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Journal : Informasi Interaktif

PERBANDINGAN METODE WEIGHTED PRODUCT DAN SIMPLE ADDITIVE WEIGHTING DALAM SELEKSI PENGURUS FORUM ASISTEN (STUDI KASUS : UNIVERSITAS AMIKOM YOGYAKARTA) Pradana, Musthofa Galih; Kusrini, Kusrini; Luthfi, Emha Taufiq
Informasi Interaktif Vol 4, No 2 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

The assistant forum is an organization built by AMIKOM Yogyakarta University which serves as an official forum for Practicum Assistants. In the Forum Assistant there are daily administrators in charge of managing the daily activities of the organization. In determining the day-to-day management, a selection process is conducted, so that the elected management is the right individual and able to make the Assistant Forum organization better. Because of the importance of the selection process, a system that is able to provide recommendations to determine eligible individuals is needed. administrator. There are many methods that can be used in decision support systems, including the Weighted Product method and Simple Additive Weighting. The data of the candidates for Forum Assistant will be applied to these two methods and a comparison is made of the ranking results of the Weighted Product method and Simple Additive Weighting. The results obtained from this study are ranking from the Weighted Product method and Simple Additive Weighting which produces the same ranking of 3 data from a total of 10 data tested or similarity percentage of 20%.Keywords: Selection, Decision Support System, Weighted Product, Selection.
PREDIKSI CUSTOMER CHURN PERUSAHAAN TELEKOMUNIKASI MENGGUNAKAN NAÏVE BAYES DAN K-NEAREST NEIGHBOR Kaharudin Kaharudin; Musthofa Galih Pradana; Kusrini Kusrini
Informasi Interaktif Vol 4, No 3 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

For a company it is very vital. Customers are the key to running a business that is run. But in reality there are loyal customers and some are churned out. Churn is defined as the tendency of customers to stop doing business with a company. It is important for companies to be able to identify customers who have a tendency to be churn customers. Then a report is needed to be able to identify and make decisions for management. Prediction method using Naïve Bayes method produces an accuracy of 76% and K-Nearest Neighbor produces information with a K = 1 value of 73%, K = 3 which is 76% and K = 5 by 78% It can be concluded that the K-Nearest Neighbor Method with K = 3 has a better value. The results of customer predictions for a company can be used to take an example for the customer so they will not churn.  Keywords: Prediction, Customer, Churn, Naïve Bayes, Telecomunication, K-Nearest Neighbor.