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All Journal Indonesian Journal on Software Engineering (IJSE) JITK (Jurnal Ilmu Pengetahuan dan Komputer) EVOLUSI : Jurnal Sains dan Manajemen Jurnal Teknologi Informasi dan Terapan (J-TIT) Indonesian Journal of Business Intelligence (IJUBI) Indonesian Journal of Networking and Security - IJNS Informatics and Computer Engineering Journal Indonesian Journal Computer Science (ijcs) Prawara Jurnal Pengabdian Masyarakat
Eka Rahmawati
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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IMPLEMENTASI SMOTE DAN ALGORITMA MACHINE LEARNING UNTUK MENINGKATKAN AKURASI REKOMENDASI HOTEL Candra Agustina; Eka Rahmawati; Denny Irawan; Vriska wahyu Tristanti
Indonesian Journal of Business Intelligence (IJUBI) Vol 7 No 2 (2024): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v7i2.5141

Abstract

Pariwisata memiliki peran penting dalam perekonomian global, dengan destinasi seperti Candi Borobudur menarik berbagai jenis pengunjung. Untuk meningkatkan pengalaman wisatawan, rekomendasi hotel yang akurat menjadi sangat penting. Namun, data yang tidak seimbang, seperti ulasan positif yang terlalu dominan, sering kali mengurangi kinerja model machine learning yang digunakan untuk rekomendasi. Penelitian ini bertujuan untuk mengatasi masalah tersebut dengan menerapkan Synthetic Minority Over-sampling Technique (SMOTE) guna menyeimbangkan dataset dan meningkatkan akurasi rekomendasi hotel. Beragam algoritma machine learning, termasuk Random Forest, Support Vector Machines, dan Neural Networks, diterapkan dan dievaluasi. Hasil penelitian menunjukkan bahwa penerapan SMOTE secara signifikan meningkatkan kinerja semua model, dengan Random Forest memberikan hasil terbaik. Studi ini menunjukkan bahwa SMOTE, dalam kombinasi dengan teknik machine learning, memberikan solusi yang kuat terhadap ketidakseimbangan kelas pada sistem rekomendasi hotel, sehingga menghasilkan rekomendasi yang lebih andal dan relevan bagi wisatawan. Temuan ini memiliki implikasi penting bagi manajemen hotel dan sektor pariwisata secara keseluruhan.
Develompent of Machine Learning Model to Predict Hotel Room Reservation Cancellations Eka Rahmawati; Galih Setiawan Nurohim; Candra Agustina; Denny Irawan; Zainal Muttaqin
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.431

Abstract

The frequent cancellations of hotel room reservations have become a pressing issue for the hospitality industry, especially in high-tourism areas such as Borobudur, Indonesia. This research develops a predictive machine learning (ML) model to identify cancellation probabilities to support proactive decision-making for hotel management. Using datasets from Borobudur-based hotels, key variables such as booking lead time, arrival month, and reservation outcomes were analyzed. Random Forest demonstrated the best performance, achieving an accuracy of 86.36% with a precision of 88.06%, recall of 93.65%, and F1-score of 90.77%. Logistic Regression demonstrated moderate effectiveness, while Bayesian Networks underperformed, highlighting the importance of robust algorithms for such tasks. The findings underscore the potential of ML models, particularly Random Forest, to reduce financial losses and enhance operational efficiency in the hospitality sector by anticipating cancellations and facilitating better resource allocation
Co-Authors ., Supriyanta Akhmad Syukron Amin Nur Rais Annida Purnamawati Candra Agustina Chandra Kesuma Denny Irawan Denny Irawan Fanny Fatma Wati Farikhin Farikhin Galih Setiawan Nurohim Heribertus Ary Setyadi Indah Sulistyowati Irawan, Denny Ismail Hasan Basri Jamhari Jamhari Mahmud Safudin Meida Intan Saputri Muhamad Abdul Ghani Muhammad Alvha Ridho Kurnia Muhammad Faittullah Akbar Purwanto Purwanto Ratna Kurnia Sari Richky Faizal Amir Rousyati Rousyati Saifudin Saifudin Tristanti, Vriska wahyu Vembria Rose Handayani Vriska wahyu Tristanti Yulianto Zainal Muttaqin