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Memi Nor Hayati
Laboratorium Statistika Terapan FMIPA Universitas Mulawarman
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Pengelompokan Data Kategorik Dengan Algoritma Robust Clustering Using Links Isma Dewi; Syaripuddin Syaripuddin; Memi Nor Hayati
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (687.398 KB)

Abstract

Cluster analysis is a technique of data mining that is used to group data based on the similarity of attributes of data objects. The problem that is often encountered in cluster analysis is the data on a categorical scale. Categorical scale data grouping can be done using the ROCK (RObust Clustering using linKs) algorithm. The ROCK algorithm is included in the of agglomerative hierarchical clustering algorithms in cluster analysis. This algorithm introduces a concept called neighbors and links in grouping data. Categorical data grouping with ROCK algorithm is done in three steps. The first step is counting similarities. The second step is determining the neighbors and the last is calculating the links between the observation objects. The value of the link is affected by θ. The optimum number of clusters in the ROCK algorithm is selected using a minimum ratio value of . The purpose of this study is to group 100 data of insurance customers of PT. Prudential Life Samarinda in 2018. Based on the analysis results, obtained that the optimum group is at θ = 0.1 with a ratio value of is 0.1371. The optimum number of groups formed is 2 clusters. The first group consisted of 42 customers and the second group consisted of 58 customers.
Model Spatial Autoregressive Moving Average (SARMA) pada Data Jumlah Kejadian Demam Berdarah Dengue (DBD) di Provinsi Kalimantan Timur dan Tengah Tahun 2016 Devi Nur Endah Sari; Memi Nor Hayati; Sri Wahyuningsih
EKSPONENSIAL Vol 11 No 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Spatial Autoregressive Moving Average (SARMA) is a spatial regression model that uses the regional approach. The weighting matrix used is an adjacency matrix which is based on the intersection between observed locations. This study was conducted to determine the SARMA model and the factors that influence the number of cases of dengue hemorrhagic fever (DHF) in the provinces of East Kalimantan and Central Kalimantan in 2016. Based on the results of the Moran's Index test, there is a spatial autocorrelation on the number of dengue events in East Kalimantan Province and Central Kalimantan in 2016. The Lagrange Multiplier (LM) test has a spatial lag on the dependent variable and the error variable, which is a parameter and that is significant to the significance level . Based on the results of SARMA modeling that the factors that influence the number of dengue events in the provinces of East Kalimantan and Central Kalimantan in 2016 are the percentage of population density, the percentage of healthy houses, and the percentage of puskesmas.
Metode Hierarchical Density-Based Spatial Clustering of Application with Noise (HDBSCAN) Pada Wilayah Desa/Kelurahan Tertinggal di Kabupaten Kutai Kartanegara Nanda Anggun Wahyuni; Memi Nor Hayati; Nanda Arista Rizki
EKSPONENSIAL Vol 12 No 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (778.141 KB) | DOI: 10.30872/eksponensial.v12i1.758

Abstract

The underdeveloped areas are generally the districts which are relatively underdeveloped compared to other regions on a national scale. Determination of underdeveloped villages is often done in order to determine the distribution of government assistance so that assistance can be distributed appropriately. The identification is based on facilities, infrastructure, access, social, population and economy provided in the Village Potential data (PODES). The concept of grouping based on regional or spatial is done to find out certain characteristics in an area. HDBSCAN is a grouping concept with a parameter called Mpts. The purpose of this study is to know the number of clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method. The Mpts parameters that is used in this study is from 2 to 6. Based on the results of the analysis, the clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method, were 3 clusters. Cluster 0 consists of 19 villages / urban areas , cluster 1 consists of 4 villages / urban areas and cluster 2 consists of 61 villages / urban areas. Based on the analysis, villages / urban areas included in cluster 1 could be the main target of the government in providing assistance and development of regional facilities / infrastructure.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (641.425 KB) | DOI: 10.30872/eksponensial.v12i2.809

Abstract

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
Klasifikasi Status Pembayaran Kredit Barang Elektronik dan Furniture Menggunakan Support Vector Machine Indah Putri Casuarina; Memi Nor Hayati; Surya Prangga
EKSPONENSIAL Vol 13 No 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (558.5 KB) | DOI: 10.30872/eksponensial.v13i1.887

