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Technological developments in Indonesia growth rapidly. Almost all systems used in daily life have been using the technology. One of its technology is communication technology. It because communication technology is a important tool for send information. All was done in order to communicate easier and faster. It is therefore important to research the condition of the existing communication technology in Indonesia. Communications technology also one of the focus of the government in national development. But not easy to know the state of communication technology in Indonesia because Indonesia has a large region and different geographically. The purpose of this research was to determine the grouping of provinces in Indonesia to increase the communication sector in order to support national development. The method used in this research is cluster hierarchical analysis method and criterion of determining the best method and many cluster optimal use Cubic Clustering Criterion (CCC). The data used is secondary data from the Statisctics Indonesia (BPS) and the Ministry of Communication and Information. The results showed that the number of cluster based on related communication technology variables are 3 cluster which 1st cluster members consist of 21 provinces, 2nd cluster members consist of 7 provinces and 3rd cluster members consist of 3 provinces.Key words : Communications Technology, Cluster Analysis, Hierarchical Method, Cubic Clustering Criterion (CCC)

Department of Statistics, Bogor Agricultural UniversityAffiliate program is one of the government's program to improve the quality of education in Indonesia. The main activity in this program is a partnership with schools in developed countries so that interactions can occur between the components of the school. Implementation of this program need support of the government and society. In order that all support can be utilized maximally, required methods to classifyand predict an affiliation status of school. Logistic discriminant analysis is one of the alternatif method to classify which the variables is a combination of numerical and categorical. Logistic discriminant function that used to classify of schools into the particular affiliate status is obtained by logit model predicted as a result of final reduction models. Through the partial parameter testing, variables that affect the status of affiliationis school type, education of headmaster, and amount of the monthly tuition of international class. The classification accuracy of the discriminant function that is equal to 66.46 Keywords-Affiliate, Logistic, Discriminant Analysis

Economic growth in the first quarter of 2021 based on YoY (Year on Year) is around -0.74%. This figure caused the Indonesian economy to recession after contracting four times since the second quarter of 2020. With positive and negative growth in the value of GDP for each category based on the business sector each quarter, can do future economic growth modelling. The prediction results can be used as an early warning for the government on factors that can maximize and factors that must improve. This study aims to predict the state of economic growth in the next quarter using Random Forest classification. Random Forest combines tree classification and bagging by resampling the data, which reduces the variance of the final model, which is for low variance overfitting. The data used in this study was scrapped from January 2021 to March 2021 on 5 Indonesian online news portals, namely Kompas, Antara, Okezone, Detik, and Bisnis. The independent variable is online news based on GDP category. The dependent variable results from data labelling on each news, up or down, carried out by the Directorate of Balance Sheet of BPS. Based on the calculations with cross-validation of 10, the modelling results obtained 96.51% accuracy, 97% precision, and 97% recall. The random forest method is good for predicting economic growth in the next quarter, namely the second quarter of 2021. Incorrectly predicted only three categories of GDP were: the construction category, the transportation and warehousing category, and the company service category

The prevalence of communicable diseases in Indonesia is still high and causes morbidity and mortality in several Indonesian provinces. This cross-sectional study classified provinces based on the prevalence of seven communicable diseases: acute respiratory infections (ARI), pneumonia, diarrhea, filariasis, malaria, hepatitis, and tuberculosis (TB). The study analyzed the 2018 Indonesian Basic Health Research data. It is essential to categorize these diseases to identify regions with low, moderate, and high prevalence and the factors that affect them to facilitate the treatment of these diseases. Grouping provinces and influenced factors were analyzed using K-means, ANOVA, and stepwise regression. The results revealed that Group 1 shows a high prevalence, except for hepatitis and diarrhea. Group 2 demonstrates a moderate prevalence, excluding hepatitis and diarrhea, while Group 3 has a low prevalence. Improved access to primary healthcare services reduced the prevalence of pneumonia and TB, while using insecticide-treated mosquito nets for less than three years increased the prevalence of ARI and pneumonia. Adequate bedroom lighting reduced the prevalence of ARI, whereas being underweight in individuals over 18 increased it. In individuals aged 16–18 with short stature, there was an increase in the prevalence of hepatitis and malaria. Regularly draining the bathtub once a week and ensuring proper bedroom ventilation reduced the incidence of diarrhea. Using improved long-lasting insecticidal nets (LLINs) for mosquitos led to a decrease in filariasis cases. Specific factors influence each communicable disease and necessitate a multisectoral approach for effective treatment.