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Sjafril Darana
Fakultas Peternakan, Universitas Padjajaran
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Journal : eProceedings of Engineering

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Identifikasi Kualitas Kesegaran Susu Sapi Melalui Pengolahan Citra Digital Menggunakan Metode Gray Level Co-occurrence Matrix Dan Local Binary Pattern Dengan Klasifikasi K-nearst Neighbor Hamdan Gustiawidi; Bambang Hidayat; Sjafril Darana
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstrak Susu sapi merupakan zat pelengkap yang kaya akan nutrisi, diantaranya protein, lemak, vitamin, mineral dan karbohidrat. Tekstur yang cair dan berwana putih menjadi ciri khas. Sering dijumpai berbagai macam tempat mulai dari penjual pinggiran maupun di super market susu sapi dijual, akan tetapi banyak penjual yang mencampur dengan menambahkan air, pewarna dan pemanis buatan demi mendapat keuntungan yang lebih, sehingga kandungan gizi dan kualitas kesegarannya tidak sempurna lagi. Pada permasalahan yang ada sukar bila bukan ahlinya untuk membedakan kemurnian susu sapi, biasanya konsumen hanya dapat mengenali melalui indra penglihatan dan penciuman. Dalam Tugas Akhir, dirancang suatu sistem untuk mempermudah konsumen mengetahui kemurnian susu sapi dengan campuran air menggunakan software Matrix Laboratory (MATLAB) melalui pengolahan citra digital. metode ekstraksi ciri yang digunakan adalah Gray Level Co-Occurrence Matrix (GLCM) dan Local Binary Pattern (LBP) serta metode klasifikasi adalah K-Nearest Neighbor (K-NN). Dari hasil pengujian sistem menggunakan metode GLCM didapatkan akurasi sebesari 100% dan waktu komputasi 0,7777 detik. Sedangkan menggunakan metode LBP didapatkan akurasi sebesar 97,5% dan waktu komputasi 0,7722 detik. Kata kunci : Susu sapi, Gray Level Co-Occurrence Matrix, Local Binary Pattern, K-Nearest Neighbor Abstract Cow’s Milk is a complementary substance in food, which has a lot nutrition such as proteins, fats, vitamins, minerals and carbohydrates. The characteristic is liquid and white colored. It can found anywhere, from small traders untill the supermarkets. But some of the sellers mix and add water, dyes, and artificial sweeteners to get more profit, so it reduce the milk’s nutrition and the freshness. The problem is it diffcult to know the freshness level of milk if not by the expert, the only way the consumer to know the freshness level by sight and smell. In this Final Assignment, a sistem created to help consumer know the cow milk freshness level by using Matrix Laboratory Software (MATLAB) using image processing method. The feature extraction by using Gral Level Occurrence Matrix (GLCM) method and Local Binary Pattern (LBP) method and classified by K-Nearest Neighbor (K-NN). From the results of system testing using GLCM method obtained 100% accuracy and computation time 0.7777 second, While using the LBP method obtained accuracy of 97.5% and computing time 0.7722 second. Keywords: Cow’s Milk, Gray Level Co-Occurrence Matrix, Local Binary Pattern, K-Nearest Neighbor
Identifikasi Kualitas Kesegaran Susu Sapi Melalui Pengolahan Citra Digital Menggunakan Metode Watershed Dan Klasifikasi Learning Vector Quantization (lvq) Mohamad Fikri Permana; Bambang Hidayat; Sjafril Darana
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Tingginya minat masyarakat terhadap susu, membuat para produsen melakukan inovasi agar mendapatkan keuntungan yang lebih dengan menurunkan kualitas asli dari susu sapi. Misalnya menambahkan bahan tambahan yang dapat merusak nilai gizi dari susu dengan dicampur air agar volumenya lebih banyak. Cara umum dalam membedakan kualitas susu, yaitu dari aroma dan rasa, namun hal tersebut tidak efektif karena indra perasa setiap orang dapat berbeda. Pada era masa kini diperlukan teknologi yang dapat membedakan susu murni dengan susu yang sudah dicampur bahan lain. Penelitian dilakukan dengan mengidentifikasi kualitas kesegaran susu sapi melalui pengolahan citra digital menggunakan metode Watershed, dimana proses ekstraksi ciri menggunakan Local Binary Pattern serta diklasifikasikan menggunakan Learning Vector Quantization. Sistem tersebut telah diaplikasikan melalui penggunaan perangkat lunak Matlab dengan mengidentifikasi dan mengklasifikasikannya pada tekstur susu sapi. Pengambilan data dilakukan dengan cara mengambil sampel susu sapi murni dan sampel susu sapi yang dicampur air sebanyak 25%, 50%, dan 75%. Hasil penelitian identifikasi kualitas kesegaran susu diperoleh tingkat akurasi sebesar 92.5% dan waktu komputasi 0.4791 detik. Kata kunci: Susu Sapi, Watershed, Learning Vector Quantization Abstract The high public interest of milk which give many benefits for human body, urge the producers for doing an innovation to get more profit by lowered the quality of the milk adding an additional substance to the milk can impair the quality of the milk itself. A general way to distinguish whether the quality of the milk is good or not is from its aroma and taste. On the other hand, this kind of way is not effective. In this era of technology, it is needed a kind of technology which can distinguish whether the milk is pure or not. Research done by identifying the quality of fresh cow's milk through digital image processing using Watershed method, where the extraction process characteristics using Local Binary Pattern and classified using Learning Vector Quantization. The system has been applied through the use of Matlab software by identifying and classifying on texture of cow's milk. Data retrieval is done by taking a sample of pure cow's milk and cow's milk samples were mixed in the water as much as 25%, 50%, and 75%. The research of identification the quality freshness obtained accuracy of 92.5% and computational time 0.4791 seconds. Keywords: Cow’s milk, Watershed, Learning Vector Quantization
Co-Authors Bambang Hidayat Hamdan Gustiawidi Mohamad Fikri Permana