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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Teknologi Informasi Cyberku
Fenilinas Adi Artanto
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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PREDIKSI LOYALITAS PELANGGAN TELEKOMUNIKASI MENGGUNAKAN LOGISTIC REGRESSION DENGAN SELEKSI FITUR PARTICLE SWARM OPTIMIZATION Stefanus Santosa; Fenilinas Adi Artanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (246.53 KB)

Abstract

For many companies, finding a reason to lose customers, measurement of customer loyalty and regain customers have become essential, including for telecommunication companies. The telecommunications company is one of the industry, where the customer really needs special attention because it is very influential in maintaining the stability of the company's revenue. The telecommunications industry has always faced the threat of financial loss resulting from customer loyalty. The customer who leaves the service is usually called churners. Find churners can help telecommunications companies in retaining customers and keep the company financially. This study used Logistic Regression algorithm with feature selection Particle Swarm Optimization to predict customer loyalty telecommunications. The test results obtained using ANN algorithm accuracy value amounted to 94.80%, and Logistic Regression Algorithm with Particle Swarm Optimization feature selection shows the value of accuracy of 97.65%, and the AUC value of 0.99, then the Logistic Regression algorithm with feature selection Particle Swarm Optimization can improve the accuracy of prediction telecommunications customer loyalty
Algoritma Bidirectional Long Short Term Memory untuk Analisis Sentimen Berbasis Aspek pada Aplikasi Belanja Online dengan Cicilan Edy Subowo; Fenilinas Adi Artanto; Isna Putri; Wahyu Umaedi
JURNAL FASILKOM Vol 12 No 2 (2022): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v12i2.3759

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen berbasis aspek dalam review aplikasi. Penjualan barang dengan cara mencicil untuk mengetahui kelebihan dan kekurangan dari waktu ke waktu menurut pemakai sehingga dapat dijadikan sebagai bahan evaluasi peningkatan kualitas. Kombinasi pendekatan Bidirectional Long Short Term Memory dan klasifikasi aspek menggunakan Support Vector Machine diterapkan untuk menganalisis sentimen pada perusahaan online yang menyediakan Layanan Belanja dengan Cicilan seperti Kredivo, Akulaku, Indodana, dan BRI Ceria. Jumlah review 1000 review di google play store dengan aspek yang digunakan adalah customer service, tampilan aplikasi dan produk yang ditawarkan. Corpus Text digunakan untuk autolabelling sehingga akurasi sentimen 81% dan akurasi aspek 78%. Hasil visualisasi tren triwulanan pada aspek yang harus ditingkatkan untuk setiap aplikasi juga disajikan dalam penelitian ini.
Co-Authors Edy Subowo Isna Putri Stefanus Santosa Wahyu Umaedi