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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Putri, Atsilah Daini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Library & Information Science

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Prediksi Pemilihan Warna Hijab Berdasarkan Tone Kulit Menggunakan Algoritma K-Nearest Neighbor (KNN) Putri, Atsilah Daini; Adrianto, Sopan; Mulyana, Dadang Iskandar
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1580

Abstract

Choosing the right hijab color that matches a person's skin tone is essential for many Muslim women to achieve a harmonious and attractive appearance. However, selecting a suitable color is often subjective and requires specific knowledge of color compatibility. This study aims to develop an automated prediction system that recommends hijab colors based on the user’s skin tone using the K-Nearest Neighbor (KNN) algorithm. KNN was chosen for its simplicity and effectiveness in classifying data based on proximity. The dataset used includes skin tone and corresponding hijab color data, collected through both primary and secondary sources. The classification process involves extracting color features from images and calculating Euclidean distances to determine the best hijab color prediction. The experimental results show that the KNN model provides fairly accurate predictions in recommending hijab colors based on skin tone. This system is expected to assist users in selecting appropriate hijab colors in a more objective and efficient manner.
Prediksi Motif Batik dengan Menggunakan Metode Gabor Filter Convolution Neural Network Bili, Yudisman Ferdian; Tundo; Sutisna, Nandang; Putri, Atsilah Daini; Yuliantoro, Dita Tri; Nurmayanti, Laily
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 3 (2025): JULI-SEPTEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i3.3798

Abstract

This research aims to develop a batik motif classification system by utilizing Convolutional Neural Network (CNN) and Gabor Filter, in order to increase accuracy in texture feature extraction. The batik dataset used goes through a preprocessing stage, which includes normalization and data augmentation. During training, the model was tested with 10,000 iterations, using the Adam optimizer and the Categorical Cross-Entropy loss function, and evaluated via a confusion matrix. Test results show accuracy reaching 87%, with a precision and recall value of 90% each, and an F1-score of 89%. This method has proven effective for classifying batik motifs and has the potential to be applied in the fields of education, textile industry and cultural preservation.
Co-Authors Bili, Yudisman Ferdian Dadang Iskandar Mulyana` Nurmayanti, Laily SOPAN ADRIANTO Sutisna, Nandang Tundo Yuliantoro, Dita Tri