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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) 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 Abdi Kimia : Jurnal Pengabdian Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Ruliana
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Spatial Regression Analysis to See Factors Affecting Food Security at District Level in South Sulawesi Province Safitri, Irma Yani; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience740

Abstract

Spatial regression is a development of classical linear regression which is based on the influence of place or location. To determine the location/spatial effect, a spatial dependency test was performed using the Moran Index, and the Lagrange Multiplier (LM) test was used to determine a significant spatial regression model. In this study, spatial regression was applied to the case of food security in each district in South Sulawesi Province. The results of the analysis show that there is a negative spatial autocorrelation, meaning that the spatial effect does not affect the level of food security. The significant spatial regression model is the SEM (Spatial Error Model) model. The equation of the SEM model produces variables that have a significant effect, namely the ratio of normative consumption per capita to net availability, percentage of population living below the poverty line, percentage of households with a proportion of expenditure on food more than 65 percent of total expenditure, percentage of households without access to electricity, percentage of households without access to clean water, life expectancy at birth, ratio of population per health worker to the level of population density, the average length of schooling for women above 15 years, and the percentage of children under five with height below standard (stunting). Thus, the resulting distribution pattern is a uniform data pattern. This means that each adjacent district tends to have different characteristics.
Comparison of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) Methods for Classification of Poverty Data in Papua Fauziah; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience741

Abstract

Classification is a job of assessing data objects to include them in a particular class from a number of available classes. The classification method used is the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) methods. The data used in this study is data on poverty in Papua with the category of the number of low/high level poor people. Of the 29 regencies/cities that were sampled, 15 regencies/cities represent the number of low-level poor people and 14 districts/cities are the number of high-level poor people. The results of the analysis obtained are the k-Nearest Neighbor (k-NN) method with a value of k=15 producing an accuracy of 58.62%, while the Support Vector Machine (SVM) method with Parameter cost = 1 using the RBF kernel produces an accuracy value. by 93.1%. The classification criteria to find the best method is to look at the Root Mean Square Error (RMSE) which states that the Support Vector Machine (SVM) method is better than the k-Nearest Neighbor (k-NN) method.
Nonparametric Model For Poverty Data: The Effect of Internal Factors Using Multi-Predictor Spline Regression in Indonesia Ruliana; Hidayat, Rahmat; Hardianti Hafid; Sudarmin
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.60319

Abstract

Poverty, as a multidimensional issue affecting national welfare and development, is the main focus of this research. This study investigates the impact of demographic and educational factors on the percentage of the poor population in Indonesia using a nonparametric Spline regression approach. The variables studied include the average population growth rate, the availability of schools in villages, and school enrollment rates. The best model, selected based on the lowest Generalized Cross Validation (GCV) value (0.204) and a high coefficient of determination (94.67%) is a nonparametric Spline regression model with an optimal combination of knot points. The analysis shows that all three predictor variables significantly influence the poverty rate. The model also meets standard statistical assumptions. These findings highlight the vital role of education and demographic factors in addressing poverty, thus strengthening education and controlling population growth should be a priority in poverty alleviation policies in Indonesia.
PENERAPAN METODE HYBRID SSA-ARIMA PADA PERAMALAN INDEKS HARGA KEBUTUHAN PERTANIAN YANG DIBAYAR PETANI DI PROVINSI SULAWESI SELATAN: indonesia Fahmuddin S, Muhammad; Ruliana; Muh. Imam Shadiq
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/variansiunm317

Abstract

This study aims to determine the results and accuracy of forecasting the farmer's price index (IHDP) in South Sulawesi Province using Hybrid SSA-ARIMA. Hybrid SSA-ARIMA is a combination of two good time series methods to improve forecasting accuracy, especially for IHDP data that contains trend and seasonal elements. The data used is the South Sulawesi IHDP data from January 2019 to June 2024 which is sourced from the official website of the Central Statistics Agency. The results of the IHDP forecast in South Sulawesi for the next 12 months from July 2023 to June 2024 tend to increase with the largest increase in September 2024 of 1.184 with a forecast accuracy based on the Mean Absolute Percentage Error (MAPE) of 1.59%. This shows that Hybrid SSA-ARIMA has very good forecasting capabilities
Penerapan Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Standar Pencemar Udara (ISPU) di Kota Makassar Sudarmin; Ruliana; Sasa Arisa, Sasa Arisa
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/variansiunm400

