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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 Journal of Mathematics Education and Science 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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Perbandingan Metode Klasifikasi Naïve Bayes Dan Jaringan Saraf Tiruan: Studi Kasus: Pt Asuransi Jiwa Bersama Bumiputera Tahun 2018 Ardyanti, Hesti; Goejantoro, Rito; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new object. Naïve Bayes is a classification technique for predicting future probability based on past experiences with a strong assumption of independence. Artificial neural network is one of the data mining analysis tools that can be used to create data on classification. Model selection in artifial neural networks requires various factors such as the selection of optimal number of hidden neuron. This research has a goal to compare the level of classification accuracy between the Naïve Bayes method and artificial neural network on payment status of the insurance premium. The data used is insurance costumer’s data of PT AJB Bumiputera Samarinda in 2018. The result of the comparison of accuracy calculation from the two analyzes indicate that artificial neural network has a higher level of accuracy than naïve Bayes method. Classification accuracy result of Naïve Bayes is 82,76% and artificial neural network is 86,21%.

Pemodelan Indeks Pembangunan Manusia (IPM) Menggunakan Analisis Regresi Probit: Studi Kasus: Indeks Pembangunan Manusia (IPM) di Pulau Kalimantan Tahun 2017 Christyadi, Santo; Satriya, Andi M Ade; Goejantoro, Rito
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Ordinal probit regression analysis is non-linear regression analysis that used to find affected independent variables for ordered categorical dependent variable and regression model in this analysis used Normal cumulative distribution function. Parameter estimation in this model used Maximum Likelihood Estimation (MLE) method. This model has been applied to Human Development Index (HDI) in Borneo Island in 2017 case study. HDI is the most important measurement in improving the human development quality in all cities/regencies in Indonesia. Some factors that affected to IPM, they are Life Expectancy (X1), School Expectancy (X2), Spending per Capita (X3), Average School Duration (X4), and Labour Force Participation Rate (X5). Based on research that was performed by researcher, resulted two factors affecting to HDI, those are Life Expetancy and Average School Duration. This model has classification accuracy of 89,29%, APER (Apparent Error Rate) value of 10,71%, and AIC (Akaike Information Criterion) value of 39,75; this model was very good because prediction value is almost approaching to observation value (actual value).

Penerapan Metode Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Kesejahteraan Rakyat Tahun 2020 Nurmin, Deviyana; Hayati, Memi Nor; Goejantoro, Rito
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Clustering is a method of grouping data into several clusters or groups so that data in one cluster has a high level of similarity and data between clusters has a low level of similarity. The clustering method used in this research is Fuzzy C-Means (FCM). FCM is a data grouping technique in which the existence of each data point in a cluster is determined by the degree of membership. To optimize the grouping results, it is necessary to validate the number of clusters using Partition Coefficient (PC). The purpose of this study is to obtain optimal grouping results from the FCM method using the PC validity indices from the people's welfare indicator data in 56 regencies/cities on the island of Kalimantan in 2020. Based on the results of the analysis, the conclusion is that the optimal number of clusters is three clusters. The first cluster consists of 24 regencies/cities on the island of Kalimantan, the second cluster consists of 17 regencies/cities on the island of Kalimantan, and the third cluster consists of 15 regencies/cities on the island of Kalimantan.

Regresi Logistik dengan Metode Bayes untuk Pemodelan Indeks Pembangunan Manusia Kabupaten/Kota di Pulau Kalimantan Syafitri, Febriana; Goejantoro, Rito; Wasono, Wasono
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v12i2.802

Human Development Index (HDI) is an indicator that can measure success in efforts to build the quality of human life. HDI is also a measure of the prosperity of a region which is observed based on three dimensions, namely health, education and economy. Based on HDI publication by the Central Statistics Agency in 2018, it showed that the scores of HDI for 56 districts/cities in Kalimantan Island only has two categories of HDI which are medium and high. Bayesian method is a parameter estimation technique that combines the likelihood and prior distribution functions. The estimation with Bayesian method was solved using Markov Chain Monte Carlo simulation (MCMC) with Gibbs Sampler algorithm. The aim of this study is to examine the modelling of the factors that influence the HDI of districts/cities in Kalimantan Island and determine the accuracy of the model classification using logistic regression with Bayesian method. The data used is the HDI of districts/cities in Kalimantan Island in 2018. Bayesian method is a parameter estimation technique that combines the likelihood and prior distribution functions. The estimation with Bayesian method was solved using Markov Chain Monte Carlo simulation (MCMC) with Gibbs Sampler algorithm. The results of modelling and analysis on districts/cities HDI data on Kalimantan Island showed that the factors that significantly influence HDI are the number of paramedic, the number of health facility and the participation rate of high school. The results of the classification accuracy of the model amounted to 82,14% which resulted in 37 districts/cities are categorized as the HDI medium category and 19 districts/cities are categorized as the HDI high category.

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.

