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All Journal Media Statistika Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Varian Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Abdi Insani ESTIMASI: Journal of Statistics and Its Application International Journal Of Science, Technology & Management (IJSTM) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Jurnal Siger Matematika Journal of Research Applications in Community Services Eksponensial STATISTIKA Journal of Education Technology and Information System
Sifriyani, Sifriyani
Statistics Study Program, Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Mulawarman University, Samarinda
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Stunting Prevalence Modeling Using Nonparametric Regression of Quadratic Splines Tutik Handayani; Sifriyani Sifriyani; Andrea Tri Rian Dani
Jurnal Varian Vol 7 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.2916

Abstract

The nonparametric regression approach is used when the shape of the regression curve between the response variable and the predictor variable is assumed to be of unknown shape. The advantages of nonparametric regression have high flexibility. A nonparametric regression approach that is often used is truncated spline which has an excellent ability to handle data whose behavior changes at certain sub-sub intervals. The purpose of this study is to obtain the best model of multivariable nonparametric regression with linear and quadratic truncated spline approaches using the Generalized Cross Validation (GCV) and Unbiased Risk (UBR) methods and to find out the factors that influence the prevalence of stunting in Indonesia in 2021. The data used were the prevalence of stunting as a response variable and the predictor variable used was the percentage of infants receiving exclusive breastfeeding for 6 months, the percentage of households that have proper sanitation, the percentage of toddlers who get Early Initiation of Breastfeeding (IMD), the percentage of poor people, and the percentage of pregnant women at risk of SEZ. The results showed that the best quadratic truncated spline nonparametric regression model in modeling stunting prevalence was quadraic truncated spline using the GCV method with three knot points. This model has a minimum GCV value of 7.29 with an MSE value of 1.82 and a R2 value of 94.07%.
Pemodelan Produk Domestik Regional Bruto (PDRB) di Indonesia Periode 2018-2021 dengan Analisis Regresi Data Panel Kesuma, Ahmad Rizky; Rinanda, Farikah Ayu; Astafira, Ilyas; Afriani, Nur; Fadlirhohim, Rizki Dwi; Lestari, Tri Septi Ayu; Sifriyani, Sifriyani
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i2.27522

Abstract

High and sustainable economic growth is the main condition or a must for the continuity of economic development and increased welfare. GRDP is defined as the total added value generated by all business units in an area. The analytical method used in this study is panel data regression analysis. Panel data regression is used to observe the relationship between one dependent variable and one or more independent variables. This study aims to determine the panel regression model of Gross Regional Domestic Product (GDP) in Indonesia for the period 2018 to 2021 and to find out whether the domestic investment investment variable and the cooperative business volume variable affect GRDP in Indonesia for the 2018-2021 period. The results obtained in this study are that the best panel regression model for modeling GRDP is the FEM model and the variable Domestic Investment Investment and Cooperative Business Volume are variables that have a significant effect on the GRDP variable in Indonesia for the 2018-2021 period.
Penerapan Metode K-Means Dalam Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator Pendidikan Messakh, Gerald Claudio; Hayati, Memi Nor; Sifriyani, Sifriyani
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.v14i2.1103

Abstract

Cluster analysis is an analysis that aims to classify data based on the similarity of spesific characteristics. Based on the structure, cluster analysis is divided into two, namely hierarchical and non-hierarchical methods. One of the non-hierarchical methods used in this study is K-Means. K-Means is a partition-based non-hierarchical data grouping method. This purpose of this study is to obtain the best results of grouping regencies/cities on the island of Kalimantan based on education indicators using the K-Means method based on the smallest ratio of standard deviation. Based on the results of the analysis, it can be concluded that the best grouping results based on the smallest ratio of standard deviation is 0.6052 which produces optimal clusters of 2 clusters with the first cluster consisting of 14 Regencies/Cities while the second cluster consists of 42 Regencies/Cities on Kalimantan Island
Perbandingan Metode Klasifikasi Naive Bayes dan K-Nearest Neighbor: Studi Kasus : Status Kerja Penduduk Di Kabupaten Kutai Kartanegara Tahun 2018 Novalia, Viona; Goejantoro, Rito; Sifriyani, Sifriyani
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 (979.072 KB) | DOI: 10.30872/eksponensial.v11i2.659