Abstract

Air quality is a comprehensive indicator that reflects air pollution. The air quality index is defined as a description or value of the transformation of individual parameters of interrelated air pollution, such as PM10, SO2, CO, O3, NO2 into one value or a set of values ​​so that it is easy to understand for the general public. Therefore, predictions of the Air Pollutant Standard Index in the future are needed for future policy decision making. SVR is a development of Support Vector Machine (SVM) for regression cases. The aim of this study is to predict the Air Pollutant Standard Index (ISPU) in the future using the SVR method. In the SVR method, the best kernel is used as an aid in solving non-linear problems, the Min-Max Normalization method for data normalization, division of training and testing data, selection of the best model with Grid Search Optimization. The best prediction results were obtained using a radial kernel with values with parameters ​​ε = 0.1, C = 10, and γ = 1 with the smallest error of 0.0086, with an RMSE of 0.0894. The RMSE value indicates that the model's ability to follow data patterns well. From the model on the radial kernel, the predicted results of the Air Pollution Standard Index from January 1, 2025 to July 31, 2025 were obtained which were not constant or fluctuating with an interval range of 41,14 – 58,69 and based on the Air Pollution Standard Index category, it was in the fairly good category index.
APPLICATION OF TIME SERIES REGRESSION (TSR) AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) IN RICE PRODUCTION FORECASTING IN INDONESIA Fahmuddin S, Muhammad; Ruliana; Fahmi, Nurul
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/variansiunm412

Abstract

Rice production plays a crucial role in supporting food security in Indonesia. The annual fluctuations in rice yield necessitate accurate forecasting methods to support agricultural planning. This study aims to forecast rice production in Indonesia using two time series forecasting approaches: Time Series Regression (TSR) and Autoregressive Integrated Moving Average (ARIMA). The data used consist of monthly rice production from January 2020 to December 2024. The analysis results show that both methods are capable of modeling the data well, with high forecasting accuracy based on the Mean Absolute Percentage Error (MAPE). The TSR model yielded a MAPE of 13.838%, while the ARIMA(2,1,0)(0,1,0)12model achieved a lower MAPE of 13.1439%, indicating that the ARIMA model provides more accurate forecasting results. This study is expected to serve as a reference for policy-making and strategic planning in rice production management in the future.
A Comparative Study of M-Estimation and S-Estimation in Robust Regression Models: A Case Study on Crime Rates in Indonesia Hidayat, Rahmat; Ruliana; Mar'ah, Zakiyah; Jumriani
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 14 No 1 (2026): VOLUME 14 No 1, 2026
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v14i1.60365

Abstract

Robust regression is a regression method used to handle outliers in statistical analysis. Robust regression consists of five estimation methods, namely M-estimation (Maximum Likelihood type), LMS estimation (Least Median of Squares), LTS estimation (Least Trimmed Squares), MM estimation (Method of Moments), and S-estimation (Scale). M-estimation is known for having the smallest variance among the estimators, with high efficiency reaching up to 95%, while S-estimation is based on the residual scale of M-estimation and is characterized by a high breakdown point of up to 50%. The 2022 crime rate data in Indonesia contains outliers. According to data from the Central Statistics Agency, there was a drastic increase in crime incidents in 2022, reaching 372,965 reported cases. Therefore, a comparison of robust regression estimation methods was conducted to obtain the best model that explains the factors influencing crime rates in Indonesia. This study employs robust regression using M-estimation and S-estimation with Tukey bisquare weighting. The dependent variable in this study is the number of crimes in Indonesia in 2022, while the independent variables include population density (x₁), open unemployment rate (x₂), number of poor people (x₃), mean years of schooling/MYS (x₄), and labor force participation rate/TPAK (x₅). The results indicate that S-estimation in robust regression provides the best performance among the methods analyzed.
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%.
APPLICATION OF SVM FOR SENTIMENT ANALYSIS REGARDING THE EFFICIENCY OF APBN AND APBD IN 2025 Nursya'bani, Nabilah; Ruliana; Aidid, Muhammad Kasim
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/variansiunm512

Abstract

The policy on expenditure efficiency in the 2025 APBN and APBD has triggered diverse public responses on social media, necessitating sentiment analysis to identify emerging opinion trends. The analysis employs the Support Vector Machine (SVM) method, a margin-based classification algorithm that constructs an optimal separation between classes through the identification of the best hyperplane, where optimality is achieved when the separating margin is maximized. This study aims to identify sentiment patterns and classify public opinion regarding the budget efficiency policy to provide a measurable quantitative overview beyond subjective assessment. Data were collected from the X platform during the period 15 January–25 March 2025 using the keyword “efisiensi anggaran.” The results indicate that negative sentiment dominates at 53%, while positive sentiment accounts for 47%. The SVM model achieved an accuracy of 99%, indicating strong performance in classifying sentiment related to the 2025 budget efficiency policy
Co-Authors Ahmad Imdad Aidid, Muhammad Kasim Amir, Arfan Shalihin Ankaz As Sikib Aswi, Aswi Fahmuddin S, Muhammad Fahmuddin S., Muhammad Fahmuddin, Muh. Fauziah Hardianti Hafid Indrayasaro Jumriani Lili Maghfirah Rahma Sudirman Mar'ah, Zakiyah Mar’ah, Zakiyah Muh. Imam Shadiq Muhammad Arif Tiro, Muhammad Arif Mukhtazam Aqil Mukhtar Mustika M, Sitti Sri Nursya'bani, Nabilah Nurul Fahmi Rahmat Hidayat Rais, Zulkifli Safitri, Irma Yani Sasa Arisa, Sasa Arisa Sudarmin Suhendra, Ogi Suwardi Annas Zulkifli Rais