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Menampilkan 1–4 dari 4 artikel
Integrating Multimodal Data Processing Techniques to Enhance User Experience Evaluation in Interactive Digital Platforms
Indonesian Journal of Infomatics
Vol 1
, No 1
(2026)
User experience (UX) evaluation plays a crucial role in understanding how users interact with digital platforms and in improving product design. Traditional UX evaluation methods, such as surveys and interaction logs, often rely on a single data source, which limits the depth of analysis. This study explores the integration of multimodal data processing techniques in UX research, aiming to enhance the accuracy and comprehensiveness of UX evaluations. By combining interaction logs, visual attenti...
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Machine Learning-Based Spatiotemporal Modeling for Detecting Disease Hotspots in Primary Care Data
Rachmatika, Rinna
; Desyani, Teti
; Khoirudin
Journal of Information Technology and Computer Science
Vol 1
, No 4
(2025)
Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in...
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A Systematic Review on Data Fusion Techniques for Agri-cultural Yield Prediction: Integrating Satellite Imagery with Climatic Data
Systematic Literature Review Journal
Vol 1
, No 4
(2025)
An answer to the worldwide need for solutions to food security, data fusion technology that combines climate data with satellite imagery greatly improves the accuracy of agricultural yield predictions; this study intends to examine the advancements, methods, and key contributions of this area. By sifting through 62 papers pulled from Scopus, this research employs the SLR methodology. Document type, data source, open access, subject area, and year of publication (2020–2024) are some of the catego...
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Feature Extraction Using Discrete Wavelet Transform and Zero Sequence Current for Multi-Layer Perceptron Based Fault Classification
International Journal of Engineering and Applied Science
Vol 2
, No 4
(2025)
Application of Multi-Layer Perceptron neural network to fault classification in high-voltage transmission lines is demonstrated in this paper. Different fault types on protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of Discrete Wavelet Transform energy features combined with zero sequence current magnitude as input features for neural network classifier. The proposed method uses eight extracted features to learn hidden relationship...
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