Aligning Software Architecture with Cost Structure: A Comparative Study Using ATAM and Lean Canvas in Early Startup Development

Startups in the early phase often face challenges in balancing operational efficiency with resource constraints. This research find how startups can choose software architecture to align with cost structures with the Lean Canvas framework and the Architecture Trade-off Analysis Method (ATAM). Lean canvas allows for startups to identify cost structures at an early stage and align with market demands efficiently and ATAM helps to evaluate software architecture systematically by analysing trade-offs and quality attributes. Although microservice architecture offers modularity and scalability, its implementation can lead to higher operational costs making it unsuitable for startups with limited budgets. On the other hand, monolithic architecture is more cost-effective, easy to manage and suitable for the needs of early-stage startups. This research emphasizes that systematic evaluation of software architecture based on business goals and resource limitations is essential for startup growth for sustainability. By combining Lean Canvas for business validation and ATAM for architectural decision making, startups can optimize operational and technical strategies, analyse risks, and identify trade-offs that are implemented according to business development

Leveraging Convolutional Block Attention Module (Cbam) For Enhanced Performance In Mobilenetv3-Based Skin Cancer Classification

As the incidence of skin cancer continues to rise globally, effective automated classification methods become crucial for early detection and timely intervention. Lightweight neural networks such as MobileNetV3 offer promising solutions due to their minimal parameters, making them suitable for environment with low resource. This study aims to develop an automated multiclass skin cancer classification system by enhancing MobileNetV3 with the Convolutional Block Attention Module (CBAM). The primary goal is to achieve high classification accuracy without significantly increasing computational demands. We employed Bayesian optimization to automatically fine-tune model parameters and applied targeted data augmentation techniques to address class imbalance. CBAM was integrated to highlight diagnostically relevant regions within images. The proposed method was evaluated using the ISIC 2024 SLICE-3D dataset, which includes over 400,000 dermatoscopic images categorized into benign, basal cell carcinoma, melanoma, and squamous cell carcinoma classes. Preprocessing involved standardized resizing, normalization, and extensive geometric and photometric augmentations. Results demonstrated that our method achieved an accuracy of 98.97%, precision of 98.99%, recall of 98.97%, and an F1-score of 98.98%, surpassing previous state-of-the-art models by 1.86–6.52%. Remarkably, this improvement was achieved with minimal additional parameters due to the effective integration of CBAM. These results represent an advancement in automated medical image analysis, particularly for low resource settings, by combining lightweight CNNs with attention mechanisms and systematic hyperparameter exploration.

Real-Time Rice Leaf Disease Diagnosis: A Mobile CNN Application with Firebase Integration

Rice, the staple food for the majority of Indonesia's population, faces significant production threats from leaf diseases, which can decrease yields and jeopardize national food security. Traditional manual identification of these diseases is a major challenge for farmers, as it is often subjective, prone to misdiagnosis leading to incorrect treatments, time-consuming, demands specialized expertise, and is difficult to implement widely for effective real-time early prevention, allowing diseases to spread and significantly impact crop yields. This research addresses these challenges by developing an automated and easily accessible rice leaf disease diagnosis system. The system is manifested as a mobile application that integrates a Convolutional Neural Network (CNN) model, specifically utilizing the EfficientNetB0 architecture, for the classification of rice leaf images and leverages key Firebase services such as its Realtime Database for data synchronization and Cloud Storage for image management to ensure a scalable and responsive backend. The methodology involved several key stages. Firstly, the CNN model was developed by employing a transfer learning approach on the pre-trained EfficientNetB0 architecture. Secondly, the model underwent comprehensive testing using a dataset of 1,000 new rice leaf images, which were independently validated by agricultural experts. The results demonstrated that the developed CNN model achieved a global accuracy of 85.9%, with an average precision of 86.1% and recall of 85.9% (macro-average) in the expert validation testing phase with the 1,000 new images. However, the study also identified variations in the model's performance across different disease classes, highlighting areas that require further optimization to enhance detection effectiveness for specific types of rice leaf diseases. The primary benefit of this research is the provision of a practical rice leaf disease diagnosis tool that is readily accessible to farmers via a mobile application, empowering them with timely and accurate information for effective crop management. This can lead to reduced crop losses, improved yield quality, and contribute significantly to national food security. Furthermore, this research contributes to the field of applied machine learning and mobile computing in resource-constrained agricultural environments, offering valuable insights for the development of impactful informatics solutions

Enhanced U-Net Cnn For Multi-Class Segmentation And Classification Of Rice Leaf Diseases In Indonesian Rice Fields

