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All Journal EKSAKTA: Journal of Sciences and Data Analysis Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Indonesian Journal of Statistics and Its Applications ESTIMASI: Journal of Statistics and Its Application PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS

NURTITI SUNUSI

Department Of Statistics, Faculty Of Mathematics And Natural Science, Hasanuddin University, Makassar, Indonesia.

Author-ID : 393105

Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Other

Published : 11 Documents Claim Missing Document

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Estimasi Hazard Rate Temporal Point Process Nurtiti Sunusi
Jurnal Matematika, Statistika dan Komputasi Vol. 9 No. 1: July 2012
Publisher : Department of Mathematics, Hasanuddin University

sifatnya acak baik dalam ruang maupun waktu. Point process dikarakterisasikan oleh intensitas bersyaratnya (conditional Intensity). Pada penelitian ini intensitas bersyarat proses titik temporal (temporal point process) dipandang sebagai proses pembaruan (renewal process) dimana selisih waktu sejak kejadian terakhir tidak tergantung pada selang sebelumnya. Intensitas bersyarat yang bersesuaian pada model ini disebut hazard rate. Untuk mengestimasi parameter hazard rate digunakan metode Hazard Rate Single Decrement (HRSD) yang diadaptasi dari metode estimasi dalam studi aktuaria yang dipakai dalam pembentukan tabel mortalita. Pada metode ini, satu individu diasosiasikan dengan satu kejadian. Jika informasi yang digunakan pada pembentukan tabel mortalita adalah tanggal lahir dan tanggal meninggal, maka pada temporal point process digunakan informasi waktu mulai dan berakhirnya suatu kejadian. Selanjutnya pada bagian akhir, ditinjau dua kasus yaitu estimasi hazard rate dengan waktu antar kejadian berdistribusi uniform dan eksponensial.

ANALISIS KOVARIANSI RANCANGAN PETAK TERBAGI PADA RANCANGAN ACAK KELOMPOK (RAK) DENGAN DATA HILANG YULIANA.DEWI Putri; Nasrah Sirajang; Nurtiti Sunusi
Jurnal Matematika, Statistika dan Komputasi Vol. 14 No. 2 (2018): January 2018
Publisher : Department of Mathematics, Hasanuddin University

Pada skripsi ini akan di kaji analisis kovariansi rancangan petak terbagi (split plot design) pada Rancangan Acak Kelompok (RAK) dengan satu data hilang. Analisis kovariansi pada rancangan petak terbagi dilakukan melalui dua analisis yaitu analisis petak utama dan analisis anak petak. Sebagai aplikasi selanjutnya diberikan studi kasus pada percobaan dengan satu data hilang. Dari hasil analisis rancangan petak terbagi pada Rancangan Acak Kelompok (RAK) dengan satu data hilang diperoleh nilai koefisien keragaman dari analisis variansi lebih kecil dibandingkan analisis kovariansi. Hal ini menunjukkan analisis variansi lebih baik dibandingkan analisis kovariansi pada rancangan petak terbagi (split plot design) pada Rancangan Acak Kelompok (RAK) dengan satu data hilang.

Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Sparse Georgina M. Tinungki; Nurtiti Sunusi
Jurnal Matematika, Statistika dan Komputasi Vol. 15 No. 2 (2019): JMSK Vol. 15, No. 2, January 2019
Publisher : Department of Mathematics, Hasanuddin University

Abstract Sparse Principal Component Analysis (Sparse PCA) is one of the development of PCA. Sparse PCA modifies new variables as a linier combination of p old variables (original variable) which is yielded by PCA method. Modifying new variables is conducted by producing a loading yang sparse matrix, such that old variable which is not effective (value of loading is zero) able be exit from PCA. In this study, Sparse PCA method was applied on data of Indonesia Poverty population in 2015, that contains 13 variables and 34 observation with variable reduction such that yields 4 (four) new variables, which can explain 80.1% of total variance data. This study show, the loading matrix that has been yielded by using Sparse PCA method to become sparse with there exist 11 elements (loading value) zero entry of matrix, such that the model that has been produced to be simpler and easy to be interpreted. Keywords: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse Abstrak Sparse Principal Component Analysis (Sparse PCA) merupakan salah satu pengembangan dari metode PCA. Sparse PCA memodifikasi variabel-variabel baru yang merupakan kombinasi linear dari variabel lama (variabel asli) yang dihasilkan oleh metode PCA. Pemodifikasian variabel baru ini dilakukan dengan dengan menghasilkan matriks loading yang sparse sehingga variabel lama yang tidak efektif (memiliki nilai loading sama dengan nol) dapat dikeluarkan dari model PCA. Pada penelitian ini, metode Sparse PCA diterapkan pada data Indikator Kemiskinan Penduduk Indonesia Tahun 2015 yang memuat 13 variabel dan 34 observasi dengan reduksi variabel menghasilkan 4 (empat) variabel baru yang telah mampu menjelaskan 80,1% dari total variansi data. Hasil penelitian menunjukkan, matriks loading yang dihasilkan menggunakan metode Sparse PCA menjadi sparse dengan terdapat 11 elemen (nilai loading) matriks bernilai nol sehingga model yang dihasilkan menjadi lebih sederhana dan mudah untuk diinterpretasikan. Kata Kunci: Principal Component Analysis, Sparse Principal Component Analysis, reduksi dimensi, matriks loading yang sparse

