Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Journal of Management and Informatics Jaksa: Jurnal Kajian Ilmu Hukum Dan Politik Jurnal Ilmiah Sistem Informasi
Nikhlis, Neilin
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Law, Crime, Criminology & Criminal Justice Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Deteksi Dini Pencurian Data pada Perangkat Seluler Menggunakan Machine Learning Nikhlis, Neilin; Muhammad Jamal Udin Ghofur
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 2 (2025): Mei : Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/fz0xzt26

Abstract

The increasing use of mobile devices has increased the risk of data theft, posing significant security challenges for individuals and organizations. This study proposes an early detection system for data theft on mobile devices using machine learning algorithms. The system is designed to identify suspicious patterns in application usage, network access, and CPU/memory activity, providing early warnings to prevent potential data loss. By employing algorithms such as Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the developed models demonstrated significant performance: CNN achieved the highest accuracy of 95.1%, with a precision of 94.2%, recall of 93.5% , F1-score of 93.8%, and AUC-ROC of 0.96. Random Forest and SVM also showed competitive performance with accuracy rates of 94.7% and 92.5%, respectively. These findings highlight the high potential of machine learning algorithms for real-time detection of data theft threats, providing adaptive protection against evolving cyberattack methods. This approach offers a promising solution to strengthen mobile device security frameworks and safeguard user data against increasingly sophisticated cyber threats.
Soft System Methodology (SSM) Analysis to Increase the Number of Prospective Students Nikhlis, Neilin; Iriani, Ade; Hartomo, Kristoko Dwi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 4 No 1 (2020): February 2020
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (279.957 KB) | DOI: 10.29407/intensif.v4i1.13552

Abstract

The competition between campus, whether it’s a public college and private college in Central Java, is very tight with the increasing number of interested students for prospective students from various regions. The close competition requires many campuses to compete to provide the best facilities and services. The research objective is expected to support the "XY" university promotion strategy to help the university in the knowledge capture process. Data collection was carried out using the group discussion forum (FGD) method with a structured interview process for university leaders, university officials, marketing departments, and students. The technique used in this study is a soft system methodology (SSM). The results of this study model knowledge capture (KC) on the "XY" university promotion strategy and produce knowledge documentation that provides benefits in making policy strategies and has an impact on increasing the number of prospective new college students by optimizing digital marketing.
Implementasi Prinsip Due Diligence dalam Kewajiban Negara Mencegah Cyber Attacks: Tinjauan Hukum Internasional Kontemporer Pratiwi, Berliant; Nikhlis, Neilin
Jaksa : Jurnal Kajian Ilmu Hukum dan Politik Vol. 4 No. 1 (2026): Januari : Jurnal Kajian Ilmu Hukum dan Politik
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/172frz12

Abstract

The rapid evolution of cyberspace has transformed it into a strategic domain of international relations, raising complex legal challenges regarding state responsibility for preventing cross-border cyberattacks. This study explores the implementation of the due diligence principle in international law as a normative foundation for evaluating a state’s obligation to prevent harmful cyber activities originating from its territory. Utilizing a normative-legal approach, the research analyzes global legal instruments, state practices, and Indonesia’s position on due diligence. Findings reveal a significant gap between the doctrinal recognition of due diligence and its operational application, especially in developing countries lacking institutional and regulatory capacity. The study proposes practical recommendations for integrating due diligence into national Cybersecurity strategies while contributing to the broader development of binding international cyber norms. By focusing on Indonesia, the research aims to strengthen both national legal preparedness and its international legal standing in cyberspace governance.
Enhancing Decision Quality and Transparency via Machine Learning-Based Goodwill Impairment Estimation in Banks Wibisono, Gunawan; Nikhlis, Neilin; Wicaksono, Yosep Aditya; Faradila, Silvia
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.233

Abstract

Goodwill impairment assessment remains a judgment-intensive process in banking institutions, where managerial discretion, information asymmetry, and regulatory complexity often challenge the quality of decisions and transparency. While prior studies have widely applied machine learning to financial risk assessment and credit analytics, they have paid limited attention to its role in improving managerial accountability in goodwill impairment decisions. This study aims to address this gap by developing and evaluating a machine-learning–based estimation framework to enhance the quality of decisions and transparency in bank-level goodwill impairment assessments. Using simulation-based analysis on synthetic financial statements, the proposed framework evaluates the performance of impairment estimation using quantitative metrics that capture predictive accuracy, decision consistency, and traceability. The findings demonstrate that ML-assisted estimation can systematically improve decision quality while strengthening transparency and accountability compared to traditional judgment-driven approaches. Beyond technical performance, the results indicate that machine learning can function as a governance-supporting mechanism by enabling more traceable and internally auditable impairment decisions. The study contributes theoretically by operationalizing transparency and accountability as measurable decision outcomes in corporate finance, and practically by offering banks a simulation-based tool for internal evaluation that does not rely on field experiments or sensitive proprietary data. Overall, the research highlights the potential of ML-enabled decision support systems to enhance both the quality and governance of goodwill impairment practices in the banking sector.
Co-Authors Ade Iriani Faradila, Silvia Gunawan Wibisono Kristoko Dwi Hartomo Muhammad Jamal Udin Ghofur Pratiwi, Berliant Wicaksono, Yosep Aditya