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Rito Goejantoro

Laboratorium Statistika Komputasi FMIPA Universitas Mulawarman

Author-ID : 6295453

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Perbandingan Metode C-Means dan Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator IPM Tahun 2019 Mahmudi Mahmudi; Rito Goejantoro; Fidia Deny Tisna Amijaya
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 Dezty Adhe Chajannah Rachman; Rito Goejantoro; Fidia Deny Tisna Amijaya
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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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.

Penerapan Metode Fuzzy Subtractive Clustering Nur Azizah; Desi Yuniarti; Rito Goejantoro
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Cluster analysis is a statistical analysis to classify the objects to be some clusters based on checked variables and similarity of character between the objects. Quality of human living or society has been influenced by many things. In reality, population density is very influential to the quality of human living because high population density will cause many problems that impact on deterioration of quality of human living. Fuzzy Subtractive Cluster (FSC) methods using the data as a candidate of cluster center, so that duty of computation is hanging on the number of data and is not hang at dimension of data. This study aims is to determine the results of FSC at clustering the district in East Borneo based on wide of the district and total of population in 2015. The result shows there is 8 until 24 districts which have high population density. From validity of cluster, it isfounded that the best result for clustering the district in East Borneo based on wide of the district and sum of citizen in 2015 is 2 clusters, there are narrow district with many citizen and wide district with few citizen.

Perbandingan Metode Klasifikasi Naive Bayes dan K-Nearest Neighbor pada Data Status Pembayaran Pajak Pertambahan Nilai di Kantor Pelayanan Pajak Pratama Samarinda Ulu Fatihah Noor Rahmaulidyah; Memi Nor Hayati; Rito Goejantoro
EKSPONENSIAL Vol 12 No 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Classification is a systematic grouping of objects into certain groups based on the same characteristics. The classification method used in this research are naive Bayes and K-Nearest Neighbor which has a relatively high degree of accuracy. This research aims to compare the level of classification accuracy on the status data of value-added tax (VAT) payment. The data used is data on corporate taxpayers at Samarinda Ulu Tax Office in 2018 with the status of VAT payment being compliant or non-compliant and used 3 independent variables are income, type of business entity and tax reported status. Measurement of accuracy using APER in the Naive Bayes method is 17.07% and in K-Nearest Neighbor method is 19,51%. The comparison results of accuracy measurements between the two methods show that the naive Bayes method has a higher level of accuracy than the K-Nearest Neighbor method

Multi-Attribute Decision Making dengan Metode Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) Oktri Mayasari; Yuki Novia Nasution; Rito Goejantoro
EKSPONENSIAL Vol 9 No 1 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Fuzzy TOPSIS is a method that is used for identifying solution from one limited alternative set. The basic principle is that the chosen alternative must have the shortest distance from the positive ideal solution and the furthest distance from the negative ideal solution to determine relative proximity from an alternative with optimal solution. Fuzzy numbers in this method give effectiveness to determine the value of decision matrix. The purpose of this research is to find out the recommendation of investment in ADHI, PTPP, WIKA, and WSKT stocks by using fuzzy TOPSIS method. The alternatives that is used in this research are four stocks in the building construction sector on LQ45, from February to July 2017 namely Adhi Karya (Persero) Tbk. (ADHI), PP (Persero) Tbk. (PTPP), Wijaya Karya (Persero) Tbk. (WIKA), and Waskita Karya (Persero) Tbk. (WSKT) with the attributes that consist of nine financial ratios, namely Earnings Per Share (EPS), Book Value Per Share (BV), Debt to Assets Ratio (DAR), Debt to Equity Ratio (DER), Return on Assets (ROA), Return to Equity (ROE), Gross Profit Margin (GPM), Operating Profit Margin (OPM) and Net Profit Margin (NPM) on June 2016. The result of the research with fuzzy TOPSIS analysis generates preference value from stocks of ADHI amount 0,1711, stocks of PTPP amount 0,6169, stocks of WIKA amount 0,6310, and stocks of WSKT amount 0,7488. The result of preference value shows that stocks of WSKT with the highest preference value become the best recommendation option to invest rather than the stocks of ADHI, PTPP, or WIKA.

Analisis Regresi Logistik Multinomial Bayes untuk Pemodelan Minat Peserta Didik MAN 2 Samarinda Tahun Ajaran 2018/2019 Era Tri Cahyani; Rito Goejantoro; Meiliyani Siringoringo
EKSPONENSIAL Vol 13 No 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Currently, Senior High School and Madrasah Aliyah have implemented student specialization. The specialization includes Natural Science, Social Science and Language. There are several criteria for determining interest in Senior High School and Madrasah Aliyah which include academic scores, student interests and IQ. The multinomial logistic regression model is used to examine these factors because the dependent variable has more than 2 categories. Bayes method is used to estimate the parameters of the multinomial logistic regression. The Bayesian method is a parameter estimation technique that combines the likelihood and prior distribution function. The estimation with the Bayesian method was solved using Markov Chain Monte Carlo simulation (MCMC) with the Gibbs Sampler algorithm. The data used were new students at MAN 2 Samarinda on 2018/2019 with the results of interest namely Natural Science, Social Science and Language. Independent variables were used, namely the score of the Junior High School in subjects Natural Science, Social Science, Language and the rate of National Test. The results of modeling and analysis showed that the factors that significantly influenced were the score of the junior high school in the subject of Natural Science and the rate of National Test. The classification accuracy of the model was 63,10%.

