Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Technomedia Journal Aptisi Transactions on Management Journal of Computer Science and Technology Application
Steven Harazaki Lase
Universitas Raharja
Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Other

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

Found 3 Documents
Search

 1 
Data Driven and Sustainable Innovation Strategies for Long Term Product Market Fit in SMEs Sri Lestari Pujiastuti; Nanda Septiani; Adam Faturahman; Steven Harazaki Lase; Mitra Trima Dessincer Putri; April Lansonia
APTISI Transactions on Management (ATM) Vol 10 No 2 (2026): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v10i2.2615

Abstract

In an increasingly dynamic and sustainability-conscious marketplace, startups and SMEs face mounting pressure to sustain product relevance and strategic resilience over time. This study investigates how integrated data-driven strategies support long-term product–market fit (PMF) through the alignment of real-time analytics, structured customer feedback loops, and sustainability-oriented innovation practices. Drawing on Resource-Based and Organizational Capability perspectives, the study conceptualizes digital capability formation as a strategic asset that strengthens adaptive market alignment under structural constraints. Using PLS-SEM analysis on data collected from 110 SMEs, five key constructs are examined: technology utilization, data-driven decision-making, customer feedback integration, sustainable innovation capability, and market responsiveness. The results indicate that technology utilization and data-driven decision-making exert significant positive effects on long-term PMF, while customer feedback integration facilitates iterative product refinement and market consistency. However, sustainable innovation capability and market responsiveness demonstrate negative path coefficients, suggesting that without structured governance, digital maturity, and prioritization mechanisms, these capabilities may generate operational strain or reactive strategic behavior that weakens long-term positioning. The findings extend the Data Strategy–Sustainability convergence literature by validating an integrative model that bridges digital capability development and responsible innovation in SME contexts. Managerially, the study highlights the importance of phased digital adoption and disciplined sustainability integration to ensure durable competitive alignment within evolving industrial ecosystems
Deep Learning Enabled Security Monitoring for Intrusion Detection in Smart Campus Networks Ruli Supriati; Nuke Puji Lestari Santoso; Steven Harazaki Lase; Carlos Perez
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/a88eeq63

Abstract

The increasing complexity of smart campus networks has heightened the need for advanced cybersecurity measures to protect sensitive data and ensure seamless operations. Traditional Intrusion Detection Systems (IDS) often struggle to cope with the dynamic and heterogeneous nature of network traffic in smart campus environments, necessitating the development of more effective solutions. This study aims to propose a deep learning-based intrusion detection system for smart campus networks, utilizing a Hybrid CNN-LSTM model to enhance security monitoring. The proposed methodology integrates Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in network traffic. The model was trained and evaluated on a combination of publicly available datasets and simulated smart campus data, measuring performance through key metrics such as accuracy, precision, recall, and F1-score. Results show that the Hybrid CNN-LSTM model outperforms traditional machine learning models, achieving an accuracy of 97.3% and a ROC-AUC of 0.99, demonstrating superior detection of both known and unknown intrusions. The findings suggest that deep learning models, especially when tailored to smart campus contexts, offer significant advantages in real-time threat detection and adaptive learning. This research contributes to the growing body of knowledge on AI-driven network security and provides practical insights for improving cybersecurity infrastructures in higher education institutions.
Business Intelligence Implementation to Support Data Driven Strategic Decision Making in Digital Organizations Tessa Handra; Agung Rizky; Mungkap Mangapul Siahaan; Steven Harazaki Lase; Kamal Arif Al-Farouqi
Technomedia Journal Vol 11 No 1 (2026): June
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v11i1.2640

Abstract

Digital transformation has encouraged organizations to generate large volumes of data that must be managed effectively to support strategic decision-making. Business Intelligence has become a solution for integrating, analyzing, and presenting data into valuable information. This study aims to analyze the implementation of Business Intelligence in digital organizations, its role in supporting data-driven strategic decision-making, as well as the benefits and challenges of its implementation. This study used a qualitative method with a literature review approach by examining journals, scientific articles, and other academic publications related to the research topic. The data were analyzed using descriptive qualitative analysis. The results show that the implementation of BI through data warehouses, dashboards, reporting systems, and data analytics can improve operational efficiency, accelerate access to information, and support performance monitoring and business trend analysis more effectively. BI also supports faster and more accurate data-driven decision-making. Business Intelligence plays an important role in supporting strategic data-driven decision-making in digital organizations. However, its implementation still faces challenges related to data quality, information security, limited human resource competencies, and high technology implementation costs.
Co-Authors Adam Faturahman Agung Rizky April Lansonia Carlos Perez Kamal Arif Al-Farouqi Mitra Trima Dessincer Putri Mungkap Mangapul Siahaan Nanda Septiani Nuke Puji Lestari Santoso Ruli Supriati, Ruli Sri Lestari Pujiastuti, Sri Lestari Tessa Handra