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All Journal SISFOTENIKA JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Informasi Interaktif Jurnal Teknologi Informasi dan Multimedia Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Reswara: Jurnal Pengabdian Kepada Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Kaharuddin
Universitas Universal
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Informasi Interaktif

 1 
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.
KLASIFIKASI JENIS REMPAH-REMPAH BERDASARKAN FITUR WARNA RGB DAN TEKSTUR MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR Kaharruddin Kaharruddin; Kusrini Kusrini; Emha Taufiq Luthfi
Informasi Interaktif Vol 4, No 1 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

 Indonesia is a country famous for its spices wealth, spices have many benefits such as cooking and can also be used as medicine, but nowadays there are many Indonesian people who cannot distinguish each type of spices especially the rhizomes that will be used due to their shape quite similar, even though the selection of the right type of spices in accordance with the needs is very important because the spices used for cooking or medicine have different taste and efficacy, therefore the use of computer technology needs to be used to facilitate and accelerate humans in conducting classifications, this research classifies spices based on RGB and Texture colors using K-Nearest Neighbor Algorithm and distance measurement using Euclidean Distance, from 30 times the test experiment gets the result that the level of truth with K = 1 is 76%, K = 3 is equal to 67% and K = 5 by 63%. From these results it is known that based on GE colors and computer textures can classify spices but with a fairly low accuracy so that further development is needed such as adding form features. Keywords: classification, spices, K-Nearest Neighbor.
Co-Authors Dony Arius Eka Wahyu Sholeha Elvis Pawan Emha Taufiq Luthfi Ilwan Syafrinal Ismail, Asriani Kurniawan Hamidi Kusrini Kusrini Marfuah Marfuah Musliadi K H Musthofa Galih Pradana Pernando, Yonky Raymond Erz Saragih Rifqi Hammad Roza, Yuni Ulfa Laela Rambega, Ulfa Laela Veny Cahya Hardita Verdian, Ihsan Yunita, Selviana