial Autoregressive Fixed Effect (SAR-FE) model. The rate of economic growth and the district/city minimum wage factors that significantly influence the HDI of districts/cities in East Kalimantan Province from 2017 to 2020 based on the SAR-FE model is the rate of economic growth and the district/city minimum wage. Keywords : Panel Data, Spatial Data, Panel Data Spatial Regression, SAR-FE, HDI

Pemodelan Jumlah Kasus Tuberkulosis Paru di Indonesia dengan Geographically Weighted Negative Binomial Regression Putri, Ditha Reginna; Fathurahman, M.; Suyitno, Suyitno
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1303

Geographically Weighted Negative Binomial Regression (GWNBR) model is the development of Negative Binomial Regression (NBR) model applied to spatial data. The parameter estimation of GWNBR model is performed at each observation location using spatial weighting. The purpose of this study is to determine the GWNBR model of the number of pulmonary tuberculosis cases in Indonesia in 2021 and determine the factors that influence pulmonary tuberculosis cases in Indonesia in 2021. The research data are secondary data obtained from the Indonesian Ministry of Health and Indonesian Central Agency on Statistics. Parameter estimation method is Maximum Likelihood Estimation (MLE). Spatial weighting is calculated by using the Adaptive bi-square weighting function and the optimum bandwidth is determined by using the Cross-Validation (CV). The research results showed that the exact Maximum Likelihood (ML) estimator could not be obtained analytically and the approximation of ML estimator was obtained by using the Newton-Raphson iterative method. Based on the results of the parameter testing of GWNBR model, it was concluded that the factors affecting the number of tuberculosis cases were local and varied in 34 provinces. The factor affecting locally are population density, the percentage of districts/cities implementing GERMAS, and number of hospitals.

Regresi Binomial Negatif untuk Memodelkan Kematian Bayi di Kalimantan Timur Fathurahman, M
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Negative Binomial Regression (NBR) is an alternative regression model to model the relationship between the dependent variable in overdispersion count data and one or more independent variables. Overdispersion is a problem in Poisson regression modeling. Namely, the variance of the dependent variable is more than the mean. If there is overdispersion, then the parameter estimator of the Poisson regression model has a standard error value that is not under-estimated. The NBR model was applied to modeling infant mortality in East Kalimantan in 2019. Data on infant mortality in East Kalimantan in 2019 indicated overdispersion. Infant mortality is an indicator that can measure the progress of development outcomes in the health sector in a region. In the last three years, from 2017 to 2019, infant mortality data in East Kalimantan has increased. Therefore, it is necessary to do modeling to get the factors that cause it. The modeling results with NBR show that the percentage of the complete neonatal visit of KN3, the percentage of infant health services, and the percentage of visits by pregnant women K4 significantly affect infant mortality in East Kalimantan in 2019.

Prediksi Ketepatan Klasifikasi Status Predikat Lulusan Program Sarjana FMIPA Universitas Mulawarman Menggunakan Regresi Logistik Biner dan Neural Networks Khasanah, Lisa Dwi Nurul; Fathurahman, M.; Hayati, Memi Nor
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1301

Classification is a learning technique for identifying categorical groups from a data set whose group member categories are known. Several methods that can be used in classification include binary logistic regression and neural networks. This research aims to compare the prediction results for the accuracy of the classification of predicate status for graduates of the FMIPA Mulawarman University undergraduate program in 2021. In the binary logistic regression method, the model parameters are estimated using the maximum likelihood estimation and Fisher scoring iteration methods. The neural networks used the backpropagation algorithm. The results of the research show that the classification accuracy using the confusion matrix obtained with binary logistic regression and neural networks is the same, namely 87.5%.

Penerapan Algoritma Divisive Analysis dalam Pengelompokan Provinsi di Indonesia Berdasarkan Prevalensi Stunting Suyono, Ari Krisna; Hayati, Memi Nor; Siringoringo, Meiliyani; Prangga, Surya; Fathurahman, M.
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1341

Cluster analysis is an analysis that aims to group data (objects) based only on the information contained in the data that describes objects and the relationships between the objects. Divisive analysis is a clustering method using a top-down approach which starts by placing all objects into one cluster or what is called a hierarchical root and then dividing the cluster root into several smaller clusters. This research aimed to group 34 provinces in Indonesia into 2,3, and 4 clusters based on stunting prevalence data and factors causing stunting in 2022 using a divisive analysis algorithm. The results showed that for 2 clusters, cluster 1 consisted of 32 provinces with low stunting prevalence, and cluster 2 consisted of 2 provinces with high stunting prevalence. For 3 clusters, cluster 1 consisted of 26 provinces with moderate stunting prevalence, cluster 2 consisted of 6 provinces with low stunting prevalence, and cluster 3 consisted of 2 provinces with high stunting prevalence. For 4 clusters, cluster 1 consisted of 21 provinces with moderate stunting prevalence, cluster 2 consisted of 5 provinces with low stunting prevalence, cluster 3 consisted of 6 provinces with high stunting prevalence, and cluster 4 consisted of 2 provinces with very high stunting prevalence.

