Stemming words to (usually) remove suffixes has applications in text search, machine translation, document summarization, and text classification. For example, English stemming reduces the words "computer," "computing," "computation," and "computability" to their common morphological root, "comput-." In text search, this permits a search for "computers" to find documents containing all words with the stem "comput-." In the Indonesian language, stemming is of crucial importance: words have prefixes, suffixes, infixes, and confixes that make matching related words difficult.
This work surveys existing techniques for stemming Indonesian words to their morphological roots, presents our novel and highly accurate CS algorithm, and explores the effectiveness of stemming in the context of general-purpose text information retrieval through ad hoc queries.
Many COVID-19 spread predictions have been implemented using various method. However, most of the prediction are missed because of many factors influence the COVID-19, e.g. geographic condition, socio-economic, government policy, etc. To handle this problem, the scenariobased prediction is proposed in this study to predict COVID-19 spread in Indonesia. This study proposed two methods to be used, i.e. Support Vector Regression (SVR) and Susceptible-Infectious-Recovered (SIR) Model. The prediction run for bestcase scenario and worst-case scenario. Whereas best-case scenario used current daily case as a maximum case, worst-case scenario used another country' s maximum case, i.e. India. SVR regression showed different end of epidemic, whereas best-case scenario on 21 January 2021, the worst-case scenario on 5 March 2021. SIR-Model showed the similar end of epidemic on January 2021 for both scenarios but showed the dramatically increase of infectious people from 450,000 people in best-case scenario to 5,500,000 people in worst-case scenario. The prediction can be used as an insight for the policy maker in combating the COVID-19 pandemic.
Penelitian ini bertujuan untuk membangun sebuah sistem informasi yang dapat menunjang sebuah toko dalam menentukan kombinasi item dan tata letak barang berdasarkan kecenderungan pembelian konsumen untuk meningkatkan penjualan pada grosir. Pada penelitian ini, data yang digunakan adalah data penjualan di Grosir Sembako Lina dan metode yang digunakan adalah metode algoritma apriori. Algoritma apriori adalah algoritma pengambilan data dengan aturan asosiatif untuk menentukan hubungan asosiatif suatu kombinasi item, sehingga data mining dengan algoritma apriori merupakan salah satu algoritma yang cocok untuk memprediksi persediaan sembako yang ada di grosir lina. Dengan metode ini, pemilik grosir dapat mengetahui barang apa saja yang sering dibeli oleh konsumen, dan tau bagaimana cara mengatur tata letak penyimpanan barang sehingga konsumen tidak merasa bingung saat mencari barang yang dia butuhkan.
Kata Kunci: Data Mining, Apriori, Itemset, Data Transaksi Penjualan
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