Analisis Pengendalian Kualitas Produksi Menggunakan Peta Kendali U dan Diagram Kontrol Decision On Belief (DOB). Nurul Rahmahani; Rito Goejantoro; Desi Yuniarti
EKSPONENSIAL Vol 10 No 1 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Statistical quality control is a problem solving technique used to monitor, control, analyze, manage, and improve products. There are two kinds of control charts, namely the attribute control chart and the variable control chart. The Decision On Belief (DOB) control chart is an attribute control chart based on Bayes's Theorem. In this study, to determine the comparison of control chart U and the DOB control chart the degree of control sensitivity in identifying out of control data on the production quality control data banner of Lineza digital printing in Samarinda. Based on the result of the research, it is found that quality control using U control chart and DOB control diagram has not been statistically controlled because there is still data out of control and in better sensitivity level in detecting out of control data is a DOB control chart because this diagram detects 65% while the U control chart is only 15%.

Analisis Distribusi Frekuensi dan Periode Ulang Hujan Widyawati Widyawati; Desi Yuniarti; Rito Goejantoro
EKSPONENSIAL Vol 11 No 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Increasing water demand for various needs can be a complex problem so it is necessary to manage water resources. Analysis of hydrological data is very necessary to get information about water resources where the information can be used as a benchmark for planning a water resources builder. One of hydrological analysis is the analysis of rainfall data where this analysis uses frequency distribution analysis and rain return periods. There are four types of distribution used, namely normal distribution, normal log distribution, Gumbel distribution and type III log Pearson distribution. The goodness of fit test uses the Kolmogorov-Smirnov method, Chi-Square and Anderson-Darling. Rainfall return calculation is calculated when it is known the type of distribution of the data studied. This research uses rainfall data of Long Iram Sub-Distric, West Kutai Distric in 2013 to 2017 obtained from the Meteorology, Climatology and Geophysics Agency (BMKG) of Samarinda City. The results from research showed that the Gumbel distribution was the right distribution or distribution that was the best with the results of the return period of rain for the return period of 2 years obtained by rainfall of 519 mm, 5-year return period of 796 mm, 10-year return period of 980 mm, return period 20 years of 1.154 mm, a 50 year return period of 1.348 mm and in a 100 year return period of 1.752 mm.

Optimalisasi K-Means Cluster dengan Principal Component Analysis pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Tingkat Pengangguran Terbuka Muhammad Rais; Rito Goejantoro; Surya Prangga
EKSPONENSIAL Vol 12 No 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Data mining or often also called knowledge discovery in databases is an activity that includes collecting, using historical data to find regularity, patterns, or relationships in large data sets resulting in useful new information. Cluster analysis is an analysis that aims to group data based on its likeness. This research uses the K-Means method combined with PCA. The K-Means method groups data in the form of one or more clusters that share the same characteristics. While the PCA method was used to reduce research variables. This grouping method was applied to the data indicator of the unemployment rate of districts/cities in Kalimantan Island in 2018. The cluster validation used in this study was the Davies-Bouldin Index (DBI). Based on the results of the analysis, it was concluded that the number of principal components formed was as many as 2 principal components. The most optimal grouping of districts/cities in Kalimantan island in 2018 was to use 2 clusters with a DBI value of 0,507. The grouping of districts/cities in Kalimantan Island in 2018 produced 2 clusters, cluster 1 consisting of 51 districts/cities and clusters of 2 consisting of 5 districts/cities. Cluster 1 was a cluster that has the highest percentage of the poor population and the highest labor force participation rate when compared to cluster 2. While cluster 2 was a cluster that has an index value of human development, population, number of the labor force, number of unemployed, population density, and the minimum wage of district/city was high compared to cluster 1.

Penaksiran Kandungan Klorida di Sungai Mahakam Wilayah Samarinda Tahun 2017 dengan Metode Cokriging Eko Prasatyo Putra; Rito Goejantoro; Suyitno Suyitno
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Cokriging is the interpolation method of value of an unsampled data by minimizing the variance of the estimation error by utilizing cross correlations between the main variable and the additional variable. This study aims to estimate the chloride content in the Mahakam River in Samarinda by using the cokriging method. The data of this study are spatial data that consists of the main variable data is chloride content and additional variable data is the pH of the water, as well as the coordinates of the observation location. Semivariogram (matrix covariance) is determined based on the best model, namely theoretical semivariogram. The best theoretical semivariogram model for cross variables is the exponential model, while the best theoretical semivariogram model for the main variable and additional variables are the spherical model. The selected theoretical semivariogram model was used to determine the semivariogram matrix in estimating chloride content in IPA Bantuas and Teluk Lerong. The results of estimation of chloride content in IPA Bantuas and Teluk Lerong are 1.91 mg/l and 1.64 mg/l. Based on the estimated chloride content in IPA Bantuas and in Teluk Lerong, it shows that the chloride content is still below the maximum threshold and meets the water chloride content standard for consumption by the Ministry of Health of the Republic of Indonesia, which is a maximum of 250 mg/l.

Co-Authors Andi M Ade Satriya Andi M Ade Satriya Desi Yuniarti Dezty Adhe Chajannah Rachman Eko Prasatyo Putra Endah Mulia Murdani Era Tri Cahyani Fatihah Noor Rahmaulidyah Fidia Deny Tisna Amijaya Hesti Ardyanti Ika Purnamasari Irfan Fadil Kristin Rulin Katianda M Fathurahman Mahmudi Mahmudi Meiliyani Siringoringo Memi Nor Hayati Muhammad Rais Mulyta Anggraini Nur Azizah Nurhasanah Nurhasanah Nurul Rahmahani Oktri Mayasari Prangga, Surya Rebeka Norcaline Septilasse Rival Rinaldi Santo Christyadi Sifriyani, Sifriyani Sri Wahyuningsih Suyitno Suyitno Syaripuddin Syaripuddin Syeli Ariessela Viona Novalia Widyawati Widyawati Yenni Safitri Yuki Novia Nasution

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