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All Journal TEKNIK INFORMATIKA Indonesian Journal on Computing (Indo-JC) JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science eProceedings of Engineering Nuansa Informatika
Hani Nurrahmi
Telkom University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates Primanda Sayarizki; Hasmawati; Nurrahmi, Hani
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.934

Abstract

In this modern era, Indonesian society widely utilizes social media, particularly Twitter, as a means to express their opinions. Every day, various opinions of Indonesian citizens are disseminated on this platform, including their views on prospective presidential candidates for the year 2024. Analyzing public opinions regarding prospective presidential candidates in 2024 is crucial to understanding the sentiment of the people toward these candidates. Such sentiment analysis can be conducted using deep learning techniques such as IndoBERT to acquire knowledge regarding the classification of sentiments as positive, neutral, or negative. IndoBERT is employed to generate vector representations that encapsulate the meaning of tokens, words, phrases, or texts. These representation vectors can then be input into a classification model to perform sentiment analysis. The sentiment classification model undergoes testing with a diverse set of tweets in the test dataset, which represent a wide range of public opinions. The evaluation results indicate an overall accuracy rate of 80%, with precision rates of 62% for negative sentiment, 81% for neutral sentiment, and 85% for positive sentiment. Additionally, the recall rates for each sentiment are 64% for negative, 81% for neutral, and 84% for positive, with corresponding F1-scores of 63%, 81%, and 85%, respectively.
Diversity Balancing in Two-Stage Collaborative Filtering for Book Recommendation Systems Muttaqien, Rifqi Fauzia; Nurjanah, Dade; Nurrahmi, Hani
JURNAL TEKNIK INFORMATIKA Vol. 16 No. 2 (2023): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v16i2.36580

Abstract

A book recommender system is a system used to provide relevant book recommendations for readers. One approach that is often used in recommender systems is Collaborative Filtering (CF). CF provides book recommendations based on books liked by other similar users. However, CF only provides recommendations for items that are popular, so items that are less popular will be difficult to recommend. Therefore, we propose a book recommendation system based on Two-stages CF using the Diversity Balancing method. Diversity Balancing method in CF is used to balance diversity in the recommendation results by replacing popular items with less popular relevant items. System accuracy is measured using precision and recall, while diversity is measured using personal diversity and aggregate diversity. The test results show that the accuracy of the proposed system increases with the increasing number of recommended items. meanwhile, the diversity of recommended items continues to decrease as more items are included in the recommendation list. In consideration of the trade-off between accuracy and diversity, our system achieves a recall score of 0.301, a precision score of 0.282, a PD score of 0.048, and an AD score of 0.095 with a recommendation list size of 8 items.
Analisis Sentimen Review Film Menggunakan Naive Bayes Classifier Dengan Fitur TF-IDF Razaq, Muhammad Thaariq; Nurjanah, Dade; Nurrahmi, Hani
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstrak-Penilaian mengenai isi dari review film dapat disebut dengan sentiment analysis. Sentiment analysis pada review film terbagi menjadi 2 yaitu berupa review positif dan review negatif. Salah satu algoritma data mining yang paling sering digunakan dalam penelitian adalah Naïve Bayes karena bekerja dengan cepat dan efisien sebagai metode pengklasifikasian teks tetapi memiliki kekurangan yang sangat sensitif dalam pemilihan fitur. Pada umumnya, data review film memuat isi yang sangat panjang sehingga diperlukan feature selection atau pemangkasan fitur yang berguna untuk mengurangi dimensi pada saat proses klasifikasi. Pada penelitian ini menggunakan fitur Tf-Idf sebagai salah satu solusi untuk mempermudah dan mempercepat pencarian informasi yang sesuai adalah dengan meringkas konten tersebut. Tf-Idf (Term Frequency Inverse Document Frequency) merupakan metode pembobotan dalam bentuk integrasi antar term frequency dengan inverse document frequency. Metode Tf-Idf digunakan pada penelitian ini untuk memilih fitur sebagai hasil ringkasan, dengan penerapannya pada seleksi fitur bobot kata. Sebelum proses klasifikasi, dilakukan tahapan preprocessing yang meliputi data cleaning dan case folding, stop words removal, stemming, dan tokenization. Pada penelitian ini menghasilkan nilai akurasi mencapai 86.48%. Sehingga Naïve Bayes dengan fitur Tf-Idf pada masalah analisis klasifikasi sentimen review film terbukti memberikan akurasi yang akuratKata kunci- sentiment analysis, film, Naïve Bayes, TF-IDF
Co-Authors Agung Toto Wibowo Arbaatun, Cepthari Ningtyas Dade Nurjanah Dade Nurjannah Dea Reskyadita, Feddy Diffo Elza Pratama Hasmawati Hasmawati Hasmawati Irfansyah, Muhammad Rafi Maulana , Daris Muhammad Rafi Irfansyah Muttaqien, Rifqi Fauzia Perwira Hanif Zakaria Primanda Sayarizki Primanda Sayarizki Putri Waisnawa, Ni Luh Putu Mawar Silveria Razaq, Muhammad Thaariq Rifqi Fauzia Muttaqien Selly Meliana