K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation

Hidayati, Nur; Hermawan, Arief

doi:10.21831/jeatech.v2i2.42777

Cited by 27 publications

(11 citation statements)

References 10 publications

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“…In this study, the indigo k used was 3 with a resulting accuracy rate of 92.07%, while the k value of less than 3 and more than 3 accuracy levels produced was less than 92.07%. The results of this study are different from the results of research from Hidayati & Hermawan (2021) which showed that the maximum k value with the highest accuracy was 7 with an accuracy rate of 85.25%. However, the difference in results is more due to the use of different measure types and mixed measures.…”

Section: Discussioncontrasting

confidence: 99%

Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

Kartianom

Arpandi

Kassymova

et al. 2022

Ekspose J. Penelit. Huk. dan Pendidik.

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This study aims to determine the prediction model of the graduation status of prospective teacher students at IAIN Bone in terms of attributes, accuracy levels, and differences in the level of accuracy produced in the attributes of decision tree C4.5, Naïve Bayes, and k-NN data mining algorithms. This research uses a quantitative approach by adopting the Data Mining method. This research was conducted at IAIN Bone. The data collection process in this study used documentation techniques in the form of data on alumni of the Tarbiyah Faculty of IAIN Bone. The data analysis used was a descriptive analysis using decision tree C4.5, Naive Bayes, and k-NN data mining algorithms assisted by the RapidMiner application. The results of this study show that (1) model prediction of the graduation status of prospective teacher students in IAIN Bone in terms of attributes generated in the Decision Tree C4.5 and Naïve Bayes data mining algorithms consist of gender, age, Semester 1 IP, Semester 2 IP, Semester 3 IP, Semester 4 IP, and GPA, while the attributes produced in k-NN data mining algorithm consists of gender, regional origin, number of siblings, age, IP Semester 1, IP Semester 2, IP Semester 3, IP Semester 4, and GPA; (2) model prediction of graduation status of iain bone teacher candidate students in terms of the accuracy rate generated in the Decision Tree C4.5 data mining algorithm of 93.90%, Naïve Bayes by 90.24%, and k-NN of 92.07%; and (3) there was no significant difference between the accuracy rate produced by decision tree's data mining algorithm. C4.5 and Naïve Bayes (p-value = 1.00); Decision Tree C4.5 and k-NN (p-value = 1.00); as well as Naïve Bayes and k-NN (p-value = 1.00) in predicting the graduation status of iain bone teacher candidate students.

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Section: Discussioncontrasting

confidence: 99%

Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

Kartianom

Arpandi

Kassymova

et al. 2022

Ekspose J. Penelit. Huk. dan Pendidik.

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show abstract

“…This algorithm was executed through the Rapidminer software, and it was tested on a dataset comprising 380 training data points and 163 test data points. The findings reveal that employing the Euclidean and Manhattan distance methods for classifying graduating students yielded the highest accuracy rate of 85.28% when the value of k was set to 7 [25]. Research Application of Data Mining in Classifying Student Data Based on Academic Data Before College and Study Period Using the K-Medoids Method with the variables average math scores [26].…”

Section: Related Researchmentioning

confidence: 99%

Comparative Analysis of Naive Bayes and K-NN Approaches to Predict Timely Graduation using Academic History

Riadi,

Umar,

Anggara

2024

IJCDS

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Graduation is a pivotal moment in higher education, significantly impacting institutional accreditation and public perception. This study aims to ensure that all students graduate punctually, recognizing the critical role of higher education in achieving this goal. Central to this effort are comprehensive datasets that capture academic performance throughout both undergraduate and graduate studies. These datasets include details such as university and program specifics, undergraduate and master's GPAs, TOEFL scores, and the duration of study. By leveraging classification techniques within data mining, particularly K-NN and Naive Bayes, a comparative analysis was conducted to precisely predict the on-time completion of graduate students. The process of predicting graduation involves several stages, including data preprocessing, transformation, and the segmentation of data into training and testing sets. Subsequently, the selected methods are applied, and analyses are undertaken to accurately forecast graduation outcomes. Experimental findings reveal an 80% accuracy rate for Naive Bayes and 73% for K-NN. Notably, Naive Bayes demonstrates superior efficacy in predicting on-time graduation. However, to further refine accuracy, it is necessary to expand datasets and diversify the variables used in the analysis, such as incorporating additional academic and non-academic factors that may influence graduation timelines. The insights derived from this research hold significant implications for academic institutions, offering valuable guidance for implementing proactive measures to support students in completing their studies within the expected timeframe. By utilizing the findings of this study, educational institutions can develop tailored strategies and interventions to address potential barriers to timely graduation, such as enhancing academic advising, providing targeted support services, and optimizing course scheduling. These efforts will ultimately foster student success, improve institutional outcomes, and contribute to the overall excellence of higher education institutions.

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“…i = Variabel data. Algoritma k-NN nilai k dapat berupa konstanta yang ditentukan atau variabel acak, dimana metrik jarak yang digunakan dapat berupa jarak Euclidean, Minkowski, Manhattan, Chebychev, Korelasi, dan lain-lain [26].…”

Section: ) K-nnunclassified

Optimasi Klasifikasi Data Stunting Melalui Ensemble Learning pada Label Multiclass dengan Imbalance Data

Prasetyo,

Nugroho

2024

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Salah satu permasalahan kesehatan yang sering ditemui di banyak negara termasuk Indonesia adalah stunting. Stunting telah mendapat banyak perhatian di Indonesia, terlihat dari alokasi APBN masing-masing sebesar Rp48,3 triliun dan Rp49,4 triliun pada tahun 2022 dan 2023 untuk bidang ini. Pada tahun 2022, Kementerian Kesehatan merilis temuan dari Survei Status Gizi Indonesia (SSGI) yang menyatakan bahwa angka stunting di Indonesia mencapai 21,6% pada saat Rapat Kerja Nasional BKKBN pada 25 Januari 2023.Hal ini menunjukkan pentingnya untuk mengerti pemahaman mendalam tentang faktor-faktor yang mengidentifikasi anak-anak berisiko tinggi terkena stunting. Banyak penelitian sebelumnya yang membahas faktor resiko stunting, namun masih sedikit penerapannya dalam metode machine learning, dalam data yang kompleks dan tidak seimbang.Penelitian ini mengevaluasi kinerja dari berbagai metode machine learning yang bertujuan dapat memberikan kontribusi penting dalam bidang kesehatan anak dan analisis data. Diantara metode machine learning yang dipilih metode Bagging Decision Tree mendapatkan nilai accuracy yang terbaik sebesar 78,93%, precision 78% dan recall sebesar 77,99%. Dalam penelitian ini menunjukkan bahwa metode ensemble learning mampu bekerja dengan baik dalam atribut multiclass dan data yang tidak seimbang pada dataset pertumbuhan balita.

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K-Nearest Neighbor (K-NN) algorithm with Euclidean and Manhattan in classification of student graduation

Cited by 27 publications

References 10 publications

Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

Comparative Analysis of Naive Bayes and K-NN Approaches to Predict Timely Graduation using Academic History

Optimasi Klasifikasi Data Stunting Melalui Ensemble Learning pada Label Multiclass dengan Imbalance Data

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