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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 Jambura Journal of Mathematics Variance : Journal of Statistics and Its Applications ESTIMASI: Journal of Statistics and Its Application Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal Statistika dan Komputasi (STATKOM) Eksponensial

Rito Goejantoro, Rito

Statistics Study Program, Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Mulawarman University, Samarinda

Author-ID : 2469461

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

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Estimasi Parameter Model Regresi Linier dengan Pendekatan Bayes Katianda, Kristin Rulin; Goejantoro, Rito; Satriya, Andi M Ade
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Two types of viewpoints in statistics are Frequentist and Bayesian Method. In Bayesian method sees a parameter as a random variable, so the value is not single. Frequentist method that are often used in linear regression are Ordinary Least Square (OLS) and Maximum Likelihood Estimation (MLE). But along with developments, several studies show the results of modeling that are better at using Bayesian method than the Frequentist method. The data used is Poverty data in 2017 from BPS East Kalimantan. The purpose of this study is to estimate the parameters of the regression model with the Bayesian method on data on the number of poor people and regional domestic products in East Kalimantan Province in 2017. To estimate the parameters of the Bayesian linear regression model it is used by the prior conjugate distribution. Then the markov chain is designed from the posterior distribution with Gibbs Sampler as many as 50.000 iterations and the estimated parameters that are the average of the Gibbs Sampler value are = 0.9149, = 5.462, and = 0.2827. From the Gibbs Sampler values that have been obtained, a density function for each parameter is generated so that the Bayesian confidence interval (credible interval) for estimation is (0.85; 0.9836), (4.484; 6.439) and (0.2694 ; 0,296) for parameters .

Pemodelan Regresi Spasial Data Panel 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

Penerapan Metode Klasifikasi Multinomial Naive Bayes Rinaldi, Rival; Goejantoro, Rito; Syaripuddin, Syaripuddin
EKSPONENSIAL Vol 12 No 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Life insurance is a risk management service provide payment to policyholders in the event of a disaster that has been stipulated in the agreement. A classification system needs to be done to facilitate the company in making decisions to provide policies to customers. One system that can be used is multinomial Naive Bayes. Multinomial Naive Bayes is a simple probabilistic classification that has more than two groups or categories. An algorithm using Bayes theorem assumes all independent variables. The aim of this study is to obtain an accuracy level of 5 different proportions with the Naive Bayes multinomial method used in insurance customer payment status data. The data used is the customer data of PT. Prudential Life Samarinda in 2019 with the status of current premium payment, substandard and non-current and using 5 independent variables, namely income, age, amount of premium payment, sex and employment. The results of the measurement of classification accuracy using APER status premium payment on insurance customer data of PT. Prudential Life 2019 Naive Bayes multinomial method showed 22,96% misclassification at 50:50 proportion, at the proportion of 60:40 there were 21,43% misclassification, at the proportion of 70:30 there were 19,05% misclassified, at proportions 80:20 had a misclassification of 14,29%, and a proportion of 90:10 has a misclassification of 7,14%.

Penerapan Metode Klasifikasi Chi-Square Automatic Interaction Detection dan Exhaustive Chi-Square Automatic Interaction Detection Nurhasanah, Nurhasanah; Goejantoro, Rito; Suyitno, Suyitno
EKSPONENSIAL Vol 13 No 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The Chi-Square Automatic Interaction Detection (CHAID) and Exhaustive CHAID methods are nonparametric statistical methods that can be used to classify. CHAID and Exhaustive CHAID were used to determine the significant relationship between the dependent variable and the independent variables based on the chi-square independence test. This study was applied to data on the study period of students of FMIPA UNMUL batch 2014. Based on the CHAID and Exhaustive CHIAD methods, it can be seen that the dependent variable of the study period has a significant relationship with the independent variable, namely the study program and GPA predicate. Where students who graduated on time for the Statistics, Biology and Chemistry study program with a satisfactory GPA predicate of 82 students and with a very satisfactory GPA predicate and cum laude with 46 students. Meanwhile, students who did not graduate on time for the Statistics, Biology and Chemistry study program with an adequate GPA predicate of 5 students, a satisfactory GPA predicate of 41 students, very satisfactory and cum laude with 3 students. Students who graduated on time for the Physics study program were 13 students and those who did not graduate on time were 34 students. The chi-square independence test performed on the CHAID method uses fewer possible categorical pairs than the Exhaustive CHAID method which uses all possible categorical pairs so that it requires a long computational and calculation time.

