[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123812-id":3,"doc-seo-123812-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},123812,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",54,"Penelitian & Laporan","Stacking Machine Learning Model untuk Prediksi Pembatalan Pemesanan Hotel - Penelitian","Hotel menyiapkan kamar dan sumber daya sesuai pemesanan, sementara pilihan pemesanan awal menciptakan hubungan yang menegaskan kestabilan harga bagi pelanggan. Namun pembatalan pemesanan dan ketidakmampuan memenuhi calon konsumen menjadi masalah yang meningkatkan biaya operasional serta menurunkan kepuasan pelanggan. Untuk mengurangi dampak buruk pada industri perhotelan, penelitian ini memanfaatkan stacking machine learning model dua tingkat dengan base learner dan meta learner guna memprediksi pembatalan pemesanan hotel dan mengevaluasi performanya melalui metrik akurasi, sensitivitas, serta spesifisitas.","Stacking Machine Learning Model for Predict Hotel  \nBooking Cancellations  \nModel Machine Learning Stacking untuk Prediksi Pembatalan  \nPemesanan Hotel  \nJus Prasetya1, Sefri Imanuel Fallo2, Moch Anjas Aprihartha3  \n1 Alumni Magister Matematika, Universitas Gadjah Mada 2Program Studi Matematika, Universitas San Pedro 3Program Studi PJJInformatika, Universitas Dian Nuswantoro  \n[Email](Email:1jusprasetya777@gmail.com)[:](Email:1jusprasetya777@gmail.com)[1](Email:1jusprasetya777@gmail.com)[jusprasetya777@gmail.com](Email:1jusprasetya777@gmail.com) ,[2](2fallosefriimanuel@gmail.com)[fallosefriimanuel@gmail.com](2fallosefriimanuel@gmail.com)  \n,[3](3moch.anjas.aprihartha@gmail.com)[moch.anjas.aprihartha@gmail.com](3moch.anjas.aprihartha@gmail.com)  \nReceived: 5 January 2024, revised: 12 February 2024, accepted: 15 February 2024  \nAbstract  \nThe hotel prepares rooms and resources according to the room booking. Advance booking from customers is a relationship between customers and hotels that ensures price stability for customers to enjoy services. Cancellation of hotel bookings and inability to satisfy potential customers is a widespread and alarming problem that can increase hotel operating costs and affect customer satisfaction. Given that the impact on the hospitality industry can be very bad, predicting hotel cancellations can be a solution to help build an appropriate operational strategy. Method used in this research is stacking machine learning model. Stacking consists of two levels, where in this study level 0 (base learner) uses the Naive Bayes, Logistic Regression, and Gradient Boosting Machine algorithms while at level 1 (meta learner) uses the Random Forest algorithm. Accuracy value of the stacking model classification and the gradient boosting machine has the highest accuracy value of 0.87. Sensitivity value of the stacking model is 0.86 and is the highest sensitivity value which means that the stacking model classification is very precise in predicting consumers in canceling hotel reservations. Specificity value of the gradient boosting machine is 0.88 and is the highest specificity value, which means that the gradient boosting machine classification is very precise in predicting consumers who do not cancel hotel reservations. Naive bayes and logistic regression classifications have accuracy, sensitivity, specificity, precision values that are not high.  \nKeywords: Stacking Model, Base Learner, Meta Learner  \nAbstrak  \nHotel mempersiapkan kamar dan sumber daya sesuai dengan pemesanan kamar. Pemesanan diawal dari pelanggan merupakan hubungan antara pelanggan dengan hotel yang memastikankestabilan harga bagi pelanggan untuk menikmati layanan. Pembatalan pemesanan hotel dan  \nketidakmampuan untuk memuaskan calon konsumen merupakan masalah yang meluas dan mengkhawatirkan yang dapat meningkatkan biaya operasional hotel dan mempengaruhi kepuasan pelanggan. Mengingat hal itu dampaknya terhadap industri perhotelan bisa sangat buruk, makadengan memprediksi pembatalan hotel dapat menjadi solusi untuk membantu membangun strategi operasional yang sesuai. Metode yang digunakan pada penelitian ini adalah stacking machine learning model. Stacking terdiri dari dua level, dimana pada penelitian ini level 0 (base learner) menggunakan algoritma Naive Bayes, Logistic Regression, dan Gradient Boosting Machine sedangkan pada level 1 (meta learner) menggunakan algoritma Random Forest. nilai akurasi klasifikasi stacking model dan gradient boosting machine memiliki nilai akurasi tertinggi sebesar 0.87. Nilai sensitivitas stacking model sebesar 0.86 dan merupakan nilai sensitivitastertinggi yang berarti klasifikasi stacking model sangat tepat memprediksi konsumen dalampembatalan pemesanan hotel. Nilai Spesifisitas gradient boosting machine sebesar 0.88 dan merupakan nilai spesifisitas tertinggi yang berarti klasifikasi gradient boosting machine sangattepat memprediksi konsumen yang tidak melakukan pembatalan pemesanan hotel. Klasifikasinaive b","cbCaig2ea7OVyFpB","https://ap.wps.com/l/cbCaig2ea7OVyFpB","pdf",558330,4,1,13,"Indonesian","id",113,"# Pendahuluan\n## Latar Belakang Industri Pariwisata dan Perhotelan\n## Pemesanan Kamar Online dan Kebijakan Pembatalan\n# Metode Penelitian\n## Konsep Stacking Machine Learning\n## Arsitektur Level 0 (Base Learner)\n## Arsitektur Level 1 (Meta Learner)\n# Hasil dan Pembahasan\n## Evaluasi Akurasi, Sensitivitas, dan Spesifisitas","[{\"question\":\"Apa tujuan penelitian ini dalam konteks perhotelan?\",\"answer\":\"Penelitian ini bertujuan memprediksi pembatalan pemesanan hotel agar hotel dapat menyusun strategi operasional yang lebih tepat dan mengurangi dampak biaya serta penurunan kepuasan pelanggan.\"},{\"question\":\"Bagaimana arsitektur stacking machine learning yang digunakan?\",\"answer\":\"Stacking terdiri dari dua level: level 0 memakai Naive Bayes, Logistic Regression, dan Gradient Boosting Machine sebagai base learner, sedangkan level 1 memakai Random Forest sebagai meta learner.\"},{\"question\":\"Algoritma mana yang memperoleh performa tertinggi berdasarkan metrik yang disebutkan?\",\"answer\":\"Akurasi tertinggi 0.87 terlihat pada stacking model dan Gradient Boosting Machine, sensitivitas tertinggi 0.86 pada stacking model, serta spesifisitas tertinggi 0.88 pada Gradient Boosting Machine.\"}]","Stacking Machine Learning Model untuk Prediksi Pembatalan Pemesanan Hotel - 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