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Ari Abdilah, Ari
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Aplikasi Komputer dan Smartphone Berbasis Android untuk Menangani Reservasi Hotel pada Citi Smart Hotel - BSD Abdilah, Ari; Mardiyani, Elva; Nawawi, Imam
JURNAL TEKNIK KOMPUTER Vol 4, No 2 (2018): JURNAL TEKNIK KOMPUTER AMIK BSI
Publisher : AMIK BSI Jakarta

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Abstract

Presentation of information the availability rooms still manual is one of the problems experienced by Citi Smart Hotel for visitors to come to the hotel or reservation via telephone to know the availability room. It will be wasting visitor time to come to the hotel and just asking availability room and then make a reservation. This needs to made to a reservation system that can provide more detailed information on bookings and availability rooms in hotel, then built the application of a reservation hotel room based Android. This application running on a platform Android and integrating with applications residing on the server. This research talk about the design and the implementation of the application of a reservation system hotel room based Android. Through the application of this reservation, it is expected that the transaction a reservation can be done anywhere and anytime without bound with time and appropriate place the cost of also by the visitor.
Integrasi Algoritma Genetika Dan Information Gaint Untuk Menganalisis Sentimen Review Hotel Menggunakan Algoritma Naive Bayes Abdilah, Ari; Mardiyani, Elva; Safudin, Mahmud
JURNAL TEKNIK KOMPUTER Vol 4, No 1 (2018): JURNAL TEKNIK KOMPUTER AMIK BSI
Publisher : Universitas Bina Sarana Informatika

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Abstract

Input and advice is one important part of the application site, in order to assess and improve a quality and quality, Reading reviews helps consumers choose the best hotels, help companies and developers to monitor user satisfaction to improve the quality and quantity of features and services, read as a whole and in manual can spend quite a long time, if read at a glance, the information is not delivered perfectly. This study analyzes the user sentiment Agoda Hotels by automatically classifying reviews for a positive or negative opinion. To improve the accuracy of Naïve Bayes methods Feature Selection, Information Gain and genetic algorithms. This model was evaluated using 10 Fold Cross Validation. Measurements were made with the Confusion Matrix and the ROC curve, comparing accuracy before and after the addition of feature selection methods. The results showed an increase in accuracy, 60.50% to 83.00%.