Yuni Eka Achyani
STMIK Nusa Mandiri

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Journal : Jurnal Teknik Komputer AMIK BSI

SISTEM INFORMASI PENDAPATAN JASA PADA KOPERASI PDAM TIRTA PATRIOT BEKASI Yuni Eka Achyani; Eni Arviana
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 4, No 1 (2018): JURNAL TEKNIK KOMPUTER AMIK BSI
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1181.461 KB) | DOI: 10.31294/jtk.v4i1.2377

Abstract

Development of information technology is increasingly rapidly makes the need for information is increasing. Information is very important in supporting the course of a company to achieve the desired goal. With the data processing program, and the information will be more rapid, precise and accurate in its presentation. Employees Cooperative PDAM Tirta Patriot is a business entity that is engaged in trade and services. By using the Web program can perform inputting and storing data quickly and can be easier to find the data that we want, so as to reduce the mistakes that often occur. The data collection method used by the authors is the direct observation, interviews, and literature. The author's purpose of research on web-based revenue service information system on Employee Cooperation Tirta Patriot PDAM in the hope to overcome the obstacles that have occurred in the system of service revenue manually, and can assist in making reports service revenue.
Prediksi Pemasaran Langsung Menggunakan Metode Support Vector Machine Yuni Eka Achyani
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 3, No 2 (2017): JURNAL TEKNIK KOMPUTER AMIK BSI
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.171 KB) | DOI: 10.31294/jtk.v3i2.1719

Abstract

Abstract— Direct marketing is a typical strategy to increase business. The company uses direct marketing when targeting customer segments with their contact to fulfill a specific purpose. Direct marketing is one way that can be used to predict potential customers who open deposits at the bank. Direct marketing became a very important application in data mining today. Data mining is widely used in direct marketing to identify potential customers for new products, using the purchase history data, predictive models can be used to measure that customers will respond to a given promotion or offer. One method that is most widely used method of support vector machine. In this study will be used method of support vector machine for prediction of direct marketing. After testing the results obtained is a support vector machine produces an accuracy value of 88.71%, 89.47% and a precision value AUC value of 0.896 with a value of classification accuracy was very good (excellent clasification). Based on these results it can be concluded that the use of support vector machine method can be used for precise and accurate prediction of direct marketing. Keywords : Prediction, Direct Marketing, Support Vector Machine. Abstrak— Pemasaran langsung merupakan strategi yang khas untuk meningkatkan bisnis. Perusahaan menggunakan pemasaran langsung bila menargetkan segmen pelanggan dengan menghubungi mereka untuk memenuhi tujuan tertentu. pemasaran langsung merupakan salah satu cara yang dapat digunakan untuk memprediksi nasabah yang berpotensi membuka simpanan deposito pada bank tersebut. Pemasaran langsung menjadi aplikasi yang sangat penting dalam data mining saat ini. Data mining  secara luas telah digunakan dalam pemasaran langsung untuk mengidentifikasi calon pelanggan untuk produk baru, dengan menggunakan data histori beli, model prediktif dapat digunakan untuk mengukur bahwa pelanggan akan menanggapi promosi atau tawaran yang diberikan. Salah satu metode yang paling banyak digunakan adalah metode  support vector machine. Dalam penelitian ini akan digunakan  metode  support vector machine untuk prediksi pemasaran langsung. Setelah dilakukan pengujian maka hasil yang didapat adalah support vector machine menghasilkan nilai akurasi sebesar 88,71 %, nilai precision 89,47%   dan nilai AUC sebesar 0,896 dengan nilai akurasi klasifikasi sangat baik (excellent clasification). Berdasarkan hasil tersebut dapat disimpulkan bahwa penggunaan metode support vector machine dapat digunakan secara tepat dan akurat untuk prediksi pemasaran langsung. Kata Kunci— Prediksi, Pemasaran Langsung,  Support Vector Machine.