The efficacy of nationwide population control initiatives in Indonesia ought to be uniformly dispersed throughout all provinces. The decentralization policies implemented by each local government and regional differences in characteristics have, nevertheless, resulted in substantial discrepancies in TFR across all provinces in Indonesia. The efficacy of long-acting contraceptives is widely acknowledged as the primary prerequisite for mitigating the pace of population growth. In addition to reducing the TFR, the government has established goals for the equitable distribution of family planning programs across Indonesia. The implementation of family planning programs in an equitable manner will impact the birth rate reduction. The objective of this research is to examine the potential impact of contextual elements on fertility reduction via contraceptive methods in Indonesia. The findings of this research indicate that the proportion of women utilizing long-acting contraceptive methods has the most significant impact on TFR reduction in Indonesia, at -0.562. In contrast, the HDI and the number of family planning villages each have an influence of -0.12 and -0.36, respectively, on TFR reduction.

The Sustainable Development Goals (SDGs) aim to end poverty in all its forms everywhere and provide a gender-sensitive policy framework. Limited access to resources causes women to be more vulnerable to poverty and contributes to economic disadvantages, known as the feminization of poverty. Maluku Province is the fourth poorest province in Indonesia by 2022, with a poverty rate of 15.97 percent. The percentage of poor households headed by women is 7.03 percent, an increase of 0.83 percentage points from 2021. This research identifies the relationship between households headed by women and poverty in the Maluku Province. Secondary data were obtained from the National Socioeconomic Survey 2022 by the Central Bureau of Statistics. The sample size included 822 households headed by women. Binary logistic regression analysis was used in this study. The research found that area of residence, number of household members, education level, and employment sector significantly affected women's poverty status in Maluku. Furthermore, women's poverty was higher among rural residents. The findings highlight that the number of household members with family planning programs, better education, and government policies providing assistance for better agriculture can alleviate household poverty.

When the proportion of nonconformities is small, the effectiveness of the  control chart performance becomes inadequate because it has a skewness that causes asymmetryc. Therefore, the Improved Square Root Transformation (ISRT) method is used to construct the  attribute control chart to increase the accuracy of the chart control limit which is called the ISRT-  control chart. In this study, the effectiveness of the ISRT-  control chart perfomance is compared with the  control chart after being applied to the data on the number of defects in the newspaper production process at PT. Radar Sulteng Membangun. The results showed that the production process at PT. Radar Sulteng Membangun was not in a statistically controlled and the ARL value obtained on the ISRT-  control chart is much smaller than the ARL value for the  control chart, so that the ISRT-  chart is more effective and sensitive to detecting changes in the production process which produces in a small proportion of nonconformities.

The basic assumption in Poisson regression is that the mean value is the same as the variance value, which is called equidispersion. However, in some cases, this assumption is not met. A variance value that is greater than the average is called overdispersion and is called underdispersion if the variance value is smaller than the average value. So the Poisson regression model is no longer suitable for modeling this type of data because it will produce biased parameter estimates, therefore a negative binomial regression model is used. The research results show that estimating the parameters of the negative binomial regression model uses the maximum likelihood estimation method and then continues with the Newton-Raphson iteration method. The results obtained show that the negative binomial regression model overcomes the overdispersion that occurs in data on the number of dengue fever cases in Makassar City with the model  and an AIC value of 236.06647. The negative binomial regression model produces many models and then the best model with the smallest AIC criteria is selected.

Poisson regression is a nonlinear regression method used to analyse the relationship between discrete response variables. Equidispersion is the assumption that must be met in the Poisson regression. Furthermore, there are cases in which the equidispersion assumption is invalidated when using the Poisson regression model to analyze data. One such case is overdispersion, which occurs when there is an excess of zero. As a result, the Negative Hurdle Binomial (HBN) regression is implemented to solve the overdispersion issue. Maximum Likelihood Estimation (MLE) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was applied in this study to perform parameter estimation. In addition, the HBN regression model was used to analyze the data on the number of infant mortality cases in Makassar in 2017 with the variables assumed to be significant with infant mortality. The percentage of infants who were exclusively breastfed was the variable that had a significant impact on the outcome of HBN regression on the data on the number of infant mortality that experienced overdispersion.

