Meivi Kartikasari
STIKI, Malang,

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Penerapan Case Based Reasoning pada Sistem Pendukung Keputusan Penanganan Komplain Penyewa Mall (Studi Kasus : Maspion Square Mall, Surabaya) Kartikasari, Meivi; Santoso, Purnomo Budi; Yudaningtyas, Erni
Jurnal EECCIS Vol 9, No 2 (2015)
Publisher : Fakultas Teknik, Universitas Brawijaya

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Abstract

Abstract-One of the factors affecting the level of satisfaction of tenants in a mall is the speed and accuracy of the services provided by the manager of the mall, especially in terms of the handling of complaints. Case Based Reasoning is a method to resolve the issues with respect to the same events in the past, then use the knowledge or information to solve new problems. This research aims to develop a Decision Support System with Case Based Reasoning method to improve complaint handling services to tenants mall. Results of this research is a prototype complaint management system called SIPENKOM that can generate output solutions for new cases based on similar old cases. Based on test to tenants and management of Maspion Square Mall, SIPENKOM can improve complaint handling services by approximately 85%. Keywords-Decision Support Systems, Case Based Reasoning, Similarity.
Implementasi Content Based Image Retrieval Untuk Menganalisa Kemiripan Bakteri Yoghurt Menggunakan Metode Latent Semantic Indexing Kartikasari, Meivi; Oktavia, Chaulina Alfianti
IC-ITECHS Vol 1 (2014): Prosiding IC-ITECHS 2014
Publisher : IC-ITECHS

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Computer Vision technology can be implemented in a variety of fields. One implementation of computer vision in the field of biology, especially in microorganism. The problems that often exist in determining the characteristics of microorganisms is the use of manual counting technique in comparing and determining the corresponding image with the image that you saved earlier. Content Based Image Retrieval technique (CBIR) is needed in analyzing the similarity between the image-based approach yoghurt bacteria with Latent Semantic Indexing. Similarity analysis is done by means of data collection, preprocessing, feature extraction, calculation and visualization of image similarity retrieval results. From the analysis we can obtain the similarity between the tested image with the microorganism image contained in the database