Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB

Farokhah, Lia

doi:10.25126/jtiik.2020722608

Cited by 24 publications

(26 citation statements)

References 2 publications

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“…The existence of validation on training data can produce good accuracy results value [17]. This research uses an odd k value, is 3 [18], 5 [19], 7 [20], and 9 [21].…”

Section: Implementation Modified K-nearest Neighbourmentioning

confidence: 99%

“…The world is currently experiencing one of the biggest health disasters in human history, we called Corona Virus. Coronavirus Disease 2019 (Covid- 19) was first discovered in Hubei Province, China through reports of the type of Pneumonia whose cause is not yet known [1]. Based on data from the World Health Organization (WHO) on March 11, 2021, confirmed cases of Covid-19 reached 117,799,584 cases and 2,615,018 deaths spread across 223 countries.…”

Section: Introductionmentioning

confidence: 99%

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Pneumonia identification based on lung texture analysis using modified k-nearest neighbour

Saputra

Taufik

Hidayat

et al. 2022

J. Phys.: Conf. Ser.

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Covid-19 is a virus that was first discovered in China, which has the impact of mild and severe respiratory infections such as pneumonia. Pneumonia is inflammation and consolidation of lung tissue due to infectious agents. Generally pneumonia has a high mortality rate, as do Covid-19 patients. For now, it is very difficult to distinguish between Pneumonia and Covid-19, due to the high similarity of X-Ray image results. The high similarity has an impact on the difficulty of difference between Pneumonia and Covid-19 patients. This research aims to be able to different Pneumonia and Covid-19 patients based on texture analysis of the Gray Level Co-Occurrence Matrix using Modified k-Nearest Neighbour as a classifier. The calculations used in the Gray Level Co-Occurrence Matrix method are Contrast, Correlation, Energy, and Homogeneity which will be input for the Modified k-Nearest Neighbour classifier. The results showed that the highest accuracy is when the value of K = 3 using Manhattan Distance and 80%:20% data percentage, which is 87.5%. For the values of K = 7 and K = 9 there is no change in accuracy, so it can be concluded that the value of K that affects accuracy only occurs at the values of K = 3 and K = 5. Then, the higher the K value, the lower the resulting accuracy.

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“…The existence of validation on training data can produce good accuracy results value [17]. This research uses an odd k value, is 3 [18], 5 [19], 7 [20], and 9 [21].…”

Section: Implementation Modified K-nearest Neighbourmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pneumonia identification based on lung texture analysis using modified k-nearest neighbour

Saputra

Taufik

Hidayat

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Hasil yang didapatkan nilai K terbaik K=2 dan tingkat akurasi fitur warna mencapai 55,95%. Penelitian ini [8] KNN untuk klasifikasi bunga dengan ekstraksi fitur warna RGB. Hasil penelitian menunjukkan tingkat akurasi mencapai 90-100% pada K=5.…”

Section: Pendahuluanunclassified

Ekstraksi Fitur Warna Citra Daun Untuk Klasifikasi Skala Klorofil dan Rekomendasi Pemupukan

Guntara

2022

jmp

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Rekomendasi pemupukan berdasarkan tingkat kehijauan daun sudah lumrah dilakukan. Namun saat ini masih terbatas dilakukan secara tradisional yaitu dengan mengandalkan bagan warna daun. Belum ada penelitian khususnya pada tanaman jeruk yang mengklasifikasikan tingkat skala klorofil daun untuk rekomendasi pemupukannya. Pada penelitian ini akan menggunakan teknologi API Clarifai dan algoritma K-Nearest Neighbor (KNN) untuk membangun aplikasi berbasis android yang dapat secara otomatis mengukur tingkat skala klorofil daun jeruk serta dapat memberikan rekomendasi pemupukan yang tepat. API Clarifai digunakan sebagai alat bantu pemrosesan citra daun. Proses yang dilakukan adalah ekstraksi ciri/fitur pada citra tersebut. Ciri yang dieksraksi adalah ciri warna yang paling dominan mendominasi citra daun. Sedangkan algoritma K-Nearest Neighbor (KNN) digunakan sebagai metode klasifikasi citra daun tersebut. Klasifikasi berdasarkan tingkat kehijauan daun sesuai dengan bagan warna daun standar (BWD). Hasil penelitian menunjukkan tingkat akurasi mencapai 90% untuk ekstraksi warna. Sedangkan hasil klasifikasi dengan KNN menghasilkan akurasi mencapai 90% denan nilai K=2. Berdasarkan hasil penelitian dapat disimpulkan bahwa penggunaan API Clarifai dan KNN untuk klasifikasi tingkat kehijauan daun memiliki akurasi yang tinggi. Untuk penelitian lebih lanjut, dapat dilakukan dengan penambahan ekstraksi fitur lainnya seperti bentuk, tekstur, dan lainnya. Selain itu, penggunaan metode klasifikasi lainnya untuk meningkatkan kinerja algoritma KNN dapat juga diterapkan.

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“…Tujuan dari algoritma k-NN adalah untuk mengklasifikasi objek baru berdasarkan atribut dan training samples [15]. Dimana hasil dari sampel uji yang baru diklasifikasikan berdasarkan mayoritas dari kategori pada k-NN [16]. Pada proses pengklasifikasian, algoritma ini tidak menggunakan model apapun untuk dicocokkan dan hanya berdasarkan pada memori [17].…”

Section: K-nearest Neighborunclassified

Analisis Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu

Gunawan¹,

Zarlis

Roslina

2021

mib

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Students are one of the important pillars in the life cycle of a university. In the process of developing, a university can be influenced by how many bachelor degree (S1) graduates from the university are. The number of graduations of a college sometimes has a low ratio when compared to the number of students admitted in the same school year. This low passing rate of students can be caused by several factors, such as the number of student activities that are participated in, economic factors, and several other unexpected factors. This makes a university must have a scheme or a formula that can predict whether the student can graduate on time. Normally, a bachelor (S1) student takes 8 semesters of education. But the existence of several factors that have been mentioned can make the time to take S1 education to be more, or even fail to graduate. This study will try to compare the results of the analysis of the two methods in the classification algorithm to predict student graduation. The algorithm used is the K-Nearest Neighbor and Naïve Bayes Algorithm. This study also aims to identify the best algorithm among the two classification algorithm choices. This research concluded that the Naïve Bayes algorithm has the same level of accuracy as the KNN algorithm in predicting the graduation of students in the Medical Education study program, which is 90%

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Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB

Cited by 24 publications

References 2 publications

Pneumonia identification based on lung texture analysis using modified k-nearest neighbour

Pneumonia identification based on lung texture analysis using modified k-nearest neighbour

Ekstraksi Fitur Warna Citra Daun Untuk Klasifikasi Skala Klorofil dan Rekomendasi Pemupukan

Analisis Komparasi Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu

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