Article Per Year (5 Year)
p-Index From 2020 - 2025
1.798
P-Index
This Author published in this journals
All Journal EQUILIBIRIA Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK) Djtechno: Jurnal Teknologi Informasi Jurnal Pengabdian Masyarakat Nusantara Jurnal Manajemen dan Profesional International Journal of Management Science and Information Technology (IJMSIT) Jurnal Suara Pengabdian 45 Jurnal Manajerial dan Bisnis Tanjungpinang Serat Acitya
Yuniarti, Nur Atika
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
HYBRID DEEP LEARNING RANDOM FOREST OPTIMASI PEMILIHAN FITUR UNTUK PREDIKSI CHURN INDUSTRI TELEKOMUNIKASI Mutiarachim, Atika; Marutho, Dhendra; Yuniarti, Nur Atika; Pramudya, Ryan Arya; Tyoso, Jaluanto Sunu Punjul
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7052

Abstract

Customer churn merupakan tantangan kritis dalam industri telekomunikasi yang berdampak signifikan terhadap profitabilitas perusahaan. Penelitian ini mengusulkan pendekatan hybrid machine learning untuk memprediksi customer churn dengan mengintegrasikan deep learning dan random forest serta mengoptimalkan performa melalui seleksi fitur chi-square dan information gain. Dataset IBM Telco Customer Churn yang terdiri dari 7.043 sampel dengan 31 atribut digunakan dalam penelitian ini. Metodologi penelitian meliputi preprocessing data, implementasi 10-fold cross validation, aplikasi metode seleksi fitur, dan evaluasi performa menggunakan confusion matrix serta metrik klasifikasi biner. Hasil penelitian menunjukkan bahwa implementasi seleksi fitur secara signifikan meningkatkan akurasi prediksi, di mana akurasi tanpa seleksi fitur mencapai 97.00% (Deep Learning) dan 98.68% (Random Forest), sedangkan dengan seleksi fitur chi-square meningkat menjadi 97.97% (Deep Learning) dan 98.72% (Random Forest). Performa terbaik dicapai oleh kombinasi Random Forest dengan seleksi fitur information gain yang menghasilkan akurasi 98.75%, precision 98.37%, recall 99.96%, dan F-measure 99.16%. Temuan ini membuktikan efektivitas kombinasi algoritma ensemble dengan teknik seleksi fitur dalam mengoptimalkan prediksi customer churn untuk mendukung strategi retensi pelanggan yang lebih tepat sasaran
The Role of Driver Services and Application Quality in Enhancing Gojek Customer Loyalty Through Satisfaction Mutiarachim, Atika; Yuniarti, Nur Atika
SIMAK Vol. 22 No. 02 (2024): Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK)
Publisher : Faculty of Economics dan Business, Atma Jaya Makassar University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35129/simak.v22i02.562

Abstract

The purpose of this research is to determine the influence of Driver Services and Application Quality on Customer Loyalty through Customer Satisfaction, both directly and indirectly. Primary data was obtained through a Google Form questionnaire, the link of which was distributed online to Gojek customers throughout Indonesia resulting 120 respondents. Four data contained missing value and outliers so 116 data were used. Data was processed using Smart PLS 4.0. This research propose seven hypotheses. The results show five hypotheses can be accepted, however hypothesis of Application Quality on Customer Loyalty and Driver Services on Customer Loyalty through Customer Satisfaction obtained p-value > 0.05 so concludes to not influential.
Co-Authors Deewar Mahesa Dhendra Marutho Jaluanto Sunu Punjul Tyoso Kartika, Dwi Anna Mahesan, Deewar Mutiarachim, Atika Pamudya, Ryan Arya Puji Setya Sunarka Ryan Arya Pramudya, Ryan Arya Sukardi Sukardi