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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Ilmiah Media Sisfo Budimas : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Pengabdian Masyarakat Unama
Rohaini, Eni
Universitas Dinamika Bangsa
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences Other

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Journal : Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)

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Word Embedding Feature for Improvement Machine Learning Performance in Sentiment Analysis Disney Plus Hotstar Comments Jasmir, Jasmir; Nurhadi, Nurhadi; Rohaini, Eni; Pahlevi B, M Riza; Pardamean Simanjuntak, Daniel Sintong
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28799

Abstract

In this research we apply several machine learning methods and word embedding features to process social media data, specifically comments on the Disney Plus Hotstar application. The word embedding features used include Word2Vec, GloVe, and FastText. Our aim is to evaluate the impact of these features on the classification performance of machine learning methods such as Naive Bayes (NB), K-Nearest Neighbor (KNN), and Random Forest (RF). NB is very simple and efficient and very sensitive to feature selection. Meanwhile, KNN is known for its weaknesses such as biased k values, overly complex computations, memory limitations, and ignoring irrelevant attributes. Then RF has a weakness, namely that the evaluation value can change significantly with just a slight change in the data. Feature selection in text classification is crucial for enhancing scalability, efficiency, and accuracy. Our testing results indicate that KNN achieved the highest accuracy both before and after feature selection. The FastText feature led to the highest performance for KNN, yielding balanced accuracy, precision, recall, and F1-score values.
Co-Authors Abdurrahman Ade Saputra Agus Siswanto Aldino Ramadhani Amin Syam, Atha Bugis Andrianti, Ari Ari Andrianti Aryani, Lies Ayu Alysia Despita Meisak Dodi Sandra Errissya Rasywir Fadillah, Yosi Feranika, Ayu Gunardi Hendri Hendri Ibnu Sani Wijaya Indana Arum , Refi Inggrit Tia Pratiwi Inggrit Tia Septiana Jasmir Jasmir, Jasmir Joni Devitra, Joni Jovansah, Afif Lely Febriyanti Lies Aryani Lola Yorita Astri, Lola Yorita M Riza Pahlevi M. Taufik Aufa Meisak, Despita mery Muhammad Afif Dzaky Khairullah Muhammad Fadri Adam Muhammad Fajar Muhammad Riza Pahlevi Muhammad Rizki Saputra Nasrul Ahlunaza NURHADI Nurhadi Nurhadi Nurhadi Nurhayati Nurhayati Oktavianus, Welsen Oldie Versileno, Satria Pahlevi B, M Riza Pahlevi, M. Riza Pardamean Simanjuntak, Daniel Sintong Purnama, Benni Putra Ramadhan, Muhammad Alif Ramadhan Saputra, Tri reza saputra, Fharis Rezky Pramudia, Muhammad Rico Natanael Rifky Januardi Risky Handayani Riza Pahlefi Riza Pahlevi, Riza Setiawan Assegaf Silvia Rianti Agustini Sirait, Gusnaldi Triardo Stevent Chandra Dinata The, Felix Filbert Virginia Casanova Andiko William William WILLY RIYADI Xaverius Sika Yossinomita Yovi Pratama