Implementation of K-Nearest Neighbour (Knn) Algorithm to Predict Student’s Performance

Wiyono, Slamet; Abidin, Taufiq

doi:10.24176/simet.v9i2.2424

Cited by 11 publications

(7 citation statements)

References 9 publications

(9 reference statements)

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“…K-Nearest Neighbor merupakan algoritma pengklasifikasian terhadap kumpulan data melalui pembelajaran yang sudah terklasifikasikan, dimana hasil jarak euclid terbaru akan diklasifikasi menurut nilai terbanyak yang memniliki jarak terdekat melalui parameter k sebagai jumlah tetangga terdekat. Algoritma KNN merupakan algoritma pembelajaran mesin sangat bergantung pada jumlah kernel untuk mendapatkan hasil prediksi [10], dimana penemuan jarak terdekat pada tahapan proses yang terjadi melalui algoritma K-NN dilakukan melalui pendekatan pelatihan data. Tahapan pada Algoritma klasifikasi K-NN adalah [11] Pada penelitian ini digunakan model perancangan UML, dimana diagram UML ideal digunakan untuk mendemonstrasikan model sistem Informasi agar lebih mudah dalam membangun kerangka sebuah sistem berorientasi objek [12].…”

Section: Metode Penelitianunclassified

Implementasi Algoritma K-Nearest Neighbor Terhadap Penentuan Risiko Kredit Usaha Mikro Kecil Dan Menengah

Ida¹,

Baharuddin²,

Faisal³

et al. 2023

jimik

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This research was carried out in the context of implementing the K-NN algorithm so that a source of information can be produced as a basis for supporting decisions on initial credit applications by customers so that they can help cooperative managers more as knowledge of the progress of credit proposals that are carried out at the Micro, Small and Medium Enterprise Cooperative Service Office ( SMEs) South Sulawesi Province. The K-Nearest Neighbor algorithm is used to classify objects based on attributes and training samples. Among them, from k objects, the k-Nearest Neighbor algorithm uses neighbor classification as the predicted value. The results show that the algorithm produces a classification with a faster calculation time based on the prediction of customer data resulting from the calculation.

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Section: Metode Penelitianunclassified

Implementasi Algoritma K-Nearest Neighbor Terhadap Penentuan Risiko Kredit Usaha Mikro Kecil Dan Menengah

Ida¹,

Baharuddin²,

Faisal³

et al. 2023

jimik

View full text Add to dashboard Cite

show abstract

“…Metode K-Nearest Neighbor (KNN) merupakan metode klasifikasi klasik yang paling sederhana (Wiyono & Abidin, 2018). Metode KNN sering juga disebut dengan Instance Based Learning (Zhang et al, 2018), KNN melakukan klasifikasi terhadap objek berdasarkan jarak (Muhathir et al, 2020) antara objek tersebut dengan objek lain (Siringoringo, 2018).…”

Section: K-nearest Neighbor (Knn)unclassified

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K â€“ Nearest Neighbor

Rambe

Tanjung

Muhathir

2022

JITE

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Pattern recognition is one of the sciences used to classify things based on quantitative measurements of the main features or properties of an object. Pattern recognition has been widely used in various fields of research. One of the pattern recognition that is often discussed is facial recognition. The face is one of the human biometrics that is often used as the main information of a person. Face recognition is a field of research with many applications in applications such as attendance, population data collection, security systems, and others. The research utilizes feature extraction of PCA (Principal Component Analysis), and K-NN (K – Nearest Neighbor) with variations of the distance formula by applying facial recognition attendance at the Safiatul Amaliyah School. This research is expected to get accurate results in detecting, recognizing, and comparing a person's face with a small error rate. The distance formula with accuracy level is presented with the equation Cityblock < Euclidian < Minkowski < Chebychev. The effect of applying the variation of the distance formula on the performance of the facial attendance recognition model is not too big, but it is better.

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“…Hasil ini diperoleh dengan mencoba nilai K adalah 3 sampai 60. Nilai prediksi kemudian dibandingkan, hasil prediksi yang salah dengan persentase terkecil adalah yang terbaik [7].…”

Section: Algoritma K-nn Machineunclassified

Akurasi Klasifikasi Pengguna terhadap Hotspot WiFi dengan Menggunakan Metode K-Nearest Neighbour

et al. 2021

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The Current wireless technology is used to find out where the user is in the room. Utilization of WiFi strength signal from the Access Point (AP) can provide information on the user position in a room. Alternative determination of the user's position in the room using WiFi Receive Signal Strength (RSS). This research was conducted by comparing the distance between users to 2 or more APs using the euclidean distance technique. The Euclidean distance technique is used as a distance calculator where there are two points in a 3-dimensional plane or space by measuring the length of the segment connecting two points. This technique is best for representing the distance between the users and the AP. The collection of RSS data uses the Fingerprinting technique. The RSS data was collected from 20 APs detected using the wifi analyzer application, from the results of the scanning, 709 RSS data were obtained. The RSS value is used as training data. K-Nearest Neighbor (K-NN) uses the Neighborhood Classification as the predictive value of the new test data so that K-NN can classify the closest distance from the new test data to the value of the existing training data. Based on the test results obtained an accuracy rate of 95% with K is 3. Based on the results of research that has been done that using the K-NN method obtained excellent results, with the highest accuracy rate of 95% with a minimum error value of 5%

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Implementation of K-Nearest Neighbour (Knn) Algorithm to Predict Student’s Performance

Cited by 11 publications

References 9 publications

Implementasi Algoritma K-Nearest Neighbor Terhadap Penentuan Risiko Kredit Usaha Mikro Kecil Dan Menengah

Implementasi Algoritma K-Nearest Neighbor Terhadap Penentuan Risiko Kredit Usaha Mikro Kecil Dan Menengah

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K â€“ Nearest Neighbor

Akurasi Klasifikasi Pengguna terhadap Hotspot WiFi dengan Menggunakan Metode K-Nearest Neighbour

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