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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Teknologi
Malecita Nur Atala Singgih
Universitas Islam Indonesia
Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering

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Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM untuk Mendapatkan Estimasi Robust Regression Terbaik dalam Perkara Pidana di Indonesia: Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM Malecita Nur Atala Singgih; Achmad Fauzan
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.18630

Abstract

Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98  
Analisis Pengaruh Mobilitas Penduduk terhadap Kasus Covid-19 Selama Masa Pandemi di Indonesia Menggunakan Regresi Linier Berganda Dining Dwi Suci Riyani; Malecita Nur Atala Singgih; Zumrotul Wahidah; Edy Widodo
Jurnal Teknologi Vol 14 No 2 (2021): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknologi Industri, Institut Sains & Teknologi AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v14i2.3636

Abstract

Mobility of population is closely related and is thought to have an effect on the number of COVID-19 cases. From time to time, the movement of people's mobility is mapped geographically in various categories, namely retail and recreation, grocery stores and pharmacies, parks, public transportation centers, workplaces, and residential areas. Population mobility is very closely related and is thought to have an effect on the number of Covid-19 cases, population movements greatly affect the social interaction of the population itself, the more activities residents carry out, the more interactions are created and can cause the spread of the virus chain to increase. Therefore, population mobility can be used as a means for the government to take more efficient policies in solving the problems of the COVID-19 pandemic. This research uses the Multiple Regression method which aims to find out what categories of factors affect Covid-19 cases in Indonesia during the pandemic. The results found that the mobility category of residential areas, parks, and public transportation had a significant effect on Covid-19 cases in Indonesia during the pandemic.
Co-Authors ACHMAD FAUZAN Dining Dwi Suci Riyani Edy Widodo Zumrotul Wahidah