This study discusses the development of a prediction model for the classification of rainfall based on time in Java. The method used in this research is naive Bayes and simple kriging. Naive Bayes is used for classification prediction, while simple kriging is an interpolation method used for mapping. There are two scenarios used, that is building a prediction model for daily and monthly rainfall classification, with data taken from 27 weather stations on the island of Java from 2010 to 2021. The results obtained in the classification process are an accuracy value of 67% for the daily model and 88% for the monthly model. The daily model data uses a spherical semivariogram with an average RMSE of 1,021. For the monthly model data using a Gaussian semivariogram with an average RMSE of 0,34. Then interpolation using simple kriging for mapping rainfall. The results of this study are predictions for the classification and mapping of daily rainfall models from April 1 to April 7 2022 and monthly models from April to September 2022. The contribution of this research is to provide predictive information and mapping of future rainfall so that public people can anticipate more.

The amount of rainfall that occurs can affect natural disasters and even food production to economic activities. the factor of the area where the rain occurs is one of the main parameters for how the change occurs. So, it is necessary to have a rainfall prediction approach that aims to find out when and what type of rain will occur. Spatial classification and interpolation are two methods used to make predictions. Random Forest is a classification method that can be used to predict rainfall. and Inverse Distance Weighted is one of the stochastic interpolation techniques to calculate the estimated rainfall from the data points of rainfall that occur so that the distribution can be visualized. In the implementation of random forest, the model that is built on a daily basis gets the best level of accuracy in the 5D model sub model C with an accuracy of 0.8238 while the monthly model gets the best level of accuracy in the sub-model B 4M 0.9362. and the results of predictions and mapping using IDW show that daily predictions from June 1-4 2022 show that Most of Java Island will experience light rain, June 5-7 2022 most of Java Island will experience sunny cloudy days. And for monthly predictions, August and June 2022 show the distribution of monthly rainfall with predictions that most of Java is cloudy, while May, July, October, September have light rainfall in most of Java

Social media is a platform that makes it easier for users to interact and get to know each other because in social media there are profiles, statuses, and user uploads. Therefore, many studies utilize social media because there is much information that can be explored on social media, one of which is research on the personality classification of social media users. However, many studies related to personality classification of social media users have failed due to too many model target classes, which result in low accuracy. In this research, the author uses the Myers-Briggs Type Indicator (MBTI) model, which is focused on only two personality classes, namely "Introvert/Extrovert" and "Sensor/Intuitive" with the features type of work and interest in information which are feature representations of the personality class used to reduce the target class. The best accuracy result is 95.87% after classifying using two personality classes.

This research proposes a visualization of Bandung City congestion map classification using machine learning and kriging interpolation methods. The machine learning methods used are Naive Bayes and Artificial Neural Network (ANN) for the congestion classification process. The kriging interpolation used is simple kriging to create a spatial location map visualization on the congestion classification prediction. They are based on the classification results of both methods. Naïve Bayes is ideal supervised learning for classification, while ANN is ideal unsupervised learning for prediction. The classification was performed on arterial and collector roads with 11 intersections that are congestion points. The data used is traffic counting data for Bandung City in April 2022. The congestion classification is divided into four categories based on the congestion level. This category division causes data imbalance, so the Random Oversampling technique is used to overcome data imbalance. The result is that the ANN method has better performance, with an accuracy rate of 93% and an RMSE value of 0.9746, while the Naïve Bayes method has an accuracy rate of 90% and an RMSE value of 0.9381. The resulting classification map shows that in April 2022, the southern area of Bandung City experienced the highest congestion compared to the northern, western and southern areas. This research provides the best algorithm between the two methods. It provides information on congestion in Bandung City by visualizing the congestion classification map to reduce traffic congestion in the city of Bandung.

This research provides information about land prices in Jakarta by classifying using the Random Forest method.  Where Random Forest is a data mining technique that is usually used to perform classification and regression.  Random Forest is one of the best classification methods.  It is found that classification accuracy will increase dramatically as a result of voting to select class types and ensemble tree growth.  The method helps in providing information about the classification of land prices with the class of land prices per meter less than IDR 15 million, land prices per meter with a price range of IDR 15 to 25 million and land prices per meter more than IDR 25 million. With a fairly good accuracy of 82%, this method can classify where the permeter land price data that is tested will match the predicted classification accurately.  Classification is performed on unbalanced data which is then oversampled using the ADASYN method.  Assisted by doing spatial interpolation with the Ordinary Kriging method using Semivariogram, information about the classification of land prices can be seen on the distribution of the Jakarta area map.  Ordinary Kriging can predict the estimated price per meter of land around the area of land that has a known price.  The Root Mean Square Error (RMSE) results of the best Semivariogram model are obtained from the lowest RMSE value, namely the Spherical model with a value of 1.014896e7.  The contribution of this research is to provide information about a reliable classification method, namely Random Forest and Ordinary Kriging performance as a spatial analysis method that can predict land prices per meter at unknown points so as to provide information about the distribution of land prices in Jakarta with each price class.