Peramalan Curah Hujan Di Kota Samarinda Menggunakan Vector Error Correction Model Astafira, Ilyas; Siringoringo, Meiliyani; Fathurahman, M.
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1377

The Vector Error Correction Model (VECM) was one of the multivariate time series models that was a development of the Vector Autoregressive (VAR). VECM could be used to forecast non-stationary time series variables that had cointegration relationships. This study used monthly data of rainfall, minimum air temperature, and maximum air humidity variables from January 2015 to December 2023 to form the VECM model. The purpose of this study was to obtain a VECM model for rainfall in the city of Samarinda and to forecast rainfall in the city of Samarinda using VECM. The results of the study showed that the VECM model that formed was VECM(1) with two cointegration relationships. The rainfall forecasted results with VECM(1) indicated a downward trend until April 2024 and a horizontal pattern from May to December, with the highest rainfall in January at 214 mm and the lowest rainfall in April at 182.5 mm. The forecasted results ranged between 180-300 mm, which was categorized as moderate, with forecasting accuracy using a MAPE value of 32.369%, which was considered quite good.

Analisis Klaster Menggunakan Metode Average Linkage dengan Validasi Multiscale Bootstrap (Studi Kasus: Indikator Pendidikan di Indonesia Tahun 2021) Rasidia, Fikri; Roejantoro, Rito; Fathurahman, Muhammad
EKSPONENSIAL Vol. 16 No. 1 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v16i1.1392

The average linkage method is one of the hierarchical cluster analyses, where the clustering process starts by finding two objects that have the closest distance to the average rule of the two groups. The multiscale bootstrap method is a method used to see the validity of the cluster analysis results. This study aims to determine the clusters formed using the average linkage method, as well as to determine the validity of the clusters formed based on education indicators in each province in Indonesia. The result of the study is one cluster with AU (Approximately Unbiased) ≥ 0.95 so that the cluster is considered to be able to represent the actual population.

Klasifikasi Data Pasien Penyakit Tuberkulosis Paru Menggunakan Metode Probabilistic Neural Network (Studi Kasus: Puskesmas Telaga Sari Kota Balikpapan) Ramadhini, Laila Thalia; Fathurahman, M.; Wulan Sari, Nariza Wanti
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v16i2.1514

Pulmonary tuberculosis is an infectious disease that remains a major health problem in Indonesia. Early detection of this disease is very important to improve the effectiveness of treatment and prevention of its spread. The purpose of this study is to classify laboratory test data of pulmonary tuberculosis patients using the Probabilistic Neural Network method. The data used are medical records of patients with pulmonary tuberculosis disease at Puskesmas Telaga Sari, Balikpapan City in 2023-2024. The variables used are age, weight, systolic blood pressure, diastolic blood pressure, cough duration, fever duration, shortness of breath, and loss of appetite. The classification process involves the stages of encoding, data normalization, division of training data and testing data using a proportion of 80:20, and calculation of accuracy using confusion matrix. The results showed that classification using the Probabilistic Neural Network method was appropriate in classifying pulmonary tuberculosis disease and obtained the best smoothing parameter ( ) value of 0.1 with an accuracy value of 82.95% for training data and 95.45% for testing data.

Prediksi Curah Hujan di Kabupaten Berau Menggunakan Support Vector Regression Patiallo, Monalisa Randang; Fathurahman, M.; Prangga, Surya; Nadhilah Widyaningrum, Erlyne
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/v16i2.1508

Machine learning is an analytical approach that is able to predict the output of a system based on patterns that have been formed from previous data. One of the machine learning methods used in this research is Support Vector Regression (SVR). SVR is the application of the support vector machine method in the case of regression. The concept of the SVR algorithm is to obtain a function with the minimum error rate so as to produce a good predictive value. The advantage of SVR lies in its ability to handle nonlinear data using the kernel functions. This study aims to determine the results of rainfall prediction in Berau Regency using the SVR method. The data used is rainfall data in Berau Regency from January 2014 to December 2023 as much as 120 data, and uses five predictor variables namely temperature, humidity, air pressure, wind speed, and solar irradiation. The kernel function used is a polynomial kernel with parameter values and . The results showed that the best SVR model to predict rainfall in Berau Regency is the SVR model with parameter values and . This model provides good prediction performance, with an RMSE value of 0,1786.

Co-Authors Abda Abda Abidin, Ahmad Aliful Aisha, Putri Andrea Tri Rian Dani Annisa Zuhri Apridayanti Apridayanti, Annisa Zuhri Arif, Zainul Astafira, Ilyas Dani, Andrea Tri Rian Darnah Darnah, Darnah Dini Elizabeth Erlyne Nadhilah Widyaningrum Fachrian Bimantoro Putra Fauziyah, Meirinda Goenjatoro, Rito Hasanah, Siti Hadijah Ika Purnamasari Khairun Nida Khasanah, Lisa Dwi Nurul Meiliyani Siringoringo Meirinda Fauziyah Memi Nor Hayati Memi Nor Hayati Muh. Irfan Murdani, Endah Mulia Nariza Wanti Wulan Sari Ni'matuzzahroh, Ludia Ningsih, Eva Lestari Nurmayanti, Wiwit Pura Oroh, Chiko Zet Orvanita Patiallo, Monalisa Randang Putra, Fachrian Bimantoro Putri, Ditha Reginna Ramadhini, Laila Thalia Rasidia, Fikri Ratna Nila Puspitasari Regita Putri Permata Rito Goejantoro, Rito Roejantoro, Rito Rut Esra Sekar Nur Utami Sifriyani Sifriyani Sinaga, Julia Oriana Siringoringo, Meiliyani Surya Prangga Suryaningsih, Ruliq Suyitno Suyitno Suyitno Suyitno Suyono, Ari Krisna Syamsiar, Syamsiar Umi Kalsum Yusrian Paliling

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