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All Journal Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika
Dwi Sulistya Kusumaningrum
Buana Perjuangan Karawang University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Implementation U-Net for Semantic Segmentation and Perspective Transformation in Floor Tile Virtual Try-On Systems Fahrul Rozi; Ayu Ratna Juwita; Yusuf Eka Wicaksana; Deden Wahiddin; Sutan Faisal; Dwi Sulistya Kusumaningrum
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/68r7wj29

Abstract

Conventional floor tile VTO systems rely on printed catalogs, physical samples, or marker-based AR, making it difficult for consumers to visualize ceramic products in real rooms. This study proposes a markerless VTO framework integrating U-Net floor segmentation and homography-based perspective transformation. The model was pre-trained on 5,285 SUN RGB-D images and fine-tuned on 1,338 customer-room images from PT Concord Industry, divided into training, validation, and independent test sets using a 70:20:10 ratio. Segmentation was evaluated using IoU, Dice Coefficient, Pixel Accuracy, Precision, and Recall. Perspective alignment was evaluated using four-corner Mean Angular Error (MAE) on two room images representing conditions with and without excessive furniture, while system functions were validated through black-box testing. The fine-tuned U-Net with a ConvNeXt-V2-Huge encoder achieved an IoU of 0.9483, a Dice coefficient of 0.9671, a pixel accuracy of 0.9891, a precision of 0.9675, and a recall of 0.9702. The perspective module achieved an average MAE of 5.92° and produced visually coherent tile alignment. These results demonstrate the framework's potential to support realistic floor tile visualization and reduce uncertainty during product selection.
Classification Model of Public Sentiments About Electric Cars Using Machine Learning Nurul Romadoni; Amril Mutoi Siregar; Dwi Sulistya Kusumaningrum; Tatang Rohana
Scientific Journal of Informatics Vol. 11 No. 2: Regular Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.1309

Abstract

Purpose: This research compared the accuracy level of six algorithms based on the ROC method and the Confusion Matrix evaluation on data regarding public sentiments towards electric cars. Methods: Data collection was conducted for data sourced from TikTok. Next, the data underwent text preprocessing (data cleaning and case folding) and text processing (stemming, tokenizing, stopword removal, word frequency, word relation, TF-IDF, scoring, and labeling). Modeling was then conducted using supervised (labeled) algorithms consisting of the Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, K-Neighbor, and Logistic Regression. Finally, an evaluation was conducted (confusion matrix and ROC). Result: The results revealed that the Decision Tree algorithm with the Confusion Matrix and ROC evaluation obtained the highest result of 87%. The algorithm with the lowest result is KNN, which has an accuracy of 56%. The classification result for the neutral sentiment has a percentage of 57.1%, followed by negative sentiment at 26.8% and positive sentiment at 16.1%. The KNN algorithm is suitable for large and low-dimensional data, SVM is suitable for data with many features and clear separation between classes, and Naive Bayes is efficient for large datasets with many low-quality features. Additionally, the Random Forest algorithm could overcome overfitting and unbalanced data. Logistic regression is also suitable for linear data without assuming a certain distribution. The Decision Tree algorithm is good for complex data as it provides a visual explanation of predictions. In this study, the Decision Tree algorithm obtained high results because it has the best characteristics and is a linear technique. Novelty: This study found that based on the ROC method and the Confusion Matrix evaluation conducted, the Decision Tree algorithm is more accurate than the other algorithms studied.
Co-Authors Amril Mutoi Siregar Ayu Ratna Juwita Deden Wahiddin Fahrul Rozi Nurul Romadoni Sutan Faisal Tatang Rohana Yusuf Eka Wicaksana