Abstract

Classification is a technique to build a model and assess an object to put in a particular class. Naive Bayes is one of algorithm in the classification based on the Bayesian theorem, which assumes the independencies of one class with another class. K-nearest neighbor is an algorithm in the classification method for classifiying based on data that has a closest distance between one object and another object. Naive Bayes and k-nearest neighbor methods are used in classification of the employment status of citizen in Kutai Kartanegara regency because has a good accuracy and produce a small error rate when using large data sets. This research aim to compared optimal performance accuracy of both methods on the classifiying of the employment status of citizen. The data used are employment status of citizen in Kutai Kartanegara Regency based on SAKERNAS of East Kalimantan Province in 2018 and used 5 factors namely age, sex, status in the household, marital status, and education to predict employment status of citizen. Based on the analysis, classification the employment status of citizen with naive Bayes method has accuracy of 90,08% and in the k-nearest neighbor has accuracy of 94,66%. To evaluate the accuracy of classification used calculation of Press’s Q. Based on Press’s Q value showed that both of classification methods are accurate. From that analysis, can be concluded that the k-nearest neighbor method works better compared with the naive Bayes method for the case of the employment status of citizen in Kutai Kartanegara Regency.
Analisis Regresi Linier Berganda Dalam Estimasi Indeks Pembangunan Manusia di Indonesia Khotimah, Ariska Khusnul; Rahman, Athaya Azahra; Alam, Muhammad Zainul; Adawiyah, Rabiatul; Nur, Yumi Handayani; Aufi, Tresna Restu; Sifriyani, Sifriyani
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1318

Abstract

The human development index has an important role in determining and measuring achievements in developing the quality of life and development ranking of a country. By increasing the level of the part forming the human development index, it will greatly influence various aspects in terms of health, longevity, quality of life, and improving the quality of human resources. Therefore, this research aims to determine the influence of the percentage of young people who have never attended school, the percentage of the population with higher education, the minimum wage, the percentage of young people who are married under age, the average per capita food expenditure, the number of people receiving 4G LTE signals, and the level of open unemployment on the index. Human Development . This research uses a Multiple Linear Regression analysis method which can be used to look for patterns of relationship between one response variable and only one predictor variable. The data in this study is secondary data obtained from the Central Agency covering 34 provinces in Indonesia in 2023. In the test results using multiple linear regression, a p-value coefficient of determination was obtained of 0,8073, indicating that there was 80,73% variation what occurs in the Human Development Index is caused by the variables Percentage of Youth Never Attending School, Percentage of Population Having Higher Education, Minimum Wage, Percentage of Youth Married Under Age, Average Per Capita Food Expenditure, Number of Receive 4G LTE Signals, and Open Unemployment Rate. This indicates that there are around 19,27% other variables that influence the Human Development Index.
IMPLEMENTATION OF GEOGRAPHICALLY WEIGHTED PANEL REGRESSION WITH GAUSSIAN KERNEL WEIGHTING FUNCTION IN THE OPEN UNEMPLOYMENT RATE MODEL Saska, Indria; Sifriyani, Sifriyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp733-742

Abstract

This study analyzes the factors influencing the Open Unemployment Rate in Kalimantan using the Geographically Weighted Panel Regression (GWPR) model with Gaussian kernel weighting functions. The GWPR model, a local panel regression approach for spatial data, is compared with the global Fixed Effect Model (FEM). Spatial weighting for parameter estimation employs Fixed Gaussian and Adaptive Gaussian kernels, with the optimum bandwidth determined through Cross Validation (CV), resulting in a minimum CV value of 25.536 for the Adaptive Gaussian Kernel. Local factors identified as influencing the Open Unemployment Rate include the Labor Force Participation Rate ( ), Expected Years of Schooling ( ), Average Years of Schooling ( ), Total Population ( ), Number of Poor People ( ), and the Growth Rate of Gross Regional Domestic Product at Constant Prices ( ). The results underscore the importance of spatial heterogeneity in understanding regional unemployment dynamics, as local variations in these factors significantly affect unemployment rates. Moreover, the GWPR model exhibits a notable improvement in predictive accuracy and goodness of fit compared to the global panel regression model, achieving a coefficient of determination of 77.96% and a Root Mean Square Error (RMSE) of 0.2726. These findings highlight the GWPR model's potential in regional economic studies and policymaking, offering precise insights into local determinants of unemployment and facilitating the development of targeted and effective interventions.
GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING OF POVERTY RATES IN TROPICAL RAINFOREST AREAS OF KALIMANTAN Mumtaz, Ghina Fadhilla; Suyitno, Suyitno; Sifriyani, Sifriyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp903-916

