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Journal of Information Technology and Computer Science. IMPLEMENTASI DATA MINING PEMILIHAN PELANGGAN POTENSIAL MENGGUNAKAN ALGORITMA K-MEANS IMPLEMENTATION OF DATA MINING FOR POTENTIAL CUSTOMER SELECTION USING K-MEANS ALGORITHM. R., Wadisman, C., Sains, F., Teknologi, D., Pembangunan, U., & Medan, P. IEEE Communications Surveys and Tutorials. IMPLEMENTASI DATA MINING PEMILIHAN PELANGGAN POTENSIAL MENGGUNAKANة. New book classification based on Dewey Decimal Classification (DDC) law using tf-idf and cosine similarity method. Nurdiansyah, Y., Andrianto, A., & Kamshal, L. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
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Ontology alignment based on word embedding and random forest classification. Nkisi-Orji, I., Wiratunga, N., Massie, S., Hui, K. Penerapan Cosine Similarity dan Pembobotan TF-IDF untuk Mendeteksi Kemiripan Dokumen. Cosine normalization: Using cosine similarity instead of dot product in neural networks. Luo, C., Zhan, J., Xue, X., Wang, L., Ren, R., & Yang, Q. Human-Centric Computing and Information Sciences. Research paper classification systems based on TF-IDF and LDA schemes.
#SKRIPSI TEKNIK INFORMATIKA BERBASIS WEB PLUS#
Implementasi Tokenizing Plus Pada Sistem Pendeteksi Kemiripan Jurnal SkripsI. International Journal Of Advance Scientific Research And Engineering Trends Using. Using Explicit Semantic Similarity for an Improved Web Explorer with ontology and TF-IDF. JISKA (Jurnal Informatika Sunan Kalijaga). PENERAPAN ANALISIS SENTIMEN PADA PENGGUNA TWITTER MENGGUNAKAN METODE K-NEAREST NEIGHBOR. SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering. Unsupervised sentence representations as word information series: Revisiting TF–IDF. Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web (Studi Kasus: Hadits Shahih Bukhari-Muslim).
#SKRIPSI TEKNIK INFORMATIKA BERBASIS WEB CODE#
So that OPD can choose the product code as desiredĪmrizal, V. By using the cosine sismilarity algorithm and TF-IDF, it is hoped that it can improve the accuracy of the search for product codification. This research produces the calculation of cosine similarity and TF-IDF weighting and is expected to be applied to the SiPaGa application so that the search process on the SiPaGa application is more accurate than before. The search keywords were processed using the Cosine Similarity method to see the similarities and using TF-IDF to calculate the weighting. Codification of goods processed in this study were 14,417 data sourced from the Goods and Price Planning Information System (SiPaGa) application database. The purpose of this research is to improve the accuracy of the search for goods codification. Term Frequency and Inverse Document (TFIDF) is a way to give weight to a one-word relationship (term). Cosine Similarity is a method for calculating similarity by using keywords from the code of goods. So we need Cosine Similarity and TF-IDF methods that can improve the accuracy of the search. In the SiPaGa application, the codefication search process is still inaccurate, so OPD often make mistakes in choosing goods codes. Implementation of the application is made with PHP, so to run it can use a browser application that has installed a computer.TF-IDF, Cosine Similarity, Term Frequency, Invers Document Frequency, Search Accuracy Abstract These applications can help the production process goes smoothly. Required application that is able to predict the outcome of the actual ratio value directly without having to wait for the test results of samples which takes 4 hours. As a result, the status of stock products production is unclear whether the product is defective or not the resulting product can not be continued to the next process.
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This is due to the phase of the sample test on the products that are being produced where the stage takes 4 hours. However, the actual results of the process can not be known directly by production operators. TBINA process is done automatically by the machine by setting ¬nilai mixture ratio set on the machine. One of them is the process of calculating the ratio of raw material mixture before used as a finished product. Therefore, in any production process is necessary to check the quality from raw material to finished product prior to the hands of consumers. One supporter of the success of a manufacturing industry that is viewed in terms of the quality of products produced by the company.