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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
Ruliana
Department Of Statistics, Universitas Negeri Makassar
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Immunology & microbiology Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Optimum Control of SEIR Model on COVID-19 Spread with Delay Time and Vaccination Effect in South Sulawesi Province Syafruddin Side; Irwan Irwan; Muhammad Rifandi; Muhammad Isbar Pratama; Ruliana Ruliana; Nor Zila Abdul Hamid
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.1882

Abstract

The increasing number of cases and the development of new variants of the Covid-19 virus globally including the territory of Indonesia, especially in the province of South Sulawesi are increasingly worrying and need to be prevented. Therefore, this study aims to develop a SEIR model on the spread of Covid-19 with vaccination control, optimal control analysis, stability analysis and numerical simulation of the SEIR model on the spread of Covid-19 in South Sulawesi. This study uses the SEIR epidemic model to predict the spread of Covid-19 in South Sulawesi Province with parameters such as birth rate, cure rate, mortality rate, interaction rate and vaccination. The SEIR model was chosen because it is one of the basic methods in the epidemiological model. The method used to build the model is a time delay model by considering the vaccination factor as a model parameter, model analysis using the next generation matrix method to determine the basic reproduction number and stability of the Covid-19 distribution model in South Sulawesi. Numerical model simulation using secondary data on the number of Covid-19 cases in South Sulawesi starting in 2021 which was obtained from the South Sulawesi Provincial Health Office. The results obtained are model analysis provides evidence of the existence of optimal control in the model. Based on the results obtained, it can also be seen that vaccination greatly influences the spread of Covid-19 in South Sulawesi, so that awareness is needed for the people of South Sulawesi to follow the government's recommendation to vaccinate to prevent or reduce the rate of transmission of Covid-19 in South Sulawesi.
Text Classification on Sentiment Analysis of Marketplace SHOPEE Reviews On Twitter Using K-Nearest Neighbor (KNN) Method Zulkifli Rais; Rezky Novita Said; Ruliana Ruliana
JINAV: Journal of Information and Visualization Vol. 3 No. 1 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1389

Abstract

This research aims to know the description and result of the classification sentiment analysis by Twitter users about Shopee. The method used in this research is K-Nearest Neighbor. K-Nearest Neighbor is a method that identifies groups or classifications based on the closest k of test data (training data). The most relative distance is calculated using the Euclidean distance. The data in this research were obtained from the Twitter API which used data on July 13, 2021, and the data according to the study were 150 tweets. Based on the results of the preprocessing text, there are 10 words that appear most often conveyed by Twitter users, and these opinions are related to the features provided by Shopee. The results obtained from this research are the highest level of text classification accuracy is 90% in training data and testing data comparison 80%: 20%
Convolutional Neural Network (CNN) Method for Classification of Images by Age Nurtiwi Nurtiwi; Ruliana Ruliana; Zulkifli Rais
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1481

Abstract

Image classification is one of the studies that is currently being developed. The details of the characteristics that must be captured make researchers compete to find the most suitable method for classifying. The Convolutional Neural Network (CNN) algorithm is one of the most superior algorithms in the field of object classification and identification today. With the help of several packages contained in Google Colab for classification, this algorithm is easier to use. In this study, the target case is the age of a person who will be classified using photos or images taken from the internet which are then stored in the form of Google Drive. The research data used is divided into 2 parts, namely for training data as many as 23.440 images, and 10,046 for testing data. Then to facilitate the extraction of features from the features to be identified, the researchers carried out the preprocessing stage, namely grayscale images, and data augmentation. The purpose of this study is to implement the concept of Deep Learning with Convolutional Neural Networks (CNN) in image classification and to determine the level of accuracy of the CNN model in classifying images. After the algorithm is run and the model has been formed, an accuracy of 78.5% is obtained. It can be concluded that the Convolutional Neural Network (CNN) method is good at classifying images
Pemodelan Regresi Data Panel pada IPM di Sulawesi Selatan Zakiyah Mar'ah; Ruliana Ruliana; Ansari Saleh Ahmar; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 01 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm72

