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All Journal Dinamik Agrikultura Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-SAKTI (Jurnal Sains Komputer dan Informatika) JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY JATI (Jurnal Mahasiswa Teknik Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Sistem Informasi Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Informatika dan Rekayasa Komputer
Umaidah, Yuyun
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Implementation of Information Gain for Sentiment Analysis of PSE Policy using Naïve Bayes Algorithm Pramudja, Stevanus Ertito; Umaidah, Yuyun; Suharso, Aries
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6359

Abstract

The Ministry of Communication and Information Technology of Indonesia (Kominfo) has established the Penyelenggara Sistem Elektronik (PSE) policy as a mandatory registration requirement for both domestic and foreign Electronic Systems (ES). As a result, Kominfo will impose sanctions on all ES by temporarily suspending their access if they fail to register by July 29, 2022, at 23:59 WIB. This policy has sparked both support and opposition among the Indonesian public, and it has become a topic of discussion, including among Twitter users. Therefore, sentiment analysis is employed as a solution to identify public concerns or issues regarding the policy based on negative and positive tweets. The objective of this research is to evaluate the results of feature selection using Information Gain and the Naïve Bayes Classifier algorithm in analyzing Twitter users' sentiment towards the policies of the Information and PSE of the Ministry of Communication and Information Technology. A total of 1153 lines of tweets were collected from the Twitter platform using the keyword "PSE Kominfo," which were then analyzed using the Naïve Bayes Classifier algorithm and Information Gain feature selection with three scenarios: 90:10, 80:20, and 70:30. Based on the evaluation using the confusion matrix, overall, Scenario 1 with a 90:10 ratio and Information Gain feature selection performed the best, achieving an accuracy of 79.7%, recall of 85%, and an F-1 score of 88%. However, the best precision was observed in Scenario 2 with an 80:20 ratio, reaching 92% due to the higher proportion of positive predictions made by the model compared to other scenarios.
Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine Safitri, Tiara; Umaidah, Yuyun; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5039

Abstract

Twitter is often used as a source of public opinion and sentiment data for analysis, where the data can be used to understand public opinion about a topic. Sentiment analysis is widely used in various fields, one of which is in the marketing field. a company can carry out a sentiment analysis of the public figures they want to make Brand Ambassadors (BA), which later these sentiments can be taken into consideration for them to be able to determine the BA of their products. Sentiment analysis can also be used to distinguish the attitude of customers, users or followers towards a brand, topic, or product with the help of their reviews. Based on this, this study will analyze the sentiments of Twitter users towards music group BTS, using the Knowledge Discovery Database (KDD) research methodology, with 5 stages namely Data Selection, Data Preprocessing, Data Transformation, Text Mining and Evaluation. By using the Support Vector Machine (SVM) algorithm with a linear kernel, this study will do 3 scenarios with the distribution of training data and testing data 90:10 in scenario 1, 80:20 in scenario 2, and 70:30 in scenario 3. Confusion Matrix is used to evaluate the performance of the algorithm used and the results show that the best performance of the model formed is in scenario 1 and scenario 2.
Co-Authors Agustin, Amellia Veronica Alexander, Hizkia Anesha Lutia, Frise Apriade Voutama Arga Adi Putra, Harvian Aries Suharso Aufarrizqi, Adrian Carudin, Carudin Destiana, Tiara Eddy Ryansyah Enri, Ultach Fadhilah, Muhammad Rizqi Fadlurrahman Muchdyansyah, Raihan Farissa, Riva Arsyad Fauzan, Muh. Yoga Fitrianti, Ika garno, Garno Hakim, Reza Hannie, Hannie Haya Amalia, Salma Hidayatullah, Haikal Iqbal Maulana Jajuli, M. Jamaludin, Asep Maemunah Maharadja, Alfanda Novebrian Mahatmayana, I Ketut Manu Mayasari, Rini Meila Azzahra Sofyan, Fazrin Muhaa, Najibah Aisyah Mulyono, Larasati Mya Nur Amalia, Indira Nursidik Dinar, Ahmad Nursyahfitri, Rika Padilah, Tesa Nur Pajri, Dicki Permana, Hendi Pramudja, Stevanus Ertito Prasetya, Muhammad Erlangga Pratama, Aditiya Yoga Pratama, Sena Purwantoro Putri Riyandoro, Affani Rusfauzi Surya, Muhammad Sa'adah, Fitria Nur Safitri, Tiara Suhaeni Suhaeni, Suhaeni Susilo Yuda Irawan, Agung Suyantoputri, Fanny Trihandoko, Kardo Vito Anggoro, Alfian