Stemming Bahasa Tetun Menggunakan Pendekatan Rule Based

Guterres, Anita; Gunawan, .; Santoso, Joan

doi:10.34148/teknika.v8i2.224

Cited by 10 publications

(10 citation statements)

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“…Bekerja dengan menghapus akhiran kata, menurut aturan tata bahasa, dan menurunkan kata dari sebuah kata [10].…”

Section: Stemmingunclassified

Penerapan Metode Naïve Bayes Untuk Merekomendasikan Pekerjaan Yang Sesuai Terhadap Fresh Graduate

Ningrumsari Mulyanan,

Yusril Helmi Setyawan,

Isti Rahayu

2024

jati

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Di era teknologi yang kini berkembang, banyak lulusan baru yang menghadapi tantangan dalam mencari pekerjaan yang cocok dengan spesialisasi mereka. Sebagai solusi, sistem rekomendasi pekerjaan khusus untuk lulusan teknik informatika telah dirancang dengan memanfaatkan metode Naïve Bayes. Sistem ini dirancang untuk memberikan saran pekerjaan berdasarkan faktor seperti keahlian, riwayat pendidikan, dan pengalaman magang. Dari pengujian yang dilakukan, sistem ini menunjukkan tingkat keberhasilan prediksi sebesar 78%. Meski terdapat hambatan pada data pelatihan, sistem ini mampu mengklasifikasikan profil ke dalam subsektor IT dengan ketepatan sebesar 83%.

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“…Bekerja dengan menghapus akhiran kata, menurut aturan tata bahasa, dan menurunkan kata dari sebuah kata [10].…”

Section: Stemmingunclassified

Penerapan Metode Naïve Bayes Untuk Merekomendasikan Pekerjaan Yang Sesuai Terhadap Fresh Graduate

Ningrumsari Mulyanan,

Yusril Helmi Setyawan,

Isti Rahayu

2024

jati

View full text Add to dashboard Cite

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“…Stemming merupakan proses untuk menghilangkan imbuhan pada suatu kata (Guterres et al, 2019). Misalnya kata "mendengarkan", "dengarkan", "didengarkan" akan ditransformasi menjadi kata "dengar".…”

Section: Stemmingunclassified

Sentimen Pengguna Twitter Mengenai Isu Kebocoran Data Dengan Algoritma Naïve Bayes

Taufiqi

Nugroho

2023

Jurnal-NIK

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Sensitive data leakage occurs when it is unintentionally made available to or accessed by unauthorized individuals. Sentiment analysis is the process of analyzing text to gather different data from the web and social media in order to obtain opinions from internet users on a particular topic. Sentiment analysis is a branch of Natural Language Processing (NLP) that develops a system for extracting and identifying public opinion from text that is then transformed into structured data. One kind of technique for classifying data is Naïve Bayes. Naïve Bayes uses conditional probabilities to assign labels to instances. This study collects information from tweets sent by Twitter users on the subject of "Data Leakage" and then uses the Naïve Bayes method for sentiment analysis to determine the precision of GaussianNB

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“…Stopword removal adalah tahap menghapus kata-kata yang biasanya muncul dalam jumlah besar dan dianggap tidak memiliki makna (Stopword) [11]. Stemming adalah proses mengganti kata yang memiliki imbuhan menjadi kata dasar [12].…”

Section: Metode Penelitianunclassified

Twitter Sentiment Analysis Pedulilindungi Application Using Naïve Bayes and Support Vector Machine

Yunanto

Yulianto

2022

J. Tek. Inform. (JUTIF)

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The PeduliLindungi application is an application launched by the government during the COVID-19 pandemic, with the aim of helping government agencies carry out digital tracking to monitor the public, as an effort to prevent the spread of the Corona virus. Many people express their opinions on the PeduliLindung application on social media, one of which is through Twitter. To improve the performance of the application, of course, need input or complaints from users, opinions from the public on Twitter about the PeduliLindungi application can be input to improve or improve the performance of the application. Sentiment analysis is carried out to see how the public's sentiment towards the PeduliLindung application is, and these sentiments will be categorized into positive sentiment and negative sentiment, this sentiment can later be used as evaluation material for application development. This study aims to see and compare the accuracy of two classification methods, Naïve Bayes and Support Vector Machine in the classification process of sentiment analysis. The data used are 4636 tweets with the keyword " PeduliLindungi". The data obtained then goes to the pre-processing stage before going to the classification stage. The results obtained after classifying using the Naïve Bayes method and the Support Vector Machine show that the Support Vector Machine method has a higher accuracy of 91%, while the Naïve Bayes method has an accuracy of 90%.

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Stemming Bahasa Tetun Menggunakan Pendekatan Rule Based

Cited by 10 publications

References 0 publications

Penerapan Metode Naïve Bayes Untuk Merekomendasikan Pekerjaan Yang Sesuai Terhadap Fresh Graduate

Penerapan Metode Naïve Bayes Untuk Merekomendasikan Pekerjaan Yang Sesuai Terhadap Fresh Graduate

Sentimen Pengguna Twitter Mengenai Isu Kebocoran Data Dengan Algoritma Naïve Bayes

Twitter Sentiment Analysis Pedulilindungi Application Using Naïve Bayes and Support Vector Machine

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