Rice is a strategic food crop whose productivity is often threatened by leaf diseases and pests. This study aims to develop an Enhanced U-Net CNN model for multi-class segmentation and classification of rice leaf conditions from field images to support early detection and plant health management. The methodology includes direct field image acquisition of rice leaves, preprocessing for image quality enhancement, expert data labeling, segmentation using a U-Net architecture, and classification using CNN. The dataset was divided into training and testing data with balanced distribution across four classes: Healthy, BrownSpot, Hispa, and LeafBlast. Evaluation results show that the model can identify rice leaf conditions with high accuracy, although signs of overfitting were observed from the performance gap between training and validation data. The implementation of this model is expected to accelerate automatic disease detection in the field, reduce reliance on manual inspection, and support precision agriculture. This study achieved a testing accuracy of 76.36% with a macro-average F1-score of 0.34. While the results indicate limitations in generalization, the proposed Enhanced U-Net CNN demonstrates the feasibility of combining segmentation and classification in field conditions. This research contributes to agricultural informatics by supporting scalable deployment in precision agriculture systems, reducing reliance on manual inspection, and providing a foundation for further optimization studies

Implementation of Ant Colony Optimization in Obesity Level Classification Using Random Forest

Obesity is a pressing global health issue characterized by excessive body fat accumulation and associated risks of chronic diseases. This study investigates the integration of Ant Colony Optimization (ACO) for feature selection in obesity-level classification using Random Forests. Results demonstrate that feature selection significantly improves classification accuracy, rising from 94.49% to 96.17% when using ten features selected by ACO. Despite limitations, such as challenges in tuning parameters like alpha (α), beta (β), and evaporation rate in ACO techniques, the study provides valuable insights into developing a more efficient obesity classification system. The proposed approach outperforms other algorithms, including KNN (78.98%), CNN (82.00%), Decision Tree (94.00%), and MLP (95.06%), emphasizing the importance of feature selection methods like ACO in enhancing model performance. This research addresses a critical gap in intelligent healthcare systems by providing the first comprehensive study of ACO-based feature selection specifically for obesity classification, contributing significantly to medical informatics and computer science. The findings have immediate practical implications for developing automated diagnostic tools that can assist healthcare professionals in early obesity detection and intervention, potentially reducing healthcare costs through improved diagnostic efficiency and supporting digital health transformation in clinical settings. Furthermore, the study highlights the broader applicability of ACO in various classification tasks, suggesting that similar techniques could be used to address other complex health issues, ultimately improving diagnostic accuracy and patient outcomes

Validation of Question Classification Using Support Vector Machine and Intraclass Correlation Coefficient Based on the Revised Bloom’s Taxonomy

The assessment process must be carried out accurately as it is a crucial aspect of identifying cognitive abilities in students. Cognitive ability identification needs to be done by providing exam questions that refer to the Revised Bloom's Taxonomy for difficulty-level classification to ensure students' understanding of what has been taught. The traditional manual classification process carried out by educators often requires significant time and is susceptible to subjective variability. The classification of questions from levels C1 to C6 based on the Revised Bloom's Taxonomy shows an imbalance in the data distribution for each level, leading to inaccurate classification results. The automatic classification technique using the SVM algorithm allows educators to quickly classify questions based on their difficulty levels. The automated classification technique needs to be validated to what extent the difficulty levels classified by the machine align with the perceptions of educators and students. This research will validate the results of question classification generated from the SVM algorithm, supplemented by the oversampling technique to address data imbalance. The validation method used is ICC. Applying the SMOTE oversampling technique to handle a class imbalance in the training data shows improvement, with an accuracy rate of 91% when using SMOTE compared to 83% without it. Results of the classification suitability test with the SVM algorithm by educators and students indicate a high level of agreement. The ICC Average Measures values are as follows: SVM classification is 0,979, assessment by non-science subject educators is 0,956, assessment by science subject educators is 0,991, assessment by non-science subject students is 0,982, and assessment by science subject students is 0,984. ICC testing consistently yields excellent results in non-science and science subjects, indicating that the assessments conducted by educators and students have a very high level of agreement

the ENHANCE OBJECT TRACKING ON AUGMENTED REALITY USING HYBRID CONVOLUTIONAL NEURAL NETWORK AND FAST CORNER DETECTION

Markerless augmented reality (AR) is utilized in applications that do not require anchoring to the real world and do not require the use of physical markers (fiducial markers). Augmented object displays not only float but also allow for the automatic placement of 3D augmented reality objects on flat surfaces to enhance realism in real time. There are two challenges that need to be addressed in Markerless AR systems: object tracking and registration, as well as the influence of light intensity. Therefore, the objective of this research is to propose the use of Convolutional Neural Networks (CNN) and Features from Accelerated Segment Test (FAST) corner detection for tracking or detecting objects in markerless augmented reality systems. Testing was conducted using three epoch schemes: 10, 50, and 100. The test results were measured using several parameters, including the execution time, testing loss, and testing accuracy. The test results indicated an improvement in the performance of the tested object detection. The accuracy testing results of using the CNN and FAST corner detection methods were superior to those of the CNN-only method and FAST corner detection alone, reaching 98%. However, this method increases the processing time for object detection. Thus, the processing time of the CNN without FAST corner detection was faster

