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All Journal JSAI (Journal Scientific and Applied Informatics) Jurnal Algoritma West Science Business and Management
Ida Nurhaida
Universitas Pembangunan Jaya
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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The Effect of Financial Innovation, Risk Management, and Monetary Policy on the Stability of Fintech Startup Companies in Jakarta Husain Ali; Abdul Hadi Sirat; Ida Nurhaida
West Science Business and Management Vol. 2 No. 04 (2024): West Science Business and Management
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsbm.v2i04.1556

Abstract

This study investigates the effects of financial innovation, risk management, and monetary policy on the stability of fintech startup companies in Jakarta. Using a quantitative approach, data were collected from 35 fintech startups through structured questionnaires with responses measured on a Likert scale of 1-5. Data analysis was conducted using SPSS version 25, employing correlation and multiple regression analysis. The results reveal that financial innovation is the most significant predictor of fintech stability, followed by risk management and monetary policy. The combined influence of these factors explains 74% of the variance in fintech stability. These findings underscore the importance of integrating innovation with robust risk management practices and aligning operations with macroeconomic trends for sustained stability. This research provides valuable insights for fintech stakeholders and policymakers to foster resilience and growth in the rapidly evolving financial ecosystem.
Strategi Dan Perencanaan Outsourcing Dalam Pengembangan Sistem Informasi Dengan Memanfaatkan CMMI-ACQ Riny Nurhajati; Ida Nurhaida; Fitriyana Nuril Khaqqi
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7522

Abstract

Finance companies often face challenges in managing information system development projects through outsourcing. There is a need to improve efficiency and alignment between IT and business in the Project Planning (PP) process. By adopting the McFarlan Strategic Grid and CMMI-ACQ, mapping the current information system and development plan based on four quadrants, measuring the maturity level of the project planning process, and identifying areas that need improvement. Based on the results of the maturity level measurement in the Project Monitoring and Control area, it shows that Specific Goals (SG) have low achievements, with SG 1 (27%) and SG 2 (39%) showing great room for improvement in monitoring and corrective action management. At the Specific Practices (SP) level, practices that have been quite good are project planning monitoring (SP 1.1, 60%) and problem analysis (SP 2.1, 50%). Still, many areas need improvement, such as risk monitoring (SP 1.3, 7%), data management (SP 1.4, 20%), and stakeholder involvement (SP 1.5, 20%). These findings highlight the importance of formulating project risk management, improving project management capabilities, and strengthening collaboration between teams to achieve the success of information system development projects. By implementing the proposed approach, companies can develop more efficient PP process standards, ensure IT alignment, and optimize resource and cost allocation.
Efisiensi dan Generalisasi Gated Recurrent Unit pada Pengenalan Bahasa Isyarat Indonesia Berbasis Fitur Rangka Joshua Nathanael Zega; Ida Nurhaida
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3221

Abstract

The development of an automatic Indonesian Sign Language (BISINDO) translation system on mobile devices faces major challenges in the form of high computational costs and variability in signing styles across individuals. This study proposes a lightweight approach using MediaPipe Holistic skeletal feature extraction integrated with a Recurrent Neural Network (RNN) architecture. Specifically, the research evaluates and compares the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures in recognizing 12 classes of dynamic sign words. Unlike most previous studies that employ random data splitting, this research applies a Leave-One-Subject-Out (LOSO) validation scheme to rigorously assess model generalization to unseen users. Experimental results reveal a significant performance gap between the two architectures. The LSTM model exhibits poor generalization capability, achieving an accuracy of only 40.34%, whereas the GRU model demonstrates superior performance with an accuracy of 73.95%. In terms of resource efficiency, GRU is more optimal, with a model size of 0.83 MB (22% smaller than LSTM), 24% fewer parameters, and stable inference speed in the range of 13–14 FPS. This study concludes that GRU is a more effective and efficient architecture for implementing robust BISINDO recognition systems on resource-constrained devices.
Co-Authors Abdul Hadi Sirat Fitriyana Nuril Khaqqi Husain Ali Joshua Nathanael Zega Riny Nurhajati