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All Journal Media Statistika Jurnal Matematika Sains dan Teknologi Jurnal Varian Variansi : Journal of Statistics and Its Application on Teaching and Research JINAV: Journal of Information and Visualization Jurnal Dedikasi ARRUS Journal of Mathematics and Applied Science SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education VARIANSI: Journal of Statistics and Its Application on Teaching and Research ARRUS Jurnal Pengabdian Kepada Masyarakat SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Ruliana
Department Of Statistics, Universitas Negeri Makassar
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Penerapan Analisis Regresi Nonparametrik Spline Truncated pada Pemodelan Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Provinsi Jawa Barat Zulkifli Rais; Ruliana; Mukhtazam Aqil Mukhtar
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 03 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm199

Abstract

Unemployed is someone who has entered the workforce, but does not have a job and is looking for work, setting up a business, and who already has a job but has not yet started working. One indicator that can be used to measure unemployment is The Unemployment Rate. West Java Province is the province in first place with the highest unemployment rate in Indonesia. Based on BPS data, the unemployment rate in West Java Province in 2022 reaches 8.31%. The method that can be used to model factors that are thought to influence the unemployment rate in West Java Province in 2022 is nonparametric spline regression. The nonparametric spline regression method was used in this research because this method is very good at modeling data that has changing patterns at certain intervals. The aim of this research is to get the best model of the factors that influence the unemployment rate and find out what factors significantly influence the unemployment rate in West Java Province in 2022. Based on parameter significance testing, it was found that all the variables used, namely Labor Force Participation Rate, Percentage of Poor Population, District/City Minimum Wage, Government Expenditures, and Average Years of Schooling had a significant effect on TPT in West Java Province in 2022. The value of the determination coefficient obtained was 99.5%.
INTERVENTION ANALYSIS INTIME SERIES DATA FOR FORECASTING BBRI STOCK PRICES Andi Ilham Azhar Mangkona; Aswi Aswi; Ruliana Ruliana
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 14 No. 01 (2025): Volume 14 Nomor 1 (Maret 2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/bhvqmr60

Abstract

Intervention model analysis is a statistical technique used to assess the impact of an intervention event, caused by internal or external factors, on a time series dataset. The primary goal of this analysis is to quantify the magnitude and duration of the effects on the time series. Intervention models are generally classifiedinto two types: step function and pulse function. The step function represents an intervention event with a long-term influence, while the pulse function captures the effects of an intervention within a specific time span. This study examines the stock price data of BBRI from March 2017 to June 2020, with the intervention point identified as the onset of COVID-19 in Indonesia, specifically during the first week of March (t = 155). ARIMA modeling was applied to pre-intervention data to determine the order of intervention (b, s, r). The analysis identifiedARIMA (2, 1, 0), as the best-fitting model, characterized by a step function intervention with parametersb = 0, s = 2, and r = 0. Theaccuracy of the forecasting results was evaluated using the Mean Absolute Percentage Error (MAPE), which yielded a value of 8.48%.
Co-Authors Ahmar, Ansari Saleh Alfairus, Muh. Qodri Andi Feriansyah Andi Ilham Azhar Mangkona Annas, Suwardi Arfan Shalihin Amir Aswi, Aswi Devi Carolin Wongkar Idul Fitri Abdullah Abdullah Irwan Irwan M. Nadjib Bustan M. Nadjib Bustan Muh. Hizbul Zainul Muttaqim Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Fahmuddin S Muhammad Isbar Pratama Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Nadjib Bustan Muhammad Rifandi Mukhtazam Aqil Mukhtar Nor Zila Abdul Hamid Nur Shanty Nurtiwi Nurtiwi Nurul Madany Ogi Suhendra Rahmat Hidayat Rezki Amalia Idrus Rezky Novita Said Sitti Masyitah Meliyana R. Sudarmin Suhartin M Syafruddin Side Titi Kurnianti HR Wahidah Sanusi Wildan Mujahid Zakiyah Mar'ah Zulkifli Rais