Abstract

Classification is the process of finding a model or function that can describe and differentiate data into classes. One application of classification is Support Vector Machine (SVM). SVM is a learning system that uses a hypothetical space in the form of linear functions in a high-dimensional feature space, trained with a learning algorithm based on optimization theory by implementing machine learning derived from statistical learning theory. The concept of classification with SVM is to find the best hyperplane to separate the two data classes and use a support vector approach. This study uses the proportion of the distribution of training data and testing data, namely 50%:50%, 70%:30%, 90%:10% and uses the SVM algorithm Polynomial kernel function with parameters =0.01, r=0.5, d =2, and C=1. This study aims to determine the results of the classification of the credit payment status of electronic goods and furniture and the level of classification accuracy in the SVM method. The data used is the debtor data of PT. KB Finansia Multi Finance Bontang in 2020 as many as 133 data with current and non-current credit payment status and using 7 independent variables, namely age, number of dependents, length of stay, income, years of service, large credit payments, and length of credit borrowing. The results of the SVM classification show an average accuracy value of 72.25% and the best accuracy chosen is the proportion of training data distribution and testing data 90%:10%, which is 84.62%.
Analisis Faktor-Faktor yang Mempengaruhi Jumlah Kasus Tuberkulosis di Indonesia Menggunakan Model Geographically Weighted Poisson Regression Nabila Al Karima; Suyitno Suyitno; Memi Nor Hayati
EKSPONENSIAL Vol 12 No 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.443 KB) | DOI: 10.30872/eksponensial.v12i1.754

Abstract

Tuberculosis is a contagious disease suffered by humans caused by mycobacterium tuberculosis bacteria. Tuberculosis in Indonesia must be eradicated both preventive and treatment. One effort that can be given to the community to reduce tuberculosis cases is by providing information on the factors that influence tuberculosis cases through Geographically Weighted Poisson Regression (GWPR) modeling. The number of tuberculosis cases in Indonesia is a count data with a small chance of occurrence so that it is suspected to have a Poisson distribution. Cases of tuberculosis are spatial data (spatial heterogeneity). The purpose of this study is to determine the GWPR model of the number of tuberculosis cases in Indonesia and determine the factors that influence tuberculosis cases in Indonesia. The research data are secondary data obtained from the Indonesian Ministry of Health. Parameter estimation method is Maximum Likelihood Estimation (MLE). Spatial weighting is calculated by using the Adaptive Gaussian weighting function and the optimum bandwidth is determined by using the Cross-Validation (CV) criteria. 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 GWPR 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 the number of poor people, the percentage of houses unfit for habitation, the percentage of districts/cities that do not have a PHBS policy and the percentage of TPM not meeting health requirements, meanwhile factors influencing globally are the number of poor people.
Peramalan Menggunakan Time Invariant Fuzzy Time Series Siti Rahmah Binaiya; Memi Nor Hayati; Ika Purnamasari
EKSPONENSIAL Vol 10 No 2 (2019)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Forecasting is a technique for estimating a value in the future by looking at past and current data. Fuzzy Time Series is a forecasting method that uses fuzzy principles as the basis, where the forecasting process uses the concept of fuzzy set. This study discusses the Time Invariant Fuzzy Time Series method developed by Sah and Degtiarev to forecast the East Kalimantan Province Consumer Price Index (CPI) in May 2018. In the Time Invariant Fuzzy Time Series method using a frequency distribution to determine the length of the interval, 13 fuzzy sets are used in the forecasting process. Based on this study, using CPI data of East Kalimantan Province from September 2016 to April 2018, the forecasting results for May 2018 were obtained 135.977 and obtained the results of forecasting error values using Mean Absolute Percentage Error (MAPE) is under 10% of 0.0949%.
Pemodelan Harga Saham PT. Telekomunikasi Indonesia Tbk Menggunakan Model TSR Linier Kartika Ramadani; Sri Wahyuningsih; Memi Nor Hayati
EKSPONENSIAL Vol 13 No 1 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (560.404 KB) | DOI: 10.30872/eksponensial.v13i1.879

