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All Journal JUITA : Jurnal Informatika Panrita Abdi - Jurnal Pengabdian pada Masyarakat JTERA (Jurnal Teknologi Rekayasa) JMM (Jurnal Masyarakat Mandiri) CARADDE: Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Infotekmesin Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Community Empowerment Jurnal Data Mining dan Sistem Informasi Journal of Agribusiness Science and Rural Development Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Data Science Theory and Application Jurnal Informatika: Jurnal Pengembangan IT
Sharfina Febbi Handayani
Politeknik Harapan Bersama
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory Dwi Intan Af'idah; Dairoh Dairoh; Sharfina Febbi Handayani; Riszki Wijayatun Pratiwi; Susi Indah Sari
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1402

Abstract

Pemerintah dan pelaku industri pariwisata mengalami permasalahan dalam menentukan prioritas pengembangan suatu destinasi wisata. Karena itu, diperlukan identifikasi objek wisata yang diminati namun banyak mendapat ulasan buruk melalui ulasan dari masyarakat yang tersebar di internet. Penelitian ini bertujuan melakukan analisis sentimen terhadap ulasan objek wisata di Pulau Bali menggunakan Bi-LSTM dan Word2Vec, sehingga diperoleh model terbaik yang dapat digunakan untuk mengidentifikasi objek wisata potensial namun mendapat ulasan buruk. Bi-LSTM merupakan deep learning yang menawarkan akurasi yang lebih baik daripada LSTM biasa. Sedangkan Word2Vec merupakan pretraining yang dipilih karena dapat menangkap makna semantik teks. Penelitian ini menggunakan data ulasan objek wisata di Pulau Bali yang berasal dari situs tripadvisor.com. Penelitian dimulai dari pengumpulan data, perancangan alur program, preprocessing, pretraining Word2Vec, pembagian data uji dan data latih, pelatihan dan pengujian, serta evaluasi penentuan model terbaik. Akurasi terbaik dihasilkan oleh kombisasi Word2Vec terdiri dari CBOW, Hierarchical Softmax, dimensi 200, Bi-LSTM dengan dropout sebesar 0,5 dan learning rate sebesar 0,0001. Kombinasi tersebut menghasilkan akurasi tertinggi dari keseluruhan 108 kombinasi yaitu sebesar 96,86%, precission sebesar 96,53%, Recall sebesar 96,31%, F1 Measure sebesar 96,41%. Akurasi yang baik tersebut membuktikan bahwa kombinasi parameter Bi-LSTM dan Word2Vec cocok digunakan untuk analisis sentimen ulasan objek wisata di Pulau Bali.
Assessment of Retrieval and Generative Chatbots in Tourism Information Service Sharfina Febbi Handayani; Dairoh Dairoh; Dwi Intan Af'idah
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24182

Abstract

Chatbots are essential for improving the customer experience on tourism websites, especially when it comes to arranging travel and offering precise information. The purpose of this study is to evaluate the effectiveness of generative and retrieval-based chatbots in the tourism information service. Two retrieval-based models are MLP-based single QA and multi QA and two generative-based models namely LLaMA 2 and GEMMA were evaluated using confusion matrix, BLUE score, correctness response and naturalness response. The study found that LLaMA 2 outperformed other models, with the highest response Accuracy of 0.89, naturalness of 0.75, and BLEU score of 0.33. GEMMA received the lowest score, suggesting that it has trouble coming up with precise and organic answers. The retrieval-based models showed strong accuracy but were less natural in their responses. The ease of dataset creation for generative models, which only requires narrative text, further positions LLaMA 2 as the most suitable option for improving user experience in Tegal tourism services.
Co-Authors Achmad Miftahudin Ahmad Miftahudin Amaliyah Gian Amaliyah Gian F Ardi Susanto Arief Rachman Arief Rachman Dairoh Dairoh Dairoh Dairoh Dairoh, Dairoh Doni Cahya Adi Saputra Dwi Intan Af’idah Dwi Intan Af’idah Dyah Apriliani Dyah Apriliani Dyah Apriliani Ferian Andhika Toarsi Fina Yuniarti Ginanjar Wiro Sasmito Ilham Hanif Pamulia Irfan Triadi Saputra Irfan Triadi Saputra Irvan Akbar Koandres Koandres Laeli Nurarifiah Lolanda Hamim Annisa M Fikri Hidayatullah M. Nishom Muhammad Fikri Hidayattullah Nanda Kurnia Wardati Nurul Arifiah Gunarsih Putri Ajeng Imamah Qirani Rifa A Ratono Ratono Ratono Ratono Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Riszki Wijayatun Pratiwi Siti Handayani slamet Wiyono Slamet Wiyono Slamet Wiyono Susi Indah Sari Susi Nurindah Sari Susi Nurindah Sari Tyas Nevi Anugrahaeni Very Kurnia Bakti, Very Kurnia