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All Journal Inferensi Journal of Applied Science, Engineering, Technology, and Education Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology VARIANSI: Journal of Statistics and Its Application on Teaching and Research ARRUS Jurnal Pengabdian Kepada Masyarakat SMART: Jurnal Pengabdian Kepada Masyarakat
Rais, Zulkifli
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Workshop on Student Graduation Decisions Using Statistical Methods at Takalar State Senior High School 7 Annas, Suwardi; Ahmar, Ansari Saleh; Rais, Zulkifli; H.S, Rahmat; Tri Utomo, Agung
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.abdiku4458

Abstract

This community service program was conducted at SMA Negeri 7 Takalar to enhance teachers’ ability to utilize statistical methods specifically logistic regression to support data-driven graduation decisions. The training addressed challenges related to manual graduation assessment processes that often lack objective analytical support. Participants were introduced to the basic concepts of logistic regression, followed by hands-on practice using an interactive R Shiny dashboard to analyze student data and estimate graduation probabilities. The results indicate that teachers were able to understand and apply statistical analysis procedures, interpret logistic regression outputs, and recognize the importance of evidence-based decision-making. This activity not only improved teachers’ data literacy but also supported digital transformation efforts in education and strengthened collaboration between Universitas Negeri Makassar and SMA Negeri 7 Takalar. The program is expected to contribute to more accurate, transparent, and data-informed graduation assessments in the future.
Perbandingan Model Value-at-Risk (VaR) Hybrid GARCH-EVT dan Model Standar dalam Pengukuran Risiko Ekstrem pada Portofolio Saham Sektoral di Indonesia Annisa Syalsabila; Ikhwana, Nur; Utomo, Agung Tri; Rahmanda, Lalu Ramzy; Rais, Zulkifli
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 03 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm461

Abstract

This study aims to construct an optimal portfolio and compare the accuracy of various Value-at-Risk (VaR) models in measuring the risk of stock portfolios in the Indonesia Stock Exchange (IDX). The optimal portfolio is formed using the Minimum Variance Portfolio (MVP) method based on 11 sector-representative stocks for the period 2019–2025. The risk performance of this portfolio is then evaluated using six VaR models: Variance–Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), and the hybrid GARCH–EVT model. Model accuracy is assessed through backtesting using the Kupiec Proportion of Failures (POF) test and the Christoffersen Conditional Coverage (CC) test at the 95% and 99% confidence levels. The optimization results indicate that the MVP portfolio is dominated by defensive sectors such as consumer non-cyclicals (ICBP.JK) and large-cap banking (BBCA.JK). Backtesting results show that although all models perform adequately at the 95% level, standard models (VC, MC, GARCH) fail to capture extreme risk at the 99% level. In contrast, the GARCH–EVT model satisfies the backtesting criteria and emerges as the most accurate and superior model for predicting extreme losses.Penelitian ini bertujuan untuk membangun portofolio optimal dan membandingkan akurasi berbagai model Value-at-Risk (VaR) dalam mengukur risiko portofolio saham di Bursa Efek Indonesia (BEI). Portofolio optimal dibentuk menggunakan metode Minimum Variance Portfolio (MVP) dari 11 saham perwakilan sektor periode 2019-2025. Kinerja risiko portofolio ini kemudian diukur menggunakan enam model VaR: Variance-Covariance (VC), Historical Simulation (HS), Monte Carlo (MC), GARCH (1,1), Extreme Value Theory (EVT-GPD), dan model hybrid GARCH-EVT. Akurasi model diuji menggunakan backtesting Uji Kupiec (POF) dan Uji Christoffersen (CC) pada tingkat kepercayaan 95% dan 99%. Hasil optimisasi menunjukkan portofolio MVP didominasi oleh sektor defensif seperti consumer non-cyclicals (ICBP.JK) dan perbankan big-cap (BBCA.JK). Hasil backtesting menunjukkan bahwa meskipun semua model akurat pada tingkat 95%, model standar (VC, MC, GARCH) gagal mengukur risiko ekstrem pada tingkat 99%. Sebaliknya, model GARCH-EVT terbukti memenuhi uji dan menjadi model yang paling akurat dan superior untuk memprediksi kerugian ekstrem.
Penerapan Analisis Regresi Nonparametrik Spline Truncated pada Pemodelan Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Provinsi Jawa Barat Rais, Zulkifli; 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%.
Rainfall Classification Using Output Statistics Models Based on Classification and Regression Trees with Principal Component Analysis Preprocessing Rais, Zulkifli; Hafid, Hardianti; Bunga, Yhegi Rombe
JINAV: Journal of Information and Visualization Vol. 7 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Makassar City has a varied monsoon rainfall pattern, so rainfall prediction is an important challenge in disaster mitigation and resource management. Data mining techniques such as classification with the Classification and Regression Trees (CART) algorithm can be used to classify rainfall and analyze historical data, but the risk of overfitting high-dimensional data requires dimension reduction such as Principal Component Analysis (PCA). To improve accuracy, the Output Statistics Model (MOS) approach that combines numerical data and observations is also used. The results of dimension reduction using the Principal Component Analysis (PCA) method showed that of the initial seven variables, only three main components (, , and ) were retained because they had eigenvalues greater than 1 and were able to explain the data variance significantly. The decision tree model that was formed resulted in an accuracy rate of 72.34% in training data. Where the model can classify most of the training data into the correct rainfall category. In the data testing, the model was able to achieve an accuracy level of 71.43%, which shows that the model has good generalization ability to new data and does not experience overfitting.
A Hybrid Neural Network Approach Using SOM and LVQ for Mapping Crime Clusters in Indonesia Rais, Zulkifli; Meliyana, Sitti Masyitah; Hasbullah, Dinda Warfani
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4782

