Algoritma K-Nearest Neighbor untuk Memprediksi Prestasi Mahasiswa Berdasarkan Latar Belakang Pendidikan dan Ekonomi

Prasetyawan, Daru; Gatra, Rahmadhan

doi:10.14421/jiska.2022.7.1.56-67

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…K-Nearest Neighbors (K-NN), a simple and easy-to-implement algorithm in machine learning that is beneficial for tackling classification and regression problems [17]. K-NN searches for groupings of k objects from the training data that are most comparable to the objects in the test data [18].…”

Section: 2k-nearest Neighbormentioning

confidence: 99%

Analysis of Eye Disease Classification by Comparison of the Random Forest Method and K-Nearest Neighbor Method

Meidelfi,

Hendrick,

Sukma

et al. 2023

Int. J. of Adv. Sci. Comp. and Eng.

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Eye disease is a serious issue all over the world, and image-based classification systems play an important role in the early detection and management of eye disease. This research compares the performance between Random Forest (RF) and K-Nearest Neighbor (KNN) classification models in identifying eye disorders using image datasets divided into four classes: "normal," "glaucoma," "cataract," and "diabetic retinopathy."Â Â The dataset is converted into a feature vector and then divided into training data and test data subsets. The analysis results show that the RF model achieved an accuracy level of 80%, whereas the KNN model achieved 70%. Based on these findings, it is possible to conclude that the RF model outperforms the other models in categorizing the types of eye illnesses in the dataset. A Python-based website was also built utilizing the Flask framework to build an interactive and real-time eye illness diagnosis system. Users can upload photos of their retinas to this website and quickly receive eye disease detection results. The adoption of this technology has a tremendous impact, making eye disease detection solutions more accessible. Furthermore, this solution plays an important role in the early detection and effective management of eye health cases.

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Section: 2k-nearest Neighbormentioning

confidence: 99%

Analysis of Eye Disease Classification by Comparison of the Random Forest Method and K-Nearest Neighbor Method

Meidelfi,

Hendrick,

Sukma

et al. 2023

Int. J. of Adv. Sci. Comp. and Eng.

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“…Proses pembelajaran data mining melibatkan pengembalian dan identifikasi data, yang selanjutnya diolah menjadi pengetahuan dan wawasan yang bernillai [11] [12]. K-Nearest Neighbor (K-NN)merupakan suatu algoritma yang digunakan untuk mencari kasus dengan melakukan estimasi sejauh mana kemiripan antara kasus baru dengan kasus yang sudah ada sebelumnya [13] [14].…”

Section: Pendahuluanunclassified

Implementasi Algoritma K-Nearest Neighbor Untuk Menentukan Klasifikasi Kelulusan Mahasiswa Teknik Informatika

Andriani,

Nazir,

Candra

et al. 2023

JoSYC

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Kinerja sebuah universitas dapat dinilai semakin baik jika terdapat peningkatan jumlah mahasiswa yang berhasil menyelesaikan masa studinya tepat waktu. Perguruan tinggi harus meningkatkan kualitas akademik mahasiswa dalam proses perkuliahan untuk hasil yang optimal. Namun kenyataannya banyak keterlambatan kelulusan mahasiswa pada Universitas Islam Negri Sultan Syaif Kasim Riau terkhusus jurusan Teknik Informatika ini yang menjadi permasalahan. Oleh karena itu dibutuhkannya sebuah analisa mengklasifikasi data kelulusan mahasiswa dengan memanfaatkan proses data mining. Penelitian ini menerapkan algoritma K-Nearest Neighbor (K-NN) pada proses klasifikasi. Tujuan penelitian ini adalah hasil dari klasifikasi kelulusan ini diharapkan dapat memberikan kontribusi bagi pihak fakultas teknik dan universitas dalam melakukan evaluasi dan perbaikan terhadap sistem pembelajaran, sehingga menghasilkan lulusan tepat waktu dan berkualitas. Data yang digunakan pada penelitian ini sebanyak 613 data 5 tahun terakhir dari tahun 2016 hingga 2020 dengan pembagian data dengan rasio 80 data untuk pelatihan (training) dan 20 data untuk pengujian (testing). Hasil evaluasi confusion matrix dari K = 3, K = 5, K = 7 menghasilkan akurasi tertinggi diperoleh ketika K = 3 dengan akurasi 93,06%, presisi 99,09%, dan recall 99,58%. Dari hasil penelitian, dapat ditarik kesimpulan bahwa penerapan data mining berhasil menciptakan model klasifikasi dengan memanfaatkan algoritma K-Nearest Neighbor (K-NN) dalam mengklasifikasikan status kelulusan mahasiswa pada program sudi Teknik Informatika di Universitas Islam Negeri Sultan Syarif Kasim Riau

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“…Totally, the original data contains redundant/irrelevant features [21]; In detail, the outside features provide misleading information, which leads to a fall in the learning accuracy. For example, in K-Nearest Neighbor, the irrelevant features increase the distances between samples from the same class, which makes it more challenging to truly classify data [4,13].…”

Section: Introductionmentioning

confidence: 99%

Effective time-aware knowledge compression For recommender systems

Yazdi

Mahdavi

Kheirabadi

2022

Preprint

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Due to the big data and wide availability of various types of information, data compression plays a significant role in the current age. This task can be done in two aspects. First, it can be done by reducing the redundant features of each entity. Second, it can be employed on each record of the corresponded dataset. These techniques should maintain the crucial and useful information which presents a pivotal role in further process. This work presents an effective knowledge compression for recommender systems based on the attention mechanism. In this method, the data compression is performed in two feature and record levels. The technique is based on time windows and the activity of users. The result of this technique can be efficiently utilized for deep networks which the amount of data is one of the serious problems. The experimental results show that with the help of this technique not only does it reduce the amount of data and process time, but also it can reach acceptable and considerable accuracy in the training and testing phases of networks.

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Algoritma K-Nearest Neighbor untuk Memprediksi Prestasi Mahasiswa Berdasarkan Latar Belakang Pendidikan dan Ekonomi

Cited by 3 publications

References 6 publications

Analysis of Eye Disease Classification by Comparison of the Random Forest Method and K-Nearest Neighbor Method

Analysis of Eye Disease Classification by Comparison of the Random Forest Method and K-Nearest Neighbor Method

Implementasi Algoritma K-Nearest Neighbor Untuk Menentukan Klasifikasi Kelulusan Mahasiswa Teknik Informatika

Effective time-aware knowledge compression For recommender systems

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