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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JSAI (Journal Scientific and Applied Informatics) West Science Business and Management
Ida Nurhaida
Universitas Pembangunan Jaya
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering

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 1 
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.
SMARTSCAN-DFU: SISTEM DETEKSI DINI LUKA KAKI DIABETES MENGGUNAKAN DEEP CONVOLUTIONAL NEURAL NETWORK (CNN) Indah Hairunisah; Ida Nurhaida; Revaldo Ilfestra Metsi Zen
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7101

Abstract

Diabetes mellitus is a chronic non-communicable disease with a continuously increasing global prevalence. The number of adults living with diabetes worldwide has reached approximately 589 million, with 252 million remaining undiagnosed. One of the most serious complications is Diabetic Foot Ulcer (DFU). This study developed SmartScan-DFU, an early detection system for diabetic foot ulcers based on deep learning, by comparing four model architectures: Custom CNN, EfficientNet-B3, ResNet-18, and ResNet-50. The dataset consists of 4,446 images obtained from the Roboflow Universe platform. Evaluation results show that ResNet-50 achieved the best performance with an accuracy of 87.19%, precision of 0.87, recall of 0.87, and F1-score of 0.87. This model outperformed ResNet-18 (81.22%), EfficientNet-B3 (72.44%), and Custom CNN (61.00%). The comparison indicates that more advanced CNN architectures, particularly ResNet-50, demonstrate superior spatial feature extraction and generalization capabilities for DFU image variations. The best-performing model was then integrated into a Flask-based web interface, enabling automatic, fast, and accurate classification of DFU images. This system is expected to assist in the early digital diagnosis of diabetic foot ulcers, accelerate clinical decision-making, and contribute to achieving the Sustainable Development Goals (SDG) point 3 on good health and well-being.
Penerapan LSTM Dalam Deep Learning Untuk Prediksi Harga Kopi Jangka Pendek Dan Jangka Panjang Rifqi Muhammad; Ida Nurhaida
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5904

Abstract

Harga kopi sering mengalami berfluktuasi dalam dua tahun terakhir, terlebih harga kopi Arabika dan Robusta terus mengalami fluktuasi yang signifikan, naik dan turun secara berkelanjutan. Data historis menunjukkan variasi yang cukup signifikan dari tahun 2010 hingga Mei 2024. Penelitian ini bertujuan untuk meramalkan harga kopi Arabika dan Robusta baik dalam jangka pendek maupun jangka panjang dengan menggunakan model Long Short-Term Memory (LSTM). Metode yang digunakan yaitu data preparation, pre-processing data, model training, model testing, model evaluation dan data visualization. Performa model yang terbaik dengan menggunakan learning rate 0.0001 dan epoch 150, hal ini ditunjukan oleh tingkat error yang rendah yaitu 1021.5773 menggunakan Root Mean Squared Error (RMSE) dan 660.4265 Mean Absolute Error (MAE). Nilai tersebut diperoleh dengan data training 80% dan data testing 20%, menggunakan 60 timesteps, 225 neuron hidden layer, dan memanfaatkan metode optimasi Adam. Dengan demikian algoritma LSTM dengan performa model tersebut dapat melakukan prediksi harga yang akurat.
Implementation of Isolation Forest and Rule-Based Prioritization in the Automation of Warehouse Inventory Monitoring Using a Near Real-Time Dashboard Muhammad Windri; Ida Nurhaida
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13218

Abstract

This research implements an automated warehouse inventory monitoring system using a hybrid approach that combines Isolation Forest and rule-based prioritization for anomaly detection, while Long Short-Term Memory (LSTM) is used for demand forecasting. The system automatically detects three types of anomalies: spike (quantity surge), drop (quantity decline), and pattern shift (changes in transaction patterns). The method utilizes 341,879 inventory records over two and a half years, combining Isolation Forest for outlier detection, rule-based prioritization for spike and drop detection, and LSTM for time series forecasting. The system is equipped with a near real-time dashboard (periodic auto-refresh), API service, and a business validation mechanism involving warehouse users. The system generated 211,450 detection candidates, consisting of 187,537 operational anomaly alerts and 23,913 INFO-level records, with accuracy of 93.0%, precision of 88.7%, recall of 100%, and F1-score of 94.0%. The INFO category is not displayed on the dashboard due to low confidence scores. The near real-time dashboard displays HIGH, MEDIUM, and LOW priority alerts with auto-refresh. Business validation achieved an 84% confirmation rate. The system also provides LSTM forecast features for predicting inventory needs for 7, 14, and 30 days ahead. This research shows that AI implementation for warehouse inventory monitoring can automate anomaly detection and improve inventory issue identification. The system shows potential for medium to large-scale warehouses requiring near real-time monitoring.
Co-Authors Abdul Hadi Sirat Fitriyana Nuril Khaqqi Husain Ali Indah Hairunisah Joshua Nathanael Zega Muhammad Windri Revaldo Ilfestra Metsi Zen Rifqi Muhammad Riny Nurhajati