Journal of Informatics Development

Analysis and Visualization of Data on the Impacts of Covid-19 Globally and Locally

Muhammad Iqbal — 2025-04-24

The COVID-19 pandemic has had a profound impact on multiple aspects of human life, including food supply, mental health, and healthcare service management. This study aims to examine these impacts by applying a combination of data analysis methods such as data preprocessing, exploratory data analysis (EDA), predictive algorithms, and data visualization. The datasets utilized include information related to mental health conditions, food security, and COVID-19-related health statistics. The findings indicate a significant increase in mental health issues, such as anxiety and depression, as well as disruptions in food supply chains that have adversely affected global food security. Moreover, data visualization has proven to be a valuable tool in supporting decision-making processes in healthcare management. However, most implementations remain limited in scope and are often confined to internal agency use. Therefore, this study recommends further development in integrating data sources, enhancing the application of predictive algorithms, and optimizing data visualization for more effective decision-making in managing global health crises.

Integration of AHP and TOPSIS Methods in Decision Making Models to Identify High Achieving Students

Masud Hermansyah — 2025-04-24

Selection of outstanding students is an essential process in education to ensure that students with high achievements receive appropriate recognition and guidance. However, this process often suffers from subjectivity and the absence of a structured decision-making system. This study aims to develop an objective and accountable decision support model by integrating the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The AHP method is used to determine the weight of each selection criterion, while the TOPSIS method is used to rank students based on their proximity to the ideal solution. The study involved 10 student candidates and assessed them based on 6 criteria, including academic performance, discipline, extracurricular activities, and religious values. The results show that the integrated AHP-TOPSIS model successfully identifies students with the highest preference values as the most outstanding, while those with lower values are recommended for further coaching. The model demonstrates its effectiveness in supporting accurate, data-driven student selection at MIMA 37 Sunan Kalijogo Ambulu.

Product Demand Forecasting in E-Commerce with Big Data Analytics: Personalization, Decision Making and Optimization

Cahyasari Kartika Murni — 2025-04-24

This study explores the role of Big Data in forecasting product demand in the e-commerce sector through the application of machine learning and time series methods. A quantitative descriptive approach is used, involving data collection, preprocessing, analysis, and model evaluation. Forecasting techniques applied include ARIMA for time series prediction and XGBoost for supervised learning to identify key demand factors. Model performance is evaluated using accuracy metrics such as RMSE, MAE, and MAPE. The results indicate that the XGBoost model provides the highest forecasting accuracy at 89%, while the ARIMA model achieves 78%. These findings demonstrate that Big Data significantly supports strategic decision-making in e-commerce by enhancing personalization, optimizing inventory, and enabling data-driven marketing strategies.

Application of Three-Parameter Logistic (3PL) Item Response Theory in Learning Management System (LMS) for Post-Test Analysis

David Juli Ariyadi — 2025-04-24

In the edu-digital era, Learning Management Systems (LMS) have become pivotal in delivering and managing education. However, many LMS platforms lack sophisticated analytical tools to evaluate the quality of post-test assessments. This research explores the application of Item Response Theory (IRT) as a psychometric model integrated into an LMS to enhance post-test analysis. By leveraging IRT, the system can evaluate item difficulty, discrimination, and guessing parameters, providing more accurate insights into both test quality and student ability levels. The study implements a three-parameter logistic (3PL) IRT model and integrates it into an LMS prototype. Empirical data from real student post-tests are analyzed to validate the model's effectiveness. The results demonstrate that IRT-based analysis significantly improves the assessment feedback mechanism, allowing educators to identify poorly performing items, adapt instructional strategies, and personalize learning paths. This research contributes to the development of intelligent assessment systems in educational technology, promoting more effective, fair, and data-driven evaluation processes.

AnalysisSentimentAlun-Alun LumajangReviewusingSupportVector Machine

Maysas Yafi Urrochman — 2025-04-24

Alun-Alun Lumajangis one of room the public that becomes center activity community and tourists . Perception public to place Thiscan measured through analysis sentiment to reviews available on digital platforms such as Google Maps. Research This aiming For classifysentiment review the use Support Vector Machine (SVM) method , one of the effective machine learning algorithms Fortask classification text . Data used in the form of review collected text fromGoogle Maps, then through pre-processing data such as cleaning text , tokenization , and deletion stopword . Sentiment label determined manually to be three categories : positive, negative , and neutral . Next , the data is extracted use TF-IDF technique before classified using SVM. Research results showthat SVM algorithm is capable of classify sentiment with level high accuracy , making it proper method For analysis opinion public based on text . Findings This expected can give input for government area in increase quality services and management room public in Lumajang.