Article Per Year (5 Year)
p-Index From 2019 - 2024
1.347
P-Index
This Author published in this journals
All Journal ULUL ALBAB MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) OPSI Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Mantik Jurnal Mnemonic Journal of Computer Networks, Architecture and High Performance Computing Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Ririen Kusumawati
Unknown Affiliation
Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Analysis of Public Sentiment Towards The TikTok Application Using The Naive Bayes Algorithm and Support Vector Machine Hidayah, Ika Arofatul Hidayah; Ririen Kusumawati; Zainal Abidin; M. Imamuddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3990

Abstract

In the current digital era, social media applications such as TikTok have become an important aspect of people's lives. TikTok allows users to create and share short videos, making it a global phenomenon with millions of active users. However, this application has also been the subject of various responses and opinions from the public. This research aims to classify public sentiment towards the TikTok application based on comments on Playstore using the Naïve Bayes algorithm and Support Vector Machine (SVM). This research method involves collecting comment data from Playstore using scraping techniques, resulting in 5,000 review data. Data pre-processing stages include case folding, tokenization, normalization, stopword removal, stemming, and data labeling using a lexicon. The data that has been processed is then weighted using Term Frequency - Inverse Document Frequency (TF-IDF) before being classified using the Naïve Bayes and SVM algorithms. Algorithm performance evaluation is carried out using the Confusion Matrix to measure accuracy, precision and recall. The research results show that the SVM algorithm has higher accuracy (84%) compared to Naïve Bayes (79%). SVM also shows better precision and recall values in classifying positive and negative sentiment from user reviews. From the results of the tests that have been carried out, the SVM algorithm is more effective than Naïve Bayes in sentiment analysis of the TikTok application. This research provides insight into how public sentiment can be measured and analyzed, and underscores the importance of choosing the right algorithm for data sentiment analysis on social media platforms.
PEMETAAN SENTIMEN MASYARAKAT TERHADAP PILPRES 2024 DENGAN ALGORITMA SELF-ORGANIZING MAP Yuwono, Dwi Purbo; Santoso , Irwan Budi; Kusumawati, Ririen
Jurnal Review Pendidikan dan Pengajaran (JRPP) Vol. 7 No. 3 (2024): Volume 7 No 3 Tahun 2024
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jrpp.v7i3.28830

Abstract

Pemilihan Umum (PEMILU) adalah salah satu cara untuk memilih presiden, kepala daerah, dan anggota parlemen yang berlangsung setiap lima tahun sekali. Dalam memasuki tahun- tahun politik saat ini akan banyak bertebaran informasi dan komentar dari masyarakat terhadap pelaksanaan pemilu, komentar atau pendapat yang disampaikan akan sangat beragam dimulai dari dukungan terhadap pelaksanaan pemilu, penggiringan opini publik, ujaran kebencian dan komentar-komentar lainnya. Kemajuan teknologi saat ini mengakibatkan penyampaian pendapat dapat dengan mudah dipublikasikan melalui media sosial, salah satunya adalah melalui media twitter, twitter menjadi salah satu media sosial yang paling sering digunakan masyarakat dalam mengemukakan pendapatnya karena dianggap bebas. Oleh karena itu, pada penelitian ini diusulakan pemataan sentimen atau opini masyarakat tentang Pilpres melalui X-Twitter, baik itu positif, negatif, atau netral dengan menggunakan Term Frequency-Inverse Document Frequency (TF-IDF) dan metode Self-Organizing Maps (SOM). Dari hasil penelitian didapatkan bahwa Algoritme TF-IDF dan Self-Organizing Maps (SOM) dengan sentimen cuitan pengguna Twitter dengan Hasil pengujian masing-masing model dengan menggunakan confusionmatrix didapatkan rata-rata accuracy sebesar 81%, precision 80,3%, recall 81%, dan f-measure 80%.
Co-Authors A, Miftahul Hikmah Putri Samudera Agus Sofiyan Anwar, Agus Sofiyan Cahyo Crysdian Dita Aisha Fathurrahman Fathurrahman Fithriani Matondang, Fithriani Hidayah, Ika Arofatul Hidayah Imamudin, M Karami, Ahmad Fahmi M Imamuddin Miftachul Arif, Yunifa Muchammad Mustaqhfiri, Muchammad Santoso , Irwan Budi Sari, Nur Fitriyah Ayu Tunjung Silfiyah, Chilmiatus Sri Harini Subarkah, Aan Fuad totok chamidy Wahyuliningtyas, Lia Yuwono, Dwi Purbo Zainal Abidin Zainal Abidin