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The disparities in access to public assistances between rural and urban areas are analyzed in this study, corresponding to the intensity in which the constructs are related to expected quality, perceived quality and perceived value as influential factors in citizen satisfaction and loyalty. In Guayaquil, a survey is carried out at the household level, 428 valid questionnaires are obtained in the rural area of Tenguel and 521 Valid questionnaires in the urban area of Tarqui, applyingto the American Customer Satisfaction Index (ACSI). The research used a Structural Equation model (SEM) to evaluate the hypotheses raised, if they observe significant differences in citizen perception between inhabitants of urban and rural areas about the quality of community services, this as a determining cause in the level of citizen satisfaction and loyalty to choose their municipal authorities. The multigroup analysis allowed to identify inequalities in the observation of the quality of municipal or local communityassistances between rural and urban areas; the findings are considered to local public administrators for the design of public policy aimed at improving levels of citizen satisfaction and loyalty.Sentiment analysis is a way to automatically understand and process text data to figure out how someone feels about an opinion sentence. If there are too many reviews, it will take a lot of time and they will start to be biased. Sentiment classification tries to solve this problem by putting user reviews into groups based on whether they are positive, negative, or neutral. The dataset comes from Drone Emprit Academic. It is made up of tweets with the words "online learning method" in them, with as many as 4887 data crawled from them. Information Gain and adaboost on the C4.5 (FS+C4.5) method are used in the feature selection method. We use feature options to get rid of bias and improve accuracy. The results of the experiments will be compared to other algorithms like C4.5 and random forest. Based on the results, the accuracy of the two standard decision tree models (C4.5 and random forest) went up from 48.21% and 50.35% to 94.47 %. The value of how accurate it was went up by 44 percent. The FS+C4.5 model, on the other hand, has an RMSE of 0.204 and a correlation of 0.944. So, adding the feature selection technique to the sentiment analysis of bold learning education can make the C4.5 algorithm even more accurate.

The purpose of this research is to analyze mapping results in the form of clusters formed using clustering method measures. This is done to determine the connections that the existing clusters create. Some of the measurements used are mixed measurements, Bregman differences, and number measurements (Mixed Euclidean Distance, Generalized Divergence, Squared Euclidean Distance, Mahalanobis Distance, and Euclidean Distance). Distance measurement shall be applied on number with primary school facilities in Indonesia. The Davies Bouldin Index (DBI) is different from the cluster number test (k = 2-10) for each Distance Measure. The average DBI value in the type of measure (mixed measure) and numerical measurement (Mixed Euclidean Distance) is 0.54. The average DBI value in the type of measure (Bregman divergences) and numeric measurements (generalized IDivergence) is 0.66. The average DBI value is 0.77 for the measurement type (Bregman divergences) and numerical measurement (Squared Euclidean Distance). From the results, the measurement of distance with mixed measurement and the mixed Euclidean distance with the cluster number (k = 2), namely 0.269, have the best DBI value.

The fuzzy logic Mamdani model is a fuzzy reasoning system that can be used to calculate values based on various criteria for evaluating teacher performance. In this study, three fuzzy sets were used to determine the level of success of teachers in teaching through online media in one of the vocational schools in North Sumatra: the teacher set, the value set, and the success rate set. The teacher set is divided into three criteria: bad, sufficient, and good teachers. The set of values is also divided into three categories: bad, sufficient, and good. Furthermore, the set of success rates is divided into three categories: low, medium, and high success rates. The goal of this study is to use the mamdani fuzzy logic method to assess the level of success in teaching teachers using online media. According to the study's findings, the mamdani method could be used in this situation.

Malaria is an infectious disease throughout the world where the disease is transmitted by infected female Anopheles mosquitoes. Malaria has some symptoms that are almost like COVID-19. Malaria has several other symptoms, characterized by chills, anemia, cold sweats, nausea and vomiting, and a sudden drop in blood pressure. Identification of the type of malaria begins with preprocessing, feature extraction, and classification for identification. Image improvement is part of the preprocessing stage to improve image quality so that the malaria parasite object in the image can be seen clearly. This study tries to improve the algorithm with hybrid dark and contrast stretching. Performance evaluation of malaria parasite image improvement using Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR). The results obtained with the improvement of dark hybrids and contrast stretching can improve the image quality of malaria parasite objects with MSE value = 0.0095 and PSNR value = 22.8404, compared with dark stretching, contrast stretching, histogram equalization.

