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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM Jurnal Aplikasi Statistika & Komputasi Statistik METIK JURNAL Variance : Journal of Statistics and Its Applications Jurnal Varian Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Mandalika Mathematics and Educations Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Mathematics Education and Science Mathematics and Applications (MAp) Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Research Applications in Community Services Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Progressive Physics Journal Eksponensial Journal of Mathematics, Computation and Statistics (JMATHCOS) IJASDS: Indonesian Journal of Applied Statistics and Data Science
Memi Nor Hayati
Universitas Mulawarman
Religion Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Penerapan Metode Fuzzy Time Series Using Percentage Change Nurul Hidayah; Ika Purnamasari; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

In 1993, Song and Chissom introduce fuzzy times series is capable of handling the problem of data forecasting if historical data are the values ​​of linguistic. The study uses the modeling outline by way of fuzzy relation equations and approximate reasoning to predict the number of students. In this study, the approach to the theory of fuzzy time series used is fuzzy time series using percentage change developed by Stevenson and Porter in 2009. The case studies used in this study is the population of East Kalimantan Province. This study aims to determine how the application of fuzzy time series method using percentage change in the population of East Kalimantan from 1980 until 2013. Forecasting is done menggukan linguistic value of the fuzzy set which is formed of the differences and converted into a percentage of the universe of discourse as a value data. Based on the results of the application of the method using fuzzy time series of the percentage change obtained 12 fuzzy set which is linguistics of the data, the accuracy of forecasting value from 1981 to 2013 using MAPE (Avarage Forcasting Error Rate) that is equal to 0.557%.
Pemantauan Peramalan Akseptor KB Baru Provinsi Kalimantan Timur Menggunakan Simple Moving Average dan Weighted Moving Average dengan Metode Tracking Signal Eric Sapto Raharjo; Memi Nor Hayati; Sri Wahyuningsih
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Simple moving average (SMA) is the basic method used to measure seasonal variations. This method is done by moving the average value counted along the time series. Weighted moving average (WMA) includes selecting weights may be different for each data value and then calculating the weighted average time period of k, the value obtained as the smoothed value.The purpose of this study was to determine the method and the best forecasting model with the results of forecasting on new data on the number of new acceptors KB using tracking signal. Results of this study is to model 3 SMA method is the best monthly tracking signal with a value of -0.0349 to -0.0178 β = 0.1 and β = 0.2 for the forecasting results for the period of January, February, and March 2015 amounted to 8.151, 8.131, and 7.485. For model 3 monthly WMA method is best with a variety of weights W1 = 0.25; W2 = 0.35; W3 = 0,40 tracking signal has a value of -0.0451 to -0.0439 β = 0.1 and β = 0.2 for the forecasting results for the period of January, February, and March 2015 for 8.044, 7.893, and 7.517 , In this case the method of 3-month SMA model is the most appropriate method to forecast the number of new acceptors KB East Kalimantan province.
Penerapan Generalized Poisson Regression I Untuk Mengatasi Overdispersi Pada Regresi Poisson Iim Masfian Nur; Desi Yuniarti; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 1 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Poisson Regression model is commonly used to analyze count data is assumed to have Poisson distribution where the mean and variance values are equal or also called equdispersion. In fact, this assumption is often violated, because the value of variance is greater than the mean value, this condition is called overdispersion. Poisson regression which is applied to the data that contains overdispersion will imply the value of standard error becomes underestimates, so the conclusion is not valid. One of the models that can be used for overdispersion data is Generalized Poisson Regression I (GPR I). This research discuss the handling of overdispersion on Poisson regression using GPR I, with case study modeling the number of cervical cancer cases in East Kalimantan in 2013. In this research GPR I models meet the criteria for suitability of regression compared Poisson regression models because it has a smaller AIC value.
Metode Regresi Robust Dengan Estimasi Method of Moment (Estimasi-MM) Pada Regresi Linier Berganda Hisintus Suban Hurint; Ika Purnamasari; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Method of Ordinary Least Square (OLS) on the regression analysis is a method which is often used to estimate the parameters. In the OLS method, there are several assumptions that must be fulfilled, these assumptions are often not fulfilled when the data contains outlier, so need a method that are robust to the presence of outliers. In this research, studied method of robust regression with MM-estimation. MM-estimation is a combination of estimation methods that have a high breakdown point, namely the Scale estimation(S-estimation) and Least Trimmed Square estimation (LTS estimation) and the method that have higher efficiency point, namely the Maximum Likelihood Type estimation (M-estimation). The first step in the MM-estimation is to find the S-estimator, then set the parameter regression using the M-estimation. The purpose of this study was to determine the effect of price index of foodstuffs ( ), the price index of education ), and the price index of health ) to the CPI for the province of east borneo, where the CPI data contains outliers, namely observation to 13, 31,and 32.
Analisis Autokorelasi Spasialtitik Panas Di Kalimantan Timur Menggunakan Indeks Moran dan Local Indicator Of Spatial Autocorrelation (LISA) Nurmalia Purwita Yuriantari; Memi Nor Hayati; Sri Wahyuningsih
EKSPONENSIAL Vol 8 No 1 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

