Analisis Performa Metode K-Nearest Neighbor Untuk Identifikasi Jenis Kaca

Baharuddin, Mus Mulyadi; Azis, Huzain; Hasanuddin, Tasrif

doi:10.33096/ilkom.v11i3.489.269-274

Cited by 44 publications

(47 citation statements)

References 2 publications

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“…The distance between the training data and the test data is calculated using the Euclidean equation. [3] In this study, predictions will be made using the criteria of type, quota, price and age, on the availability of XL Prioritas Unlimited products, namely: silver, gold and platinum for the Makassar area.…”

Section: Pendahuluanmentioning

confidence: 99%

Application of the KNN Algorithm for Predicting Data Card Sales at PT. XL Axiata Makassar

Prihatmono¹,

Arni²,

Iin³

et al. 2022

conferenceseries

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XL Axiata Company, TBK. Is an internet access service provider from the many operators in Indonesia and has to provide the best service in communication in the ease of communication services. The mainstay of communication service products owned by XL Axiata, TBK for the wider community is XL Prioritas Unlimited which includes three types of packages, namely silver, gold and platinum. People using these three packages have different levels of demand. Order the number of products ordered every month by the company, should be made based on the prediction of the number of previous sales. This study was conducted to assess product sales in the form of K-Nearest Neighbor (KNN) classification modeling. The number of datasets used is 500 records with details of 90% training and 10% testing. The results obtained are predictions of 0.98%.

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Section: Pendahuluanmentioning

confidence: 99%

Application of the KNN Algorithm for Predicting Data Card Sales at PT. XL Axiata Makassar

Prihatmono¹,

Arni²,

Iin³

et al. 2022

conferenceseries

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“…Proses dalam tahap data mining terdiri dari tiga langkah Utama, yaitu data Preparation Pada langkah ini, data dipilih, dibersihkan, dan dilakukan preprocessed mengikuti pedoman dan knowledge dari ahli domain yang menangkap dan mengintegrasikan data internal dan eksternal ke dalam tinjauan organisasi secara menyeluruh. Penggunaan algoritma data mining dilakukan untuk menggali data yang terintegrasi untuk memudahkan identifikasi informasi bernilai [3]. Namun semakin besar data yang diolah maka semakin besar pula waktu prosesnya [4].…”

Section: A Data Miningunclassified

Klasifikasi Diagnosa Peradangan Kandung Kemih Menggunakan Metode Algoritma Naïve Bayes

Muharrom

2021

IJUBI

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<table width="653" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="509"><p align="center"><strong>Abstrak</strong></p><p>Dalam proses kehidupan kita perlu memperhatikan kesehatan tubuh, agar tidak mudah terserang penyakit. Berbagai cara pencegahan mengatasi terjangkitnya penyakit dengan cara menjaga pola makan dan pola hidup sehat. Karena pekerjaan, makan yang tidak teratur terkadang seseorang tidak memperdulikan kondisi tubuh mereka sendiri. Hal ini dapat berisiko dapat terkena penyakit yang berbahaya, sehingga perlu adanya pemeriksaan diri. Peradangan pada kandung kemih adalah salah satu penyakit yang ditimbulkan dari rasa nyeri saat buang air kecil, rasa nyeri itu berasal dari saluran kemih. Untuk memahami masalah dengan lebih baik, Maka perlunya penelitian agar dapat mencegah semakin parahnya penyakit yang diderita dengan cara mengklasifikasikan data pasien untuk mengakurasikan hasil penyakit yang di derita oleh pasien dan dapat memprediksi dari data yang ada. Dengan menggunakan metode naïve bayes dalam perhitungan didapatkan kesesuaian antara perhitungan manual dengan menggunakan tools rapidminer dan dihasilnya kekurata adalah 90,8% dan perhitungan ROC menghasilkan nilai AUC adalah 0.956 yang berarti <em>excellent classification</em>.</p></td></tr></tbody></table>

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“…Ada banyak cara untuk mengukur jarak kedekatan antara data baru dengan data lama (data training), diantaranya euclidean distance dan manhattan distance (city block distance), yang paling sering digunakan adalah euclidean distance. Persamaan euclidean ditunjukkan pada persamaan 1 [2], [10], [11].…”

Section: B K-nearest Neighborunclassified

Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung

et al. 2020

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Globally, the number one cause of death each year is cardiovascular disease. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels, such as coronary heart disease, heart failure or heart failure, hypertension and stroke. The purpose of this study was to measure the performance of accuracy, precision, recall and f-measure of the K-NN and Crossvalidation methods on a dataset of cardiovascular patients. The dataset used was 1000 records consisting of 11 attributes (age, gender, height, etc.) cardiovascular and non cardiovascular patient data, the dataset was obtained from the UCI Machine Learning Repository managed by the Hungarian Institute of Cardiology Budapest: Andras Janosi, MD, University Hospital, Zurich, Switzerland. The steps taken are: dividing the simulation ratio of the dataset to 20:80, 50:50 and 80:20, applying crossvalidation (k-fold = 10) and classification using the K-NN method (k = 2 to K = 900). The research results from the simulation of the dataset ratio 50:50 obtained an accuracy value of 82%, 82% precision, 82% recall and 80% f-measure at a value of K = 13, then the research results from the simulation of the dataset ratio 20:80 obtained an accuracy value of 87%, 87% precision, 97% recall and 92% f-measure at the value of K = 3, and the results of research from the simulation of the dataset ratio 80:20 obtained an accuracy value of 91%, 92% precision, 60% recall and 72% f-measure at the value K = 5.

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Analisis Performa Metode K-Nearest Neighbor Untuk Identifikasi Jenis Kaca

Cited by 44 publications

References 2 publications

Application of the KNN Algorithm for Predicting Data Card Sales at PT. XL Axiata Makassar

Application of the KNN Algorithm for Predicting Data Card Sales at PT. XL Axiata Makassar

Klasifikasi Diagnosa Peradangan Kandung Kemih Menggunakan Metode Algoritma Naïve Bayes

Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung

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