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All Journal Jurnal Sains Matematika dan Statistika ESTIMASI: Journal of Statistics and Its Application Parameter: Journal of Statistics MATHunesa: Jurnal Ilmiah Matematika
Kalondeng, Anisa
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Estimasi Parameter Model Regresi Logistik Biner dengan Conditional Maximum Likelihood Estimation pada Data Panel Fitri, Fitri; Islamiyati, Anna; Kalondeng, Anisa
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i2.13998

Abstract

Binary logistic regression models can be used on panel data with categorical responses that experience repeated measurements based on time. This study aims to determine the factors that influence the Human Development Index in South Sulawesi Province in 2015-2019. Data were analyzed through binary logistic regression with fixed effect model approach through Conditional Maximum Likelihood Estimation (CMLE) for panel data. The results of this study indicate that the variables that have a significant effect are life expectancy (X1), school length expectancy (X2) and the average length of schooling (X3). Obtained the probability value of districts/cities that have a medium low and medium high human development index with a classification accuracy of 56.25%.
Pemodelan Data Panel dengan Pendekatan Least Square Dummy Variable terhadap Faktor-Faktor yang Memengaruhi Kasus Kriminalitas di Sulawesi Selatan Nurdin, Afifah Mutiah; Arfan, Muh. Indirwan; Siswanto, Siswanto; Kalondeng, Anisa
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i1.32128

Abstract

Crime is one of the challenges that often arises in the community environment. In the years 2020-2022, South Sulawesi ranked fourth with the highest reported crime cases in Indonesia. To avoid an increase in the crime rate, an understanding of the factors impacting these cases is necessary. This research aims to determine the fixed effect model with the Least Square Dummy Variable approach to examine the percentage of the poor population, income inequality, population density, and the total population's influence on crime cases in South Sulawesi during the years 2020-2022. The most suitable model is the Least Square Dummy Variable using an individual effect with an analysis result of  of 99.9%. The variables of the percentage of the poor population, population density, and the total population are proven to significantly influence crime cases in South Sulawesi.
GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELING ON LIFE EXPECTANCY RATE IN SOUTH SULAWESI Nabila Miftakhurriza; Jelita Zalzabila; Siswanto; Kalondeng, Anisa; Andi Isna Yunita; Ania, Samsir Aditya
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17267

Abstract

Geographically Weighted Panel Regression (GWPR) is one of the panel data regression approaches used in spatial data analysis. This study uses the global Fixed Effect Model (FEM) panel regression model and the local GWPR model to examine Life Expectancy Rate (LER) at the district/city level in South Sulawesi Province in 2019-2021. LER is an important indicator that reflects the health and welfare of the community. This research aims to develop a GWPR model that can explain variations in LER and identify factors that affect that variable, so that it can help stakeholders in allocating resources and designing effective intervention programs. Parameter estimation in the GWPR model is carried out in each observation area using the Weighted Least Square (WLS) method. The calculation of spatial weights in the GWPR model used weighting functions such as fixed bi-square, fixed gaussian, fixed exponential, adaptive bi-square, adaptive gaussian, and adaptive exponential. The results showed that the use of a fixed exponential weighting function gave optimal results with the lowest cross-validation (CV) value of 44,614. Parameter analysis of the GWPR model shows that the factors that affect LER are local and not the same in each district/city in South Sulawesi Province. Factors that have a significant influence include the number of health facilities and households that have access to proper sanitation. This GWPR model has a coefficient of determination of 97,7%. The FEM model has a coefficient of determination of 58,4%. Therefore, GWPR performs LER modelling more effectively than FEM.
Pemodelan Generalized Space Time Autoregressive untuk Meramalkan Data Inflasi Bulanan di Provinsi Jawa Barat Abdia, Hikma; Akhsan, Tiara Annisa; Kalondeng, Anisa; Siswanto, Siswanto
Jurnal Sains Matematika dan Statistika Vol 11, No 1 (2025): JSMS Januari 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v11i1.26998

Abstract

Inflation is the decline in the value of money due to the continuous increase in the value of goods and services. Inflation is also an economic phenomenon that greatly affects people's daily lives and the economic stability of a country. To maintain price stability and economic growth, it is important to monitor and forecast the inflation rate. The Generalized Space Time Autoregressive (GSTAR) method is a method that is able to forecast inflation rates involving interrelationships between location and time. The data used in this study is inflation data for 7 cities in West Java, namely Bandung, Bekasi, Bogor, Cirebon, Depok, Sukabumi and Tasikmalaya in January 2018 to December 2022. This purpose of this study is to obtain the best GSTAR model and forecasting results based on inflation data in seven cities in West Java. Based on the research results, the GSTAR ( model, the MSE value and MAPE value of the 80:20 which is 0.12% and 12.20%, and the forecast results obtained for six cities relatively increased and one city experienced a decline. So that the best model for inflation data of seven cities in West Java is the GSTAR (model with uniform location location weights.
Estimasi Parameter Model Three-Factor Completely Randomized Design dengan Metode Robust MM Nurkamalia, Nurkamalia; Kalondeng, Anisa; Sirajang, Nasrah
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i1.18323

Abstract

When conducting experiments, it is often found that there are errors in the observed responses. It can cause data outliers to appear whose existence results in making conclusions inaccurate. Therefore, outliers need to be overcome by applying the robust regression method. The robust method used is the robust MM because it has a high level of efficiency and breakdown point. The Robust MM method is useful for obtaining parameter estimates in a three-factor Completely Randomized Design (CRD) which is applied to the data on average abdominal fat of broiler chickens experiencing outliers in four observations. The results showed that the presence of outliers caused no effect of differences in age of chicken and the interaction between age of chicken and feeding fermented kiambang on the average abdominal fat of broiler chickens. However, after the data was replaced with estimated data obtained from the Robust MM method to overcome outliers, it showed that there was an effect of age of chicken and the interaction between age of chicken and feeding of fermented kiambang on the average abdominal fat of broiler chickens.
Penggunaan Metode Copula Gaussian untuk Menentukan Nilai Value at Risk Investasi Saham pada Bank BCA dan Bank BRI Palungan, Kevin Ekarinaldo; Kalondeng, Anisa; Ilyas, Nirwan
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.35960

