Implementasi Natural Language Processing Dalam Pengukuran Ketepatan Ejaan Yang Disempurnakan (Eyd) Pada Abstrak Skripsi Menggunakan Algoritma Fuzzy Logic

Wangsanegara, Nisa Kurniasih; Subaeki, Beki

doi:10.15408/jti.v8i2.3185

Cited by 11 publications

(8 citation statements)

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“…Operasi perubahan string tersebut dapat berupa melakukan perubahan dari satu huruf ke huruf lainnya, menghapus satu huruf dari string, atau memasukkan satu huruf ke dalam string (Wangsanegara & Subaeki, 2015). Operasi ini digunakan dalam menghitung jumlah perbedaan dapat diperlukan dalam pertimbangan kecocokan suatu string dengan string sumber, jumlah perbedaan tersebut diperoleh dari penjumlahan semua pengubahan yang terjadi pada masing-masing operasi.…”

Section: Halaman 62unclassified

Deteksi Kemiripan Artikel Melalui Keywords Dengan Metode Fuzzy String Matching Dalam Natural Language Processing

Rumapea

2021

jmika

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Natural Language Processing (NLP) is part of artificial intelligence that focuses on natural language processing. That is the language commonly used by humans in communicating with each other. In writing, articles can also be applied. To find out the similarity of the contents of a scientific article to another will make it easier for readers to make selections and make it easier to collect similar documents. Likewise with differences in article content even though they use the same keywords. Measurements are made using the keywords contained in the abstract which consists of several words and the number of keywords. The method used is to use fuzzy string matching to get the number of keywords used in an article. Then the calculation will be done for each keyword used and will be sorted by priority according to the position of the keyword. The search will be carried out starting from the title, abstract, keywords, content, and references. The number of keywords found in each article is shared across all keywords found to generate a similarity percentage level. If the results found are the same in number from one article to another, it can even be categorized that the contents of the article have similar content. The testing process is carried out by counting the number of keywords in the source then comparing all the source keys to the destination articles in the database by searching and comparing each word.

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Section: Halaman 62unclassified

Deteksi Kemiripan Artikel Melalui Keywords Dengan Metode Fuzzy String Matching Dalam Natural Language Processing

Rumapea

2021

jmika

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“…Komunikasi ini dalam ilmu komputer dikenal dengan Natural Language Processing (NLP). Natural Language (NLP) adalah pembuatan program untuk memahasi bahasa manusia [2]. Salah satu pengolahan bahasa natural dalam NLP adalah ISSN: 2714-7975; E-ISSN: 2716-1382 REPOSITOR, Vol.…”

Section: Pendahuluanunclassified

Parsing Twitter Menggunakan Metode Left-Corner Parsing Dengan Memanfaatkan Pos Tagger

Anitia

Munarko

Azhar

2020

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AbstrakPada penelitian ini dilakukan investigasi parser dengan pendekatan left-corner untuk data tweet bahasa Indonesia. Total koleksi tweet sebanyak 850 tweet yang dibagi menjadi tiga kumpulan data, yakni data train POS Tagger, data train dan data uji. Left-corner menggabungkan dua metode yakni top-down dan bottom-up. Dimana top-down digunakan pada proses pengenalan kelas kata dan bottom-up digunakan pada proses pengenalan struktur kalimat. Adapun jenis tag yang digunakan dalam proses top-down berjumlah 23 tagset dan frasa yang digunakan untuk menentukan struktur kalimat frasa yakni frasa nomina, frasa verbal, frasa adjektiva, frasa adverbia dan frasa preposisional. Hasilnya adalah untuk pendekatan left corner mencapai nilai precision 88,29%, nilai recall 68,3% dan F1 measure 77,02%. Nilai yang diperoleh dengan pendekatan left-corner lebih besar dibandingkan nilai dengan pendekatan bottom-up. Hasil dari nilai yang diperoleh dengan bottom up mencapai nilai precision 68,79%, nilai recall 47,12% dan F1 measure 55,9%. Hal ini disebabkan penggunaan kelas kata pada proses top-down berpengaruh pada sturuktur kalimat pada proses bottom up.AbstractIn this research, we investigated parser with left-corner parser approach for data tweet in Indonesian language. The data used was consisted of 850 tweets which divided for into three data set, that is data train for POS Tagger, data train for parser and data test. The left-corner combines two methods, top-down and bottom-up methods. Top-down used for processes a sequence of words, and attaches a part of speech tag to each and bottom-up used for processes a sentence structure. We used 41 tags and the pharse used to define the sentence structure is noun phrase, verbal phrase, adjective pharse, adverd phrase and prepositional pharse. The result was that precision 88,29%, recall 68,3% and F1 measure 77,02% of left-corner approach. The value obtained by the left-corner approach is greater than the value with the bottom-up approach. The result was that precision 68,29%, recall 47,12% and F1 measure 55,9% of bottom-up approach. This is because the use of word class in top-down process affect the sentence structure in the bottom up process. that is because the use of word class in top-down process affect the sentence structure in the bottom up process.

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“…Until now, there are many kinds of research and products that discuss notifications. There are also various methods from the proposed ideas ranging from Natural Language Processing (NLP) [4], Machine Learning [1], Finite State Machine (FSM) [2] to controlling with databases.…”

Section: Introductionmentioning

confidence: 99%

Selection of Notification Based on Priority Scale with Fuzzy Algorithm

Riftiarrasyid

Diana²,

Istiqomah³

et al. 2021

IJDASEA

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Notification is one method that works as a marker that there is information waiting to be read. But along with the times, notifications are increasingly filled with information that is considered less important for device users. So there needs to be a breakthrough to overcome this. This study aims to design a system that can help users sort out notifications that are considered important. It is proven that the system can sort notifications based on the given metrics.

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Implementasi Natural Language Processing Dalam Pengukuran Ketepatan Ejaan Yang Disempurnakan (Eyd) Pada Abstrak Skripsi Menggunakan Algoritma Fuzzy Logic

Cited by 11 publications

References 0 publications

Deteksi Kemiripan Artikel Melalui Keywords Dengan Metode Fuzzy String Matching Dalam Natural Language Processing

Deteksi Kemiripan Artikel Melalui Keywords Dengan Metode Fuzzy String Matching Dalam Natural Language Processing

Parsing Twitter Menggunakan Metode Left-Corner Parsing Dengan Memanfaatkan Pos Tagger

Selection of Notification Based on Priority Scale with Fuzzy Algorithm

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