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All Journal Brilliance: Research of Artificial Intelligence Journal International Dakwah and Communication
Syahputra , Dedek
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Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Problematics of Contemporary Problems and Cultural Poverty Challenges Hakis, Hakis; Rahman, Rahman; Syahputra , Dedek; Yakir , Julia
Journal International Dakwah and Communication Vol. 2 No. 2 (2022)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (270.567 KB) | DOI: 10.55849/jidc.v2i2.190

Abstract

The more serious problems of contemporary da'wah today align with the continued development of increasingly complex social human life, one of which is the challenge of cultural poverty. Implementing the da'wah function at least motivates the da'wah target always to try to avoid poverty. The purpose of this research is to find solutions to the problems of contemporary da'wah in terms of facing the challenges of cultural poverty experienced by people who are passive in trying, including those who face fatalistic thoughts about sustenance that has been arranged by Allah SWT so that there is no need to try to find food. The research method used is qualitative research with a library research approach. The study results show that contemporary da'wah's problems have faced various challenges and difficulties, both internally and externally. The challenge of cultural poverty finds at least four classifications of discussion: forms of cultural deprivation, the impact of cultural lack, the causes of poverty, poverty, and its alleviation strategies. Furthermore, the function of da'wah in overcoming cultural poverty will only be optimally achieved if the community realizes the importance of effort and hard work, including increasing the application of social care among fellow believers, which is very much needed in social life.  
Leveraging Machine Learning for Sentiment Analysis in Hotel Applications: A Comparative Study of Support Vector Machine and Random Forest Algorithms Suryadi, Suryadi; Syahputra , Dedek; Astrianda, Nica; Syahputra, Rizki Agam; Suhendra, Rivansyah
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4877

Abstract

This research aims to conduct sentiment analysis on user reviews of hotel booking applications such as Trivago, Tiket, Booking, Traveloka, and Agoda, collected from the Google Play Store. The dataset used consists of 5,000 user reviews, with 80% of the data allocated for training and 20% for testing. Two algorithms applied in this study are Support Vector Machine (SVM) and Random Forest, with performance evaluation based on accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm delivers the best performance on the Trivago application with 94% accuracy, 94% precision, 100% recall, and a 97% F1-score. Random Forest proves to be more effective in handling diverse review data, while the Support Vector Machine (SVM) algorithm also produces good results in sentiment classification. This research contributes to the development of sentiment analysis based on user reviews, which can be utilized by app developers and hotel management to improve service quality and user experience.
Leveraging Machine Learning for Sentiment Analysis in Hotel Applications: A Comparative Study of Support Vector Machine and Random Forest Algorithms Suryadi, Suryadi; Syahputra , Dedek; Astrianda, Nica; Syahputra, Rizki Agam; Suhendra, Rivansyah
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4877

Abstract

This research aims to conduct sentiment analysis on user reviews of hotel booking applications such as Trivago, Tiket, Booking, Traveloka, and Agoda, collected from the Google Play Store. The dataset used consists of 5,000 user reviews, with 80% of the data allocated for training and 20% for testing. Two algorithms applied in this study are Support Vector Machine (SVM) and Random Forest, with performance evaluation based on accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm delivers the best performance on the Trivago application with 94% accuracy, 94% precision, 100% recall, and a 97% F1-score. Random Forest proves to be more effective in handling diverse review data, while the Support Vector Machine (SVM) algorithm also produces good results in sentiment classification. This research contributes to the development of sentiment analysis based on user reviews, which can be utilized by app developers and hotel management to improve service quality and user experience.
Co-Authors Hakis Hakis, Hakis Nica Astrianda Rahman Rahman Rizki Agam Syahputra Suhendra, Rivansyah Suryadi Suryadi Suryadi Suryadi Yakir , Julia