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All Journal Variansi : Journal of Statistics and Its Application on Teaching and Research
Muhammad Kasim Aidid
Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar, Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Public Health

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REGRESI NONPARAMETRIK SPLINE MULTIVARIAT UNTUK MENENTUKAN FAKTOR YANG MEMPENGARUHI KEMISKINAN DI SULAWESI SELATAN Muh. Qodri Alfairus; Muhammad Arif Tiro; Muhammad Kasim Aidid
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 2 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm23857

Abstract

Poverty is a condition of economic inability to meet the average standard of living of the people in an area. The percentage of poverty in Indonesia reaches 9.41% or reaches 25.14 million people. On the island of Sulawesi, the poverty percentage of the population is still quite high. One of the regions with the highest percentage of poverty in Sulawesi Island is South Sulawesi Province with a poverty percentage of 8.69%, which is ranked 18th nationally. Poverty can be seen with two indicators, namely the percentage of poor people and the poverty depth index. This study uses 5 factors that are thought to affect poverty in South Sulawesi which include the Literacy Rate, Average Length of Schooling, Open Unemployment Rate, PDRB Per Capita, and School Participation Rate. The data used in this research is data from 2018 which comes from the Central Statistics Agency of South Sulawesi. The method used to model the percentage of poor population and the depth of poverty index is a multivariate spline nonparametric regression. This method is used because it is suitable in modeling data whose patterns change at certain subintervals. The best model that is produced from the nonparametric multi-variate spline regression in South Sulawesi is the knot 2 model and the most influencing factors are the School Participation Rate and GRDP Per Capita. This method is used because it is suitable in modeling data whose patterns change at certain subintervals. The best model that is produced from the nonparametric multi-variate spline regression in South Sulawesi is the knot 2 model and the most influencing factors are the School Participation Rate and GRDP Per Capita. This method is used because it is suitable in modeling data whose patterns change at certain subintervals. The best model that is produced from the nonparametric multi-variate spline regression in South Sulawesi is the knot 2 model and the most influencing factors are the School Participation Rate and GRDP Per Capita
PENDEKATAN MARKOV CHAIN UNTUK MENGANALISIS PERENCANAAN SUMBER DAYA MANUSIA DI KEPOLISIAN SEKTOR TAMALATE KOTA MAKASSAR Suhartin M; Ruliana Ruliana; Muhammad Kasim Aidid
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 2 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm23856

Abstract

this research, an analysis of Human Resources planning in the Makassar City Tamalate Police Sector uses Markov Chain. The data used are sourced from secondary data in the Makassar City Tamalate Police Sector from the last two years period, from 2018 to 2019. The Markov Chain is used to find out transfers that occur between police positions with the first process being determining states, calculating inter-state probability values, forming a transition probability matrix, and predicting the number of police officers for the next six years. Based on the research conducted, it can be concluded that five states were formed, the ranks of the Second Police Brigadiers up to the Chief Police Brigadier are classified as Non-Commissioned Officers positions, state two the ranks of Second Police Inspector Adjutant up to First Pollice Inspector Adjutant are clarified as Warrant Officers position, state three Second Police Inspector up to Police Commissioner Adjutant are clarified Low-Rank Officers, four state Pollice Coommisioner are clarified of Mid Rank Officer, state five additional and reduction of Police Members. Based on the results of forecasting, the probability for the most Police Members in the year 2020 to 2025 is a member domiciled as a Non-Commissioned Officers, the probability of a large number of Non-Commissioned Officers is relatively stable at 0.54 from 2020 to 2024 and will decrease by 0.01 in 2025. Probabilities the number of Warrant Officers has increased quite dramatically, in 2020 amounting to 0.35 continues to increase until 2025 which is equal to 0.42. Whereas the probability for the number of Police Members to be Low-Rank Officers has decreased every year from 2020 to 0.08 to 0.03 in 2025. Then for Mid Rank Officers the probability for the number of Police Members to remain stable every year is 0.01. The probability of the number of Police officers in 2020 is 0.02 and in 2025 it is 0.01
Co-Authors Alfairus, Muh. Qodri Muhammad Arif Tiro Ruliana Suhartin M