Klasifikasi Ekspresi Wajah Menggunakan Bag of Visual Words

Muhathir, Muhathir

doi:10.31289/jite.v1i2.1426

Cited by 9 publications

(7 citation statements)

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“…Metode SVM merupakan suatu metode yang relatif baru untuk melakukan prediksi pada kasus regresi atau klasifikasi (Abd & Abd, 2017). SVM adalah model supervised (W. Wang et al, 2017) learning yang implementasinya membutuhkan tahap pelatihan menggunakan sequential training SVM dan diikuti proses pengujian (Que et al, 2020) (Muhathir, 2018). Fungsi SVM dengan memproyeksikan ruang fitur ke dalam ruang kernel (M. Wang & Chen, 2020) dan membuat kelas dapat dipisahkan secara linier.…”

Section: Support Vector Machine (Svm)unclassified

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Muhathir

Santoso²,

Larasati³

2021

JITE

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Wayang is a masterpiece of art that has been able to survive centuries of change and development as a reflection of life for the majority of society. Wayang has a high value because it does not only function as a "entertainment" spectacle, but also has many lessons and life values that can be learned from a wayang show. Puppet itself has various types and forms, and these forms have their own uniqueness, because of the many types of Puppet, many people do not know all the names and types of wayang. Therefore, in this research, we will discuss how to recognize wayang objects based on wayang images using the SVM and GLCM methods as feature extraction. The results showed that the classification of wayang using the SVM (Support Vector Machine) method and the GLCM (Gray Level Co-Occurrence Matrix) feature extraction can recognize wayang objects based on wayang images and classify them quite accurately and a maximum total accuracy of 83.2% is obtained.

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Section: Support Vector Machine (Svm)unclassified

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Muhathir

Santoso²,

Larasati³

2021

JITE

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“…Referensi (Havaldar, 2000) menjelaskan bahwa pengeditan manual pada karakter animasi diperlukan jika: 1) gerakan yang diharapkan dari model 3D belum sama/ mirip dengan pergerakan manusia, 2) wajah model 3D memiliki proporsi/ morfologi yang berbeda dari wajah manusia, 3) penempatan marker pada motion capture tidak selalu tetap dan tepat dari hari ke hari. Pengenalan pola merupakan suatu ilmu yang digunakan untuk mengklasifikasikan sesuatu berdasarkan pengukuran ciri [14]. Wajah manusia mempunyai peran sentral dalam interaksi social, sehingga pemrosesan wajah merupakan sub penting dalam pengenalan pola [15].…”

Section: Landasan Teoriunclassified

Identifikasi Ekspresi Wajah Untuk Pembayaran

Sutisnawati

Muriani

Mirawati

et al. 2021

IJIS

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Penelitian ini bertujuan untuk mengetahui akurasi dan efektivitas identifikasi pada sistem identifikasi ekspresi wajah jika digunakan sebagai sistem pembayaran transaksi jual beli suatu perusahaan. Metode penelitian ini adalah deskriptif, kualitatif, dan didukung oleh studi literatur. Hasil penelitian menunjukkan bahwa sistem identifikasi ekspresi wajah efektif jika diterapkan pada sistem pembayaran. Hal ini terlihat dari tingkat akurasi identifikasi dan efektivitas identifikasi ekspresi wajah yang lebih tinggi dari tingkat kegagalan, serta kemampuan mendeteksi wajah yang cukup cepat. Setelah itu, menggabungkan teknologi deteksi keaktifan ke dalam identifikasi ekspresi wajah akan mencegah serangan spoof yang dilakukan oleh wajah non-nyata seperti foto potret. Oleh karena itu, dapat disimpulkan bahwa sistem identifikasi ekspresi wajah dapat menjadi salah satu sistem yang paling banyak diterapkan di masa depan. Hal ini dikarenakan kemudahan bertransaksi yang akan memberikan kemudahan bagi setiap pengguna.Kata Kunci: Pembayaran, Aplikasi, Identifikasi Wajah

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“…The result of the aerial image retrieval is a separate image between one image and another. Images that have been captured can be linked using the SURF (Speed Up Robust Features) algorithm [2]. This algorithm works by specifying a key point of an image whose value is fixed when experiencing scale changes, rotation, blurring, lighting, and shape changes [24].…”

Section: Speed-up Robust Feature (Surf)mentioning

confidence: 99%

“…We can't classify images that overlap separately, but they can be character by using their textures because each image has a specific texture that determines the characteristics of the image. The classification of textures has a wide range of applications in rock classification, the introduction of wood species, facial recognition, geographic landscape segmentation, object detection, and many other image processing applications [2].…”

Section: Introductionmentioning

confidence: 99%

Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery

Muhathir

Hidayah²,

Ifantiska³

2020

ComEngApp

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Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy.

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Klasifikasi Ekspresi Wajah Menggunakan Bag of Visual Words

Cited by 9 publications

References 0 publications

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Wayang Image Classification Using SVM Method and GLCM Feature Extraction

Identifikasi Ekspresi Wajah Untuk Pembayaran

Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery

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