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All Journal Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Syntax Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Technomedia Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Ilmu Komputer dan Bisnis Systematics JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Wahana Pendidikan J-SAKTI (Jurnal Sains Komputer dan Informatika) Action Research Literate (ARL) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Sosial dan Abdimas International Journal of Health Sciences Surya : Jurnal Pengabdian Kepada Masyarakat
Mayasari, Rini
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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

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Pengaruh Stopword Removal dan Stemming Terhadap Performa Klasifikasi Teks Komentar Kebijakan New Normal Menggunakan Algoritma LSTM Santosa, Agil; Purnamasari, Intan; Mayasari, Rini
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.427

Abstract

The development of information technology has made the popularity of social media increase in recent years, one of which is Youtube. Youtube’s popularity make this platform a major source of sentiment where almost everyone tends to express their views in the form of comments. These commnent not only express people but also have more meaning about their experiences. Comments originating from social media are unstructured so that in sentiment analysis the preprocessing stage is an important task. There are many techniques used in preprocessing including stopwrod and stemming. However, several studies have shown that the use of stopword and stemming gives different result. Therefore, in this paper, the researcher further anlyzes the effect of applying stopword and stemming on Youtube video comment regarding the New Normal policy using Long Short Term-Memory. The result obtained we found that the use of stopword and stemming greatly affects the performance of the model, this is because a lot of information is lost after the stopword process and some words change meaning after stemming.
Co-Authors Adiarsa Resmana, Sopyan Andri Hendriadi, Ade Ardiansyah, Brian Nur Arip Solehudin Arnianti Arnianti Azhari Ali Ridha Baihaqi, Kiki Ahmad Betha Nurina Sari Carudin, Carudin Enri, Ultach Fauzan Osama, Mochamad Firaldi, Ilhan Galih Agustian Perdana garno, Garno Hardianto Nugroho Haya Amalia, Salma Heru Widiyanto, Muhammad Heryana, Nono Hopipah, Hopi Siti Intan Purnamasari Iqbal Maulana Jajuli, M. Jaman, Jajam Haerul Juardi, Didi Juwita, Ayu Ratna Khoirul Fahmi, Muhamad Maulana Syamil, Bangkit Muhammad Lias, Lintar Natuzzuhriyyah, Ami Nugraha, Bagja Nur Amalia, Indira Nurmiati Nurmiati Nurohmah, Yunita Padilah, Tesa Nur Pernamasari , Intan Pramudya Pratama, Tegar Pramudya, Aditya Prihandani, Kamal Qudratullah, Fyzria Rahmawati, Agustini Liviana Dwi Rahmayani, Fenny Salsabila, Syafrida Nabila Santosa, Agil Sembiring, Rinawati Sofi Defiyanti Sulhan Abdillah, M. Rio Susilo Yuda Irawan, Agung Susilo Yuda Irawan, Agung Suyantoputri, Fanny Treasa, Adisty Dwi Ula, Zumrotul Umaidah, Yuyun Vito Anggoro, Alfian Yunitasari Zahra, Almira