The purpose of this research is to analyze the C4.5 and Random Forest algorithms for classification. The two methods were compared to see which one in the classification process was more accurate. The case is the success of university students at one of the private universities. Data is obtained from the https://osf.io/jk2ac data set. The attributes used were gender, student, average evaluation (NEM), reading session, school origin, and presence as input and success as a result (label). The process of analysis uses Rapid Miner software with the same test parameters (k-folds = 2, 3, 4, 5) with the same type of sample (stratified sample, linear sample, shuffled sampling). The first result shows that the sample type test k-fold (stratified sampling) achieved an average accuracy of 55.76 percent (C4,5) and 5618 percent (Random Forest). The second result showed that the kfold (linear sampling) sample test achieved an average precision of 58.06 percent (C4.5) and 6506 percent. (Random Forest).The third result shows that the k-fold test with the sampling type has averaged 58.68 per cent (C4,5) and 60,76 per cent (shuffled sampling) precision (Random Forest). From the three test results, in the case of student success at a private university, the Random Forest method is better than C4.5.

The purpose of this research is to choose the best method by comparing two classification methods of data mining C4.5 and Naïve Bayes on Educational Data Mining, in which the data used is student graduation data consisting of 79 records. Both methods are tested for validation with 10-ford X Validation and perform a T-Test difference test to produce a table that contains the best method ranking. Different results were obtained for each method. Based on the results of these two methods, it is very influential on the dataset and the value of the area under curve in the Naïve Bayes method is better than the C4.5 method in various datasets. Comparison of the method with the 10-Ford X Validation test and the T-Test difference test is that the Naïve Bayes method is better than C4.5 with an average accuracy value of 73.41% and an under-curve area of 0.664.

The lower Davies Bouldin Index (DBI) is considered the best clustering algorithm based on the criteria that yields a cluster set. The purpose of this research is to optimize the clustering results using DBI. The data sources used are the number of villages that have school facilities and the level of education is obtained from the government website (https://www.bps.go.id). The level of education in question is senior high school and vocational high school. The method used is k-means. The results show that from the number of clusters (k = 2, 3, 4, 5, 6) the optimal DBI for (k = 2) is obtained with a value of 0.168 for Measure Type = Mixed Measures. For the value of k = 2, a mapping of areas with L0 (low) = 31 province and L1 (high) = 3 provinces is obtained. The final centroids obtained for each cluster are L0 (315 and 155) and L1 (1710 and 1259). Based on the results of mapping by optimizing k-means and DBI, more than 90% of the villages still have school facilities, especially at the high school and vocational high school levels.

Statistical data are indispensable for macro-economic planning activities such as the Gross Regional Domestic Product (GRDP) where data can determine the economic development strategies and policies that have been adopted and can be continued in the future. This study draws on quantitative data sources from the Regional Statistical Agency of Jakarta for the period 2017-2019, the subject of the Gross Regional Domestic Product based on current business prices. The aim of this research is to test and predict the level of accuracy of GRDP at current prices based on business fields using the Linear Regression method supported by Rapid Miner software. The results show that the validated Linear Regression algorithm with K-Fold values from 2 to 10 with the sampling type linear sampling and shuffled sampling can be used and implemented with the smallest Root Mean Square Error value of IDR 9,977,431 at k = 10 for the sampling.

The purpose of this study was to analyze the k-medoids method in conducting cluster mapping in the ratio of the number of students and teachers in Indonesia by region, especially at the elementary school level. The data source is secondary obtained from the Ministry of Education and Culture which is processed by the Central Statistics Agency (abbreviated as BPS) in the BPS Catalog: 4301008 concerning the Portrait of Indonesian Education. The analysis process uses the help of Rapid Miner software by using parameters of the Davies Bouldin Index (DBI) and Performance (Classification). By using three cluster labels, namely the high cluster (K1), normal cluster (K2) and poor cluster (K3), it was found that 3 provinces were in the high cluster, 9 provinces were in the normal cluster and 22 provinces were in the fewer clusters. By testing the cluster results (k = 3) through the DBI parameter the value = 0.587 was obtained. This shows that the results of the cluster formed are optimal (the smaller the better). The test results with the parameter Performance (Classification) show the results of classification error = 2.50%. The results of the research can be used as information to determine the ratio of students and teachers because the higher the value of this ratio means that the level of teacher supervision and attention to students is reduced so that the quality of teaching tends to be lower.