Article Per Year (5 Year)
p-Index From 2021 - 2026
6.595
P-Index
This Author published in this journals
All Journal Jambura Journal of Mathematics Jurnal Varian Inferensi Variansi : Journal of Statistics and Its Application on Teaching and Research Mattawang: Jurnal Pengabdian Masyarakat JINAV: Journal of Information and Visualization Panrannuangku Jurnal Pengabdian Masyarakat Daengku: Journal of Humanities and Social Sciences Innovation ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education Seminar Nasional Pengabdian Kepada Masyarakat VARIANSI: Journal of Statistics and Its Application on Teaching and Research Seminar Nasional Hasil Penelitian LP2M UNM ARRUS Jurnal Pengabdian Kepada Masyarakat Abdi Kimia : Jurnal Pengabdian Masyarakat SMART: Jurnal Pengabdian Kepada Masyarakat
Zulkifli Rais
Department of Statistics, Universitas Negeri Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 42 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 42 Documents
Search

 1   2   3   4   5 
Statistika Kategorik untuk Siswa: Meningkatkan Ketajaman Analisis dalam Karya Tulis Ilmiah Aswi, Aswi; Tiro, Muhammad Arif; Poerwanto, Bobby; Ikhwana, Nur; Rais, Zulkifli; Abidin, Muh. Zulkifli
SMART: Jurnal Pengabdian Kepada Masyarakat Vol 5, No 2 (2025): Oktober
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/smart.v5i2.77214

Abstract

Tujuan dari kegiatan ini adalah untuk meningkatkan kamampuan analisis data guru dan siswa SMAN 7 Takalar khususnya dalam mengolah dan menganalisis data kualitatif atau kategorik dalam menyusun karya tulis ilmiah. Kegiatan ini diikuti oleh 18 orang siswa. Pelaksanaan kegiatan ini dimulai dari observasi, identifikasi kebutuhan, pelatihan, pendampingan, serta monitoring dan evaluasi. Hasil dari kegiatan ini adalah peningkatan pengetahuan dan keterampilan pada topik yang dibahas. Selain itu, sekitar 83,33% peserta merasakan pengetahuan dan keterampilannya meningkat secara signifikan. Artinya kegiatan yang dilakukan memberikan dampak kepada peserta sehingga setelah narasumber meninggalkan lokasi kegiatan terjadi sharing ilmu antar peserta sehingga peserta yang belum banyak berkembang juga dapat memahami dan mengimplementasikan materi yang telah diberikan. Peningkatan keterampilan ini diharapkan dapat membantu siswa dalam penyusunan karya tulis ilmiah.
TSA App by R Shiny : Time Series Analysis Application for Univariate Series Data Tri Utomo, Agung; Ahmar, Ansari Saleh; Aidid, Muhammad Kasim; Rais, Zulkifli; Alfairus, Muh. Qodri
ARRUS Journal of Engineering and Technology Vol. 5 No. 1 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech4398

Abstract

Time series analysis is a statistical method used to model and forecast sequential data over time. This modeling is typically performed using software, but most analytical tools require paid licenses. To address this issue, the TSA App by R Shiny is developed as an open-source application that is easily accessible. The application features a dashboard-based interface designed to help users perform univariate time series analysis without requiring programming skills. This study compares the analysis results of the TSA App with other software such as R Studio, Minitab, and Python. The results show that the TSA App produces comparable outputs in terms of visualization, ARIMA modeling, and forecasting accuracy. Therefore, the TSA App provides a practical and legal solution for time series analysis, especially for users who are unfamiliar with coding.
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) UNTUK ANALISIS SENTIMEN TERHADAP DATA ULASAN APLIKASI E-COMMERCE LAZADA PADA GOOGLE PLAYSTORE Rais, Zulkifli; Muhammad Kasim Aidid; Asti Dewi Putri
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm374