Perbandingan Metode Klasifikasi Naive Bayes dan K-Nearest Neighbor Novalia, Viona; Goejantoro, Rito; Sifriyani, Sifriyani
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Classification is a technique to build a model and assess an object to put in a particular class. Naive Bayes is one of algorithm in the classification based on the Bayesian theorem, which assumes the independencies of one class with another class. K-nearest neighbor is an algorithm in the classification method for classifiying based on data that has a closest distance between one object and another object. Naive Bayes and k-nearest neighbor methods are used in classification of the employment status of citizen in Kutai Kartanegara regency because has a good accuracy and produce a small error rate when using large data sets. This research aim to compared optimal performance accuracy of both methods on the classifiying of the employment status of citizen. The data used are employment status of citizen in Kutai Kartanegara Regency based on SAKERNAS of East Kalimantan Province in 2018 and used 5 factors namely age, sex, status in the household, marital status, and education to predict employment status of citizen. Based on the analysis, classification the employment status of citizen with naive Bayes method has accuracy of 90,08% and in the k-nearest neighbor has accuracy of 94,66%. To evaluate the accuracy of classification used calculation of Press’s Q. Based on Press’s Q value showed that both of classification methods are accurate. From that analysis, can be concluded that the k-nearest neighbor method works better compared with the naive Bayes method for the case of the employment status of citizen in Kutai Kartanegara Regency.

Analisis Regresi Probit Biner Bivariat Ariessela, Syeli; Goejantoro, Rito; Purnamasari, Ika
EKSPONENSIAL Vol 12 No 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Bivariate binary probit regression is a regression analysis that uses two dependent variables and each has two categories. This regression analysis is used on education index data and expenditure index of district/city on Kalimantan island in 2017. The best model obtained in this regression analysis is a model that uses 4 independent variables namely APS 16-18 years, percentage of poor population, open unemployment rate, and GRDP ACMP (Gross Regional Domestic Product at Current Market Prices). The parameters that significantly influence the two dependent variables are the APS 16-18 years in models 1 and 2 and the percentage of poor people in model 2. In Samarinda, every change of the APS 16-18 years, the percentage of poor people, and the open unemployment rate of 1 the unit will increase the probability of Samarinda entering the education index and high expenditure index categories by 0,33 percent, 0,42 percent and 0,07 percent, respectively. Every change of GRDP ACMP by 1 unit will reduce the probability of Samarinda entering the education index and the high expenditure index by 1,63 percent.

Estimasi Parameter Model Regresi Linier Berganda dengan Pendekatan Bayes Menggunakan Prior Pseudo Isgiarahmah, Afryda; Goejantoro, Rito; Nasution, Yuki Novia
EKSPONENSIAL Vol 12 No 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The parameter estimation of a regression model can use the Ordinary Least Square (OLS) method which must fulfill the assumption of BLUE. Besides OLS, there is another method that can be used to estimate the regression parameters, namely the Bayes method. Parameter estimates using the OLS method and the Bayes method have been widely used in the field of development. One of them is on economic development, namely the Human Development Index (HDI). The purpose of this study is to know multiple linear regression models and interpretations that state the relationship between per capita expenditure, average length of school, life expectancy, and school length for the Human Development Index (HDI) with the Bayes approach using pseudo priors.

Perbandingan Metode C-Means dan Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator IPM Tahun 2019 Mahmudi, Mahmudi; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol 12 No 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The Human Development Index is an indicator used to measure one important aspect related to the quality of the results of economic development, namely the degree of human development. Data Mining is a technique or process for obtained information from large database warehouses. Based on its function, one of the data mining tasks was to group data, where the method used in this study was the C-Means and Fuzzy C-Means grouping methods. The two classification methods were applied to the human development index indicator data. The purpose of this study was to determined the best method based on the ratio of the standard deviation in clusters to the standard deviation between clusters. Based on the results of the analysis, it was concluded that the best method is the C-Means method with the value of the standard deviation value in the cluster against the standard deviation between clusters of 0.434 which results in 5 clusters, namely cluster 1 consisting of 9 districts / cities, cluster 2 consisting of 7 districts / cities, cluster 3 consists of 10 districts / cities, cluster 4 consists of 15 districts / cities and cluster 5 consists of 15 districts / cities.

