Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Cakrawala Informasi (JCI) Indo-Fintech Intellectuals: Journal of Economics and Business Journal of Innovative and Creativity
Atika Mutiarachim
Universitas 17 Agustus 1945 Semarang
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Optimasi Prediksi Pemasaran Nasabah Deposito Bank dengan Metode Klasifikasi Logistic Regression Atika Mutiarachim; Jaluanto Sunu Punjul Tyoso
Jurnal Cakrawala Informasi Vol 4 No 1 (2024): Juni : Jurnal Cakrawala Informasi
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) - Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jci.v4i1.390

Abstract

The study aims to determine the impact of the Logistic Regression method on the classification of customer bank deposits, using a public UCI Bank Marketing dataset, which contains customer-specific information of bank deposit telemarketing activities. Data has a binomial label consisting of 'yes' for subscribers and 'no' for non-subscribers. The data preprocessing phase is done with downsampling to make the amount of data more symmetrical, then data selection and data transformation to ensure that the data used values are consistent, attribute selection to select the attributes most accurately used and give significant influence. Classification is done using the Logistic Regression algorithm. Data is shared using a split method with 90% training data and 10% testing data, with the aim of optimizing the training process. The performance result consists of an accuracy of 88.53%, a classification error value of 11.4%, can be categorized as low, showing only a few errors produced by the algorithm model, a kappa value of 0.68 close to 1, so it is categorized well, a low RMSE rating of 0.3 indicates a model accurate, and a high AUC percentage of 93.4% indicates the correct algority used in this dataset, because it produces a good performance value.
Klasifikasi Pola Pembelian Kendaraan Bermotor Untuk Merancang Strategi Promosi Terarah Menggunakan Algoritma Logistic Regression Ryan Arya Pramudya; Atika Mutiarachim; Puji Setya Sunarka
Indo-Fintech Intellectuals: Journal of Economics and Business Vol. 4 No. 5 (2024): Indo-Fintech Intellectuals: Journal of Economics and Business (in-Press)
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/ifijeb.v4i5.2121

Abstract

Penelitian ini bertujuan mengetahui pengaruh metode Logistic Regression pada klasifikasi kosumen kendaraan bermotor tahun 2021. Klasifikasi konsumen dilakukan untuk mengetahui kriteria konsumen secara spesifik, sehingga dapat melakukan promosi terarah langsung kepada konsumen yang tepat. Data diperoleh dari beberapa dealer penjualan motor di area Jawa Tengah dengan berbagai merk motor, pada rentang waktu Januari sampai Desember 2024. Data memiliki label binomial yang terdiri dari ‘cash’ untuk pembelian tunai dan ‘credit’ untuk pembelian kredit. Tahap preprocessing data dilakukan dengan menyamakan atribut dataset dari masing-masing dealer, downsampling untuk membuat jumlah data lebih simetris, kemudian seleksi data dan transformasi data untuk memastikan data yang digunakan nilainya konsisten, seleksi atribut untuk memilih atribut yang paling tepat digunakan dan memberi pengaruh signifikan. Klasifikasi dilakukan dengan algoritma Logistic Regression. Data dibagi menggunakan metode split dengan 70% data training dan 30% data testing,  Hasil performance akurasi 100% nilai classification error 0%, nilai kappa 1, nilai AUC 1.00 atau 100% di interpretasikan sangat baik, menunjukkan model algoritma Logistic Rgression sangat akurat dan tepat digunakan pada dataset ini.
Employee Engagement Culture and Positive Work Behavior as Drivers of Organizational Success: A Systematic Literature Review Nur Atika Yuniarti; Puji Setya Sunarka; Ryan Arya P; Atika Mutiarachim; Fika Ulfa W
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.7729

Abstract

The study purpose of the research is to analyze the role of employee engagement culture in fostering positive work behavior and increasing organizational success. Employee engagement has become a human resource phenomenon in organizations in a dynamic business environment in the era of digital transformation. Employee involvement is not only an individual psychological state, but a very strong attachment to organizational culture and forms positive work behavior so that it can increase organizational success. However, previous studies still found several gaps, namely employee engagement and positive work behavior separately without providing an integrative understanding of how an engagement-oriented culture translates into sustainable organizational results. Materials and methods. Used a Systematic Literature Review (SLR) with the PRISMA framework, 38 peer-reviewed articles indexed in Scopus and accredited by Sinta, published between 2022 and 2025. Results. Employee engagement culture is strategic organizational resource, positive work behavior was a mediating role to increase organizational success, and organizational success must applied human resource performance, innovation, sustainability, and employee well-being. Conclusions. This study contributes to develop conceptual framework of engagement culture, positive work behavior, and organizational success. This research provides practice literacy for managers and policymakers to design inclusive, trust-based, and adaptive engagement strategies. Future research is proposed for empirical testing across sectors and cultural contexts.
Co-Authors Fika Ulfa W Jaluanto Sunu Punjul Tyoso Nur Atika Yuniarti Puji Setya Sunarka Ryan Arya P Ryan Arya Pramudya