Indonesia has abundant natural resources, especially the results of its plantations. Lots of local fruit that can be used starting from the root to the skin of the fruit. Local fruit can be consumed as fresh fruit and can also be processed into drinks and food. This is reflected in the diversity of tropical fruits found in Indonesia. Fruits that are rich in benefits and can be used as medicines such as Apples, Avocados, Apricots, and Bananas. These fruits are often found around us. In Indonesia these fruits are produced and also exported abroad. However, the limited methods and technology used to classify this fruit are interesting things to discuss and become the main focus in this research. This study analyzed using the Naive Bayes algorithm and feature extraction of HOG (Oriented Gradient Histogram) to obtain more effective classification results. The results showed that the collection of fruit using the Naive Bayes method and HOG feature extraction had not yet obtained maximum classification results, only with an accuracy of 56.52%. Keywords – Apple, Avocado, Apricot, Banana, Naive Bayes, HOG.

https://www.ojs.uma.ac.id/index.php/jite/article/download/3860/2780

Analysis Naïve Bayes In Classifying Fruit by Utilizing Hog Feature Extraction

— Fruit recognition can be automatically applied to the field of education, industry, sales, as well as science. In the vision of computer recognition of fruit relies on four basic features that describe the characteristics of the fruit, i.e., size, color, shape, and texture. The fruit recognition through the RGB image results of cameras using the features of shape and size are not reliable and effective, because in a real data image can be composed of several different sizes of fruit on each type of fruit so it can't be identified morphologically the fruit size and uniformity that can affect the results of the classification. This journal based on the feature recognition method of building colors and textures for the classification of fruit.The classification is done by K-Nearest Neighbor based on color and texture features co-occurrence. Experimental results of 1882 dataset image of fruit for 12 different classes can recognize the fruit in both color and texture features based with the highest accuracy of 92%.

/pdf/pengenalan-jenis-buah-pada-citra-menggunakan-pendekatan-5drb90ms2c.pdf

Pengenalan Jenis Buah pada Citra Menggunakan Pendekatan Klasifikasi Berdasarkan Fitur Warna Lab dan Tekstur Co- Occurrence

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.

/pdf/utilization-of-support-vector-machine-and-speeded-up-robust-2k4gbq8r2n.pdf

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

Limfoblas merupakan sel darah putih jenis sel limfosit, yang dapat menandai leukemia. Untuk mengidentifikasi limfoblas diperlukan analisis sel darah putih. Pada makalah ini diusulkan sistem otomatis berbasis komputer menggunakan teknik pengolahan citra digital untuk mendeteksi limfoblas dengan menganalisis citra mikroskopis sel darah. Metode usulan ini menyegmentasi komponen sel darah putih, yaitu sitoplasma dan nukleus, menggunakan pendekatan baru berbasis teknik pengambangan lokal adaptif. Selanjutnya, tiap komponen sel tersegmentasi tersebut diekstrak fitur geometri dan teksturnya. Fitur tekstur menggunakan deskriptor local binary pattern (LBP) dari nukleus sel. Himpunan fitur tersebut digunakan untuk melatih algoritme klasifikasi support vector machine dalam mendeteksi limfoblas. Metode yang diusulkan mampu menyegmentasi 264 dari 269 total sel darah putih dengan benar, dengan akurasi 98,14%, dari 35 citra leukemia limfoblastik akut yang diambil dengan kamera yang sama dengan kondisi pencahayaan yang sama pula. Penggunaan fitur geometri sebanyak 16 dimensi vektor fitur menghasilkan akurasi identifikasi limfoblas sebesar 88,79%, sedangkan penggunaan fitur LBP sebanyak 256 dimensi vektor fitur menghasilkan akurasi identifikasi limfoblas sebesar 89,72%. Kinerja yang lebih baik diperoleh dengan penggabungan dua fitur, yaitu geometri dan LBP sebanyak 272 dimensi vektor fitur, dengan akurasi 94,32%.

http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/download/458/377

Deteksi Limfoblas pada Citra Sel Darah Menggunakan Fitur Geometri dan Local Binary Pattern

Tomato is the most widely cultivated fruit in the world. The low production of tomato in Indonesia at planting tomato regardless quality. Then be directed to increase tomato quality with planting of the seed is available from the selection of good tomato quality. Science and digital image processing technology make it possible for tomato quality selection with automatically, by using application tomato fruit selection for superior seed by color and size using color space Hue Saturation Value (HSV) method and thresholding processing. From the result of application has 3 (three) output from the color and size of the fruit is good tomato, half as nice tomato and not nice tomato. Keyword :   Tomato, Color, Size, Hue Saturation Value (HSV), Quantization HSV, Thresholding, Otsu Threshold, Data Similarly.

Pengembangan Aplikasi Pemilihan Buah Tomat Untuk Bibit Unggul Berdasarkan Warna Dan Ukuran Menggunakan Hsv Dan Thresholding

Abstrak  Apel merupakan salah satu jenis buah yang unggul dan sangat digemari dan dikonsumsi masyarakat. Buah apel memiliki banyak varietas yang dapat dibedakan berdasarkan warna dan bentuk buah. Fitur Hue Saturation Value (HSV) dan Local Binary Patern ( LBP) digunakan pada penelitian ini sebagai ekstraksi fitur warna dan bentuk pada buah yang kemudian akan dijadikan ciri dari warna dan bentuk buah apel yang akan diteliti. Metode K-Nearest Neighbor (K-NN) adalah salah satu metode penelitian pada kecerdasan buatan yang digunakan dalam penelitian ini untuk mengklasifikasikan nilai-nilai yang didapat dari hasil ekstraksi fitur HSV dan LBP. Data yang digunakan pada penelittian ini adalah 800 citra, yang terdiri dari 600 citra latih dan 200 citra uji. Hasil evaluasi yang didapat dari metode K-Nearest Neighbor ini untuk Secara keseluruhan dapat dilihat bahwa rata-rata nilai Precision yang di dapat sebesar 94%, Recall sebesar 100%, dan Accuracy sebesar 94 %. Kata kunci : Hue Saturation Valu e, Local Binary Patern , K-Nearest Neighbor

/pdf/klasifikasi-jenis-buah-apel-dengan-metode-k-nearest-elolpltsrq.pdf

Klasifikasi jenis buah apel dengan metode k-nearest neighbors dengan ekstraksi fitur hsv dan lbp

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Klasifikasi jenis buah apel dengan metode k-nearest neighbors dengan ekstraksi fitur hsv dan lbp