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All Journal Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Made Ryan Prana Dhita
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ANALISA APLIKASI CLOUD BASE GUESTAPS UNTUK UPSELLING DAN BOOKING ENGINE PADA PT. GUESTPRO TEKNOLOGI INDONESIA I Made Ryan Prana Dhita; Gst. Ayu Vida Mastrika Giri; I Gede Arta Wibawa
Jurnal Pengabdian Informatika Vol. 3 No. 1 (2024): JUPITA Volume 3 Nomor 1, November 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Submitted: Revised: Accepted:1VOLUME xx NOMOR xx, BULANxx TAHUNxxxxANALISA APLIKASI CLOUD BASE GUESTAPS UNTUKUPSELLING DAN BOOKING ENGINE PADA PT. GUESTPROTEKNOLOGI INDONESIAI. M. R. P.Dhita1, G. A. V. M. Giri 2, dan I. G. A. Wibawa3ABSTRAKJurnal ini menjelaskan proses integrasi GuestApps dengan Property Management System (PMS) untukmeningkatkan efisiensi pengelolaan properti. Ini meliputi pengaturan produk dan layanan untuk MerchantHotel, seperti Room Dining, Spa, Things To Do, Voucher, dan Voucher Site, serta pengaturan untukMerchant Restaurant. Selain itu, jurnal ini menguraikan cara mengonfigurasi kebijakan (policy) yang berlakubagi tamu, termasuk kebijakan kamar (Room Policy) dan kebijakan pembatalan (Cancellation Policy). Padatahap lain, jurnal ini juga menjelaskan cara membuat dan mengelola tarif (Rates) dalam Booking Engine &Website. Ini mencakup pengaturan Room Type, yang melibatkan pengaturan kamar, tempat tidur, gambar,fasilitas, dan tarif (Room Rates). Selanjutnya, jurnal ini menjelaskan konfigurasi Room Rate Plan untukmengelola harga dan ketersediaan kamar. Seluruh proses ini dijelaskan dalam konteks penggunaanGuestApps untuk meningkatkan penjualan langsung dan efisiensi pengelolaan properti. Dengan pemahamanyang baik tentang langkah-langkah ini, properti dapat mengoptimalkan layanan kepada tamu danmemaksimalkan potensi pendapatan.
Implementasi Algoritma KNN Untuk Memprediksi Performa Siswa Sekolah I Made Ryan Prana Dhita; Gst. Ayu Vida Mastrika Giri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 1 No. 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2023.v01.i03.p06

Abstract

One of the factors that influences students graduation rates is their performance in learning. Predicting graduation rates based on student performance has the benefit of analyzing academically underperforming students and providing support to students who face difficulties in the learning process. There are several factors to consider in predicting students' graduation rates, such as academic grades, attitudes, and social factors. However, these factors alone are not sufficient to effectively predict students' performance, and educators also struggle to identify which factors affect students' performance.To predict the performance of school students, the KNearest Neighbor (KNN) method is utilized. The K-Nearest Neighbor method is often used in classifying students' performance due to its simplicity and ability to produce significant and competitive results. In this research, the prediction of students' graduation rates is carried out using the KNN method.The results of implementing the prediction of students' performance using the KNN method can serve as a reference for students to improve their achievements and assist educators in considering future teaching materials. 
Co-Authors Gst. Ayu Vida Mastrika Giri I Gede Arta Wibawa