Bahasa Indonesia: Bahasa Indonesia Puji Puspa Sari; Erna Tri Herdiani; Nurtiti Sunusi
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 3 (2021): May, 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v17i3.12629

Outliers are observations where the point of observation deviates from the data pattern. The existence of outliers in the data can cause irregularities in the results of data analysis. One solution to this problem is to detect outliers using a statistical approach. The statistical approach method used in this study is the Minimum Vector Variance (MVV) algorithm which has robust characteristics for outliers. The purpose of this research is to detect outliers using the MVV algorithm by changing the data sorting criteria using the Robust Depth Mahalanobis to produce maximum detection. The results obtained from this study are that RDMMVV is superior to the observed value in showing the outliers and the location of the outliers in the data plot compared to DMMVV and MMVV.

Updating Reservoir Models Using Ensemble Kalman Filter Sutawanir Darwis; AGUS YODI GUNAWAN; SRI WAHYUNINGSIH; NURTITI SUNUSI; ACENG KOMARUDIN MUTAQIN; NINA FITRIYATI
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 10, No 1 (2010)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v10i1.1007

The Ensemble Kalman Filter (EnKF) has gain popularity as a methodology for real time updates ofreservoir models. A sample of models is updated whenever observation data available. Successfulapplication of EnKF to estimate reservoir properties has been reported. A flow modeling is missing inthis research area. This paper presents the applicability of EnKF in flow modeling for three cases:infinite reservoir, bounded reservoir and one dimensional composite reservoir. The solution of flowequation was derived and used as a modeling component of state space modeling of Kalman filterupdating formula. This three reservoir models shows that the EnKF methodology can be used forupdating the reservoir models.

Estimasi Parameter Model Poisson Hidden Markov Pada Data Banyaknya Kedatangan Klaim Asuransi Jiwa Vieri Koerniawan; Nurtiti Sunusi; Raupong Raupong
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi
Publisher : Hasanuddin University

The Poisson hidden Markov model is a model that consists of two parts. The first part is the cause of events that are hidden or cannot be observed directly and form a Markov chain, while the second part is the process of observation or observable parts that depend on the cause of the event and following the Poisson distribution. The Poisson hidden Markov model parameters are estimated using the Maximum Likelihood Estimator (MLE). But it is difficult to find analytical solutions from the ln-likelihood function. Therefore, the Expectation Maximization (EM) algorithm is used to obtain its numerical solutions which are then applied to life insurance data. The best model is obtained with 2 states or m = 2 based on the smallest Bayesian Information Criterion (BIC) value of 338,778 and the average predicted number of claims arrivals is 0.385 per day.

Pemodelan Regresi Nonparametrik Spline Poisson Pada Tingkat Kematian Bayi di Sulawesi Selatan Novilia Jao; Anna Islamiyati; Nurtiti Sunusi
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.11997

Poisson regression analysis is a method used to analyze the relationship between predictor variables and response variables with a Poisson distribution. However, not all data have an orderly pattern, so the Poisson regression is not appropriate to use. To solve this problem, a multivariable Poisson nonparametric regression with a spline truncated estimator was used. In this research, the estimation parameters of multivariable Poisson nonparametric regression was applied to data of infant mortality rates in South Sulawesi in 2017. The infant mortality rate can be measured from the number of infant deaths under one year. The method of selecting the optimal knot point uses the Generalized Cross Validation (GCV) method. The best model is formed on a linear spline model with one knot point. Based on the estimation of the parameters formed, it shows that the variable number of babies with low birth weight (x1) and the number of infants who are exclusively breastfed (x3) significantly affect the number of infant deaths. Keywords: GCV, Multivariable Nonparametric Regression, Poisson, Spline Truncated, Total Infant Mortality.

