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All Journal JISA (Jurnal Informatika dan Sains) INOVTEK Polbeng - Seri Informatika
Ayu Ratna Juwita
Buana Perjuangan Karawang University
Computer Science & IT Control & Systems Engineering Electrical & Electronics 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.
Air Quality Classification Using Naive Bayes Algorithm With SMOTE Technique Based on ISPU Data Alya Febriyanti Fadhilah; Ayu Ratna Juwita; Yusuf Eka Wicaksana; Tohirin Al Mudzakir
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2181

Abstract

Air pollution in DKI Jakarta is an important issue and has a negative impact on public health. This study applies the naive Bayes algorithm to classify air quality, Utilizing the SMOTE technique effectively addresses the issue of data imbalance. The data analyzed came from air pollution index data from 2022 to 2024, taken from five air monitoring stations in Jakarta. The analysis process was carried out following the CRISP-DM stages, starting from understanding the problem to evaluating the model. The results showed that SMOTE succeeded in increasing prediction accuracy in fewer classes. Without SMOTE, the model accuracy reached 90% but appeared biased towards fewer classes, with a recall value of only 0.75 and a precision of 0.62. While SMOTE, the model accuracy became 88%, with a precision value of 0.86, recall 0.87, and f1-score 0.87, which showed more balanced results across classes.
Co-Authors Alya Febriyanti Fadhilah Deden Wahiddin Dwi Sulistya Kusumaningrum Fahrul Rozi Sutan Faisal Tohirin Al Mudzakir Yusuf Eka Wicaksana