Abstract

Classification is the process of grouping objects based on their characteristics. Various classification methods have been employed, ranging from manual grouping to using technology as an aid in the process. One commonly used classification method is the K-Nearest Neighbor (K-NN) algorithm. K-NN predicts the class of data based on the majority class of its nearest neighbors. The novelty of this research lies in using the K-NN method on the case of Lazada application user sentiment on the Google Playstore. In this study, the review classification used is positive and negative labels. Additionally, three accuracy comparisons between training and testing data were used: 80% : 20%, 70% : 30%, and 60% : 40%. Based on the research results from the classification process of Lazada application user reviews on the Google Playstore, an accuracy of 87.00% was obtained for the training and testing data comparison of 80% : 20%.
KLASIFIKASI CURAH HUJAN DI KOTA MAKASSAR MENGGUNAKAN GRADIENT BOOSTING MACHINE (GBM) Hafid, Hardianti; Rais, Zulkifli; Rezky, Akhmad Rezky Ramadhana T
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 2 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm386

Abstract

Rainfall is one of the important parameters in determining the climate of an area. Makassar, as one of the largest cities in Indonesia, has varying rainfall patterns throughout the year. This research aims to classify rainfall in Makassar City using the Gradient Boosting Machine (GBM) method. The secondary data used in this study were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG), with predictor variables including wind speed, humidity, and air temperature, and the target variable being rainfall category, consisting of no rain, very light rain, light rain, moderate rain, heavy rain, and very heavy rain. To address class imbalance in the data, this study uses the Random Undersampling (RUS) technique. The GBM model with optimal hyperparameter configuration (n_estimators, learning_rate, max_depth, subsample, min_samples_leaf, max_features) achieved a classification accuracy rate of 98.46%, precision of 93%, recall of 98%, and F1-score of 95% with a training and testing data split of 80:20. The research results show that the GBM method is able to classify rainfall very well and can be used as a tool to assist in disaster mitigation planning and water resource management in Makassar City. 95% pada proporsi data pelatihan dan pengujian 80:20. Hasil penelitian menunjukkan bahwa metode GBM mampu mengklasifikasikan curah hujan dengan sangat baik dan dapat digunakan sebagai alat bantu dalam perencanaan mitigasi bencana serta pengelolaan sumber daya air di Kota Makassar.
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.
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.
Forecasting IDR Exchange Rate to USD Using Hybrid ARIMA – LSTM Zulkifli Rais; Sitti Masyitah Meliyani R; Astrid Suwardani Sumarno; Agung Tri Utomo
ARRUS Journal of Engineering and Technology Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech4899

Abstract

Time series forecasting often involves both linear and nonlinear patterns, making the use of a single method less effective. This study aims to forecast the exchange rate of the Indonesian Rupiah (IDR) against the United States Dollar (USD) using a hybrid ARIMA–LSTM model. ARIMA is used to capture linear patterns, while LSTM is employed to model nonlinear residual components. The data used are weekly exchange rates from January 2020 to August 2025. Model performance is evaluated using Mean Absolute Percentage Error (MAPE). The results show that the hybrid ARIMA–LSTM model produces better forecasting accuracy compared to individual ARIMA and LSTM models, with the lowest MAPE value of 0.73%. This indicates that combining linear and nonlinear modeling approaches improves forecasting performance for complex time series data.
Implementasi K-Affinity Propagation dalam Pengelompokan Provinsi di Indonesia Berdasarkan Kasus Pencemaran Lingkungan Hidup Derliani Natalia D.; Suwardi Annas; Zulkifli Rais
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/variansiunm475

Abstract

Indonesia memiliki tingkat pencemaran lingkungan yang berbeda di setiap provinsi. Penelitian ini bertujuan untuk mengetahui gambaran dan hasil pengelompokan provinsi di Indonesia berdasarkan indikator pencemaran lingkungan yang meliputi pencemaran air, tanah, dan udara akibat limbah rumah tangga maupun limbah pabrik. Metode yang digunakan adalah K-Affinity Propagation (K-AP) dengan uji validasi Davies-Bouldin Index. Hasil analisis menunjukkan bahwa jumlah cluster optimum adalah 2, dimana Cluster 1 yang terdiri atas 3 provinsi dengan tingkat pencemaran lingkungan tertinggi, serta Cluster 2 yang terdiri atas 35 provinsi lainnya dengan tingkat pencemaran lebih rendah. Oleh karena itu, pemerintah perlu memberikan perhatian khusus pada provinsi yang masuk dalam Cluster 1, melalui pengawasan industri, pengelolaan limbah, serta peningkatan kesadaran masyarakat mengenai pentingnya pelestarian lingkungan.
Workshop on Student Graduation Decisions Using Statistical Methods at Takalar State Senior High School 7 Suwardi Annas; Ansari Saleh Ahmar; Zulkifli Rais; Rahmat H.S; Agung Tri Utomo
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.
Temporal Aggregation and Smoothing Parameter Sensitivity in Exponential Smoothing: Evidence from the Jakarta Composite Index Agung Tri Utomo; Abdul Rahman; Zulkifli Rais; Muh. Qodri Alfairus
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 5 No. 6 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku4925