Cluster analysis is a multivariate analysis technique that aims to cluster the observational data or variables into clusters in such a way that each cluster is homogeneous according to the factors used for clustering. This study used the Centroid linkage algorithm that was useful for forming groups based on the distance between centroids and the K-Medoids algorithm that was based on the use of the most centered object (medoid) to group districts/cities and obtained comparison results based on the education indicator data in South Sulawesi. The implementation of the Centroid Linkage Algorithm and K-Medoids on the education indicator data in South Sulawesi in 2018, showed that the grouping of districts/cities in South Sulawesi produced 2 clusters with cluster 1 of 21 districts/cities, and cluster 2 of 3. To determine the best method, it was seen from the value of the Standard Deviation ratio in the cluster 〖(S〗_W) and Standard Deviation between Clusters 〖(S〗_B) showed the same standard deviation ratio (S) in the Centroid Linkage algorithm and K-Medoids that was equal to 104,967.

Binary logistic regression is a statistical analysis method that aims to determine the relationship between variable which has two categories with the predictor variable that have categorical or continuous scale. The method that used to estimate logistic regression parameters is Maximum Likelihood Estimation (MLE) method. In estimating parameters, Least Angle Regression (LAR) algorithm is used to select the significant variables in order to get the best model from the estimation results of binary logistic regression coefficients. This LAR algorithm is applied to the risko of stunting data in two-year-old-babies at Buntu Batu Health Center working area, Enrekang Regency, South Sulawesi in 2019. This results obtained in the estimation of binary logistic regression prediction model using LAR algorithm, the standard error value is 0.018 smaller than the standard error value of binary logistic regression, which is 0.025. This shows that the binary logistic regression model using LAR algorithm is better than the usual binary logistic regression model on the risk of stunting data. Based on the results obtained, the variables that significantly affect the risk of stunting in two-year-old-babies on 2019 are father’s height, body length of birth, exclusive breastfeeding, history of infectious diseases, and history of immunization.

Korean dramas have very many fans and are spread in various countries. This study aims to determine whether the korean drama is classified as Bagus, Tidak Bagus, or Cukup Bagus and compares two methods, namely the naïve bayes classifier method and the random forest method in predicting korean drama review ratings. This study shows that the naïve bayes classifier and random forest methods are capable of predicting korean drama review ratings. In the prediction review, the random forest method obtained an accuracy value of 89%, while the naïve bayes classifier method obtained an accuracy value of 86%. In rating predictions, the random forest method obtains an accuracy value of 41%, while the naïve bayes classifier method obtains an accuracy value of 40%. The conclusion of this study is that the random forest method is superior and accurate in predicting Korean drama review ratings.

Climate change has serious effects on human life and existence in various forms. This study used Principal Component Analysis (PCA) and Mutiple Regression model (MRM) to determine the effects of meteorological factors and socio-economic factors on agricultural production. PCA showed 95.6% aggregated variation within the variables and the correlation matrix of the principal components was used to reduce the variables to six. MRM was employed for determining linear association within agricultural productions and the reduced factors showed that climate change and socio-economic factors influenced Nigerian agriculture production. The model obtained was validated with respect to coefficient of determination, adjusted coefficient of determination and Durbin Watson statistics values. Overall, this study indicated that climate change and socio-economic factors influenced the level of agriculture productions in Nigeria.

The implementation of the MB-KM program in Higher Education has a variety of obstacles based on points of view. Internship is a form of implementing the MB-KM program which involves students as executors of the program. This study aims to analyze student satisfaction with the implementation of MB-KM using the Kano method, Customer Satisfaction Index (CSI) and Quality Function Deployment (QFD). The Kano method aims to categorize variable attributes, the CSI method to calculate the overall level of satisfaction and the QFD to determine priority attribute improvements. Kano's results show that 5 attributes are in quadrant A, 30 attributes are in quadrant O, 5 attributes are in quadrant I and 13 attributes are in quadrant M. The results of the analysis of the Customer Satisfaction Index are 87.57% meaning students are "very satisfied". The final result of the HOQ attribute that is the priority is attribute .

Demand for beef is a commodity that will continue to increase. In addition to nutritious milk and protein-rich meat, cows are very beneficial to humans. The consumption trend of Indonesians which continues to increase every year also shows this. However, adequate beef production, both in terms of quality and quantity, has not been sufficient to meet the increasing demand for beef. As a result, beef production has not been evenly distributed in all Indonesian provinces. This study aims to apply the K-Means Cluster method to group provinces and determine the characteristics of the clusters formed based on the level of beef production in Indonesia in 2017-2022. With this research, it can be input to the government and the people of Indonesia so that they can handle policies for regions that are included in the low cluster as an increase in the equity of beef production. This study clustered 3 groups. The results obtained were 10 provinces included in the low cluster, 21 provinces included in the medium cluster and 3 provinces included in the high cluster.

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.