SYADZALI, Chashif and Suryono, Suryono and Suseno, Jatmiko Endro (2020) DATA MINING MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR UNTUK ANALISA PERILAKU PELANGGAN PADA SISTEM LAYANAN PENGGALANGAN DANA ONLINE. Masters thesis, School of Postgraduate Studies.

	Text 30000416410032 Chashif Syadzali cover - abstract [01-13].pdf Download (394kB)
	Text 30000416410032 Chashif Syadzali bab 1 [14-17].pdf Download (366kB)
	Text 30000416410032 Chashif Syadzali bab 2 [18-25].pdf Download (420kB)
	Text 30000416410032 Chashif Syadzali bab 3 [26-44].pdf Restricted to Repository staff only Download (607kB)
	Text 30000416410032 Chashif Syadzali bab 4 [45-63].pdf Restricted to Repository staff only Download (1MB)
	Text 30000416410032 Chashif Syadzali bab 5 - lampiran [64-71].pdf Restricted to Repository staff only Download (436kB)

Abstract

Klasifikasi pola perilaku pengguna dapat bermanfaat untuk membantu perusahaan dalam melakukan analisa business intelligence. Teknik data mining dapat melakukan klasifikasi pola perilaku pengguna menggunakan algoritma K-Nearest
Neighbor berdasarkan siklus hidup pelanggan yang terdiri dari prospect, responder, active dan former. Data yang digunakan untuk melakukan klasifikasi meliputi usia, jenis kelamin, jumlah donasi, retensi donasi dan jumlah kunjungan pengguna. Hasil
perhitungan dari 2.114 data menghasilkan klasifikasi masing-masing kategori pengguna yaitu active sebesar 1,18%, prospect sebesar 8,99%, responder sebesar 4,26% dan former sebesar 85,57%. Akurasi sistem menggunakan rentang K dari K=1 sampai K=20 menghasilkan akurasi tertinggi pada nilai K=4 dengan akurasi sebesar 94,37%. Hasil dari pembelajaran data latih yang menghasilkan klasifikasi pola perilaku pengguna dapat digunakan sebagai analisa Business Intelligence yang
bermanfaat untuk perusahaan dalam menentukan strategi bisnis dengan mengetahui target pasar optimal.
Kata Kunci: Klasifikasi, data mining, K-Nearest Neighbor, Business Intelligence, segmentasi pengguna, siklus hidup pelanggan, Customer Relationship Management.

Customer behavior classification could be useful to assist companies in conducting business intelligence analysis. Data mining techniques classified customer behavior using the K-Nearest Neighbor algorithm based on the customer's life cycle
consisting of prospect, responder, active and former. Data used to classificaltion included age, gender, number of donations, donation retention and number of user visits. The calculation resulted from 2,114 data in the classification of each customer’s category are namely active by 1.18%, prospect by 8.99%, responder by 4.26% and former by 85.57%. System accuracy using a range of K from K = 1 to K = 20 produces that the highest accuracy was 94.3731% at a value of K = 4. The results of the training data that produced a classification of user behavior was used as a Business Intelligence analysis for companies in determining business strategies by knowing the target of optimal market.
Keyword: Classification, data mining, K-Nearest Neighbor, Business intelligence, user segmentation, customer life cycle, Customer Relationship Management.

Actions (login required)

View Item