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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JURNAL MEDIA INFORMATIKA BUDIDARMA
Widi Widayat
Institut Teknologi Telkom Purwokerto, Purwokerto
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Analisis Sentimen Evaluasi Terhadap Pengajaran Dosen di Perguruan Tinggi Menggunakan Metode LSTM Muhammad Afrizal Amrustian; Widi Widayat; Arif Muhammad Wirawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3527

Abstract

Education in Indonesia is divided into several levels, from elementary education to university education. At the university education level, lecturers are asked to not only teach material but also emphasize to students that students have an important role for the future.  Due the students are considered as adults to make the decisions and take a responsibility for those decisions. During a pandemic, teaching activities are carried out online, in order the teaching activities run well, the evaluation from students is needed. Considering that students are one of the important elements in university education. In this study, sentiment analysis was carried out on the evaluation of teaching by students. The data used in this study amounted to 2280 data with the number of words in the evaluation text ranging from 3 to 50 words. The LSTM method is the method used in this study, and the results of the accuracy of using the LSTM method are 91.08%. With the analysis carried out, lecturers can improve their teaching methods based on the results of the evaluation analysis.
Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning Widi Widayat
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 3 (2021): Juli 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i3.3111

Abstract

The increasing number of internet users is directly in line with the increasing number of data on the internet that is available for analysis, especially data in text form. The availability of this text data encourages a lot of sentiment analysis research. However, it turns out that the availability of abundant text data is also one of the challenges in sentiment analysis research. Datasets that consist of long and complex text documents require a different approach. In this study, LSTM was chosen to be used as a sentiment classification method. This research uses a movie review dataset that consists of 25,000 review documents, with an average length per review is 233 words. The research uses CBOW and Skip-Gram methods on word2vec to form a vector representation of each word (word vector) in the corpus data. Several dimensions of the word vector was used in this research, there are 50, 60, 100, 150, 200, and 500, this tuning parameter is used to determine their effect on the resulting accuracy. The best accuracy around 88.17% is obtained at the word vector 100 dimension and the lowest accuracy is 85.86% at the word vector 500 dimension.
Co-Authors Arif Wirawan Muhammad Fadlan Raka Satura Faisal Dharma Adhinata Gracia Rizka Pasfica Muhammad Afrizal Amrustian Nia Annisa Ferani Tanjung