The growing accessibility of varied health data requires the creation of efficient and practical techniques for deriving actionable insights, particularly for the early identification of severe health issues. This study tackles the issue of recognizing stunting—a disorder with enduring health consequences—among children under five by employing Artificial Neural Networks (ANN) with hyperparameter optimization by GridSearchCV. The dataset, sourced from Kaggle, comprises 121,000 records detailing age, gender, height, and nutritional status according to WHO standards. Critical hyperparameters, including dropout rate, batch size, and epochs, were optimized using a five-fold cross-validation approach within GridSearchCV, ensuring a robust model that reduces overfitting and generalizes well to new data. The findings demonstrate a notable performance improvement, as the optimized ANN model attained an accuracy of 99%, exceeding the baseline model's 98%. Enhancements in accuracy, recall, and F1-score across the four stunting classifications—normal, stunted, severely stunted, and tall—underscore the improved specificity and sensitivity of the optimized model. This research demonstrates the efficacy of hyperparameter tuning in ANN for stunting prediction, offering a reliable tool for early intervention in malnutrition management.

Quality of Service (QoS) is a fundamental aspect of modern computer networks, directly influencing performance and user experience. Key parameters such as latency, throughput, packet loss, and jitter play crucial roles in determining network efficiency. Traditional QoS management approaches, often rule-based or heuristic-driven, lack adaptability to dynamic network conditions. This study explores the application of machine learning techniques to predict QoS using historical network data, enabling proactive network optimization. We employ multiple predictive models, including linear regression, random forest, and deep learning, to analyze network performance trends and forecast QoS degradation. Experimental results demonstrate that machine learning significantly enhances prediction accuracy compared to conventional methods, allowing for more effective resource allocation and congestion control. The findings highlight the potential of data-driven approaches in real-time network management, reducing latency fluctuations and improving service reliability. Moreover, deep learning models outperform traditional statistical techniques in recognizing complex patterns within network data, making them a promising solution for next-generation network optimization. The proposed methodology not only improves predictive accuracy but also offers a scalable framework for automated QoS management in cloud computing, IoT, and 5G environments. Future work will focus on refining model generalization across diverse network conditions and integrating federated learning for privacy-preserving QoS predictions. This research underscores the transformative role of machine learning in enhancing network service quality and operational efficiency.

Loan default prediction is an important aspect of risk management in financial institutions. Accurate prediction models enable banks and lending organizations to mitigate risks, allocate resources effectively, and optimize decision-making processes. This study investigates the application of decision tree algorithms in predicting loan default risk in the financial sector. Decision trees are renowned for their interpretability, adaptability to non-linear data, and ability to handle missing values, making them a valuable tool in credit risk analysis. Using a dataset consisting of borrower profiles, credit scores, income levels, and payment history, the model identifies key predictors that influence default outcomes. The study uses the C4.5 decision tree model, which will demonstrate that decision trees achieve high prediction accuracy and offer a transparent decision-making framework, enhancing their applicability in real-world scenarios. Furthermore, the paper highlights the implications of these findings for financial institutions, emphasizing the scalability and cost-effectiveness of the model. By integrating decision tree-based models into existing risk assessment systems, lenders can proactively manage loan portfolios and reduce default rates. Future research directions are proposed to explore hybrid approaches that combine decision trees with advanced combined methods to enhance predictive capabilities. The potential of decision tree algorithms in transforming credit risk assessment and supporting more accurate data-driven financial decision-making processes

To meet the ever-changing market demands, more efficient strategies are required due to the complexity of today's software development. Because it can combine Agile concepts with organizational structures to manage large-scale projects involving multiple teams and departments, the Scaled Agile Framework (SAFe) has been widely adopted. This research investigates how Flow Metrics from the SAFe framework can be used as a tool to improve productivity, efficiency and alignment of the software development process. This research examines how measurements such as flow velocity, flow efficiency, flow time, and flow load can be used to pinpoint bottlenecks, streamline processes, and improve the value delivered to clients. This research uses a qualitative methodology to examine the use of Flow Metrics in two interdependent Program Increments (PIs) by combining interviews with Agile practitioners and a literature survey. The analysis highlights how the Continuous Delivery (CD) Pipeline, backlog synchronization, and program increment planning—three critical components of SAFe—interact with each other. By highlighting the importance of metrics-based performance evaluation, collaborative planning, and continuous improvement, the findings of this research are intended to offer a useful foundation for businesses looking to implement SAFe for large-scale software development. This research advances a more comprehensive understanding of how SAFe and Flow Metrics can facilitate increasingly complex software development while guaranteeing adaptability to changing business needs.

: In the medical world, one of the biggest contributors to death in the world is heart disease. Early detection of the risk of heart disease can increase the chances of recovery and reduce mortality. This research applies the Support Vector Machine (SVM) algorithm to identify the risk of heart disease using Electrocardiogram Signals. The ECG data used was taken from a public database that contained a record of information on the electrical activity of the heart of patients with various heart health conditions. The Support Vector Machine algorithm is applied to classify ECG signals into 2 main classes, namely normal conditions and at-risk conditions. Several methods in data processing, including data normalization and feature selection are used to improve the accuracy and success of the model. The results of the evaluation with this method resulted in accuracy, precision, recall and also F1-score showed that the modeling of this algorithm produced a fairly good classification, with an accuracy of more than 90% in the identification of heart disease risk. This study shows the potential use of this algorithm in automatically detecting the risk of heart disease based on ECG signals, which can be a tool in medical diagnosis. The results show that implementing the SVM strategi with the RBF kernel appears to be a very easy execution when compared to the direct part. An important component that affects the adequacy of an SVM strategy is the parameters of the section and the way the information is handled.

