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Journal : Building of Informatics, Technology and Science

Implementasi SCRUM Pada Pengenalan Aksara Lampung Menggunakan Augmented Reality Ika Arfiani; Murien Nugraheni; Danang Sulistyono
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): Desember 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (570.905 KB) | DOI: 10.47065/bits.v3i3.1011

Abstract

Lampung Province has 20 Lampung characters and 12 Lampung scripts as characters that need to be preserved. Lampung script learning is currently still using conventional methods so that many students begin to ignore this subject because it feels less interesting and boring. This study aims to build an application that can help users introduce Lampung script in the world of education on smartphones by applying Augmented Reality technology. By applying Marker Based Tracking which is one of the methods used in the development of Augmented Reality technology. This method works by recognizing and identifying patterns on markers to bring up virtual objects into the real environment. The system development uses the waterfall method with the stages of problem identification, initial planning, design and design, implementation, testing, and evaluation. This results in an Augmented Reality application to introduce Lampung script which is equipped with features showing 3D Lampung script, pronunciation of each Lampung script object, script gallery, guide for each menu and 20 Lampung script markers. Which type of smartphone camera can affect the application's ability to see objects at a certain angle to the marker, but is still quite safe at angles between 500 to 1800.
Implementasi Bee Colony Optimization Pada Pemilihan Centroid (Klaster Pusat) Dalam Algoritma K-Means Ika Arfiani; Herman Yuliansyah; Muhammad Dzikrullah Suratin
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): Maret 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (436.104 KB) | DOI: 10.47065/bits.v3i4.1446

Abstract

Clustering is a method that is used to divide the data into several groups of parts. K-means (KM) is an algorithm that is often used in clustering, only just the result of KM often times get stuck in local optima i.e. the optimal solution (both maximum or minimal) on the candidate solution in the nearest neighbor only, not the whole of all existing solutions or what is commonly called the global optima. In this study aims to do improve the cluster determination process on the Kmeans algorithm using the Bee Colony Optimization (BCO) algorithm. BCO is an algorithm that works based on the way the bees search for food , BCO is famous for being able to escape from the local optima trap by recognizing which results are best from a series of optimal results . Combining BCO with KM begins with selecting a source of food early in random and using KM to resolve all the problems of clustering at every step BCO next and keep sources of food best in each iteration. The result of this research is that the BCOKM method has been proven to be able to solve the problem of data sharing, where the BCOKM method is able to form a good cluster, as shown by the resulting fitness value (the lowest value is 1221.53 and the highest value is 1233.28) all of which are better than the fitness value using K-means (1251.42). Likewise in terms of accuracy, where the use of BCOKM all showed better results (83.16%-83.30%) than the use of only K-means (83.09%)