Article Per Year (5 Year)
p-Index From 2020 - 2025
1.633
P-Index
This Author published in this journals
All Journal DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Jurnal Sains Manajemen dan Bisnis Indonesia International Journal of Social and Management Studies (IJOSMAS) Jurnal Abdimas Dosma (JAD) SWAGATI: Journal of Community Service INJECT Interdisciplinary Journal of Communication
Taali, Muhammad
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : INJECT Interdisciplinary Journal of Communication

 1 
BiLSTM-Based Sentiment Analysis Of Traveloka Hotel Reviews In Yogyakarta For Data-Driven Communication Strategies Frima Thousani, Hifzhan; Taali, Muhammad
INJECT (Interdisciplinary Journal of Communication) Vol. 10 No. 1 (2025)
Publisher : FAKULTAS DAKWAH UIN SALATIGA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/inject.v10i1.4338

Abstract

Online customer reviews have become a crucial medium of communication between guests and service providers in the hospitality industry. This study aims to perform sentiment analysis on hotel reviews from Traveloka to support data-driven customer communication strategies. Using a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model, 10,681 user-generated reviews related to hotels in Yogyakarta were collected, preprocessed, and classified into binary sentiment categories. To address class imbalance, Synthetic Minority Oversampling Technique (SMOTE) and class weighting were applied. The model achieved 90.17% accuracy, 93.61% precision, 95.31% recall, and 94.45% F1-score, indicating strong generalization and sentiment recognition performance. The results highlight the model's ability to extract meaningful sentiment patterns, which can enhance hotel management’s responsiveness, improve communication strategies, and support continuous service improvement based on customer feedback.
Co-Authors Abdul Aziz Amri, Lala Hucadinota Ainul Amri, Whan Augustin Ainul Farani, Dinesh Basti Farid Majedi, Farid Femilian, Fresdear Frima Thousani, Hifzhan INDAH PUSPITASARI Kholis Nur Faizin, Kholis Nur Lestariningsih, Tri Mudofir, Imam NANANG ROMANDONI Prihatinta, Triana Puspitasari, Dyah Ayu Kunthi Ramadhania, Dara Ayu Shahab, Dahris Srimiatun, Srimiatun Subkhan, Muhammad Fajar Supriyanto, Muhammad