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%.

This study aims to collect scopus indexed articles with the keyword absolute advantage in 2020, 2021, and 2022 (until July 15, 2022). In addition, we analyzed the text mining of several abstracts from these articles using the R software. we used 75 articles from top 3 journals that have most publications based on the keyword including the Journal of Cleaner Production, the Journal of Chemical Engineering, and the Journal of Applied Soft Computing. Based on data mining analysis, the word-cloud of each abstract automatically appears based on the frequency of each word that appears in the abstract.

ABSTRACT

               Multicollinearity is one of the assumption violations in regression analysis. The existence of multicollinearity causes the standard error to increase. Ridge regression is one of the regression analysis approaches that can overcome this problem. Besides multicollinearity, another problem that often occurs is outliers. The existence of outliers causes the data not to be normally distributed. Ridge Robust Least Trimmed Square Regression is a method that can be used to overcome multicollinearity and outlier problems in the data simultaneously in the regression analysis model. The purpose of this study was to obtain the estimation results of the least trimmed square ridge robust regression model on the Health Profile data of South Sulawesi in 2017. From the results and discussion it was found that the least trimmed square ridge robust regression method has an Rsquare value or ?2 which is 88% and an MSE value 1.96, thus indicating that the ridge robust least trimmed square model fits better in dealing with data containing multicollinearity and outliers.

Keywords: Robust Ridge Regression, Least Trimmed Square, Multicollinearity, Outlier, Infant Mortality Rate.

Plantation crops are an agricultural sub-sector which also plays an important role in boosting the country's economy, one of which is coffee and tea. However, it is known from the Statistics Indonesia that data shows that the production of plantation crops has decreased, this is caused by one of the factors, namely due to the reduced area of plantation land caused by changes in the conversion of plantation land. The purpose of this study was to determine the influence of land area on the production of plantation crops with linear regression. The results of the study with linear regression analysis show that the variable area of land simultaneously or partially can affect the production of plantation crops, especially coffee and tea plant.

Discriminant analysis is a method used in separating objects into different groups and allocating objects into a predetermined group. Discriminant analysis is bound by the assumption that the mean vector for each group is different, the data is normally distributed multivariate and the covariance variance matrix between groups is the same. If there is a covariance variance matrix between different groups, then quadratic discriminant analysis is used for the classification process. However, sometimes it is found that data contains outliers, so a robust estimator is used, namely the Minimun Covariance Determinant with the fast-MCD algorithm. Therefore, robust quadratic discriminant analysis can be used to classify 128 villages and 48 sub-districts in Wajo district. It was found that 106 villages were correctly classified into village groups and 22 villages were misclassified into sub-district groups and 35 sub-districts were correctly classified as sub-district groups and 13 sub-districts were misclassified into village groups and produced an accuracy of classification results of 80.11%.

Tropical diseases are common in areas with tropical and subtropical climates. As a country with a tropical climate, Indonesia is vulnerable to various tropical diseases. A large number of tropical diseases can occur in the temperate climate zone, differing only in the frequency with which they are affected. Dengue hemorrhagic fever is a tropical disease in Indonesia. DHF occurs as a result of infection with the dengue virus which is transmitted through the bite of the female Aedes aegypti mosquito. The high prevalence of DHF in East Java requires a data collection process to identify areas that are frequently infected with DHF. Therefore, we need a clustering system that can help classify areas that often experience DHF cases. This study aims to find out which districts/cities have the predominant cases of dengue fever. The clustering method used is K-Means clustering. Based on the research conducted, 2 clusters were obtained with a silhouette coefficient  value of 0.76. Cluster 1 covers 36 districts/cities and is an area with a low level of vulnerability to dengue fever, while cluster 2 covers 2 districts/cities and is an area with a high level of vulnerability to dengue fever.

High and sustainable economic growth is the main condition or a must for the continuity of economic development and increased welfare. GRDP is defined as the total added value generated by all business units in an area. The analytical method used in this study is panel data regression analysis. Panel data regression is used to observe the relationship between one dependent variable and one or more independent variables. This study aims to determine the panel regression model of Gross Regional Domestic Product (GDP) in Indonesia for the period 2018 to 2021 and to find out whether the domestic investment investment variable and the cooperative business volume variable affect GRDP in Indonesia for the 2018-2021 period. The results obtained in this study are that the best panel regression model for modeling GRDP is the FEM model and the variable Domestic Investment Investment and Cooperative Business Volume are variables that have a significant effect on the GRDP variable in Indonesia for the 2018-2021 period.

