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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Global

Gasim

Universitas Indo Global Mandiri

Author-ID : 3545307

Computer Science & IT Engineering

Published : 9 Documents Claim Missing Document

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Title

PERBANDINGAN JARAK POTRET DAN RESOLUSI KAMERA PADA TINGKAT AKURASI PENGENALAN ANGKA KWH METERMENGGUNAKAN SVM Dini Amputri; Siti Nadra; Gasim Gasim; M. Ezar Al Rivan
Jurnal Informatika Global Vol 8, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Electricity meter is a tool used to measure the electricity power consumption. Electricity meter has the numeral part known as electricity meter number which load the electricity power consumption.Usually, the recording is done once a month, by recording the number on the electric meter into the book, and then the recording officer take pictures the power meter. Electricity meter number can be analized through an image using the knowledge of pattern recognition in image processing. In analyzing picture of electicity meter number, we used Histogram Of Oriented Gradients (HOG) as the feature extraction and Supply Vector Machine (SVM) as the classification method. The result using 100 train-set and 30 test-set for each combination of category shows that the best resolution is 10 MP and 14 MP and the picture capturing distance is at 30 cm byand 10 cm by 73,33% accuracy for each image and 86,67% for each number and confusion matrix shows that presentation of all number is 75,48%. Key words:Number Recognition, Histogram Of Oriented Gradients (HOG) , Support Vector Machine (SVM)

Identifikasi Jenis Mangga Berdasarkan Bentuk Menggunakan Fitur HOG dan Jaringan Syaraf Tiruan Edgar Utama; Fikario Yapputra; Gasim Gasim
Jurnal Informatika Global Vol 9, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

AbstractMany searches have been done for object recognition based on shape using artificial intelligence with various feature extraction. also a lot of research has been done for the introduction of fruit types using various features of extraction and various methods of recognition. This research is identification of mango variant based on shape. Mango is one of the popular fruits and is often consumed by the community. Mango has many variants, each variant of mango usually has a different shape. Mango shape, among others, round with its variants and oval with its variants. the shape of mango is one of the differentiator of each type. Mango used is five variants of mango. The training data comprised 16 images for each variant (a total of 80 images for five mango variants). The test data consisted of 4 images for each variant (a total of 20 images for five mango variants). Train data and test data were obtained by photographing using optical camera, 5 MP secsor resolution, 20 cm photo spacing, and with white background. resize the image used size 50 x 50 pixels on the mango object only. This research uses feature from Histogram of Oriented Gradients (HOG) as training input and testing of recognition method. Recognition method using Artificial Neural Network with back propagation algorithm. Accuracy of recognition that can be achieved in this study is 90%.Keywords : Object recognition, Shape, HOG, Artificial Neural Network AbstrakBanyak penelian yang pernah dilakukan untuk objek recognition berdasarkan shape menggunakan kecerdasan buatan dengan berbagai ekstraksi ciri. juga banyak penelitian yang pernah dilakukan untuk pengenalan jenis buah menggunakan berbagai fitur ekstraksi dan berbagai metode recognition. Penelitian ini adalah identifikasi varian mangga berdasarkan shape. Mangga merupakan salah satu buah yang popular dan sering dikonsumsi oleh masyarakat. Mangga memiliki banyak varian, tiap varian dari mangga biasanya memiliki bentuk yang berbeda. Bentuk mangga antara lain bulat dengan variannya dan lonjong dengan variannya. the shape mangga merupakan salah satu pembeda dari masing-masing jenis. Mangga yang digunakan adalah lima varian mangga. Data latih terdiri 16 citra untuk tiap variannya (total 80 citra untuk lima varian mangga). Data uji terdiri dari 4 citra untuk tiap variannya (total 20 citra untuk lima varian mangga). Data latih dan data uji didapatkan dengan cara difoto menggunakan kamera optik, resolusi secsor 5 MP, jarak foto 20 cm, dan dengan latar putih. resize citra yang digunakan berukuran 50 x 50 piksel pada bagian objek mangga saja. Research ini menggunakan feature from Histogram of Oriented Gradients (HOG) sebagai input pelatihan dan pengujian metode recognition. Metode recognition menggunakan Jaringan Saraf Tiruan dengan algoritma propagasi balik. Akurasi pengenalan yang dapat dicapai dalam penelitian ini adalah sebesar 90%.Kata kunci : Pengenalan Objek, bentuk , HOG, Jaringan Saraf Tiruan

PERBANDINGAN TINGKAT AKURASI BENTUK FRAME MENGGUNAKAN TEMPLATE MATCHING PADA PENGENALAN WAJAH Fika Rusilawati; Hardianing Wahyu Kinasih; Gasim Gasim
Jurnal Informatika Global Vol 8, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Facial recognition research is usually done with a rectangular frame model. previously it is not known whether this model is the best model among other models in face recognition. This research conducted a comparison of face recognition accuracy with elliptical frame model, triangle, rectangle, and pentagon. RGB image is used for facial segmentation, and skin color is used as a feature. using 320 x 240 pixel image, face position is front view, shooting distance about 200 cm, and use one color for background. The method of recognition used is Template Matching. Test results on 40 test images is the highest recognition accuracy level is a rectangular frame that is 97%, and the lowest accuracy is the triangle frame that is equal to 68%.Keywords : Face recognition, Frame, Segmentation, Template Matching

PERBANDINGAN TINGKAT AKURASI JENIS CITRA KEABUAN , HSV, DAN L*a*b* PADA IDENTIFIKASI JENIS BUAH PIR Mulia Octavia; Jesslyn K; Gasim Gasim
Jurnal Informatika Global Vol 7, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Image processing has been commonly used in automatic object identification. These are some methods that can be used for automatic object identification, such as LVQ, K-NN, SVM, and Neural Network. This research specifically bring out the topic about the level accuracy comparison in identification of pear variety using grayscale, HSV, and L*a*b* images in aim to get the best image type for pear image identification using neural network. The feature are gray level co-occurrence matrix feature (energy, entropy, homogeneity, and contrast) from canny edge detection’s image and also color feature. Based on image examination result, grayscale reached its best accuracy for 90% on MSE 1e-10 with 10 hidden layer neurons, HSV reached its best accuracy for 100% on MSE 1e-5 with 20 hidden layer neurons, L*a*b* reached its best accuracy for 100% on MSE 1e-5 with 15 hidden layer neurons. HSV and L*a*b* give the better accuracy for pear variety image identification than grayscale.Keyword:Image Processing, Pear, Neural Network, Identification, Gray Level Co-occurrence Matrix, Canny, Color.

Co-Authors David Ricardo Dini Amputri Edgar Utama Fika Rusilawati Fikario Yapputra Hardianing Wahyu Kinasih Jesslyn K M Ezar Al Rivan Mulia Octavia Rizani Teguh Rusbandi Rusbandi Siti Nadra Sudiadi Sudiadi Sudiadi, Sudiadi

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