Implementasi Text Mining Pengelompokkan Dokumen Skripsi Menggunakan Metode K-Means Clustering Rachman, Dezty Adhe Chajannah; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Text mining is the text analysis that automatically discover quality information from a series of texts that is summarized in a document. K-Means Clustering method is often used because of its ability to make a group of large amounts of data with relatively fast and efficient computing time. The purpose of this study is to determine the optimal number of the groups formed from the thesis documents and determine the results of the groups formed. This study is using Nazief and Adriani algorithms for the stemming step, Euclidean Similarity to calculate document distances, and Silhouette Coefficient to test the cluster validity. The sample in this study is 119 thesis documents of Statistics Study Program, Mathematics Department, Faculty of Mathematics and Natural Sciences, graduates of 2016-2018. Based on the results of the analysis, the optimal number of groups formed is two clusters with a silhouette coefficient of 0.12. The results of the grouping formed are two clusters with the total of the first cluster is 85 documents and the second cluster is 34 documents. The first cluster is dominated by studies with data mining especially classification, time series analysis, regression analysis, survival analysis, spatial analysis and operational research, and the second cluster is dominated by studies with multivariate analysis, quality control, and insurance mathematics.

Optimasi Fuzzy C-Means Menggunakan Particle Swarm Optimization Untuk Pengelompokan Kabupaten/Kota Di Pulau Kalimantan (Studi Kasus : Data Indikator Kesejahteraan Rakyat Tahun 2020) Febriyanti, Nur Afifah; Goejantoro, Rito; Prangga, Surya
EKSPONENSIAL Vol 14 No 1 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Fuzzy C-Means (FCM) is a method of grouping data based on the degree of membership whose observation object is based on the information found in the data describing the object. The FCM method has weaknesses in the initial cluster center determination, so it can be overcome by the Particle Swarm Optimization (PSO) method that can be applied to find the optimal solution of the optimal cluster center determination. The purpose of this research is to determine the optimal number of clusters based on the validity indexes of Partition Coefficient (PC) and Modified Partition Coefficient (MPC), and obtain the results of grouping regencies/cities using the FCMPSO method. Based on the FCMPSO method with a validity index of PC and MPC, it produces an optimal cluster of two clusters, the first cluster consisting of 33 regencies/cities on Kalimantan Island and the second cluster consisting of 23 regencies/cities on Kalimantan Island.

Co-Authors Abidin, Ahmad Aliful Aditiya Risky Tizona Amanah Saeroni Andrea Tri Rian Dani Annabaa Aulia, Muzizah Ardyanti, Hesti Ariessela, Syeli Astuti, Putri Sri Athifaturrofifah Athifaturrofifah Cahyani, Era Tri Candra, Yossy Christyadi, Santo Dani, Andrea Tri Rian Darnah Darnah Andi Nohe Darnah, Darnah Desi Yuniarti Deviyana Nurmin Devy Sintya Putri Dewi Wulan Sari Dini Elizabeth Dwi Agoes Setiawan Dwi Husnul Mubiin Dwi Indra Yunistya Dyah Arumatica Novilla Etri Pujiati Fatmi’aturro’isah, Nurul Febriyanti, Nur Afifah Fidia Deny Tisna Amijaya Gerald Claudio Messakh Hairi Septiyanor Hidayatullah, Aji Syarif Ika Purnamasari Ika Purnamasari Ilham Adnan Kasoqi Irene Lishania Irfan Fadil Isgiarahmah, Afryda Juliartha, Made Angga Katianda, Kristin Rulin Khairun Nida Khoiril Anwar Lupinda, Indah Cahyani M. Fathurahman Mahmudi Mahmudi Martua Tri Januar Sinaga Meiliyani Siringoringo Memi Nor Hayati Memi Nor Hayati Memi Nor Hayati Memi Nor Hayati Mochammad Imron Awalludin Muhammad Rahmad Fadli Muhammad Rais Muhammad Yafi Mulyta Anggraini Murdani, Endah Mulia Ni Wayan Rica A Novalia, Viona Nur Annisa Fitri Nur Azizah Nurdayanti Nurdayanti Nurhasanah Nurhasanah Nurmin, Deviyana Nurul Rahmahani Oktri Mayasari Permana, Jordan Nata Primantoro, Sudhan Putra, Eko Prasatyo Putri, Nurlia Sucianti Rachman, Dezty Adhe Chajannah Rahmaulidyah, Fatihah Noor Rinaldi, Rival Satriya, Andi M Ade Sekar Nur Utami Septilasse, Rebeka Norcaline Sifriyani, Sifriyani Siringoringo, Meiliyani Siti Mahmuda Soraya, Raihana Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Suerni, Widya - Surya Prangga Suyitno Suyitno Suyitno Suyitno Syafitri, Febriana Syaripuddin Syaripuddin Syaripuddin Syaripuddin Wasono Wasono Wasono, Wasono Widyawati Widyawati Yenni Safitri Yudha Muhammad Faishol Yuki Novia Nasution Yuki Novia Nasution, Yuki Novia Yuliasari, Pratiwi Dwi Yuniarti, Desi

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