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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Infotech Journal Sistemasi: Jurnal Sistem Informasi Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Journal of Information Technology and Computer Science (JOINTECS) Produktif : Jurnal Ilmiah Pendidikan Teknologi Informasi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tekno Kompak Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) INFORMASI (Jurnal Informatika dan Sistem Informasi) Jurnal Infortech JAMI: Jurnal Ahli Muda Indonesia Journal of Applied Data Sciences Jurnal Responsif : Riset Sains dan Informatika DEVICE Jurnal Dialektika Informatika (Detika) Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Jurnal Sosial dan Abdimas JURNAL ILMU SOSIAL DAN ILMU POLITIK, STISIPOL RAJA HAJI Kohesi: Jurnal Sains dan Teknologi Ulil Albab
Yudi Ramdhani
Universitas Adhirajasa Reswara Sanjaya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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Journal : Journal of Applied Data Sciences

 1 
Exploring ADR Trends: A Data Mining Approach to Hotel Room Pricing, Cancellations, and EDA Hikmawati, Nina Kurnia; Ramdhani, Yudi; Wartika, Wartika
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.165

Abstract

This study investigates the intricacies of hotel reservation cancellations by analyzing a comprehensive dataset that includes information from both City Hotel and Resort Hotel. Through a thorough examination of various aspects, the research provides detailed insights into cancellation tendencies, daily rates, seasonal trends, and the influence of geographic factors and market segments on cancellation behavior. The overall cancellation and non-cancellation ratios indicate a notable non-cancellation rate of 62.86%, showcasing a high level of guest confidence in their reservations. Conversely, the 37.14% cancellation ratio raises concerns about potential negative repercussions. A comparative analysis between City Hotel and Resort Hotel reveals a significant difference in cancellation rates, emphasizing the need for tailored strategies at City Hotel to enhance booking stability. The study on Average Daily Rate (ADR) for both hotels bring attention to price differences and seasonal trends. Resort Hotel's higher ADR suggests potential advantages in location or amenities. Seasonal trends, particularly the highest ADR during the summer, provide valuable insights for resource planning. The variation in cancellation rates based on countries emphasizes the importance of focused strategies in regions with high cancellation rates, as seen with Portugal having the highest cancellation rate (77.70%). Analysis of hotel customer market segments identifies Online Travel Agencies (OTA) as the segment with the highest cancellation rate (46.97%). These findings present opportunities for tailored marketing and cancellation policies based on the characteristics of each segment. In conclusion, this research offers strategic insights for hotel managers to enhance booking stability, design competitive pricing policies, and understand the impact of geographic factors and market segments on cancellation behavior.
Searching Sahih Hadiths Based on Queries using Neural Models and FastText Susanti, Sari; Najiyah, Ina; Ramdhani, Yudi; Herliana, Asti; Muckti, Masaldi Kharisma; Oktaviani, Fani Rahma
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.467

Abstract

Hadith is the second source of Islamic law after the Qur’an, and the availability of accurate and easily accessible information about hadith is crucial, as it directly affects a person’s belief (aqidah). This highlights the importance of having hadith collections as essential guidance in everyday life. Today, digital versions of hadiths are available in various applications, e-books, and websites. However, users often complain that these sources are incomplete and do not contain the entire collection of the Prophet's hadiths from al-Kutub as-Sittah. Additionally, the complex presentation of these digital resources makes it difficult to find relevant hadiths efficiently. This study aims to improve access to accurate and relevant hadith information, focusing specifically on al-Kutub as-Sittah, using Information Retrieval systems that search for hadiths based on keywords. IR is employed because it has proven effective in retrieving precise documents according to the search terms. A Neural Network is used to match user queries with the document collection, while FastText word embedding is implemented for text representation. FastText is particularly useful for detecting similar meanings across different words, which is essential when interpreting Indonesian-translated hadiths that require nuanced understanding. The dataset used in this study consists of 31,275 Indonesian-translated hadiths from al-Kutub as-Sittah. In this study, it was found that many hadith translations have ancient language so that query reformulation is needed to get the right hadith because users often enter commands with currently trending words. In this study, it was also found that word2vec has less performance than FastText in weighting words in hadith translations. The results indicate that the neural network performs well in retrieving relevant hadith content according to the user’s commands or keywords. With a training data proportion of 70% and a testing data proportion of 30%, the Recall value was 0.7721 and the Precision value was 0.75112.
Co-Authors Achmad Nizar Hidayanto Ade Mubarok Adi Nurseptaji Adi Nurseptaji Adi Nurseptaji Ali Akbar Rismayadi Ali Akbar Rismayadi Alpiansah, Agung Bia Amin Fahri Andre Prayoga Arey Arey Asti Herliana, Asti B. Hariyanto, Oda I. Ce, Win Cucu Ika Agustyaningrum Djaya Siswaja, Hendy Doni Purnama Alamsyah Dwiniati, Dwiniati Dwiza Riana Dwiza Riana Dwiza Riana Erfian Junianto Fadila Andini Febriyanti Panjaitan Fitri Khoirunnisa Fitriyani Fitriyani Hafizh Cahaya Putra, Vito Hariyanti, Ifani Hery Oktafiandi Hikmawati, Nina Kurnia Hiya Nalatissifa Hizaz Zakaria Yahya Iedam Fardian Anshori, Iedam Fardian Ina Najiyah Indriyati, Susana Kanugrahan, Ghanim Marko, Niki Mayya Nurbayanti Shobary Meirynda Lastika Rahimsyah Miftah Farid Adiwisastra Miftahul Rizal Moch Iqbal Tawakal Muckti, Masaldi Kharisma Muhamad Zakhy Syahaf Muhammad Amar Mustajab Mustajab, Muhammad Amar Nadia, Putri Nadiyah Hidayati Oktafiandy, Hery Oktaviani, Fani Rahma Permai, Antika Rangga Sanjaya Rein Lantin Resdiana Pratama Riski Mardhianto Rizal Rosidin Rizki Tri Prasetio Sadikin, Nanda Dwi Husna Sadikin, Nandi Dwi Husni Salman Topiq Salsabila Ayuni Kaffah Sandini, Dwi Saputra, Irgi Mahendrata Sari Susanti Sari Susanti, Sari Satrio Rully Priyambodo Siti Rendani Anjaryanti Siti Utari, Diah Suherman, Himam Dwipratama Syarif Hidayatulloh Syarif Hidayatulloh Syarif Hidayatulloh Taufik Agung Pramana Tessa Putri Mallini Toni Arifin Wartika, Wartika