Aplikasi Penentuan Dosen Penguji Skripsi Menggunakan Metode TF-IDF dan Vector Space Model

Siregar, Riki Ruli A.; Sinaga, Fera Amelia; Arianto, Rakhmat

doi:10.24912/computatio.v1i2.1014

Cited by 8 publications

(10 citation statements)

References 1 publication

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“…In the field of education, the application of TF-IDF weighting and the Vector Space Model is carried out to determine the examiner lecturers (Siregar et al, 2017). The results of this study can recommend the examiner according to the topic based on the suitability of the title and abstract.…”

Section: Literature Reviewmentioning

confidence: 99%

Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF)

Sintia

Defit

Nurcahyo

2021

JAETS

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In the SiPaGa application, the codefication search process is still inaccurate, so OPD often make mistakes in choosing goods codes. So we need Cosine Similarity and TF-IDF methods that can improve the accuracy of the search. Cosine Similarity is a method for calculating similarity by using keywords from the code of goods. Term Frequency and Inverse Document (TFIDF) is a way to give weight to a one-word relationship (term). The purpose of this research is to improve the accuracy of the search for goods codification. Codification of goods processed in this study were 14,417 data sourced from the Goods and Price Planning Information System (SiPaGa) application database. The search keywords were processed using the Cosine Similarity method to see the similarities and using TF-IDF to calculate the weighting. 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. By using the cosine sismilarity algorithm and TF-IDF, it is hoped that it can improve the accuracy of the search for product codification. So that OPD can choose the product code as desired

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Section: Literature Reviewmentioning

confidence: 99%

Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF)

Sintia

Defit

Nurcahyo

2021

JAETS

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“…Text mining memiliki tujuan dan menggunakan proses yang sama dengan data mining, tetapi memiliki masukan yang berbeda. Masukan untuk Text mining adalah data yang tidak (atau kurang) terstruktur, seperti dokumen Word, PDF, dan kutipan teks, sedangkan masukan untuk data mining adalah data yang terstruktur (Siregar, Sinaga, & Arianto, 2017). Bidang ini mungkin lebih dikenali sebagai proses dalam menganalisis teks untuk mengekstrak informasi yang berguna untuk suatu tujuan tertentu.…”

Section: Text Miningunclassified

Analisis topik konten channel YouTube K-pop Indonesia menggunakan Latent Dirichlet Allocation

Rahmawati

Nikmah

Perwira

et al. 2021

teknol. j. ilm. sist. inf.

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The development of digital technology has brought new media, one of which is Youtube, which is now one of the most widely used applications for internet users in the world. The growth of the audience which is known as viewers, is also suported by the contribution from the content creators or also known as YouTubers from Indonesian. The more the viewers grow, the more their demand for trend content are also grwoing at surprisingly speed in one of the topics which is H-pop. In this study, the author wanted to see the dominant topics that K-pop YouTubers often upload to support content creator. This research was conducted using the Latent Dirichlet Allocation method. The analysis was carried out on after using text mining on 2563 videos from 10 K-pop YouTuber accounts with more than 100,000 subscribers. To determine the optimal number of topics by looking at the value of perplexity and topic conherence. The results obtained are the top 5 topics that are the content material in the uploaded video. These topics include reactions to dance covers, unboxing on albums and conducting reviews, riddles from K-pop dances and vlogs together to discuss about covers and reactions to sounds on K-pop songs.

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“…Cosine similarity akan menenetukan nilai kemiripan dokumen keseluruhan dari query dan dokumen pengetahuan dalam knowledge base. Beberapa bentuk implementasi dalam pengelolaan teks, diantaranya adalah kategorisasi teks [1], klasifikasi dokumen teks [2] seperti juga klasifikasi varian dokumen oleh [3], analisis cross-language sentiment [4], sistem pendeteksian kemiripan judul karya ilmiah [5], pencarian artikel dan klausa dalam dokumen UUD 1945 [6], penentuan dosen penguji [7], metode pengelolaan teks digunakan untuk mendeteksi durasi waktu berita online [8]. Dalam penelitian ini khusus digunakan unutk mendeteksi kerusakan pada system operasi.…”

Section: Pendahuluanunclassified

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Abidin¹,

Sukmadinata²

2020

oai

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Aplikasi Penentuan Dosen Penguji Skripsi Menggunakan Metode TF-IDF dan Vector Space Model

Cited by 8 publications

References 1 publication

Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF)

Product Codefication Accuracy With Cosine Similarity And Weighted Term Frequency And Inverse Document Frequency (TF-IDF)

Analisis topik konten channel YouTube K-pop Indonesia menggunakan Latent Dirichlet Allocation

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