Pengelompokan Kabupaten/Kota Di Pulau Kalimantan Berdasarkan Indikator Indeks Pembangunan Manusia Tahun 2020 Menggunakan Optimasi K-Means Cluster Dengan Principle Component Analysis (PCA) Anwar, Khoiril; Goejantoro, Rito; Prangga, Surya
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Data mining is a technique or process to obtain useful information from a large database. Based on its functionality, one of the tasks of data mining is to group data. Cluster analysis is an analysis that aims to group objects based on the information found in the data. One of the cluster analysis methods is the K-Means cluster method, which is a non-hierarchical grouping method by dividing the data set into a number of groups that do not overlap between one group and another. This study aims to classify districts/cities on the island of Kalimantan based on indicators of the human development index and obtain the sillhoutte coefficient value from the optimal cluster analysis using the K-Means algorithm on principle component analysis. The data used is the 2020 human development index data in districts / cities on the island of Kalimantan and used 8 variables from the human development index indicator. The results of the optimal cluster formed in the grouping of regencies/cities on the island of Kalimantan using the K-Means cluster method on the principle component analysis are 4 clusters. Cluster 1 has 20 regencies/cities, cluster 2 has 3 regencies/cities, cluster 3 has 26 regencies/cities and cluster 4 has 7 regencies/cities. The sillhoutte coefficient value for data validation from district/city clustering on the island of Kalimantan using the K-Means cluster method on principle component analysis produces 4 clusters of 0.540 which states that the cluster structure formed in this grouping is a medium structure.

Aplikasi K-Nearest Neighbor Dengan Fungsi Jarak Gower Dalam Klasifikasi Kelulusan Mahasiswa: Studi Kasus : Mahasiswa Program Studi Statistika, Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Mulawarman Fadil, Irfan; Goejantoro, Rito; Prangga, Surya
EKSPONENSIAL Vol. 13 No. 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The results of the reaccreditation of the Statistics Study Program, Mulawarman University in 2019 remain accredited B. One of the assessment indicators used in reaccreditation is the student's timely graduation status. Therefore, it is necessary to predict the graduation status of Statistics students, Mulawarman University.. The prediction method used in this research is K-Nearest Neighbor (K-NN). K-NN is a classification method based on studying previously classified data. This method is very easy to understand, easy to applied and also non-parametric method, so that no certain assumptions are needed in the process. The independent variables used in this study were student profiles, including gender, regional origin, cumulative Grade Point Average (GPA) and single tuition fee. The dependent variable in this study is the graduation status of students, namely graduating on time and not graduating on time. The data used were students of the Mulawarman University, Statistics Study Program in 2014, 2015, and 2016. The results showed at k = 7 and the distribution of training and testing data with the proportion of 80:20 obtained optimal accuracy of 0,909 with a TPrate of 0.500, a TNrate. in the amount of 1,000 and AUC value of 0,75 that means fair classification.

Perbandingan Diagram Kontrol Demerit dan Fuzzy u: Studi Kasus : Kecacatan Produk Kayu Lapis (Plywood) di PT. Segara Timber Mangkujenang, Samarinda Provinsi Kalimantan Timur Tahun 2019 Septilasse, Rebeka Norcaline; Goejantoro, Rito; Wahyuningsih, Sri
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Control chart is a graph that provides a picture of a running process whether under controlled conditions or not. Demerit control chart and fuzzy u control chart are very suitable for production quality control. This study was applied to the data of defects of plywood products at pt. segara timber, samarinda, east Kalimantan in 2019. The purpose of this study is to get the results of a comparison of the decision of Demerit control chart and fuzzy u control chart. The results of this study shows the demerit control chart is more thorough than the fuzzy u control chart due to the demerit control chart found 12 out of control observations and the fuzzy u control chart only found 1 out of control observations.

Klasifikasi Status Hipertensi Pasien UPTD Puskesmas Sempaja, Kota Samarinda Menggunakan Metode K-Nearest Neighbor Soraya, Raihana; Hayati, Memi Nor; Goejantoro, Rito
EKSPONENSIAL Vol. 14 No. 2 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1009

Data mining is a method of selecting, exploring and modeling large amount of data to find knowledge and clear patterns or interesting relation of the data and useful in the process of data analysis. In data mining there are several techniques that have different function and one of them is classification tehcnique. The classification process itself is the process of finding patterns or differences between classes or data that can be used to predict object classes whose class labels are unknown. K-nearest neighbor (K-NN) is one of the methods in classification algorithm. This study discusses the classification using K-NN algorithm which is applied to the data hypertension status. The aim is to find out the optimal neighborliness value (K) accuracy value and the best propotion of the data hypertension status. The data used is the data of patients UPTD health center Sempaja, Samarinda city from February to May 2022 with dependent variabel is hypertension status and uses 4 independent variables, age, gender, diabetes mellitus and heart disease. Based on the research that has been done, obtained an accuracy value of 62,60% with K = 5 in the best proportion of the data is 70%:30%.

Estimasi Parameter Model Regresi Linier dengan Pendekatan Bayes: Studi Kasus: Kemiskinan di Provinsi Kalimantan Timur pada Tahun 2017 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 .

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 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 Messakh, Gerald Claudio Mochammad Imron Awalludin Muhammad Rahmad Fadli Muhammad Rais Muhammad Yafi Mulyta Anggraini Murdani, Endah Mulia Ni Wayan Rica A Nida, Khairun 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 Suerni, Widya - Surya Prangga Suyitno Suyitno Suyitno Suyitno Syafitri, Febriana Syamsiar, Syamsiar 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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