Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal SKANIKA: Sistem Komputer dan Teknik Informatika Journal of Students‘ Research in Computer Science (JSRCS)
Agustin, Syafira Cessa
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
CHATBOT BERBASIS NLP UNTUK REKOMENDASI PRODUK SKINCARE LOKAL PADA TELEGRAM Agustin, Syafira Cessa; Syafina, Prilia Hashifah; Rachmatin, Nida; Pratama Yusuf, Ajif Yunizar
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 1 (2024): Jurnal SKANIKA Januari 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i1.3141

Abstract

This research applies a Natural Language Processing (NLP)-based chatbot to provide recommendations for local skincare products. Telegram, as a widely used communication platform, is an ideal medium to present this innovative solution to consumers looking for appropriate skincare products. The chatbot is designed to understand the user's needs regarding skin type, and skin concerns. By utilizing artificial intelligence, the chatbot can provide personalized recommendations of suitable local skincare products, improving consumers' access to product information and facilitating the process of selecting the right product. This research is expected to make chatbot an effective tool in finding skincare products that suit the skin, as well as increasing consumer participation in supporting the local skincare industry through instant messaging platform, telegram.
Metode Naïve Bayes dan Support Vector Machine untuk Mengolah Sentimen Ulasan dan Komentar di Platform Digital Herlawati; Srisulistiowati, Dwi Budi; Agustin, Syafira Cessa; Syafina, Prilia Hashifah; Rachmatin, Nida; Setiawati, Siti
Journal of Students‘ Research in Computer Science Vol. 5 No. 2 (2024): November 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/dby15h32

Abstract

This study analyzes sentiment from user Reviews of the FLO app, Taman Mini Indonesia Indah (TMII), and public comments on infidelity cases on Instagram, using Naïve Bayes and Support Vector Machine (SVM) algorithms. FLO, an app that helps users track reproductive health, was analyzed based on 1,393 Reviews on Google Play Store. Of these, 796 Reviews expressed positive sentiment, while 597 were negative. Although both Naïve Bayes and SVM achieved an accuracy of 74%, SVM performed better in recall (74%) and precision (71%). For TMII Reviews, the analysis involved 1,616 Google Reviews, with 1,263 showing negative sentiment, indicating complaints about facilities and services, and 353 expressing positive sentiment. SVM outperformed Naïve Bayes, achieving an accuracy of 85% and an f1-score of 87%, compared to Naïve Bayes’ 82% accuracy and 83% f1-score. Additionally, the analysis of 1,200 public comments on Instagram accounts @lambe_turah and @awreceh.id revealed 918 negative comments and 282 positive ones. SVM once again demonstrated superior performance with an accuracy of 91%, precision of 87%, recall of 96%, and an f1-score of 92%, surpassing Naïve Bayes, which achieved an accuracy of 86%. These findings confirm that SVM is more effective for sentiment classification across various digital Platforms, including social issues and service evaluations. The results can be applied to develop public opinion analysis systems that support strategic decision-making and enhance service quality based on user feedback.
Co-Authors Dwi Budi Srisulistiowati Herlawati Herlawati Pratama Yusuf, Ajif Yunizar Rachmatin, Nida SITI SETIAWATI Syafina, Prilia Hashifah