In the last few decades has developed statistical methods relating to spatial science, is the spatial statistics. Spatial Statistics aims to analyze spatial data. The case studies in this study was the amount of hotspots in East Kalimantan by Regency/City in years 2014-2016. This study aimed to analyze the existence of spatial autocorrelation in the data the amount of hotspots as well as determine the level of vulnerability to potential areas of forest and land fires in East Kalimantan by Regency/City in 2014-2016. The method used to analyze the global spatial autocorrelation is the Moran Index method and Local Indicators of Spatial Autocorrelation (LISA) for analyze spatialautocorrelation locally. The results of the analysis of global spatial autocorrelation using the Moran index with α = 20% showed there spatial autocorrelation amount of hotspots in East Kalimantan in 2014, 2015, and 2016. Meanwhile, the analysis results locally using LISA showed that there spatial autocorrelation in several Regency/City in East Kalimantan in 2014, 2015 and 2016. The analysis results Regency/City that belong to the vulnerable category of forest and land fires is Bontang City, Kutai Barat Regency, Kutai Kartanegara Regency, Mahakam Ulu Regency, dan Penajam Paser Utara Regency and Samarinda City.
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 Jumlah Kematian Bayi di Provinsi Nusa Tenggara Timur Tahun 2015 Dengan Regresi Poisson Pratama Yuly Nugraha; Memi Nor Hayati; Desi Yuniarti
EKSPONENSIAL Vol 8 No 2 (2017)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Poisson regression is one of the non-linear regression analysis whose response variable is modeled with Poisson distribution. The parameter estimation Poisson regression model using Maximum Likelihood Estimation (MLE). This study aims to model the number of infant mortality in East Nusa Tenggara Province in 2015 and what factors affect the occurrence of cases of infant mortality in East Nusa Tenggara Province using Poisson regression. The results of research with Poisson regression factors influencing the number of infant mortality is the number of deliveries assisted by health personnel (x1), the percentage of pregnant women receiving FE3 tablets (x2), the number of obstetric complications handled (x4), the percentage of low birth weight babies (x5), the number of exclusively breastfed babies (x6), the percentage of households Live clean and healthy (x7), and the number of deliveries is helped by non-medical personnel (x8).
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.
Optimasi Fuzzy C-Means Menggunakan Particle Swarm Optimization Untuk Pengelompokan Kabupaten/Kota Di Pulau Kalimantan (Studi Kasus: Data Indikator Kesejahteraan Rakyat Tahun 2020) Deviyana Nurmin; Memi Nor Hayati; Rito Goejantoro
EKSPONENSIAL Vol 14 No 1 (2023): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (948.946 KB) | DOI: 10.30872/eksponensial.v14i1.1002

Abstract

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.
Model Regresi Logistik Spasial Tiara Nurul Ma’ala; Desi Yuniarti; Memi Nor Hayati
EKSPONENSIAL Vol 7 No 2 (2016)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

