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All Journal Brahmana : Jurnal Penerapan Kecerdasan Buatan
Heru Satria Tambunan
STIKOM Tunas Bangsa Pematangsiantar, Sumatera Utara, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Analisis Faktor Kompetensi Pedagogik Guru Dalam Proses Pembelajaran Menggunakan Algoritma C45 Ingkana Devi Cahaya; Heru Satria Tambunan; Harly Okprana
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 2, No 2 (2021): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v2i2.69

Abstract

Pedadogic competencies are the competencies that teachers must have in managing learning so that the process can run well. Mining data is a series of processes to explore the added value obtained from information or knowledge that can form a decision pattern that will later be used in analyzing pedagogical competency factors the teacher in the learning process for one semester is based on the criteria of understanding students, designing learning, evaluating learning outcomes, learning that educates and dialogues and develops students to share their potential. Pedagogic assessment is done by summing the value of each criterion and using a standard value to determine the final value. The application of the c45 algorithm can be used to prodece a decission tree that will be taken to determine the form of rules or rules that will be used as a decision tree
Analisis Data mining dengan Metode C.45 pada Klasifikasi Kenaikan Rata-Rata Volume Perikanan Tangkap Muhammad Ridho Matondang; Muhammad Ridwan Lubis; Heru Satria Tambunan
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 2, No 2 (2021): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v2i2.68

Abstract

Increasing the amount of demand for natural resource needs is increasing. One of them is natural resources in the sea and coast. The current condition of capture fisheries in Indonesia is not yet optimal. This is indicated by the increase in the volume of capture fisheries production which is very slow. The purpose of this study is to make data classification for the prediction of the average volume increase in capture fisheries with data mining techniques. Data mining techniques are applied to determine the data patterns of the capture fisheries dataset, so the results of the classification can be applied to evaluate the factors that affect the volume of capture fisheries. The classification algorithm used is C45. The results of the classification were tested with rapidminer in classifying data. The level of performance is indicated by the accuracy value. The accuracy value is obtained by testing the results of the classification of training data and testing data. Comparison of accuracy values between the algorithms used can be seen the best algorithm in making the classification of capture fisheries data.
Co-Authors Ingkana Devi Cahaya Muhammad Ridho Matondang Muhammad Ridwan Lubis Okprana, Harly