Novhirtamely Kahar
Program Studi Teknik Informatika STMIK Nurdin Hamzah Jalan Kolonel Abunjani , Sipin, Jambi

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Journal : Arcitech: Journal of Computer Science and Artificial Intelligence

Penerapan Data Mining Untuk Memprediksi Daya Serap Lulusan Siswa Menggunakan Algoritma Native Bayes Waru, Daka; Astuti, Reny Wahyuning; Kahar, Novhirtamely
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 1 No. 1 (2021): June 2021
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (791.95 KB) | DOI: 10.29240/arcitech.v1i1.3294

Abstract

The importance of predicting the absorption of Vocational High School (SMK) graduates in the world of work, especially SMK Negeri 9 Muaro Jambi which is not yet known about the prediction of the world of work that accepts SMK graduates so that the purpose of this study is to analyze the prediction of the accuracy of the absorption of graduates of SMK Negeri 9 Muaro Jambi as material. a reference to see whether the graduates of SMK Negeri 9 Muaro Jambi have achieved the expected goals or not so that this analysis can be used as input for schools to improve the competence of SMK students. This implementation is assisted by using the Rapidminer and WEKA applications with 100 alumni work data input. The attributes used in this analysis process are Department, Waiting Time and Field of Work and Class of Work Field Accuracy. The process in this analysis is carried out with data that has been provided with the Naïve Bayes Classification Method to predict the absorption of graduates. The results of this study the highest accuracy value in the Rapidminer application is at 100% and WEKA is at 100%.
Penerapan Data Mining Untuk Memprediksi Daya Serap Lulusan Siswa Menggunakan Algoritma Native Bayes Waru, Daka; Astuti, Reny Wahyuning; Kahar, Novhirtamely
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 1 No. 1 (2021): June 2021
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (791.95 KB) | DOI: 10.29240/arcitech.v1i1.3294

Abstract

The importance of predicting the absorption of Vocational High School (SMK) graduates in the world of work, especially SMK Negeri 9 Muaro Jambi which is not yet known about the prediction of the world of work that accepts SMK graduates so that the purpose of this study is to analyze the prediction of the accuracy of the absorption of graduates of SMK Negeri 9 Muaro Jambi as material. a reference to see whether the graduates of SMK Negeri 9 Muaro Jambi have achieved the expected goals or not so that this analysis can be used as input for schools to improve the competence of SMK students. This implementation is assisted by using the Rapidminer and WEKA applications with 100 alumni work data input. The attributes used in this analysis process are Department, Waiting Time and Field of Work and Class of Work Field Accuracy. The process in this analysis is carried out with data that has been provided with the Naïve Bayes Classification Method to predict the absorption of graduates. The results of this study the highest accuracy value in the Rapidminer application is at 100% and WEKA is at 100%.
Predicting Early Childhood Readiness to Enter Elementary School Using the Naive Bayes Classification Puspitorini, Sukma; Kahar, Novhirtamely; Kartika, Ikah
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.11635

Abstract

This study aims to examine the readiness and maturity of early childhood in entering elementary school using the Naïve Bayes method. This analysis involves variables such as gender, age, aspects of physical-motor, cognitive, social-emotional development, and literacy skills which include reading, writing, arithmetic, and children's level of independence. The readiness category is classified into two classes, namely "ready" and "not ready". This prediction model is designed to provide a comprehensive understanding of the factors that affect the classification results, so that the evaluation process can be carried out in a transparent, objective, and data-driven manner. This research is expected to be a reference for other educational institutions in implementing a similar model to evaluate student readiness systematically. By adjusting variables and data according to local needs, this model has the potential to support more accurate and standardized decision-making, as well as improve the quality of early childhood preparation in entering formal education. The results show that the Naïve Bayes method is able to achieve an accuracy level of 93.33%, confirming its effectiveness in identifying early childhood readiness optimally.