Abstract

When applied to spatial panel data, the Geographically Weighted Panel Regression (GWPR) model is a localized version of the linear regression model. The Fixed Effect Model (FEM) inside estimator is used as a global model in this investigation. The purpose of this research is to obtain a GWPR model and identify the variables that affect the proportion of the impoverished in 56 districts and cities located in Kalimantan's humid tropical forest region between 2019 and 2022. The Weighted Least Square (WLS) approach, which provides geographic weighting in addition to the Least Square method, is used for estimating the parameters of the GWPR model. The optimal weighting function chosen from the adaptive bisquare, adaptive tricube, and adaptive gaussian weightings is the spatial weighting function used in the GWPR model estimate in this work. For determining the ideal bandwidth, the Cross Validation (CV) criterion is applied. According to the study's findings, the optimal weighting function is adaptive gaussian, which yields the best GWPR model with a CV of 8.8740 at the lowest. The GWPR model parameters were tested, and the results showed that both local and global influences affect the percentage of the population living in poverty. The gross domestic product (GDP), the open unemployment rate, the average length of education, the number of workers, and life expectancy are local factors that affect the percentage of the poor; on the other hand, the number of workers is a global factor that affects the percentage of the poor.
I-Regs (Internet-Regression Analysis) as a Statistical Innovation in Nonparametric Regression Modeling Dani, Andrea; Budiantara, I Nyoman; Nuraini, Ulfa Siti; Yustanti, Wiyli; Sifriyani; Putra, Fachrian Bimantoro
Journal of Education Technology and Information System Vol. 1 No. 02 (2025): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jetis.v1i02.35288

Abstract

This research develops an information system based on the R-Shiny Dashboard, allowing users to perform nonparametric regression modeling. Internet-Regression Analysis (I-Regs) is the name of a dashboard that has been successfully developed. I-Regs provides a complete model library in regression analysis modeling, including parametric, nonparametric, and semiparametric regression. It is hoped that I-Regs can become a valuable tool for researchers, practitioners, and students in modeling regression analysis and solving various data analysis problems.
PELATIHAN ANALISIS DATA DENGAN SOFTWARE R BAGI SISWA SMA NEGERI 8 SAMARINDA Sari, Nariza Wanti Wulan; Sifriyani, Sifriyani; Suyitno, Suyitno; Wahyuningsih, Sri; Yuniarti, Desi; Purnamasari, Ika; Mahmudah, Siti; Nurmayanti, Wiwit Pura; Widyaningrum, Erlyne Nadhilah; Nugraha, Pratama Yuly; Pangruruk, Thesya Atarezcha; Hidayanty, Nurul Ilma; Kosasih, Raditya Arya; Bahriah, Ayu
Jurnal Abdi Insani Vol 12 No 7 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i7.2136

Abstract

Students of SMA Negeri 8 Samarinda have received material on statistics since grade X. In the learning process, teachers use Microsoft Office Excel software which is closed source. So through this community service activity, a solution is provided by disseminating data analysis and alternative open source software 'R'. Community service activities are packaged in the form of training. Evaluation of activities in the form of pretest and posttest questionnaires and activity feedback surveys. This activity was carried out on September 11, 2024 in the Computer Laboratory Room of SMA Negeri 8 Samarinda. The number of students who participated in this activity consisted of 36 students. Based on the analysis of the pre-test and post-test data, it was concluded that there was an increase in student understanding after the training. The results of the feedback stated that the training material was easy, the explanations given were considered interesting, and the training activities were considered useful by the participants. Furthermore, participants hope that there will be follow-up activities to hold similar activities again.
REGRESI NONPARAMAETRIK SPLINE PADA DATA LAJU PERTUMBUHAN EKONOMI DI KALIMANTAN Purnaraga, Tirta; Sifriyani, Sifriyani; Prangga, Surya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 3 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1252.884 KB) | DOI: 10.30598/barekengvol14iss3pp343-356