Abstract

The study examines the structural sensitivity and predictive performance of the Single Exponential Smoothing (SES) model when applied to varying temporal aggregation levels of the Indonesian stock market index. This study employs time-series data of the Jakarta Composite Index (JCI / IHSG) spanning from January 2, 2020, to May 31, 2025, which are structurally categorized into three distinct frequency domains: daily observations, weekly aggregated averages, and monthly aggregated averages. Methodologically, the optimal smoothing parameter (?) for each aggregation tier is determined through maximum likelihood estimation by minimizing the Mean Absolute Percentage Error (MAPE). The results reveal an extreme structural behavior where the optimal ? approaches its upper asymptotic boundary across all temporal frameworks, specifically 0.9943 for daily data, 0.9999 for weekly data, and 0.9999 for monthly data. Concurrently, the predictive error amplifies systematically as the aggregation window widens, yielding MAPE values of 0.74%, 1.30%, and 2.88% for daily, weekly, and monthly frameworks, respectively. The findings of this study suggest that the JCI movement exhibits strong adherence to the random walk hypothesis, wherein the mathematical framework of SES reacts almost exclusively to the most recent historical innovation. Consequently, temporal aggregation does not induce a structural smoothing effect on parameter convergence but rather introduces informational attenuation that compromises short-term forecasting precision.
Co-Authors A. Bida Purnamasari Abdul Rahman Abidin, Muh. Zulkifli Agung Tri Utomo Agung Tri Utomo Ahmar, Ansari Saleh Aidid, Muhammad Kasim Alfairus, Muh. Qodri Annas, Suwardi Annisa Syalsabila Asmar Asmar, Asmar Asmi, Nia Nurul Asti Dewi Putri Astrid Suwardani Sumarno Aswi, Aswi Bunga, Yhegi Rombe Derliani Natalia D. Fahmuddin, Muh. Ferigo Taufani Tri Hakiki Fithriyah Azzahrah Gunawan, Andi Restu Hardianti Hafid Hardianti Hafid Ikhwana, Nur Irwan Irwan Irwan Irwan Irwan Irwan Isnaeni R Jiran Julita Karunia Rahayu Ayu Lili Maghfirah Rahma Sudirman Marni Marni, Marni Mar’ah, Zakiyah Misveria Villa Waru Muh. Hizbul Zainul Muttaqim Muhammad Arif Tiro Muhammad Arif Tiro, Muhammad Arif Muhammad Fahmuddin Muhammad Fahmuddin Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Kasim Aidid Muhammad Refaldy Mukhtazam Aqil Mukhtar Nurdin Nurdin Nurhamidah Mursyidin Nurtiwi Nurtiwi Nurul Madany Nusrang, Muhammad Poerwanto, Bobby Rahmanda, Lalu Ramzy Rahmat H.S Rahmat Hesha Safei Rezky Novita Said Rezky, Akhmad Rezky Ramadhana T Riska Aprianti Rizal Bakri Ruliana Ruliana Ruliana Ruliana, Ruliana Ruliana, R. Rusdianto S. Sudarmin, S. Shopia Risqi Sitti Masyitah Meliyana R. Sitti Masyitah Meliyani R Sudarmin Sudarmin Sudarmin Sudarmin Surahman Nur, Surahman Suwardi Annas Uca Sideng Utomo, Agung Tri Yusal, Muh Sri Yusniar Rasjid Yusniar Rasjid, Yusniar Zakiyah Mar'ah