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All Journal Parameter: Journal of Statistics VARIANSI: Journal of Statistics and Its Application on Teaching and Research ARRUS Jurnal Pengabdian Kepada Masyarakat
Sudarmin
Universitas Negeri Makassar
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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PKM Workshop Pembuatan Micromodul Digital untuk Meningkatkan Keterampilan IT dalam Pengajaran Para Guru SMA Negeri 7 Takalar Sitti Masyitah Meliyana; Ruliana; Sudarmin; Rahmat Hidayat; Muh. Qodri Alfairus
ARRUS Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.abdiku4283

Abstract

Perkembangan teknologi digital menuntut guru untuk memiliki keterampilan dalam memanfaatkan media pembelajaran berbasis teknologi. Namun, guru-guru di SMA Negeri 7 Takalar masih menghadapi kendala dalam pembuatan dan penggunaan micromodul digital yang interaktif. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan meningkatkan keterampilan teknologi informasi para guru melalui workshop pembuatan micromodul digital menggunakan aplikasi Canva dan Heyzine Flipbook. Metode pelaksanaan meliputi sosialisasi, pelatihan, penerapan teknologi, pendampingan, evaluasi, dan keberlanjutan program. Kegiatan diikuti oleh 25 guru dengan latar belakang mata pelajaran yang beragam. Hasil menunjukkan bahwa 85% peserta mampu membuat micromodul digital sesuai standar, 75% berhasil mengintegrasikannya ke dalam Rencana Pelaksanaan Pembelajaran (RPP), dan 80% merasakan peningkatan interaktivitas pembelajaran di kelas. Kesimpulannya, pelatihan ini efektif meningkatkan keterampilan IT guru dan berdampak pada peningkatan kualitas pembelajaran di SMA Negeri 7 Takalar.
Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models (Case Study: Crime in South Sulawesi) Indi Nur Ridwan; Sudarmin; Zakiyah Mar'ah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm503

Abstract

The Geographically Weighted Regression (GWR) model operates by taking into account how the relationships between different factors change across geographic space. Meanwhile, the Mixed Geographically Weighted Regression (MGWR) model permits certain variables to exhibit spatially varying (local) effects, while other variables are assumed to have constant effects across all locations. Both models are relevant to be applied in crime studies influenced by variations in regional conditions. The objective of this study is to evaluate the GWR and MGWR approaches in selecting the best model to explain factors associated with crime cases in South Sulawesi. The data used include the number of crime cases in South Sulawesi in 2024 along with factors presumed to influence them. The investigation's outcomes suggest the GWR model demonstrates higher appropriateness compared to the MGWR model, evidenced by its reduced Akaike Information Criterion (AIC) score and a 98.44% coefficient of determination . Based on the best-fitting model, population density and the number of poor residents were identified as the main factors influencing criminality in South Sulawesi in 2024.
SPATIAL AUTOREGRESSIVE MODEL (SAR) AND SPATIAL ERROR MODEL (SEM) MODELING ON LIFE EXPECTANCY DATA IN SOUTH SULAWESI PROVINCE 2022 Ayu Pebriyanti; Hardianti Hafid; Sudarmin
Parameter: Journal of Statistics Vol. 5 No. 1 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2025.v5.i1.17397

Abstract

Spatial regression is a development of classical linear regression which takes into account the spatial or spatial effects of the data being analyzed. The Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM) methods include spatial regression models show that spatial effects on response variables and predictor variables. This research aims to model the factors that influence life expectancy in South Sulawesi Province in 2022. The analysis method used in this research is the SAR and SEM methods. The results show that based on the Lagrange Multiplier test values, there are lag and error dependencies. Based on the research results, it was found that the SAR and SEM models each had Akaike’s Information Criterion (AIC) values of 94.0069 and 90.6410, so the best model for analyzing the influence life expectancy value was the SEM model because the smallest had Akaike’s Information Criterion (AIC) value was obtained. The factors that have a significant influence on life expectancy are average years of schooling and gross regional domestic product which have a positive effect. Then, the percentage of poor population and per capita expenditure have a negative effect.
APPLICATION OF THE COPULA FRANK FOR ESTIMATING VALUE AT RISK (VAR) IN TELECOMMUNICATION SUB SECTOR STOCKS Mutiara; Sudarmin; Hardianti Hafid
Parameter: Journal of Statistics Vol. 5 No. 2 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2025.v5.i2.17823

Abstract

Investment is investing capital with the aim of getting money or additional profits. When investing, you need to pay attention to risks that can cause losses for investors. One method that is widely used to measure investment risk is Value at Risk (VaR). VaR often has limitations, especially in capturing non-linear dependencies between variables, so a copula function is needed that can handle moderate to strong dependencies. One of the copulas used is the Archimedian copula with Frank subcopula. This article aims to estimate investment risk using the Value at Risk (VaR) method based on the Frank copula approach and to analyze the dependency structure between stock returns. The main steps in estimating VaR using the Frank copula are calculating the return of each stock, estimating the parameters of the Frank copula, carrying out data simulations using Frank copula parameters, calculating the VaR value using Frank copula. The data used in this research comes from shares of PT. Telkom Indonesia Tbk and shares PT. Indosat Ooredoo Hutchison Tbk. These two stocks have a positive correlation of 0.136. However, such a low correlation may still indicate for non-linear dependencies or tail dependencies that cannot be captured by linear correlations, so additional analysis, namely Frank copula, is required. The estimated Frank copula parameter value is 0.825. From the VaR estimation results, the risk obtained at a 90% confidence level is -0.0222, at a 95% confidence level it is -0.0281 and at a 99% confidence level it is -0.0383.
Co-Authors Alfairus, Muh. Qodri Ayu Pebriyanti Hardianti Hafid Hardianti Hafid Indi Nur Ridwan Mutiara Rahmat Hidayat Ruliana Sitti Masyitah Meliyana R. Zakiyah Mar'ah