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All Journal Indonesian Journal of Business Intelligence (IJUBI) Djtechno: Jurnal Teknologi Informasi International Journal of Management Science and Information Technology (IJMSIT) 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 Control & Systems Engineering 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

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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.
Digital Agility and Knowledge Sharing to Increase MSMEs Performance : An Empirical Study in MSMEs in Semarang City Nur Atika Yuniarti; Atika Mutiarachim
International Journal of Management Science and Information Technology Vol. 4 No. 2 (2024): July - December 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v4i2.2730

Abstract

This study investigates how digital agility and knowledge-sharing culture affect the performance of Micro Small and Medium Enterprises (MSMEs) in Semarang City, with innovation performance as a mediating factor. In today's rapidly evolving technological and business landscape, digital agility enables organizations and individuals to adapt swiftly, maintaining relevance and competitiveness. Knowledge-sharing culture facilitates the exchange of expertise and experiences, fostering new ideas through the combination of diverse knowledge. The research proposes seven hypotheses. First, digital agility's effect on innovation performance. Second, knowledge sharing's impact on innovation performance. Third, digital agility's influence on MSMEs performance. Fourth, knowledge sharing's effect on MSMEs performance. Fifth, innovation performance's impact on MSMEs performance. The indirect effect, digital agility has a positive and significant effect on MSMEs performance through innovation performance as a hypothesis 6, then knowledge sharing has a positive and significant effect on MSMEs performance through innovation performance as a hypothesis 7. The study surveyed 100 MSMEs owners in Semarang City, categorized by their business net income. Data analysis was conducted using SMART-PLS 4 software. Results indicate that both digital agility and knowledge sharing positively and significantly influence innovation performance. While digital agility has a positive but insignificant direct effect on MSMEs performance, knowledge sharing demonstrates a positive and significant direct impact. Importantly, the study reveals that digital agility and knowledge sharing indirectly enhance MSMEs performance through innovation performance. This research emphasizes the importance of digital agility and knowledge-sharing culture in improving MSMEs performance. As MSMEs reach critical growth stages, implementing more productive methods through digital agility and knowledge sharing becomes crucial. These practices generate new ideas, fostering creativity and innovation within the organization. The resulting innovation competitiveness allows MSMEs to develop unique products or services, creating a competitive advantage and attracting new customers.
PREDIKSI KEBERHASILAN TRANSFORMASI DIGITAL PADA UKM Atika Mutiarachim
Indonesian Journal of Business Intelligence (IJUBI) Vol 8 No 1 (2025): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v8i1.5679

Abstract

Industri 4.0 menuntut pelaku industri skala besar sampai kecil untuk mampu beradaptasi dengan percepatan teknologi. Transformasi digital terbukti berdampak signifikan terhadap kinerja industri, termasuk mempertahankan eksistensi UKM. Keberhasilan UKM dalam memanfaatkan teknologi terbukti mampu meningkatkan keuntungan dan efisiensi operasional. Pelaku UKM harus memastikan transformasi digital berhasil diterapkan. Prediksi transformasi digital memudahkan pelaku UKM untuk mengetahui tingkat keberhasilan usahanya dalam menerapkan transformasi digital, sehingga UKM dapat mengembangkan strategi inovasi mengoptimalkan teknologi digital untuk menjaga eksistensi, memperoleh keuntungan optimal, meningkatkan daya saing serta efisiensi operasional. Prediksi dilakukan dengan membandingkan ID3, C4.5 dan CART untuk menemukan rule dengan performance terbaik, yang paling efektif untuk memprediksi keberhasilan suatu UKM dalam menerapkan transformasi digital. Hasil menunjukkan ID3 memperoleh performance terbaik dengan akurasi 89.19% dan AUC 0.959. Atribut ATP menjadi root node pada seluruh pohon keputusan yang dihasilkan. Ini berarti variabel Peran Digital Marketing, Model TOE, Strategi Bisnis Digital, Adopsi technopreneurship mempunyai peranan besar atau dampak yang signifikan terhadap UKM dalam melaksanakan transformasi digital pada bisnisnya.
HYBRID DEEP LEARNING RANDOM FOREST OPTIMASI PEMILIHAN FITUR UNTUK PREDIKSI CHURN INDUSTRI TELEKOMUNIKASI Atika Mutiarachim; Dhendra Marutho; Nur Atika Yuniarti; Ryan Arya Pramudya; Jaluanto Sunu Punjul Tyoso
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
OPTIMALISASI PREDIKSI SAHAM APPLE DAN SAMSUNG DENGAN ALGORITMA BPNN Atika Mutiarachim; Yupie Kusumawati; Nurchayati Nurchayati; Aulia Indriawati
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.v6i2.6707

Abstract

Pasar saham global khususnya bidang teknologi mengalami volatilitas yang signifikan, dengan saham Apple Inc. dan Samsung Electronics Co., Ltd sebagai pemain utama. Prediksi signifikan sangat diperlukan untuk mengurangi resiko investasi. Penelitian ini menganalisis dan membandingkan kinerja Backpropagation Neural Network (BPNN) dalam memprediksi pergerakan saham Apple dan Samsung. Dataset publik diperoleh dari Kaggle, saham Samsung dengan 6128 data periode 4 Januari 2000 sampai 13 Juni 2024 dan saham Apple dengan 2476 data periode 2 Januari 2014 sampai 31 Oktober 2023. Metode BPNN diterapkan dengan optimasi parameter learning rate, momentum, dan training cycle, pembagian data 10-fold cross validation, evaluasi nilai Root Mean Square Error (RMSE). Hasil terbaik menunjukkan konfiguasi optimal diperoleh dari learning rate 0.1, momentum 0.9, error epsilon 1.0E-4 dan training cycle 60. Nilai RMSE terbaik saham Apple 0.802 0.263 dengan akurasi 99.85%, dan pada saham Samsung RMSE terbaik 399.806 102.670 dengan akurasi 99.36%. Penelitian membuktikan BPNN dengan pola 0.1-0.9-60 sangat efektif memprediksi harga Close sehingga mampu memberikan kontribusi signifikan bagi investor dalam melakukan evaluasi investasi sebagai strategi meminimalisir resiko saham.
Co-Authors Aulia Indriawati Dhendra Marutho Fika Ulfa W Jaluanto Sunu Punjul Tyoso Nur Atika Yuniarti Nurchayati Nurchayati Puji Setya Sunarka Ryan Arya P Ryan Arya Pramudya Yupie Kusumawati