Abstract

Economic Growth Rate (EGR) is an important indicator for measuring the success of an economy's development. The welfare and progress of an economy is determined by the amount of growth shown by changes in the quantity of goods and services produced nationally. High economic growth is a goal that is expected to be achieved in a developing country. Many factors affect EGR in Kalimantan, so it is necessary to do modeling to find out the factors that significantly affect EGR. This study uses 6 factors that are suspected to influence EGR, namely the labor force participation rate, the number of large and medium industries, the average length of schooling, regional income and expenditure budgets, general allocation funds and rice productivity. The data is 2017 data obtained from the Central Bureau of Statistics in 5 provinces in Kalimantan. The method used to model the LPE is spline nonparametric regression and the optimal knot point is 3 knot points based on the smallest Generalized Cross Validation (GCV) value of 1.208. The research results, the best model is obtained with a R2 value of 82.15 percent and a Mean Square Error (MSE) of 0.805. The results of the study provide information that the factors that influence the LPE are the level of labor force participation, the number of large and medium industries, the average length of schooling, regional income and expenditure budgets, general allocation funds and rice productivity.
Co-Authors A'yun, Qonita Qurrota Afif Nurdiansyah, Mochamad Afriani, Nur Alam, Muhammad Zainul Andrea Tri Rian Dani Angeline Seru, Indra Anggraeni, Sitti Anisar, Anggi Putri Asnita, Asnita Astafira, Ilyas Atarezcha Pangruruk, Thesya Aufi, Tresna Restu Aulia, Nabila Bahriah, Ayu Chairunnisa, Nurul Rizky Christian, Diego Clemensius Arles Damayanti, Elok Dani, Andrea Dani, Andrea Tri Rian Darnah Andi Nohe Darnah, Darnah Dedi Rosadi Deni Sunaryo Eka Nur Amaliah Erlyne Nadhilah Widyaningrum Etty Puji Lestari Fadlirhohim, Rizki Dwi Fatia Fatimah Fauziyah, Meirinda Febriana Rinda Sihotang Febriyani, Eka Riche Fidia Deny Tisna Amijaya Gerald Claudio Messakh Hadi Koirudin Hidayanty, Nurul Ilma Hillidatul Ilmi I Nyoman Budiantara Ika Purnamasari Ilmi, Hillidatul Kesuma, Ahmad Rizky Khoiruddin, Ahmad Zulfikar Khotimah, Ariska Khusnul Kosasih, Raditya Arya Lestari, Tri Septi Ayu M. Fariz Fadillah Mardianto Mahmuda, Siti Mar'ah, Zakiyah Mar’ah, Zakiyah Meirinda Fauziyah Memi Nor Hayati Memi Nor Hayati Messakh, Gerald Claudio Mohammad Nurul Huda Muhammad Hunaipi Pratama Mumtaz, Ghina Fadhilla Nabilla, Maghrisa Ayu Nadia Serena NARITA YURI ADRIANINGSIH Nariza Wanti Wulan Sari Nasywa, Syarifah Novalia, Viona Nugraha, Pratama Yuly Nur, Yumi Handayani Nuraini, Ulfa Siti Nurmayanti, Wiwit Pura Padatuan, Aprianti Boma Pangruruk , Thesya Atarezcha Pangruruk Paradilla, Yunda Sasha Pasarella, Muhammad Danil Purnaraga, Tirta Putra, Fachrian Bimantoro Putri, Asyifa Charmadya Rabiatul Adawiyah Rahman, Athaya Azahra Rahmania Rahmania Raihani, Risti Rian Dani, Andrea Tri Rinanda, Farikah Ayu Rito Goejantoro, Rito Salsabila, Adellia Saputri, Marisa Nanda Sari, Ar Ruum Mia Saska, Indria Shalihatunnisa, Shalihatunnisa Siti Mahmuda SITI MAHMUDAH Sitohang, Frans Karta Sayoga Sri Wahyuningsih Sri Wahyuningsih Surya Prangga Suyitno Suyitno Suyitno Suyitno Tamba, Felicia Joy Rotua Tandi Kala, Ezra Alfrianto Tutik Handayani Tutik Handayani, Tutik Vita Ratnasari Wasono, Wasono Wianita Noviani Wiyli Yustanti Yuniarti, Desi Zarkasi, Rifka Nurfaiza Zen, Muhammad Aldani