Perbandingan Penggunaan Jarak Manhattan, Jarak Euclid, dan Jarak Minkowski  dalam Klasifikasi Menggunakan Metode KNN pada Data Iris

Setiawan, Adi

doi:10.24246/juses.v5i1p28-37

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The definition of accuracy is shown in Equation (1) and precision is shown in Equation (2). Recall is shown in Equation (3) and F1-Score is shown in Equation (4) [12]. Accuracy is the ratio of correct predictions to all data.…”

Section: ) Conversion Of Datamentioning

confidence: 99%

Performance Comparison of Decision Tree and Logistic Regression Methods for Classification of SNP Genetic Data

Setiawan,

Setivani,

Mahatma

2024

BAREKENG: J. Math. & App.

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This research was conducted to compare the accuracy when decision tree and logistic regression methods are used on some data. Decision tree is one method of classification techniques in data mining. In the decision tree method, very large data samples will be represented as smaller rules, and logistic regression is a method that aims to determine the effect of an independent variable on other variables, namely dichotomous dependent variables. Both algorithms were written and analyzed using R software to see which method is better between the decision tree method and the logistic regression method applied to SNP (Single Nucleotide Polymorphism) genetic data, namely Asthma data. SNP Genetic Data was obtained from R software with the package name "SNPassoc" and the data name "asthma". Asthma data has 57 features, namely Country, Gender, Age, BMI, Smoke, Case control, and SNP (Single Nucleotide Polymorphism) genetic code. Comparative analysis was carried out based on the results of the accuracy values obtained in the two methods. Variations in the proportion of the test data used were 40%, 30%, 20% and 10% and were simulated 1000 times on the grounds of obtaining a better accuracy value. The results obtained show that the decision tree method obtains an accuracy value of 0.5793, 0.5777, 0.5745, 0.5526, respectively, while the logistic regression method is 0.7696, 0.7729, 0.7763, 0.7788, respectively and they are achieved at the proportion of test data of 40%, 30%, 20%, 10%. Thus it can be concluded that in this case the logistic regression method is better than the decision tree method in classifying Asthma data.

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Section: ) Conversion Of Datamentioning

confidence: 99%

Performance Comparison of Decision Tree and Logistic Regression Methods for Classification of SNP Genetic Data

Setiawan,

Setivani,

Mahatma

2024

BAREKENG: J. Math. & App.

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“…KNN bekerja dengan mencari K objek terdekat dalam data latihan yang memiliki kesamaan dengan objek pada data uji. pendekatan untuk mencari kasus dengan memperhitungkan kesamaan bobot dari beberapa atribut yang ada dengan menghitung jarak antara data latihan (x) dan data uji (y), didefinisikan menggunakan rumus jarak metrik Euclidean, Manhattan dan Minkowski [9] [10]. Ada salah satu penelitian yang telah dilakukan menggunakan algoritma K-NN dalam menyelesaikan masalah seperti salah satu penelitian yang dilakukan Esty Purwaningsih dan Ela Nurelasari dengan judul "Penerapan K-Nearest Neighbor untuk Klasifikasi Tingkat Kelulusan Pada Siswa".…”

Section: Pendahuluanunclassified

Perbandingan Jarak Metrik pada Klasifikasi Jamur Beracun Menggunakan Algoritma K-Nearest Neighbor (K-NN)

Suarisman,

Nazir,

Syafria

et al. 2023

JoSYC

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Mushrooms are organisms from the kingdom fungi that have a fleshy body structure and can be consumed, but there are some species of mushrooms that are not safe to eat and have specific characteristics, so distinguishing between edible and poisonous mushrooms can be tricky due to the almost identical appearance of various mushroom species. Errors in identifying edible mushrooms can impact the health of consumers who consume the mushrooms. Evaluating the performance of various methods on a dataset is a key step in determining the most suitable classification method. This research is about how to measure the performance of classification methods on toxic mushroom datasets using the K-Nearest Neighbor algorithm with several metrics such as euclidean, manhattan and minkowski, which is a method for classifying new data based on proximity to existing training data. The results obtained in this study with several distance metrics can be concluded that the accuracy value of the manhattan metric is better than the euclidean and minkowski metrics. Because the manhattan metric gets the highest accuracy result of 99% with K = 100 and the lowest 82% with K = 3000, while the euclidean metric gets accuracy results with a value of 98% with K = 100 and 72% with K = 3000, and the minkowski metric gets accuracy results with a value of 96% at K = 100 and 64% at K = 3000.

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“…Penelitian ini menggunakan perhitungan jarak Euclidean, Manhattan (city block), Chebyshev, Minkowski, dan Hamming. Formula yang digunakan dalam menghitung jarak terdapat pada Persamaan (3) sampai ( 7) [18]- [27]. Metode CNN merupakan algoritma deep learning yang saat ini memiliki performa terbaik dalam pengklasifikasian citra sebab algoritma CNN bekerja dengan cara meniru visual cortex manusia dalam mengenali dan mengolah sebuah citra [30].…”

Section: K-nearest Neighborunclassified

Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor

Basri,

Purnawansyah,

Darwis

et al. 2023

JTMI

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The number of herbal plants in Indonesia is 30,000, but only about 1,200 plants are used in medicine. The large number of herbal plants makes it difficult for people to distinguish one type of herbal plant from another. From these conditions, this research has conducted tests to compare the performance of the K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN) methods using Fourier Descriptor (FD) feature extraction on herbal plants, namely moringa (moringa oleifera) and katuk (sauropus androgynus). The amount of data used is 480 data using image conditions, namely dark and light images which are then divided into 20% testing data and 80% training data. Classification is done using the KNN method using 5 distance calculations (Euclidean, Chebyshev, Manhattan, Minkowski, and Hamming) and CNN with FD feature extraction. From the tests that have been carried out, it is found that the use of FD feature extraction for the KNN method produces the best performance on both light and dark image data. While the use of the CNN method, for dark image data, the best accuracy results are obtained with FD feature extraction and CNN. Meanwhile, for bright image data, the best performance accuracy results are obtained in the CNN method without going through feature extraction. Of these three methods, using FD and KNN feature extraction is more recommended because it produces 100% accuracy in moringa and katuk images with light and dark intensity.

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Perbandingan Penggunaan Jarak Manhattan, Jarak Euclid, dan Jarak Minkowski dalam Klasifikasi Menggunakan Metode KNN pada Data Iris

Cited by 6 publications

References 6 publications

Performance Comparison of Decision Tree and Logistic Regression Methods for Classification of SNP Genetic Data

Performance Comparison of Decision Tree and Logistic Regression Methods for Classification of SNP Genetic Data

Perbandingan Jarak Metrik pada Klasifikasi Jamur Beracun Menggunakan Algoritma K-Nearest Neighbor (K-NN)

Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor

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