Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction

Raja Ayu Mahessya — 2025-06-19

Post-operative kyphosis represents a significant complication following pediatric spinal corrective surgery, necessitating sophisticated prediction methods to identify high-risk patients. This study developed and evaluated machine learning models for kyphosis prediction using a dataset of 81 pediatric patients by comparing the logistic regression and decision tree approaches. Despite achieving a higher overall accuracy (82%), the logistic regression model failed to identify any kyphosis cases, rendering it clinically ineffective. Conversely, the decision tree model demonstrated superior clinical utility by successfully identifying 33% of kyphosis cases while maintaining 71% accuracy. Feature importance analysis established starting vertebral position as the dominant predictor (importance=0.554), followed by patient age (0.416), with vertebrae count contributing minimally (0.030). The decision tree identified critical thresholds for risk stratification: operations beginning at or above T8-T9, particularly in children aged 5-9 years, carried a substantially elevated kyphosis risk. Our methodological approach emphasizes sensitivity over conventional accuracy metrics, recognizing that missing high-risk patients have greater clinical consequences than unnecessary monitoring. This study demonstrates the capacity of decision tree models to extract clinically meaningful patterns from small, imbalanced surgical datasets that elude conventional statistical approaches.

Performance Comparison of Monolithic and Microservices Architectures in Handling High-Volume Transactions

Mastura Diana Marieska — 2025-06-19

Monolithic and microservices are two distinct approaches for designing and developing applications. However, these architectures exhibit contrasting characteristics. In monolithic architecture, all components of an application form a unified entity with closely interconnected parts, whereas microservices decompose an application into independent, lightweight services that can be developed, deployed, and updated separately. Microservices are often regarded as superior to monolithic architectures in terms of their performance. This study aims to compare the performance of monolithic and microservices architectures in handling a high volume of transactions. It is important to observe how the two architectures behave when handling transactions from a large number of concurrent users. A prototype of an online ticketing system was implemented for both architectures to enable comparative analysis. The selected performance metrics were response time and error rate. The experimental results reveal that under high-load conditions, microservices outperform monolithic architectures, demonstrating 36% faster response times and 71% fewer errors. However, under overload conditions—when CPU usage exceeds 90%—the performance of microservices degrades significantly. This does not imply that microservices cannot handle a large number of concurrent users but highlights the necessity for enhanced resource management.

Enhancing Tomato Leaf Disease Detection via Optimized VGG16 and Transfer Learning Techniques

Sandy Putra Siregar — 2025-06-18

Identification of tomato leaf disease remains difficult because standard approaches are frequently incorrect in identifying distinct signs. Convolutional Neural Networks (CNNs) perform well in image classification and pattern identification, although they are prone to overfitting. Thus, max pooling was employed to reduce dimensionality while retaining crucial information. This paper offers an improved CNN through hyperparameter tuning and compares it to Transfer Learning models such as InceptionV3, NASNetMobile, and VGG16, which were chosen for their efficiency and accuracy. The dataset comprises 7,178 photos classified as Healthy, Leaf Late Blight, Septoria Leaf Spot, and Yellow Leaf Curl Virus, collected from Kaggle.. The dataset is separated into three sections: training, validation, and testing, with a ratio of 70:15:15. The results of this study revealed that the proposed method achieved the highest accuracy of 98.24%. In the application of transfer learning, the inceptionV3 model achieved an accuracy of 96.94%, whereas NASNetMobile obtained 97.50%, and VGG16 showed an accuracy of 96.76%. The evaluation is based on accuracy, precision, recall, F1-score and Inference time to determine the optimum model for accuracy and computing efficiency. This project uses the proposed method and Transfer Learning Techniques to categorize illness images on tomato leaves. These findings will drive further research to improve tehe performance of the proposed method for foliar disease classification and comparable applications.

Prediction of Financial Distress in Retail Companies Using Long-Short Term Memory (LSTM)

Wahyuni Windasari — 2025-06-18

Financial distress is a condition in which an entity struggles to meet its debt and operating obligations.. Financial distress can lead to bankruptcy or company closure if corrective action is not taken. This study aims to forecast financial distress in retail companies by utilizing key financial ratios, including Total Asset Turnover (TATO), Current Ratio (CR), Return on Assets (ROA), and Debt-to-Equity Ratio (DER). The analysis is based on secondary data from Indonesian retail companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. The dataset exhibited missing values and class imbalance, which were addressed using mean imputation and the Synthetic Minority Oversampling Technique (SMOTE), respectivelyTo perform predictions, a Long Short-Term Memory (LSTM) model was implemented. The integration of SMOTE contributed to enhanced detection of the minority class; however, it was accompanied by a slight reduction in overall predictive accuracy. The model demonstrated a performance accuracy of 86%, with a recall rate of 85%, a precision of 100%, and an F1-score of 92%.

UDAWA Gadadar: Agent-based Cyber-physical System for Universal Small-scale Horticulture Greenhouse Management System

I Wayan Aditya Suranata — 2025-06-18

Digitalization in agriculture is becoming increasingly important for improving efficiency and sustainability, but small-scale farmers often face difficulties in adopting digital technologies because of various constraints. This study proposes an open-source intelligent system platform called UDAWA (Universal Digital Agriculture Workflow Assistant) to assist small-scale farmers in digitizing greenhouse management processes. The first variant of this platform, UDAWA Gadadar, was designed as a cyber-physical agent to control and monitor greenhouse instruments. UDAWA Gadadar was built using a 5C architecture approach and farmer-centric design thinking, utilizing an ESP32 microcontroller and a power sensor module to ensure performance and energy efficiency. The UDAWA Gadadar prototype was tested in a small-scale greenhouse with promising results, with an average remaining memory of 175 KB in the non-SSL mode and 122 KB in the SSL mode. Cost analysis indicates that this platform is relatively affordable for small-scale farmers, with a total component cost of USD 33.7 per unit. A decision matrix analysis involving five different greenhouse models in Pancasari Village, Buleleng Regency, Bali, showed that UDAWA Gadadar has high relevance and potential for adoption, particularly in models GH3 and GH5, with compatibility scores of 0.27. This study contributes to the development of appropriate and accessible digitalization solutions for small-scale agriculture, with future work focusing on developing other physical agent variants and a digital twin for enhanced cultivation simulations.