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All Journal Jurnal Transformatika Jurnal Sistem Informasi, Manajemen, dan Akuntansi (SIMAK) Indonesian Journal of Business Intelligence (IJUBI) Jurnal Pengabdian Masyarakat Nusantara International Journal of Management Science and Information Technology (IJMSIT) Jurnal Cakrawala Informasi (JCI) Jurnal Suara Pengabdian 45 Jurnal Manajerial dan Bisnis Tanjungpinang Serat Acitya
Mutiarachim, Atika
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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Optimasi Prediksi Pemasaran Nasabah Deposito Bank dengan Metode Klasifikasi Logistic Regression Mutiarachim, Atika; 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.
Transformasi Strategis Manajemen Risiko dan Pemulihan Finansial melalui Arsitektur Digital Twins: Analisis Komprehensif dan Proyeksi Masa Depan: Systematic Literature Review Sumarno, Nurchayati; Parju, Parju; Mutiarachim, Atika
Serat Acitya Vol. 15 No. 1 (2026): April Jurnal Ilmiah Serat Acitya
Publisher : Universitas 17 Agustus 1945

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56444/8fn1q774

Abstract

Penelitian ini mengkaji transformasi strategis manajemen risiko finansial melalui penerapan Digital Twins (DT) dengan pendekatan Systematic Literature Review (SLR). DT berevolusi dari sekadar model manufaktur menjadi sistem cerdas yang mampu memprediksi perilaku entitas finansial secara real-time. Hasil kajian menunjukkan bahwa DT mendukung kerangka Prevention, Preparedness, Response, Recovery (PPRR), memperkuat resiliensi rantai pasok, serta meningkatkan efektivitas stress testing perbankan sesuai regulasi Basel III. Selain itu, DT berperan dalam deteksi penipuan, akuntansi karbon, dan simulasi kebijakan makroekonomi. Studi ini menegaskan bahwa DT mampu meningkatkan akurasi prediksi risiko hingga 95% dan mengurangi downtime operasional sebesar 20%. Namun, keterbatasan standar global, integrasi data lintas sistem, serta hambatan regulasi masih menjadi tantangan utama. Agenda riset masa depan diarahkan pada pengembangan interoperabilitas global, integrasi teknologi AI, serta evaluasi ROI jangka panjang untuk memperkuat ketahanan finansial berbasis DT.
Co-Authors Aditya Putra Ramdani Basirudin Ansor Fari Katul Fikriah Jaluanto Sunu Punjul Tyoso Jaluanto Sunu Punjul Tyoso Pamudya, Ryan Arya Parju, Parju Puji Setya Sunarka Ryan Arya Pramudya, Ryan Arya Sumarno, Nurchayati Yuniarti, Nur Atika