Abstract

HDI is an important indicator to measure success in efforts to build the quality of human life (community/population). HDI can determine the rank or level of development of a region/country. For Indonesia, HDI is strategic data because apart from being a measure of government performance, HDI is also used as an allocator for determining the General Allocation Fund (DAU). The development of HDI in Indonesia has always increased from year to year. In South Sulawesi, the HDI has increased significantly in the last 10 years. Where in 2012 the HDI of South Sulawesi was at 67.26 to 72.82 in 2022. This is measured based on three essential aspects, namely longevity and healthy living, knowledge, and a decent standard of living. Along with HDI, other indicators also show an increase from year to year. To find out how much these variables affect the increase in HDI during the 2018-2022 period, the panel data regression method is used which is a combination of time series data and cross section data. The regression model that is suitable for South Sulawesi HDI data from 2018-2022 is a panel data regression model with one-way random effects, namely individual effects. The model is written as follows IPM=(-1.9360e+01) + (1.0734e+00) UHH + (1.4014e-03) PPK + e
EVALUASI MODEL-MODEL BAYESIAN SPASIAL CONDITIONAL AUTOREGRESSIVE UNTUK PEMODELAN KASUS KEMATIAN CORONA VIRUS DISEASE (COVID-19) DI INDONESIA Andi Feriansyah; Aswi Aswi; Ruliana
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 1 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i1.4864.2023

Abstract

Covid-19 cases in Indonesia occurred for the first time on 2 March 2020. By 30 September 2022, Indonesia had 158,173 Covid-19 deaths. Several studies have been done in modelling Covid-19 cases. However, research in modelling the number of Covid-19 deaths using the Bayesian Spatial Conditional Autoregressive (CAR) model is still rare. The Bayesian spatial CAR model has high flexibility in relative risk (RR) modeling. CAR models can include various types of spatial effects and can include covariates in the model. RR represents the ratio of the risk of outcome (Covid-19) in the exposed group compared to the population average (the unexposed group). This study aims to evaluate the BYM, Leroux, and Localised models with five hyperpriors, to obtain the best model for estimating the RR of Covid-19 deaths in Indonesia and to create RR maps. This study used aggregate data on Covid-19 deaths (2 March 2020 - 30 September 2022). Data on the total population and population density of each province in 2021 were also used. The best model selection is based on the lowest Watanabe Akaike Information Criterion (WAIC) and Deviance Information Criterion (DIC) values, and Modified Moran's I (MMI) residual values. The result showed that the CAR BYM model with covariates and with Inverse-Gamma IG(0.5; 0.0005) prior distribution had the lowest DIC and WAIC. As the BYM model does not converge, the model cannot be used in determining the RR of Covid-19 deaths in Indonesia. From the other three models that converge, the Bayesian CAR Leroux model without covariate with IG(0,5;0,0005) has the lowest DIC(393,76), and WAIC(400,12), and its MMI value (-0,26) is approximate to zero. Therefore, the Bayesian CAR Leroux model without covariate with IG(0,5;0,0005) is preferred. The province with the highest RR (2,76) and the lowest RR (0,22) are Yogyakarta and Papua, respectively.
Application of K-Medoids Algorithm in Provincial Grouping in Indonesia Based On Case of Environmental Pollution Muh. Hizbul Zainul Muttaqim; Ruliana Ruliana; Zulkifli Rais
SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education Vol. 12 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/sainsmat1775

Abstract

Cluster analysis is a method for grouping objects that have the same characteristics. One of the methods in cluster analysis used to group data is the K-Medoids method. In this study the K-Medoids method was applied to classify provinces in Indonesia based on environmental pollution. The variables used are: the number of sub-districts/villages that experience water pollution from factory waste, the number of sub-districts/villages that experience water pollution from household waste, the number of sub-districts/villages that experience soil pollution from factory waste, the number of sub-districts/villages that experience soil pollution from household waste, the number of sub-districts/villages that experience air pollution from factory waste and the number of sub-districts/villages that experience air pollution from household waste. Based on the Davies Bouldin Index, the 2 best clusters were obtained where the first cluster consisted of 31 provinces which had low environmental pollution and the second cluster consisted of 3 provinces which had high environmental pollution.
Analisis Regresi Nonparametrik Spline Truncated untuk Menganalisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Provinsi Sulawesi Selatan Devi Carolin Wongkar; Ruliana Ruliana; Muhammad Fahmuddin S
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm101

Abstract

The nonparametric regression analysis is a regression model used to determine the relationship between response variable and independent variables with unknown regression curve shapes. In the nonparametric approach, one of the frequently used estimators is the spline truncated. Spline truncated model is a segmented polynomial truncation model. The advantage of this model is that it is flexible because it has knot points that can show changes in data patterns. The unemployment rate in South Sulawesi Province in 2021 reached 5.72% and became the province with the second highest unemployment rate on Sulawesi Island. Therefore, spline truncated nonparametric regression modelling will be carried out in the case of unemployment rate with each of the factors that are thought to be influential because the regression curve is found not to form a certain pattern. Based on the analysis results, the best truncated spline nonparametric regression model was obtained using three knot points and obtained the minimum GCV value of 0.38 with a coefficient of determination (R2) value of 89%. Factors that have a significant effect on the unemployment rate in South Sulawesi are mean years of schooling (x1) and labour force participation rate (x2).
APLIKASI FUNGSI TRANSFER MULTIVARIAT UNTUK PERAMALAN CURAH HUJAN DI KOTA MAKASSAR Idul Fitri Abdullah Abdullah; Ruliana Ruliana; Muhammad Fahmuddin S
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 02 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm117