Abstract

Investment is capital for one or more assets over a long period of time to obtain profits. Besides being able to provide profits, stock investment also contains an element of risk. Therefore, risk measurement needs to be done so that the risk is within a controlled level so as to reduce the occurrence of investment losses. This study uses the Gaussian Copula to calculate Value at Risk on the closing price data of PT. Bank Central Asia Tbk and PT. Bank Rakyat Indonesia Tbk for the period January 02, 2020 to December 30, 2022. For the Kendall's correlation value τ=0.3307 produces a Pearson correlation value of ρ=0.4965 which is also used as an estimate of the Copula Gaussian parameter. The results of the VaR calculation on a portfolio with a weight of 50% shares of PT Bank Central Asia Tbk and 50% shares of PT Bank Rakyat Indonesia Tbk average VaR at the 95% confidence level of -0.0269 means that if investors invest their funds by 50% in PT Bank Central Asia Tbk shares and 50% in PT Bank Rakyat Indonesia Tbk shares there is a risk that the maximum loss is 2.69% of the invested funds.
Pengelompokan Kemiskinan di Provinsi Sulawesi Selatan Tahun 2023 dengan Metode K-Means Clustering Wulandari, A. Elisha; Baso, Andi M. Alfin; Fajri, Belia Nurul; Kalondeng, Anisa; Islamiyati, Anna; Pannu, Abdullah; Fadil, Muhammad; Vallarino, Alfian Akbar; Rahman, Anugrah Nur Isnaeni
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.45824

Abstract

Poverty remains a significant social and economic issue in South Sulawesi Province. This study aims to classify districts/cities in South Sulawesi based on poverty levels using the K-Means Clustering method. The data used were obtained from the Central Bureau of Statistics (BPS) for 2023, including indicators such as the percentage of poor population, education level, and employment sector. The Silhouette Index method was applied to determine the optimal number of clusters. The results indicate that South Sulawesi is divided into two clusters, representing high and low poverty levels. The scatter plot further reveals that cluster 1 is more varied, while cluster 2 is more concentrated. These findings can serve as a foundation for formulating more targeted policies to reduce poverty in South Sulawesi.
PEMODELAN REGRESI NONPARAMETRIK DENGAN ESTIMATOR SPLINE POLYNOMIAL TRUNCATED PADA DATA JUMLAH WISATAWAN NUSANTARA Arman, Agym Nastiar; Lemido, Ryo; Siswanto, Siswanto; Kalondeng, Anisa
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 1 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p127-133

Abstract

The nonparametric regression approach is a statistical method used to determine the relationship between predictor variables and the dependent variable when the assumed pattern is unknown. Truncated spline is an estimator used in nonparametric regression to handle data with varying behaviors. Nonparametric regression modeling with truncated polynomial spline was applied to local Indonesian tourist visitation data obtained from BPS for the years 2017-2019, for each month. The optimal knot points were selected based on the smallest Gross Cross Validation values. Based on the analysis, the optimal model is a second-order spline with the smallest Gross Cross Validation value of 17,95 and the optimal knot points are in the 2nd, 6th, and 7th months. The goodness of the model is evident from an value of 81,88% and an MSE of 12,46. The best model obtained shows a fairly accurate ability to explain the estimated number of domestic tourists so that it can be a basis for stakeholders to make key decisions in planning and managing the tourism industry as an effort to increase domestic tourism interest.
OPTIMASI METODE JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK PERAMALAN CURAH HUJAN BULANAN DI KOTA DENPASAR Nailah, Fadia; Larasati, Dwi Ina; Siswanto, Siswanto; Kalondeng, Anisa
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 1 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p134-140

Abstract

Rainfall is a natural phenomenon that depends on many factors that are an important part of life on earth. The high intensity of rainfall can lead to disasters. Therefore, this study aims to forecast monthly rainfall. The data used was obtained from BMKG Bali Province, namely monthly rainfall data for Denpasar City from 2009 to 2019. The method used is backpropagation artificial neural network. The artificial neural network method is an information processing method inspired by the human nervous system. Optimal backpropagation network architecture is needed so that the prediction results have a low error rate, by optimizing the use of training data and test data taken from sample data. Based on the results of the testing and prediction process with the parameters of one hidden layer with 50 neorons, epoch 11 and learning rate 0.01, the results obtained with the MSE value in network testing are 0.037. So it can be concluded that the backpropagation artificial neural network method has good accuracy results used as a reference for decision making in predicting monthly rainfall in Denpasar City in the future.
Co-Authors Abdia, Hikma Akhsan, Tiara Annisa Andi Isna Yunita Ania, Samsir Aditya Anna Islamiyati Arfan, Muh. Indirwan Arman, Agym Nastiar Baso, Andi M. Alfin Fajri, Belia Nurul Fitri Fitri Jelita Zalzabila Larasati, Dwi Ina Lemido, Ryo Muhammad Fadil Nabila Miftakhurriza Nailah, Fadia Nirwan Ilyas Nurdin, Afifah Mutiah Nurkamalia, Nurkamalia Palungan, Kevin Ekarinaldo Pannu, Abdullah Rahman, Anugrah Nur Isnaeni S Siswanto Sirajang, Nasrah Siswanto Vallarino, Alfian Akbar Wulandari, A. Elisha