Clustering is a method needed to group data or objects based on the required level between data, K-means is one of the clustering methods used that can be used easily in its implementation, there are some additions to this method according to the data center and on the weighting of the distance between data, the weighting of the distance between data on K-Means traditionally can be done using Euclidean Distance, Canberra Distance and Manhattan Distance, making this an analysis of the accuracy generated from the method produced by a combination of the Z-score and Min-Max Normalization methods, and is carried out Cluster homogeneity test using the Silhouette Coefficient method. The results of this method show that the Canberra method is superior to Euclidean and Manhattan on Iris dataset and the Canberra combination method with Z-score and Min-Max can increase the value on the glass without using the Normalization Method 37. 44% to 67.46% use the Z-score and 56.52% use Min-Max and use an increase in the average value of the Silhouette Coefficient.

The learning process undertaken by each school is face-to-face between educators and students; of course, this method will lose its appeal to meet digital education 4.0. So that education personnel and students should be able to adjust the p Indonesia Economic technology and information that constitute a significant role as a medium for help, search, analyze, and obtain information about learning. The learning process to welcome digital education 4.0 cannot be separated from various computer devices, the internet, which can be done online (e-learning). To get good learning outcomes, of course, technological development (e-learning) must be able to benefit right, without having to leave face-to-face learning (face-to-face). For this reason, there are teaching models/strategies, teaching delivery, and the right quality of teaching, one of which is the Blended Learning Model. Blended learning is a learning model that combines the advantages of face-to-face learning models with e-learning learning models. With blended learning interaction and communication between educators and students can take place because this model is one of the active learning methods to deal with digital education 4.0.

K-Means is a clustering algorithm based on a partition where the data only entered into one K cluster, the algorithm determines the number group in the beginning and defines the K centroid. The initial determination of the cluster center is very influential on the results of the clustering process in determining the quality of grouping. Better clustering results are often obtained after several attempts. The manhattan distance matrix method has better performance than the euclidean distance method. The author making the result of conducted testing with variations in the number of centroids (K) with a value of 2,3,4,5,6,7,8,9 and the authors having conclusions where the number of centroids 3 and 4 have a better iteration of values than the number of centroids that increasingly high and low based on the iris dataset.

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Pandemi COVID-19 memicu perubahan pembelajaran Nasional, memunculkan “Belajar Dari Rumah”. Penyelenggaraan Pendidikan menjadi Pembelajaran Jarak Jauh (PJJ), tidak terkecuali untuk jenjang Taman Kanak-kanak (TK). PJJ membutuhkan teknologi informasi dan komunikasi termasuk aplikasi edutainment. Aplikasi edutainment dapat mendukung pembelajaran pada jenjang TK. Paper ini memuat penelitian untuk memperoleh aplikasi-aplikasi edutainment yang dapat mendukung PJJ TK merujuk Standar Nasional PAUD (SN PAUD). Penelitian kualitatif ini menggunakan teknik observasi  terhadap aplikasi-aplikasi pada Google Play Store dengan platform Android. Selanjutnya, melakukan analisis eksperimen terhadap konten dari aplikasi edutainment yang telah dikumpulkan. Bagian akhir paper ini memberikan daftar aplikasi edutainment yang penggunaannya mendukung PJJ jenjang TK, bukan untuk menggantikan materi pembelajaran yang telah disusun untuk tatap muka langsung atau daring. Aplikasi-aplikasi edutainment tersebut memuat konten berkesesuaian dengan Standar Isi pada SN PAUD, sehingga  siswa memperoleh lebih banyak manfaat “Belajar Dari Rumah” dan memungkinkan guru mengeksplorasinya untuk pembelajaran.

Forecasting is one of the main topics in data mining or machine learning in which forecasting, a group of data used, has a label class or target. Thus, many algorithms for solving forecasting problems are categorized as supervised learning with the aim of conducting training. In this case, the things that were supervised were the label or target data playing a role as a 'supervisor' who supervise the training process in achieving a certain level of accuracy or precision. Time series is a method that is generally used to forecast based on time and can forecast words in social media. In this study had conducted the word forecasting on twitter with 1734 tweets which were interpreted as weighted documents using the TF-IDF algorithm with a frequency that often comes out in tweets so the TF-IDF value is getting smaller and vice versa. After getting the word weight value of the tweets, a time series forecast was performed with the test data of 1734 tweets that the results referred to 1203 categories of Slack words and 531 verb tweets as training data resulting in good accuracy. The division of word forecasting was classified into two groups i.e. inactive users and active users. The results obtained were processed with a MAPE calculation process of 50% for inactive users and 0.1980198% for active users.

Abstract. Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database : Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%.

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