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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Computing Insight: Journal of Computer Science
Muhdhor, Umar
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Computer Science & IT

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Evaluation of MobileNet-Based Deep Features for Yogyakarta Traditional Batik Motif Classification Muhdhor, Umar; Yohannes
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15668

Abstract

Batik is an Indonesian intangible cultural heritage that embodies profound philosophical, aesthetic, and cultural values. Yogyakarta batik motifs, such as Parang, Kawung, and Truntum, reflect Javanese wisdom and identity through distinctive geometric and floral patterns. In the digital era, artificial intelligence based image processing provides a promising approach to support the preservation and automatic recognition of traditional batik motifs. The objective of this study is to evaluate the effectiveness of MobileNet-based feature extraction combined with Support Vector Machine (SVM) classification for Yogyakarta batik motif recognition. The proposed method employs MobileNet as a convolutional feature extractor and SVM as a decision model to separate motif classes in the feature space. Experiments were conducted on 685 batik images consisting of three motif classes, with class imbalance handled using Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using weighted accuracy, precision, recall, and F1-score under five-fold cross validation. The results show that MobileNetV3Large achieved the best performance with a weighted accuracy of 98.36%, followed by MobileNetV3Small and MobileNetV4Small. Statistical significance tests using the Friedman test and Wilcoxon signed-rank analysis confirm that the performance differences among the evaluated models are statistically significant. These findings indicate that MobileNetV3 architectures provide robust and discriminative feature representations for batik motif classification on limited yet structured datasets. This study contributes a validated MobileNet–SVM framework for batik recognition and supports ongoing efforts in the digital preservation of Indonesia’s cultural heritage. Future work will explore larger motif sets and cross-dataset evaluation to further improve generalization performance.
EKSTRAKSI BERITA HOAX PADA TURN BACK HOAX BERBASIS PENDEKATAN TF-IDF & COSINE SIMILARITY HIDAYAT, WILLIAM; Ong, Jesen; Muhdhor, Umar; Irsyad, Hafiz; Rahman, Abdul
Computing Insight : Journal of Computer Science Vol 7 No 2 (2025)
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/comp_insight.v7i2.26678

Abstract

Perkembangan teknologi telah membawa perubahan besar dalam kehidupan masyarakat. Salah satunya akses terhadap berita dan artikel yang semakin mudah, dan bebas. Namun, fenomena ini juga memunculkan permasalahan serius, yaitu penyebaran berita hoaks yang sangat cepat dan masif. Penelitian ini bertujuan untuk mengekstraksi informasi penting dari artikel hoaks yang dipublikasikan di situs TurnBackHoax.id menggunakan pendekatan text mining berbasis TF-IDF dan cosine similarity. Data artikel hoaks diperoleh melalui teknik web scraping dengan pustaka Python seperti requests dan BeautifulSoup, diikuti oleh tahap prapemrosesan teks yang meliputi case folding, penghapusan tanda baca, angka, serta stopwords, dan stemming. Teks yang telah diproses kemudian direpresentasikan dalam bentuk vektor numerik menggunakan metode TF-IDF untuk menentukan bobot kata berdasarkan frekuensi dan kelangkaannya dalam korpus. Selanjutnya, cosine similarity digunakan untuk mengukur tingkat kemiripan antar dokumen, sementara kata kunci diekstraksi berdasarkan bobot TF-IDF tertinggi. Visualisasi Word Cloud juga diterapkan untuk menggambarkan kata-kata dominan secara visual. Berdasarkan hasil evaluasi, metode yang digunakan dalam penelitian ini berhasil mencapai tingkat ketepatan sebesar 93,15%, menunjukkan efektivitas pendekatan TF-IDF dan Cosine Similarity dalam menganalisis dan mengelompokkan artikel hoaks. Hasil penelitian menunjukkan bahwa pendekatan ini efektif dalam mengidentifikasi kata kunci penting dan mengelompokkan artikel hoaks berdasarkan kemiripan konten. Kata kunci : Cosine Similarity,Hoaks,Text Mining, TF-IDF, kata kunci
Co-Authors Abdul Rahman Hafiz Irsyad Hidayat, WIlliam Ong, Jesen Yohannes, Yohannes