The city of Semarang is one of the metropolitan cities that has a fairly dense population. During the years 2010-2021 the City of Semarang has a population fluctuation. It is necessary to make predictions of population data in order to plan the development in the City of Semarang can be better planned and can regulate the fluctuation of population in the future. In this study, the results of the prediction of the population of the City of Semarang were analyzed using the Neural Network approach with the backpropagation method. After training and testing, the best architectural model was obtained with 2 neurons on the input layer, 2 neurones on the hidden layer and 1 neuron on the output layer. Based on the results of the best architectural model, the MSE score was 9.39749 x 10-6 and the average MAPE value was 0.884552461%. The evaluation result with the MAPE value is very accurate because it is < 10%. In this study, the results of the forecast of the population of the city of Semarang in 2022-2025 consecutive are 1,863.121 people, 1,878.634 people, 1.891.865 people, and 1,902.947 people.

One of the prevalent nutritional issues affecting toddlers worldwide is stunting. Several studies on stunting cases have been conducted in Indonesia. However, modeling using the Geographically Weighted Regression (GWR) method in South Sulawesi has not been carried out. This study aims to identify the variables that affect the incidence of stunting in each district in South Sulawesi based on spatial modeling using the GWR method. Data on the number of stunting cases, the pproportion of low-birth-weight infants, the percentage of under-five who are malnourished, the percentage of proper drinking water, and the percentage of poor people in South Sulawesi in 2020 were used. The results show that the GWR model has an  value of 86.64%, which is higher than that of the global regression model. The factors that influence the percentage of stunting based on the GWR modeling results are the percentage of under-five who are malnourished and the percentage of proper drinking water. The findings of this study are anticipated to help the government address the issue of stunting in South Sulawesi. Early prevention may then be implemented.

Application of the asymmetric power logistic smooth transition autoregressive (APLSTAR) model proposed by [1] to naira/Euro exchange rate spanning from January, 2006 to April, 2021, which is a nonlinear  macroeconomic time series was considered. The APLSTAR model was justifiably fitted to the series and the fit of the APLSTAR model compared with the fits of the competing models revealed that the APLSTAR model fits the data exchange rate of naira to Euro better than the other asymmetric STAR models. Lagrange Multiplier tests for autoregressive conditional heteroscedastic (ARCH) effects were carried out and there was no substantial evidence to reject the presence of ARCH effects in the set of residuals used. Hence, we compared hybrid smooth transition autoregressive-generalized ARCH (STAR-GARCH) models using model evaluation criteria. On balance, the APLSTAR-GARCH (0, 1) model outperforms the other models under consideration.

Regression analysis is a method to examine the relationship between variables and determine their influence. However, the problem of multicollinearity often arises in linear regression analysis and can cause interpretation problems. To handle multicollinearity, Principal Component Analysis (PCA) is used. However, this method has a weakness when the data contains outliers. Therefore, it was developed into robust PCA using the Minimum Covariance Determinant (MCD) method and the Least Trimmed Square (LTS) estimation method. This study uses Gross Regional Domestic Product data in Indonesia in 2020, which has problems with multicollinearity and outliers. This data is modeled using two robust PCA methods, namely MCD and LTS. The robust PCA model with MCD has an adjusted  value of 87.87% and an MSE value of 0.0700. However, in the robust PCA regression model with LTS, the adjusted  value is 98.93% and the MSE value is 0.0325. Thus, the effective method in handling multicollinearity and outliers is the LTS method because it shows better results.

Panel data regression analysis is a method for modeling the influence of independent variables on dependent variables, on a combination of cross-section and time-series data. This research aims to estimate a panel data regression model with a generalized effects model using the least squares method, estimate a fixed effects model with the Least Square Dummy Variable and estimate a random effects model with Generalized Least Square on inflation and human development index data. The results obtained show that the factors that have a significant influence at the 5% level on the inflation rate in 2014-2019 are the dollar exchange rate with a coefficient of determination of the general effects model of 61.06%, then the HDI level in South Sulawesi in 2011-2017 is significantly influenced by factors such as average length of schooling and life expectancy with a coefficient of determination of the fixed effects model of 89.73%, and the HDI level in South Sulawesi in 2016-2019 is significantly influenced by the factors of life expectancy, per capita expenditure and poverty with a coefficient of determination of the random effects model amounting to 63.07%.