The investment awareness among the Indonesian populace is rising, particularly during the Covid-19 pandemic. Mutual funds are the most prevalent investment vehicle due to their accessibility, requiring minimal cash, and offering competitive returns, as they are managed by seasoned investment professionals. This is due to the relative ease of investing in this instrument with accessible capital and competitive returns, as fund management is conducted by seasoned investment managers. Conversely, the rapid advancement and proliferation of technology present new challenges for firms in the financial sector, particularly in banking and financial technology, as they strive to innovate and provide convenient services that cater to the needs of customers and investors. This research seeks to develop Enterprise Architecture for the integration of Mutual Fund services into digital channels via mobile banking, utilizing the TOGAF framework as the foundational design approach, supplemented by SWOT analysis to assess the strategic position and market potential. The research employed a qualitative methodology utilizing the ArchiMate program to illustrate diagrams in Enterprise Architecture (EA) across the public banking sector. The research indicated that architectural design can facilitate improved data access and enhance adaptability to technology and market advancements, hence removing inefficiencies and streamlining the review process. In conclusion, the application of EA in the incorporation of mutual fund services into the digital banking platform will optimize the company's performance processes to attain objectives, while also enhancing agility in evaluating technological risks to facilitate the monitoring, metrics, and analysis of information technology, thereby ensuring the achievement of business goals.

In the era of Society 5.0, information security is an important issue along with the increasing use of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and big data. ISO 27001 acts as a globally recognized standard framework for managing information security. The ISO 27001 standard provides a systematic framework for identifying, assessing, and managing information security risks so as to ensure the integrity, confidentiality, and availability of data in an organization. This research aims to evaluate the implementation of ISO 27001 as an information security solution in the Society 5.0 era through a systematic literature review. Using the Systematic Literature Review (SLR) method, this research collects and analyzes relevant literature to identify benefits, challenges, and recommendations related to the application of ISO 27001 in an era of increasingly integrated technology. The results showed that the implementation of ISO 27001 in the Society 5.0 era proved to make a significant contribution in improving organizational information security. This is done through a PDCA (Plan-Do-Check-Act) approach that integrates information security policies into business processes, strengthens risk management, technology infrastructure, and human resource competencies. In conclusion, the implementation of ISO 27001 in the Society 5.0 era not only improves information security, but also supports the achievement of operational efficiency and organizational sustainability amid rapid technological developments.

Rainfall plays a critical role in the global water and energy cycle, influencing surface water availability and recharge processes both spatially and temporally. Traditional rainfall data collection using ombrometers provides accurate live data, but often faces the challenge of missing data due to equipment failure or transmission, especially in agencies such as BMKG. This problem of missing data greatly impacts hydrological analysis and requires an effective data recovery process through imputation. This study aims to assess the accuracy of rainfall data imputation techniques using the Inverse Distance Weighting (IDW) and Artificial Neural Network (ANN) methods. In this study, we utilize data from 31 observation stations in Semarang City for more than three decades. The findings show that the spatial distribution of rainfall is variable and exhibits a cyclic pattern despite fluctuations. The ANN model performed very well in overcoming missing data, especially in the dry season with an RMSE of 0.9489 and a coefficient of determination (R2) of 0.9926. By demonstrating the superiority of the ANN model in accurately predicting rainfall, this study offers an effective approach to improve the quality of BMKG climate data. This is expected to support disaster mitigation decisions and sustainable development planning. This approach demonstrates that the selection of an appropriate method is critical for accurate and reliable analysis of rainfall time series data. In addition to making an academic contribution, these results also provide an alternative imputation method for various time series.

In today's digital era, social media data provides valuable insights into public opinion. This study implements the Graph Regularized Probabilistic Latent Semantic Analysis (GPLSA) method to analyze topics from social media data surrounding the 2024 Indonesian Presidential Election (Pemilu), as well as to evaluate the efficiency of the Probabilistic Latent Semantic Analysis (PLSA) algorithm. The research stages include collecting social media data on presidential debates and elections, text pre-processing, and applying the GPLSA method to identify main topics. The analysis results show that PLSA without graph achieved a topic coherence score of 0.653, indicating good consistency, while GPLSA decreased to 0.5, suggesting that the addition of graph regularization did not significantly enhance coherence. Additionally, PLSA without graph achieved a perplexity score of 12.138, indicating good predictive capability, while GPLSA increased to 12.511, showing that graph regularization did not improve the prediction of new words. PLSA without graph also produced topics relevant to election issues, while GPLSA generated topics influenced by graph regularization, though without significant improvement in topic quality. Sentiment analysis of social media posts provides insights into public responses to debates and election issues. Validation of the GPLSA model ensures relevant topic representation. This research contributes to the development of text analysis methods and offers valuable information for elections and democratic participation. These results can be utilized by stakeholders to make more strategic and informed decisions.

This research explores public discourse on financial policies by analyzing tweets mentioning the keyword 'Kemenkeu' (Ministry of Finance). Using Latent Semantic Analysis (LSA), the study examined 10,099 tweets to uncover key topics that reflect public sentiment toward the Ministry’s policies. Preprocessing steps, such as stopword removal and stemming with Sastrawi, were essential to ensure the effectiveness of the analysis. The results revealed three main topics: Finance and Budget, Salaries and Employee Welfare, and Excise and Customs Regulations. These insights provide a better understanding of public opinion on financial issues and highlight the importance of proper text preprocessing in topic modeling. This approach demonstrates how LSA can be used as a tool for analyzing large-scale social media data, offering valuable input for policymakers. Future research could expand on this by using more advanced models or larger datasets to gain deeper insights.