Abstract

This study aims to determine the transfer function model and factors that significantly affect the level of rainfall in Makassar city. This study uses rainfall data as the output series and air humidity (X1), air temperature (X2) and wind speed (X3) as the input series. The data used is monthly data with a period of January 2013 - December 2022. The initial stage of modeling is done by determining the ARIMA model of each input series which is then used to calculate the identification of the transfer function model Based on the research obtained multivariate transfer function model X1 (b=3, r=0, s=0) X12(b=0, r=0, s=0) ARIMA (2,1,0)(1,1,0)12 with air humidity and air temperature being significant factors affecting rainfall in Makassar city.
PEMODELAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA INDEKS HARGA SAHAM GABUNGAN (IHSG) TAHUN 2018 – 2023 Zakiyah Mar'ah; Ruliana Ruliana; Magfirah Septiana
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm144

Abstract

Nonparametric regression is one of the methods used to estimate the pattern of the relationship between response variables and predictor variables where the shape of the regression curve is unknown and is generally assumed to be contained in an infinite dimensional function space and is a smooth function (Eubank, 1999). The MARS method is one method that uses a nonparametric regression approach and high-dimensional data. These namely data has a number of predictor variables of 3 ≤ k ≤ 20 and data samples of size 50 ≤ n ≤ 1000. This research discusses Multivariate Adaptive Regression Spline (MARS) Modeling on the Composite Stock Price Index (JCI) 2018 - 2023. MARS modeling is obtained from a combination of basis function (BF), maximum interaction (MI), and minimum observation (MO) based on the minimum Generalized Cross Validation (GCV) value. The results of this study were obtained from the combination value of BF = 16, MI = 1, and MO = 2 with GCV = 60710.98. The factors that affect the Jakarta Composite Index (JCI) are Inflation (X1), Rupiah to USD Exchange Rate (X3), and Money Supply (X4).
Perbandingan Metode ARIMA dan Single Exponential Smoothing dalam Peramalan Nilai Ekspor Kakao Indonesia Muhammad Fahmuddin S; Ruliana Ruliana; Sitti Sri Mustika M
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm193

Abstract

Indonesia is a country with an open economy, one of the sources of foreign exchange needed by a country with an open economy is exports. Cocoa is one of Indonesia's main export commodities that makes an important contribution to the country's economy, but the value of Indonesian cocoa exports fluctuates, that is there are inconsistent changes from time to time. The purpose of this study is to determine the results of forecasting the value of Indonesian cocoa exports, as well as to determine the best method for forecasting. This research compares the ARIMA and Single Exponential Smoothing methods to determine the best forecasting method. The best method is selected based on the smallest MAPE value. Based on the results of data analysis, the best forecasting model using the ARIMA method is the ARIMA (1, 0, 1) model, which has a MAPE value of 10.38060%. Meanwhile, the best forecasting model using the Single Exponential Smoothing method is with α = 0.16, which has a MAPE value of 10.92874%. So that the best method for forecasting the value of Indonesian cocoa exports is the ARIMA method
Co-Authors Ahmar, Ansari Saleh Andi Feriansyah Annas, Suwardi Arfan Shalihin Amir Aswi, Aswi Devi Carolin Wongkar Fauziah Hasnita Hasnita Idul Fitri Abdullah Abdullah Irma Yani Safitri Irwan Irwan M. Nadjib Bustan M. Nadjib Bustan Magfirah Septiana Miguel Botto-Tobar Muh. Hizbul Zainul Muttaqim Muhammad Arif Tiro 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 Nor Zila Abdul Hamid Nurtiwi Nurtiwi Nurul Madany Ogi Suhendra Rezki Amalia Idrus Rezky Novita Said Sitti Sri Mustika M Suhartin M Syafruddin Side Taufiq Hidayat Titi Kurnianti HR Wahidah Sanusi Wildan Mujahid Zakiyah Mar'ah Zakiyah Mar'ah Zulkifli Rais Zulkifli Rais