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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika The Scientific Journal of Information Systems
Nurul Badriah
Universitas Utpadaka Swastika
Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Environmental Science

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Analysis of Priority-Based Communication Feature Using UI/UX Design Thinking Model: A Case Study of WhatsApp Nurul Badriah; Sony Veri Shandy; Fajar Muttaqi; Moh Alfaujianto
Scientific Journal of Information System Vol. 4 No. 1 (2026): Scientific Journal of Information System
Publisher : Universitas Utpadaka Swastika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70429/sjis.v4i1.333

Abstract

The rapid growth of instant messaging applications has significantly transformed the way individuals communicate in both personal and professional contexts. However, the increasing volume of incoming messages often leads to information overload, making it difficult for users to distinguish between important and less relevant conversations. This study aims to design and implement a priority-based communication feature using a Design Thinking approach, with WhatsApp as a case study. Unlike conventional chronological message ordering, the proposed system allows users to manually define priority contacts through a “High Priority Mode” feature, enabling important conversations to be automatically positioned at the top of the chat list. In addition, the system introduces visual differentiation in notifications to highlight messages from priority contacts. A prototype interface is developed to support intuitive configuration and improve usability. The results indicate that the proposed feature enhances message visibility, reduces the risk of overlooking important communications, and improves overall user efficiency. This study demonstrates that a user-centered, rule-based approach can provide a practical and effective solution for managing communication priorities in messaging applications.
Deep Learning-Based Classification of Cikadu Batik Motifs Using ResNet50 and MobileNetV2 Rizki Ripai; Fajar Mahardika; Fazar Sidik; Nurul Badriah; Angga Maulana Purba
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16368

Abstract

Batik motif recognition is essential for cultural heritage preservation and the digitization of traditional Indonesian textile knowledge. This study proposes a deep learning-based framework for the automatic classification of Cikadu Batik motifs from Tanjung Lesung, Banten — a regionally distinct batik pattern that has not been systematically studied in prior computational literature. Two convolutional neural network (CNN) architectures were implemented and comparatively evaluated under identical experimental conditions: ResNet50, a high-capacity model employing residual skip connections, and MobileNetV2, a lightweight model utilizing depthwise separable convolutions and inverted residual blocks. A curated dataset of 2,500 images spanning five motif classes was constructed through collaboration with local batik artisans, preprocessed via resizing (224×224), pixel normalization, and augmentation (rotation, zoom, horizontal flip, brightness adjustment), and partitioned using a stratified 70:15:15 split. Both models were trained with transfer learning from ImageNet weights, using the Adam optimizer (lr=0.0001), categorical cross-entropy loss, batch size of 32, and early stopping over 30 epochs. Model evaluation employed accuracy, precision, recall, F1-score, AUC-ROC, inference time, and parameter count. ResNet50 achieved 95.51% accuracy, 95.67% precision, 95.34% recall, 95.50% F1-score, and 99.56% AUC-ROC, with an inference time of 18.2 ms and 25.64 million parameters. MobileNetV2 achieved 92.13% accuracy, 92.28% precision, 91.98% recall, 92.13% F1-score, and 98.89% AUC-ROC, with an inference time of 8.7 ms and only 3.54 million parameters — approximately 7× lighter and 2× faster. These results empirically establish a clear accuracy-efficiency trade-off, with ResNet50 favored for accuracy-critical server-based systems and MobileNetV2 better suited for real-time mobile deployment. This study constitutes the first published benchmark for deep learning-based Cikadu Batik classification and provides a principled basis for architecture selection in regional batik recognition applications
Co-Authors Angga Maulana Purba Fajar Muttaqi Fazar Sidik Mahardika, Fajar Moh Alfaujianto Rizki Ripai Sony Veri Shandy