International Journal for Applied Information Management

Clustering Students Based on Academic Performance and Social Factors: An Unsupervised Learning Approach to Identify Student Patterns

Felinda Rahma, Siti Zayyana Ulfah — Mon, 01 Sep 2025 00:00:00 +0000

This study explores the application of K-Means clustering, an unsupervised learning method, to group students based on academic performance and social factors. The primary objective is to uncover hidden patterns among students by analyzing academic scores in mathematics, reading, and writing, as well as demographic attributes including gender, ethnicity, parental education level, and lunch type. Data preprocessing steps, such as normalization and one-hot encoding, were conducted to prepare the dataset for clustering. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, with K=3 selected for its balance between cluster quality and interpretability. The clustering results revealed three distinct groups of students: low performers, average performers, and high performers. These clusters were visualized using PCA and t-SNE, which showed clear separation and internal consistency. Interpretation of the clusters suggests that social factors may influence academic outcomes, with students from disadvantaged backgrounds more likely to fall into the lower-performing group. The study highlights the importance of data-driven approaches in understanding student diversity and designing targeted interventions. Furthermore, this research underlines the potential of clustering techniques to inform educational strategies by identifying students' needs more precisely. However, limitations include reliance on academic and basic demographic variables, and sensitivity of the K-Means algorithm to outliers and the predefined number of clusters. Future research should incorporate additional factors such as emotional well-being and learning preferences to develop more comprehensive educational models. Overall, the study demonstrates that clustering can serve as a valuable tool for enhancing the effectiveness and equity of educational programs

Comparative Study of Traditional and Modern Models in Time Series Forecasting for Inflation Prediction

Henderi Henderi, Sofa Sofiana — Mon, 01 Sep 2025 00:00:00 +0000

Time series forecasting plays a crucial role in economic analysis, particularly in anticipating inflation and policy planning. This study compares the performance of seven different time series forecasting models, namely ARIMA, SARIMA, ETS, Prophet, LSTM, XGBoost, and TCN, in predicting inflation rates. Each model was applied to four years of inflation data to test its accuracy and reliability. The evaluation was conducted using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) to measure the performance of each model. The results indicate that deep learning models, particularly LSTM and TCN, achieved the highest accuracy with the lowest MSE and RMSE values, specifically 0.0008 and 0.0015 for LSTM, and 0.0007 and 0.0013 for TCN, indicating their capability in capturing complex temporal patterns. Traditional models such as ARIMA and SARIMA, while effective in capturing trends and seasonality, showed limitations in handling non-linear patterns and sudden changes, with MSE and RMSE values of 0.0012 and 0.0024 for ARIMA, and 0.0011 and 0.0023 for SARIMA, respectively. ETS, with the highest MSE and RMSE values of 0.0013 and 0.0025, demonstrated limitations in dealing with the complexity of inflation data. XGBoost also showed good performance with MSE and RMSE values of 0.0009 and 0.0018, combining flexibility and robustness in handling complex data. Prophet achieved an MSE of 0.0010 and RMSE of 0.0020, indicating that while it effectively captures seasonal trends, there is room for improvement in handling rapid inflation increases. This research provides in-depth insights into the strengths and weaknesses of each model, as well as recommendations for practical applications in inflation forecasting. By presenting a comprehensive comparative analysis, this study aims to assist researchers and practitioners in selecting the most suitable forecasting model for their specific needs

Anomaly Detection in Corporate Balance Sheets for Financial Risk Assessment Using Isolation Forest from 2020 to 2023

Khabib Nugroho, Turino — Mon, 01 Sep 2025 00:00:00 +0000

This study aims to evaluate corporate financial risk by analyzing changes in balance sheet accounts from 2020 to 2023 using anomaly detection methods. Employing the Isolation Forest algorithm with a 5% contamination rate, we identified a consistent 3,264 anomalies each year out of a total of 65,296 entries, focusing on key accounts, including Accumulated Depreciation (61 anomalies), Additional Paid-In Capital (17 anomalies), Accounts Payable (9 anomalies), and Accounts Receivable (6 anomalies). These anomalies highlight areas of potential financial risk associated with asset valuation, capital structure, and cash flow management. The steady presence of anomalies suggests underlying, possibly systemic factors influencing financial stability. Findings indicate that significant fluctuations in Accumulated Depreciation and Additional Paid-In Capital may impact the company’s asset valuation and investor perceptions, while irregularities in Accounts Payable and Accounts Receivable suggest short-term liquidity risks. Recommendations include regular monitoring of high-risk accounts, trend analysis to identify cyclical patterns, and examining correlations with macroeconomic conditions to understand root causes. Future research should explore advanced anomaly detection models and integrate real-time detection capabilities to enhance proactive financial risk management. This study demonstrates the effectiveness of anomaly detection in identifying critical financial risks, supporting improved decision-making and corporate resilience

Predicting Future Electric Vehicle (EV) Sales: A Time Series Forecasting Approach Using Historical EV Sales Data

Bhavana Srinivasan — Mon, 01 Sep 2025 00:00:00 +0000

Accurate forecasting of Electric Vehicle (EV) sales is essential for supporting strategic decisions by policymakers, manufacturers, and investors amid the global shift toward sustainable transportation. This study compares the performance of two time series models, AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) using historical EV sales data from 2010 to 2023. The ARIMA model, which is suited for linear trend projection, forecasts continued exponential growth, estimating sales to surpass 103 million units by 2025. In contrast, the LSTM model, known for capturing non-linear and complex patterns, projects a more moderate trend, with sales peaking at around 11.5 million units in 2022 before gradually declining. Evaluation using Mean Squared Error (MSE) shows that LSTM significantly outperforms ARIMA, achieving a lower error value (2.23 × 10¹⁴ vs. 4.44 × 10¹⁵), indicating superior predictive accuracy. These results suggest that while ARIMA may be effective for short-term forecasting in stable markets, it can lead to overestimations in more dynamic environments. LSTM, with its ability to learn complex temporal dependencies, presents a more flexible and realistic tool for long-term planning in the evolving EV sector. The study contributes methodologically by offering a comparative analysis of two popular forecasting techniques and practically by guiding stakeholders on model selection. However, it is limited by its reliance on historical data and exclusion of external variables such as energy prices or policy changes. Future work should incorporate hybrid models and multi-source data to enhance forecasting robustness in the fast-changing EV market

Deciphering Weather Dynamics and Climate Shifts in Seattle for Informed Risk Management

Nevita Ramadani, Agi Nanjar — Mon, 01 Sep 2025 00:00:00 +0000

This research presents a comprehensive analysis of the weather characteristics in the city of Seattle over the past few years. Through a detailed understanding of the distribution of maximum and minimum temperatures, the findings indicate significant fluctuations between summer and winter seasons. The increasing temperature trend from year to year provides insights into the potential climate changes in the region. Additionally, rainfall data reveals consistent increases over time, particularly during the winter, with significant impacts on the environment and daily life. Wind speed stability throughout the year provides insights into wind dynamics, influencing the transportation and maritime sectors. Annual averages of rainfall, sunshine hours, snowfall, and foggy days provide foundational information for long-term planning and risk management in Seattle. The percentage of rainy and clear weather throughout the year gives a comprehensive overview of the seasons, facilitating daily activity planning. Through these findings, the research aims to make a significant contribution to the understanding of the general public, natural resource managers, and economic sectors regarding the potential impacts and opportunities arising from future weather changes. It is hoped that this research can serve as a solid foundation in efforts to mitigate and adapt to the continually changing weather dynamics in the city of Seattle