Enhancing Prediction of Treatment Duration in New Tuberculosis Cases: A Comprehensive Approach with Ensemble Methods and Medication Adherence

Tuberculosis (TB) remains a significant global health problem, with treatment duration varying among patients. TB patients have difficulty following a long-term treatment regimen. After the final diagnosis is determined, it is necessary to know the predicted duration of treatment for a patient. By increasing patient compliance with taking medication, the percentage of TB patients will increase, and this can reduce cases of multi-drug resistant patients and dropouts. This study aims to build a prediction model for the duration of treatment for new cases of Pulmonary TB patients by adding medication compliance parameters using the ensemble method. The research methodology uses CRISP-DM. This study begins with identifying problems and objectives, collecting data, preprocessing and analyzing data, modeling, evaluating, and validating models. The results showed that adding medication compliance parameters can improve model performance. However, the results of model exploration with feature selection techniques and various ensemble methods have not shown good performance. The medication adherence parameters used in this study are the number of medications swallowed in Phase I and Anti-Tuberculosis drug compliance in Phase I. These parameters had never been used in previous studies. The prediction model can be used as an early warning for a patient. If a patient is predicted to have a treatment duration of more than six months, then the patient will receive stricter drug intake supervision. Thus, this proposed model is expected to help achieve the target of eliminating Tuberculosis in 2030 to reduce the death rate by 90% compared to 2019

Forecasting Indonesian Banking Stock Prices Using Prophet, XGBoost, and Ridge Regression: A Comparative Analysis

This study investigates the efficacy of Prophet, XGBoost, and Ridge Regression in forecasting stock prices of four major Indonesian banks—Bank Central Asia (BBCA.JK), Bank Negara Indonesia (BBNI.JK), Bank Rakyat Indonesia (BBRI.JK), and Bank Mandiri (BMRI.JK)—using daily historical data from January 2020 to March 2025, sourced from Yahoo Finance. The banking sector's volatility, evidenced by year-to-date declines ranging from 7.59% (BBCA) to 22.69% (BMRI) as of May 1, 2025, underscores the need for accurate predictive models. Performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), revealing Ridge Regression as the superior method, consistently achieving the lowest errors (i.e., MAE of 23.81 for BBNI.JK and RMSE of 55.75 for BBCA.JK). Prophet exhibited the highest errors, suggesting its seasonal focus is less suited to stock price unpredictability, while XGBoost performed moderately better but lagged behind Ridge Regression. The results highlight Ridge Regression’s effectiveness in handling multicollinearity and noise in financial data. Our discussions emphasize the importance of model selection based on data characteristics, with implications for investment decision-making in the Indonesian market. This research contributes to the field of computational finance by providing a comparative analysis that not only identifies Ridge Regression as a superior method for forecasting stock prices but also illuminates the limitations of popular models like Prophet and XGBoost in handling financial data's unique characteristics, guiding future model selection and development. Future research could explore hybrid models to enhance accuracy across varied market conditions, addressing the study’s 60-day forecasting horizon limitation

Comparative Analysis of LSTM and GRU for River Water Level Prediction

Accurate river water level prediction is essential for flood management, especially in tropical areas like Palembang. This study systematically analyzes the performance of two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for real-time water level forecasting using hourly rainfall and water level data collected from automatic sensors. A series of experiments were conducted by varying window sizes (10, 20, 30) and the number of layers (1, 2, 3) for both models, with model performance assessed using RMSE, MAE, MAPE, and NSE. The results demonstrate that both window size and network depth significantly influence prediction accuracy and computational efficiency. The LSTM model achieved its highest accuracy with a window size of 30 and a single layer, while the GRU model performed best with a window size of 20 and two layers. This work contributes by systematically analyzing hyperparameter configurations of LSTM and GRU models on hourly rainfall and water level time series for flood-prone regions, offering empirical insight into parameter tuning in recurrent neural architectures for hydrological forecasting. These findings highlight the importance of careful parameter selection in developing reliable early warning systems for flood risk management