Klasifikasi Tingkat Kematangan Buah Kopi Berdasarkan Deteksi Warna Menggunakan Metode KNN Dan Pca

Raysyah, Siti Raysyah; Arinal, Veri; Mulyana, Dadang

doi:10.30656/jsii.v8i2.3638

Cited by 17 publications

(14 citation statements)

References 6 publications

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“…So to be able to extract the color of an image in the PHP programming language, you can use the "imagecolorat" function with three parameters [21], namely: a. Image object b. X-Image c. Y-Image To be able to extract the color in an image using the imagecolorat function, [23] you need to know the number of pixels in the following image: It is known that it has a height of 18px and a width of 10px, so it uses the looping method to retrieve the color value for each pixel using the PHP programming language as follows [5]: The value returned by the function above is in the form of a multi-dimensional array of RGB color data which amounts to 180 obtained from the following calculation [29], [32] : Furthermore, the researcher will test the data that has been obtained, this is the exercise data inputted in this study [28]: The researcher conducted a test using a test image which would calculate the distance between the color dimensions and the training data that had been input into the system. In the test image from each color data that has been extracted and taken as many as 16 colors according to the number of colors in the training data, [29][24]the table below is the result of color extraction from the test image.…”

Section: Resultsmentioning

confidence: 99%

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Ardianto

Sriani

Hasugian

2023

J. Teknik Informatika CIT Medicom,

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Computerization requires system testing and further system development, namely color feature extraction with KNN. Avocado is one that has a high protein content in it. This research uses the KNN algorithm method and feature extraction in order to get more effective results, the purpose of this research is to make it easier for people to choose the ripeness level of butter avocados because people still don't know about the maturity level of butter avocados. In this study, testing was carried out by bringing the avocado fruit closer to the cellphone camera connected to the researcher's internet, after which the application will automatically match the color of the avocado. to the system, the system will produce output based on that color with output in the form of the ripeness level of the avocado, whether it is ripe, ripe, half ripe, rotten and also generates information on how much longer the avocado will ripen. All stages of system development are carried out by analyzing data first, then taking sample data, training and testing datasets, then the results of the system will become benchmarks. The test data in this study used several types of avocado objects, namely: Raw, Half Ripe, Ripe, Ripe, Rotten. It consisted of 55 data samples consisting of 11 raw avocado samples, 11 half-ripe avocado samples, 11 ripe avocado samples, 11 ripe avocado samples and 11 rotten avocado samples. Obtained euclidean distance values for each type of avocado butter. After that, the sum is done to get the overall level of accuracy by adding up the total euclidean distance with the total euclidean distance for each type of avocado. After getting the added value multiply it by 100%. Then the overall accuracy results obtained are 98.38%.

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Section: Resultsmentioning

confidence: 99%

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Ardianto

Sriani

Hasugian

2023

J. Teknik Informatika CIT Medicom,

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“…Principal Component Analysis (PCA) merupakan metode untuk mereduksi dimensi atribut dalam dataset, sehingga nilai yang terbentuk sangat berbeda dengan bentuk aslinya [10]. Metode PCA digunakan untuk meringkas struktur dari dataset dengan dimensi yang banyak sehingga memiliki jumlah variabel yang lebih kecil [11].…”

Section: Principal Component Analysisunclassified

Analisis Pengaruh Komposisi Data Training dan Data Testing pada Penggunaan PCA dan Algoritma Decision Tree untuk Klasifikasi Penderita Penyakit Liver

