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All Journal Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Intelligent Computing and Advanced Data Science
Novriza Rahayu
Universitas Satya Terra Bhinneka
Computer Science & IT

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Klasifikasi Jenis Kelamin Berdasarkan Citra Mata Menggunakan Fitur HSV dan HOG Dengan Algoritma SVM Novriza Rahayu; Marta Tabita Anggi Silitonga; Dimas Jordan; Salwa Nabila; Andre Pratama
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 1 (2026): Januari
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i1.423

Abstract

Abstrak: Identifikasi jenis kelamin melalui analisis citra mata menjadi pendekatan yang relevan dalam aplikasi forensik dan keamanan, khususnya dalam situasi ketika identifikasi wajah tidak dapat dilakukan. Penelitian ini mengembangkan sistem klasifikasi jenis kelamin berdasarkan citra mata menggunakan kombinasi fitur HSV dan HOG dengan algoritma Support Vector Machine (SVM). Permasalahan yang diangkat adalah bagaimana mengekstraksi fitur citra mata secara representatif dan mengoptimalkan akurasi klasifikasi jenis kelamin melalui pendekatan metode yang tepat. Metode penelitian ini mencakup tahap pengumpulan data, pra-pemrosesan citra, ekstraksi fitur dengan memanfaatkan HSV untuk memperoleh karakteristik warna serta HOG untuk mengidentifikasi karakteristik bentuk, penerapan SVM sebagai algoritma klasifikasi, serta evaluasi model guna menilai performa sistem secara keseluruhan. Dataset penelitian terdiri dari citra mata dari subjek laki-laki dan perempuan dengan berbagai kondisi. Sistem klasifikasi yang dikembangkan berhasil mencapai accuracy sebesar 90,24%, precision 90,26%, recall 90,24%, dan F1-score 90,22%. Hasil penelitian menunjukkan bahwa kombinasi fitur HSV dan HOG dengan algoritma SVM mampu memberikan tingkat akurasi yang konsisten dan reliabel dalam mengklasifikasi jenis kelamin berbasis citra mata. Pendekatan ini dapat diterapkan sebagai solusi alternatif dalam sistem keamanan, analisis forensik, dan layanan personalisasi yang membutuhkan estimasi jenis kelamin ketika hanya bagian mata yang dapat diamati.Kata kunci: Citra Mata, HOG, HSV, Jenis Kelamin, Support Vector MachineAbstract: Identifying gender through eye image analysis is a relevant approach in forensic and security applications, especially in situations where facial identification is not possible. This study develops a gender classification system based on eye images using a combination of HSV and HOG features with the Support Vector Machine (SVM) algorithm. The problem addressed is how to extract representative eye image features and optimize gender classification accuracy through the appropriate methodological approach. Research method includes data collection, image preprocessing, feature extraction using HSV to obtain color characteristics and HOG to identify shape characteristics, application of SVM as a classification algorithm, and model evaluation to assess the overall system performance. The research dataset consists of eye images from male and female subjects under various conditions. The developed classification system achieved an accuracy of 90.24%, precision of 90.26%, recall of 90.24%, and an F1-score of 90.22%. The results show that the combination of HSV and HOG features with the SVM algorithm is capable of providing consistent and reliable accuracy in classifying gender based on eye images. This approach can be applied as an alternative solution in security systems, forensic analysis, and personalized services that require gender estimation when only the eyes are observable.Keywords: Eye Image, Gender , HOG, HSV, Support Vector Machine
Banana Leaf Disease Classification Using HSV and LBP Feature Extraction with Support Vector Machine Novriza Rahayu; Farhan Muhammad; Sylvia Indri Yani; Agung Fadillah; Akbar Idaman
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 2 (2026): July 2026
Publisher : Universitas Satya Terra Bhinneka

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Banana leaf diseases are one of the primary factors contributing to the decline in the quality and productivity of banana plants, as manual identification remains time-consuming, subjective, and prone to misclassification due to the similarity of symptoms among different diseases. This raises the question of whether a combination of Hue, Saturation, Value (HSV) and Local Binary Pattern (LBP) feature extraction can provide an effective alternative for banana leaf disease classification using a Support Vector Machine (SVM). While previous studies have relied on deep learning methods or combined HSV with Histogram of Oriented Gradients (HOG) features for this task, the combination of HSV and LBP for banana leaf disease classification remains largely unexplored. Using the Banana Leaf Spot Diseases (BananaLSD) dataset, comprising four classes (Cordana, Healthy, Pestalotiopsis, and Sigatoka), the original images were first divided into training and testing sets using an 80:20 ratio to prevent data leakage, after which data augmentation was applied exclusively to the training set. All images were center cropped before HSV and LBP features were extracted, combined, and classified using an SVM with a Radial Basis Function (RBF) kernel. On an independent test set of original, non-augmented images, the proposed model achieved an accuracy of 87.30%, precision of 89.06%, recall of 87.30%, and F1-score of 87.80%, with consistent results confirmed through Stratified Group 5-Fold Cross-Validation and an ablation study showing that the HSV and LBP combination outperformed either feature type alone. These findings indicate that combining HSV and LBP features offers a reliable, feature based alternative to deep learning for automated banana leaf disease identification.
Co-Authors Agung Fadillah Akbar Idaman Andre Pratama Dimas Jordan Farhan Muhammad Marta Tabita Anggi Silitonga Salwa Nabila Sylvia Indri Yani