Abstract

Crime ratehigh crime rates in Indonesia are one of the important issues that need to be addressed with data-based strategies. This study aims to group provinces in Indonesia based on crime patterns using Self-Organizing Map (SOM) and classify the results using Learning Vector Quantization (LVQ). The results of the clustering analysis using SOM show that the optimal number of clusters is two, as supported by validation using Connectivity, Dunn Index, and Silhouette Score. Cluster 1 consists of 31 provinces with lower crime rates, while Cluster 2 includes 3 provinces with higher crime rates. To improve understanding of the clustering results, classification was carried out using the LVQ method, which produced an accuracy of 91.43%.
Classification of Family Welfare Card Recipients in Makassar City Using Decision Tree Algorithms Rais, Zulkifli; Fahmuddin S, Muhammad; Musfira, Musfira
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4783

Abstract

This study aims to analyze the factors influencing the determination of recipients of the Family Welfare Card (KKS) program in Makassar City and evaluate the level of accuracy of the decision tree model in the classification process. The KKS program is a government effort to accelerate poverty alleviation, so it is important to ensure that the selection process for program recipients is carried out on target. The decision tree method is used in this study because of its ability to simplify the decision-making process through an easy-to-understand tree structure. This study utilizes KKS recipient data with various variables, such as income, number of dependents, employment status, asset ownership, and education level, to build a classification model. The results of the study indicate that the variable of the Head of Household's (KRT) Highest Education Level (X4) has the highest level of importance in determining KKS recipients, followed by the variable Number of Family Members (X1), and the variable Ownership of Residential Buildings (X5). The decision tree model that was built has an accuracy level of 84.21%, which states the model's ability to classify KKS recipients effectively. This study also provides insight into the description of factors influencing KKS receipts, which can be used as a basis for formulating more efficient and targeted policies.
Co-Authors Abidin, Muh. Zulkifli Ahmar, Ansari Saleh Aidid, Muhammad Kasim Akbar Iskandar Alfairus, Muh. Qodri Amira, Husnul Annisa Syalsabila Asti Dewi Putri Aswi, Aswi Azrahwati Botto-Tobar, Miguel Bunga, Yhegi Rombe Fahmuddin S, Muhammad H.S, Rahmat Hardianti Hafid Haris, A. Sulfiana Hasbullah, Dinda Warfani Hasnita, Hasnita Ikhwana, Nur Indrayasaro Mar'ah, Zakiyah Muhammad Arif Tiro, Muhammad Arif Muhammad Kasim Aidid Mukhtazam Aqil Mukhtar Musfira, Musfira Nusrang, Muhammad Poerwanto, Bobby Purnamasari, A. Bida Rahmanda, Lalu Ramzy Rahmat, Rahmat Wahyudi Refaldy, Muhammad Rezky, Akhmad Rezky Ramadhana T Risqi, Shopia Ruliana Ruliana Ruliana, Ruliana Rusdianto Saida, Saida Sitti Masyitah Meliyana R. Sudarmin Sudarmin Sudarmin Suwardi Annas Syahputra, Akbar Taufiq Hidayat Triutomo, Agung Utomo, Agung Tri Yuniar, Eka Citra Yusniar Rasjid, Yusniar