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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi Compiler JOURNAL OF SCIENCE AND APPLIED ENGINEERING IJID (International Journal on Informatics for Development) Informatika : Jurnal Informatika, Manajemen dan Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Cyber Security dan Forensik Digital (CSFD) International Journal of Health Science and Technology Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer

Muhammad Habibi

Universitas Jenderal Achmad Yani Yogyakarta

Author-ID : 1734466

Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Nursing Public Health Social Sciences Other

Published : 26 Documents Claim Missing Document

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Title

Implementation of Cosine Similarity in an Automatic Classifier for Comments Muhammad Habibi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 3 No. 2 (2018): September 2018
Publisher : UIN Sunan Kalijaga Yogyakarta

Classification of text with a large amount is needed to extract the information contained in it. Student comments containing suggestions and criticisms about the lecturer and the lecture process on the learning evaluation system are not well classified, resulting in a difficult assessment process. So from that, we need a classification model that can classify comments automatically into classification categories. The method used is the Cosine Similarity method, which is a method for calculating similarities between two objects expressed in two vectors. The data used in this study were 1,630 comment data with several different categories. The test in this study uses k-fold cross-validation with k = 10. The results showed that the percentage accuracy of the classification model was 80.87%.

Journal Classification Based on Abstract Using Cosine Similarity and Support Vector Machine Muhammad Habibi; Puji Winar Cahyo
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 4 No. 3 (2020): Januari 2020
Publisher : UIN Sunan Kalijaga Yogyakarta

One of the problems related to journal publishing is the process of categorizing entry into journals according to the field of science. A large number of journal documents included in a journal editorial makes it difficult to categorize so that the process of plotting to reviewers requires a long process. The review process in a journal must be done planning according to the expertise of the reviewer, to produce a quality journal. This study aims to create a classification model that can classify journals automatically using the Cosine Similarity algorithm and Support Vector Machine in the classification process and using the TF-IDF weighting method. The object of this research is abstract in scientific journals. The journals will be classified according to the reviewer's field of expertise. Based on the experimental results, the Support Vector Machine method produces better performance accuracy than the Cosine Similarity method. The results of the calculation of the value of precision, recall, and f-score are known that the Support Vector Machine method produces better amounts, in line with the accuracy value.

Co-Authors ABIYOGA BAGUS MUSTRIYANTO Adri Priadana Alfun Roehatul Jannah Aris Wahyu Murdiyanto dayat subekti Dedy Hariyadi Fajar Syahruddin Iqbal Hadi Subekti Irmma Dwijayanti Jenny Meilila Azani Cahya Permata Kusumaningtyas, Kartikadyota M. Wahyu Indriyanto Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Nasron, Ulil Amri Netania Indi Kusumaningtyas Nurul Hikmah Pangky Putra Aziztiya Puji Winar Cahyo Ramses Caniago Rifqi Maâ€™arif, Muhammad Risky Setyadi Putra Rizky Fauzi Akbar Saputra, Andika Bayu Siti Rahmawati Sumiyarini, Retno Ulil Amri Nasron

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