In the text mining there are stages that must be passed namely the text preprocessing stage. Text preprocessing is the stage to do the data selection process in each document, including case folding, tokenizing, filtering, and stemming. The results of the preprocessing process can affect the accuracy of document classification. In documents Bahasa Indonesia, there are still often over-stemming and under-stemming, so improvements are needed in the stemming process. In this study, it is proposed to use sastrawi libraries to improve the results of previous studies that are still not optimal in the results of preprocessing, especially in the filtering and stemming process. From the results of the study, the sastrawi library is able to reduce over stemming and under stemming and a faster processing time compared to using a Tala stemmer.
Pada bulan Maret 2020 Presiden Indonesia dan berdasar Surat Edaran Kemendikbud Nomor 4 Tahun 2020 memandatkan untuk siswa belajar dari rumah akibat dari pandemi Covid-19. Adapun studi ini bertujuan untuk menguji keefektifan penggunaan media edutainment saat belajar dari rumah mulai diberlakukan akibat dari Covid-19. Data dianalisis menggunakan metode kuantitatif dan kualitatif. Adapaun data didapatkan menggunakan suatu instrumen tes dan non tes yang dapat diakses secara online. Sebanyak 2 jenis angket yang digunakan pada penelitian ini, yakni angket guru dan angket siswa. Sampel penelitian terdiri dari 232 siswa (M = 15 tahun; SD = 0,5 tahun) serta 32 guru matematika (M = 34 tahun; SD = 0,4 tahun). Hasil studi menunjukkan bahwa mayoritas siswa dan guru memiliki persepsi yang positif untuk menggunakan media edutainment (pada fase kuesioner). Selain itu melalui media edutainment pada pembelajaran via daring, pencapaian aspek kognitif siswa (pada fase tes) juga memperoleh hasil yang positif. Sehingga hasil secara keseluruhan menunjukkan bahwa media edutainment efektif dalam menemani siswa belajar dari rumah. Kata kunci: Game edukasi; media pembelajaran edutainment; pandemi Covid-19.
Pemrograman web atau aplikasi berbasis web memliki kelebihan di bandingkan dari pada aplikasi berbasis dekstop karena : Akses Informasi lebih mudah karena bisa darimana saja melalui jaringan baik intranet maupun internet. Pemrograman web merupakan pemrograman server side jadi Jika terdapat perubahan aplikasi atau program maka client tidak perlu melakukan update cukup di lakukan di server. Data disimpan di server sehingga akses data untuk client dapat di atur sesuai dengan kebutuhan. Aplikasi dapat diakses melalui komputer dengan berbagai sistem operasi (cross-platform) yang memiliki browser.
Weather forecast in an area is unpredictable. This is due to the fact that human factors cannot predict it. The weather forecast is by applying data mining using the algorithm Naive Bayes, K-nearest Neighbor (K-NN), and C.45. Bayesian Classification is a statistical classification method that is useful for the process of determining the probability of a class membership. KNN Algorithm is a classification algorithm based on the similarity between one data and another data. C4.5 algorithms is an easy-to-use classification method interpreted. The best level of accuracy between the three algorithms can be determined by comparison. Comparison of algorithm aims to get the algorithm that is considered accurate, precision, recall and f-measure to make a prediction of a problem. the results of the comparison of the k-Nearest Neighbor, Naïve Bayes and C4.5 classification algorithms used in weather prediction case studies stating that the KNN classification algorithm is a classification algorithm that has the highest accuracy with k = 7 and fold = 5 in predicting the weather compared to Naïve Bayes classification algorithm with fold = 3 and C.45 which reached 71.58% followed by C.45 with fold = 20 having an accuracy of 69.83%. and finally Naïve Bayes 68.77%.
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