Abstract

The movement of the stock price of PT. Telekomunikasi Indonesia Tbk from time to time is relatively erratic, but in 2020 the movement shows an decreasing trend pattern in January-October and an increasing trend pattern in November-December. There needs a stock price modeling for PT. Telekomunikasi Indonesia Tbk which is useful for investors as a consideration in making decisions to invest. In this study, modeling the stock price of PT. Telekomunikasi Indonesia Tbk uses a Time Series Regression (TSR) Linear model. The results of this study obtained a model for the proportion of data in sample 90, a model for the proportion of data in sample 80, and a model for the proportion of data in sample 70. It was found that the residual value of the TSR linear model the white noise assumption and normally distributed is not valid, so it can be concluded that TSR Linear model has not been able to understand all information on stock price data of PT. Telekomunikasi Indonesia Tbk.
Penerapan Model Mixed Geographically Weighted Regression dengan Fungsi Pembobot Adaptive Tricube pada IPM 30 Kabupaten/Kota di Provinsi Kalimantan Timur, Kalimantan Tengah dan Kalimantan Selatan Tahun 2016 Ranita Nur Safitri; Suyitno Suyitno; Memi Nor Hayati
EKSPONENSIAL Vol 11 No 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Mixed Geographically Weighted Regression (MGWR) model is a Geographically Weighted Regression (GWR) model which has global (equal value) and local (inequal value) parameters at every different observation location. The goal of this study is to obtain MGWR model of the Human Development Index (HDI) data and find out significant factors influencing the HDI in each district (city) East Kalimantan, Central Kalimantan and South Kalimantan province in 2016. Parameter estimation method is conducted in two stages namely local parameter estimation and global parameter estimation. Local parameter estimation method is Maximum Likelihood Estimation (MLE), with spatial weighting is calculated by adaptive tricube weighting function and optimum bandwidth determination uses the Akaike Information Criteria (AIC). Global parameter estimation method is Ordinary Least Square (OLS). Based on the result of MGWR parameter testing, it was concluded that the school enrollment rates (SMP) and poor people percentage affected the HDI of 30 districts (cities) in East Kalimantan, Central Kalimantan and South Kalimantan. Meanwhile the population density affected the HDI of two districts namely HDI of Samarinda and Bontang.
Analisis Model Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) dan Model Exponential Generalized AutoregressiveConditional Heteroskedasticity (EGARCH) Julia Julia; Sri Wahyuningsih; Memi Nor Hayati
EKSPONENSIAL Vol 9 No 2 (2018)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

In the field of finance, Autoregressive Integrated Moving Average (ARIMA) is one of the models that can be used. Financial data usually have a non constant variance error. Thus, Autoregressive Conditional Heterokedasticity (ARCH )model can be used to solve the problem. In addition, it also can be used the development of ARCH model that is Generalized Autoregressive Conditional Heterkadasticity (GARCH) model. The symmetry of residual data can be determined by using the model of Threshold Generalized Autoregressive Conditional Heterkadasticity (TGARCH) and the model of Exponential Generalized Autoregressive Conditional Heterkadasticity (EGARCH). The purpose of this research is to know the best model among the model of TGARCH and the model of EGARCH in predicting Indonesia Composite Index (ICI) and the results of ICI forecasting by using the best model for the period of July 2017 until December 2017. The best model in the ICI case study from January 2011 to June 2017 is the model of ARIMA (1,1,1) -GARCH (1,2) -EGARCH (1). The results of ICI forecasting by using the model of ARIMA (1,1,1) -GARCH (1,2) -EGARCH (1) obtained an upward trend in the period of July 2017 to December 2017.
Co-Authors Desi Yuniarti Devi Nur Endah Sari Edy Fahrin Fatihah Noor Rahmaulidyah Fidia Deny Tisna Amijaya Ika Purnamasari Indah Putri Casuarina Isma Dewi Julia Julia Kartika Ramadani Lili Widyastuti Masrawanti Masrawanti Meiliyani Siringoringo Muhammad Jainudin Nabila Al Karima Nanda Anggun Wahyuni Nanda Arista Rizki Nur Aini Ayu Lestari Nur Fajar Apriyani Prangga, Surya Putri Ayu Dwi Lestari Ranita Nur Safitri Rina Fauzia Riska Putri Utami Rito Goejantoro Ronald Tediwibawa Siti Rahmah Binaiya Sri Wahyuningsih Sri Wahyuningsih Suyitno Suyitno Syaripuddin Syaripuddin Yuki Novia Nasution