Comparison of Negative Binomial Regression Model and Geographically Weighted Poisson Regression on Infant Mortality Rate in South Sulawesi Province Siswanto Siswanto; Edy Saputra R; Nurtiti Sunusi; Nirwan Ilyas
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p170-179

The number of infant mortality cases is an important indicator to assess the quality of a country's public health. A number of studies argue that the case of infant mortality has a close relation to the living area condition and the social status of the parents. Indirectly, the quality of life of babies in a country will impact the nation's quality of life in general. Therefore, many efforts are required to reduce the infant mortality in Indonesia. One of the steps that could be done to overcome this issue is to analyze the causative factors. The statistical method that has been developed for data analysis taking into account current spatial factors is the Geographically Weighted Poisson Regression (GWPR) with a weighted Bisquare kernel function. Based on the partial estimation with the GWPR model, there are seven groups based on significant variables that affect the number of infant deaths in South Sulawesi Province. Of the seven groups formed, the first group is the Selayar Islands where all variables have a significant effect. This needs to be a concern for the South Sulawesi provincial government to improve facilities and infrastructure in the Selayar Islands, of course the location which is very far from the city center can affect access to drug reception, medical personnel and so on. Based on the results of the analysis of the factors that affect the number of infant deaths in South Sulawesi Province using a negative binomial regression approach and GWPR with a bisquare kernel weighting, it can be concluded that the GWPR model used is the best for analyzing the number of infant deaths in South Sulawesi Province because it has an AIC value. The smallest is 167.668.

Pemodelan Proporsi Kasus Tuberkulosis di Sulawesi Selatan Menggunakan Sparse Least Trimmed Squares Trigarcia Maleachi Randa; Georgina Maria Tinungki; Nurtiti Sunusi
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art6

The deadliest infectious disease in Indonesia is tuberculosis (TB), and South Sulawesi is one of the provinces that contributed the most tuberculosis cases in Indonesia in 2018 with 84 cases per 100,000 population. This study aims to identify variables that could explain the proportion of TB cases in South Sulawesi. The data used has many explanatory variables, and there are outliers. Sparse Least Trimmed Squares (LTS) analysis can be used to handle data that has many explanatory variables and outliers. The resulting sparse LTS model successfully selects and shrinks the variables to 14 variables only. In addition, based on the value of R2 and RMSE for the model evaluation, the sparse LTS shows satisfying results rather than classical LASSO. The government can focus on these factors if they want to reduce the proportion of TB cases in South Sulawesi.

Analisis Peluang Steady State Pada Kasus Covid-19 di Indonesia Menggunakan Rantai Markov Ika Pratiwi Haya; Andi Kresna Jaya; Nurtiti Sunusi
ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.24802

Covid-19 in Indonesia began to be recorded on March 2, 2020 with the number of positive patient cases as many as 2 people with the passage of time Covid-19 cases in Indonesia are always increasing. To see the development of Covid-19 cases in the future period, the opportunity for the number of Covid-19 cases can be used using the Markov chain. The Markov chain method is carried out using a transition probability matrix which is seen from the number of additions to positive Covid-19 patients in a steady state or a situation for a long period of time. Based on the results of the range of additions to the number of positive cases of Covid-19, 6 states were used. Furthermore, the calculation of the Markov Chain in the stationary state of Covid-19 cases in Indonesia after 328 days or 11 months obtained the probability of each state, namely state 1 of 0.0005, state 2 of 0.0069, state 3 of 0.1707, state 4 of 0.1462, state 5 of 0.1884 , and state 6 is 0.4873. Prediction of the addition of positive Covid-19 patients obtained results as many as 2058 patients in state 5 for July 1, 2022 with actual data as many as 2049 patients.

Co-Authors Aceng Komarudin Mutaqin Agus Yodi Gunawan Andi Kresna Jaya Anna Islamiyati Edy Saputra Rusdi Erna Tri Herdiani Georgina M. Tinungki Georgina Maria Tinungki Hedi Kuswanto Ika Pratiwi Haya Nasrah Sirajang Nina Fitriyati Nirwan Ilyas Nirwan Nirwan Novilia Jao Nur Aminah Ahmad Puji Puspa Sari Raupong, Raupong Siswanto Siswanto Sri Wahyuningsih Sutawanir Darwis Trigarcia Maleachi Randa Vieri Koerniawan YULIANA.DEWI Putri

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