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Abstract

Logistic regression modeling procedure is applied to model the response variable (Y) which is based on one or more categorical explanatory variable (X) which is categorical or continuous. In the application of logistic regression is often found that there are spatial influences that affect the model. The existence of spatial relationships between regions that cause necessary to accommodate the spatial diversity into the model, so that the analysis used logistic regression spatial. First law of geography says that everything is related to everything else, but near things are more related than distant things. Then, when a region becomes a major cause of the spread of a disease is suspected, the region will provide the spread of a disease to the new area adjacent to it. The way to find out the adjacent area with the same characteristics can be done with spatial logistic regression method.The spread of TB disease in Samarinda City is quite high. TB is a chronical disease which has been known by the public and feared of its infection. This study’s aim is to determine the appropriate model to estimate the spread of TB disease. From this model it is known that the factors that influence the number of people with TB disease in every village in Samarinda City in the year 2013 are the number of primary school in every village and the spatial effect. This means that there is the influence of spatial factors to the spread of TB disease in every village in Samarinda City in the Year 2013.
Co-Authors - Purhadi Abda Abda Alifta Ainurrochmah Amanah Saeroni Anak Agung Gede Sugianthara Andi M. Ade Satriya Anjani Anjani Annabaa Aulia, Muzizah Asnita, Asnita Astuti, Putri Sri Cahyaningsih, Ariyanti Candra Dewi, Ni Luh Ayu Casuarina, Indah Putri Damayanti, Elok Dani, Andrea Tri Rian Darnah Darnah Darnah, Darnah Desi Yuniarti Deviyana Nurmin Dewi, Isma Diani, Milda Alfitri Dini Elizabeth Dwi Husnul Mubiin Edy Fahrin Emi Harmianti Eric Sapto Raharjo Fatma wati Fauzia, Rina Fauziyah, Meirinda Fidia Deny Tisna Amijaya Goenjatoro, Rito Hadisti, Zahrah Dhafina Hadistii, Zahrah Dhafiinia Hidayatullah, Aji Syarif Hisintus Suban Hurint Ibrahim, Rizky Nur Iim Masfian Nur Ika Purnamasari Ika Purnamasari Ika Puspita, Ika Ineu Sintia Julia Julia Julnita Bidangan Karima, Nabila Al Kartika Ramadani Khairun Nida Khasanah, Lisa Dwi Nurul Krisna Rendi Awalludin Lestari, Nur Aini Ayu Lili Widyastuti Lupinda, Indah Cahyani M. Fathurahman Mahmuda, Siti Marsandy, Aldwin Falah Hasan Masrawanti Masrawanti Meiliyani Siringoringo Messakh, Gerald Claudio Mochammad Imron Awalludin Muhammad Jainudin Nabilla, Maghrisa Ayu Nana Nirwana Nanda Arista Rizki Nida, Khairun Ningsih, Eva Lestari Nohe, Darnah Andi Nur - Azizah Nur Annisa Fitri Nur Azizah Nur Fajar Apriyani Nurmalia Purwita Yuriantari Nurmin, Deviyana Nurul Hidayah Oroh, Chiko Zet Paradilla, Yunda Sasha Pratama Yuly Nugraha Pratiwi, Reni Purhadi - Putri Ayu Dwi Lestari, Putri Ayu Dwi Putri, Nurlia Sucianti Rahmah, Putri Aulia Rahmaulidyah, Fatihah Noor Ramadani, Kartika Riska Veronika Rito Goejantoro, Rito Ronald Tediwibawa Safitri, Ranita Nur Sari, Devi Nur Endah Sa’diyah, Lita Vindiyatus Sekar Nur Utami Sembiring, Rinawati Sifriyani, Sifriyani Sinaga, Julia Oriana Siringoringo, Meiliyani Siti Mahmuda Siti Rahmah Binaiya Soraya, Raihana Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Suerni, Widya - Sumartini Sumartini Surya Prangga Suyitno Suyitno Suyitno Suyitno Suyitno Suyitno Suyitno Suyono, Ari Krisna Syamsiar, Syamsiar Syaripuddin Syaripuddin Tiara Nur Hikmaulida Tiara Nurul Ma’ala Utami, Riska Putri Verawaty Bettyani Sitorus Wahyuni, Nanda Anggun Yuki Novia Nasution, Yuki Novia Yuniarti, Desi