Azmi¹,

Hermawan²,

Avianto³

2023

jtim

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Liver disease is one of the diseases that is difficult to detect and becomes the largest contributor to deaths because it is considered a silent killer without symptoms. Liver disease can be detected based on abnormalities in the number of contents in the human body. The Indian Liver Patient Dataset (ILPD) dataset has many variables related to content in the body of liver patient data which are used as parameters in the classification of liver disease patients. Previous studies have shown that only two variables influence the ILPD dataset. The purpose of this study is to examine the use of the Principal Component Analysis (PCA) method to determine the optimal number of features in the context of classification of liver disease and examine the percentage distribution of data training and data testing which produces the best accuracy. The ILPD dataset was obtained from the UCI Machine Learning website with a total of 583 rows of data and 11 features. The percentage of training data and testing data used is 50%:50%, 60%:40%, 70%:30%, 73%:27%, 75%:25%, 80%:20%, 83%:17%, 85%:15% and 90%:10%. The use of different training and testing data percentages serves to find the best accuracy. The PCA result feature is used as input for the Decision Tree C4.5 classification algorithm. The experimental results show that using the training and testing data distribution percentage of 90%:10% and after the application of PCA produces the highest accuracy, namely 78.40% which is obtained for the number of PCA components n = 8.

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“…Perubahan warna pada buah kopi memberikan informasi terkait tingkat kematangannya. Buah kopi arabika berwarna hijau menandakan usia muda, berwarna kekuningan sampai kemerahan menandakan usia setengah tua, berwarna merah terang hingga merah gelap menandakan sudah tua dan siap untuk dipanen [2], [3]. Pengolahan buah kopi dapat meliputi proses sortasi buah dan proses pemisahan kulit buah dengan biji.…”

Section: Pendahuluanunclassified

Prototype Sistem Penyortir Buah Kopi Arabika Berdasarkan Tingkat Kematangan Menggunakan Metode Support Vector Machine

Rusman

Pasae

2023

teknika

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Salah satu proses peningkatan mutu kopi adalah menyeleksi buah kopi yang matang dan belum matang pasca panen yang dilakukan dengan cara memilih satu per satu. Namun dengan cara tersebut terdapat kelemahan yaitu inkonsistennya hasil sortiran karena faktor subjektifitas dan intentitas cahaya. Sebagai solusi, pada penelitian ini didesain alat bantu dalam bentuk prototipe untuk menyortir buah kopi berdasarkan kematangannya. Kopi matang ditandai dengan warna merah, kopi setengah matang dengan warna kuning kemerahan dan kopi mentah dengan warna hijau. Kamera diletakkan dalam kotak guna mengurangi intentitas cahaya eksternal, digunakan untuk mengambil citra buah kopi kemudian citra buah kopi akan dipisahkan dengan background dengan metode segmentasi warna hue, saturation, value (HSV). Selanjutnya citra buah kopi diekstraksi untuk mendapatkan nilai setiap warna dengan parameter red, green, blue (RGB) dan HSV guna membentuk model klasifikasi metode support vector machine (SVM). Parameter SVM optimum yaitu cost (C)= 10,0 dan gamma (γ)= 0,001. Prototipe yang dibangun berbentuk persegi panjang dengan panjang 70 cm, lebar 10 cm dan tinggi 12 cm. Komponen penyortir yaitu servo yang memutar ke sudut 90o untuk klasifikasi “matang”, sudut 45o untuk klasifikasi “matang setengah” dan sudut 135o untuk klasifikasi “mentah” sedangkan conveyor belt digerakkan oleh motor DC 12 V. Meskipun pada proses prediksi perangkat lunak terdapat kesalahan prediksi namun perangkat keras telah bekerja dengan baik ditandai dengan servo berhasil memutar link ke sudut yang telah ditentukan sesuai hasil prediksi. Dengan demikian, dapat disimpulkan bahwa prototipe sistem penyortir buah kopi arabika berdasarkan kematangannya berhasil dibangun.

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Klasifikasi Tingkat Kematangan Buah Kopi Berdasarkan Deteksi Warna Menggunakan Metode KNN Dan Pca

Cited by 17 publications

References 6 publications

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter

Analisis Pengaruh Komposisi Data Training dan Data Testing pada Penggunaan PCA dan Algoritma Decision Tree untuk Klasifikasi Penderita Penyakit Liver

Prototype Sistem Penyortir Buah Kopi Arabika Berdasarkan Tingkat Kematangan Menggunakan Metode Support Vector Machine

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