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All Journal Techno.Com: Jurnal Teknologi Informasi JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan JAIS (Journal of Applied Intelligent System) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal ICT : Information Communication & Technology Infotekmesin JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Innovation Information Technology and Application (JINITA) Brahmana : Jurnal Penerapan Kecerdasan Buatan JOINT (Journal of Information Technology Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Puan Indonesia Tedc Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Journal of Informatics and Electronics Engineering Journal Of Artificial Intelligence And Software Engineering Competitive Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics Journal of New Trends in Sciences JPPkM: Jurnal Pengabdian dan Pemberdayaan kepada Masyarakat Journal of Applied Information Technology and Innovation
Castaka Agus Sugianto
Politeknik TEDC
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Journal : Infotekmesin

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OPTIMALISASI ALGORITMA C4.5 MENGGUNAKAN ALGORITMA GENETIKA UNTUK PREDIKSI KELULUSAN SISWA SMKN 2 CIMAHI Sugianto, Castaka Agus; Pujiyanita, Nadya Selvi
Infotekmesin Vol 10, No 2 (2019): Infotekmesin: Juli 2019
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (530.677 KB) | DOI: 10.35970/infotekmesin.v10i2.35

Abstract

SMK 2 Cimahi is an educational institution Vocational high school in Cimahi. Vocational high schools play an important role in creating the best graduates for the needs of the workforce. The graduation process is the latest activity of student management. Student graduation criteria for national exams must meet minimum scores, complete all subjects, take school exams, national exams, and competency tests. In the 2015 class of 300 students, students who did not pass the national exam were 1 student, in the 2016 class of 299 students, students who did not pass the national exam were 1 student, whereas in the 2017 class totaling 302 students 100% students graduate on time. However in the coming year, it is still unknown whether students will be 100% graduated or not. Therefore this study was conducted to predict student graduation using the C4.5 algorithm + Genetic Algorithm. C4.5 algorithm has an accuracy rate of 99.78%, precision of 99.78%, 100% recall and 1 second execution time. While the C4.5 + genetic algorithm has an accuracy rate of 99.78%, precision of 99.78%, 100% recall AUC 0.500, and 36 second execution time and after T-test testing between the C4.5 Algorithm and C4 Algorithm .5 +, Genetic Algorithm is said to be significant if alpha = 0.050. The results of this study obtained alpha = 1,000.
OPTIMALISASI ALGORITMA C4.5 MENGGUNAKAN ALGORITMA GENETIKA UNTUK PREDIKSI KELULUSAN SISWA SMKN 2 CIMAHI Castaka Agus Sugianto; Nadya Selvi Pujiyanita
Infotekmesin Vol 10 No 2 (2019): Infotekmesin: Juli 2019
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v10i2.35

Abstract

SMK 2 Cimahi is an educational institution Vocational high school in Cimahi. Vocational high schools play an important role in creating the best graduates for the needs of the workforce. The graduation process is the latest activity of student management. Student graduation criteria for national exams must meet minimum scores, complete all subjects, take school exams, national exams, and competency tests. In the 2015 class of 300 students, students who did not pass the national exam were 1 student, in the 2016 class of 299 students, students who did not pass the national exam were 1 student, whereas in the 2017 class totaling 302 students 100% students graduate on time. However in the coming year, it is still unknown whether students will be 100% graduated or not. Therefore this study was conducted to predict student graduation using the C4.5 algorithm + Genetic Algorithm. C4.5 algorithm has an accuracy rate of 99.78%, precision of 99.78%, 100% recall and 1 second execution time. While the C4.5 + genetic algorithm has an accuracy rate of 99.78%, precision of 99.78%, 100% recall AUC 0.500, and 36 second execution time and after T-test testing between the C4.5 Algorithm and C4 Algorithm .5 +, Genetic Algorithm is said to be significant if alpha = 0.050. The results of this study obtained alpha = 1,000.
Co-Authors Ade Yuliana Aditia Gusman Aldy Noer Tjahyo Apnena, Riri Damayanti Aqmal Mulqy Bagja Laksana Ari Sudrajat Aris Haris Rismayana Arya Aditya Ayu Hendrati Rahayu Candra Irawan Christy Atika Sari Dadan Saepul Ramdan Dahlan Dahlan Dini Rohmayani Eko Hari Rachmawanto Faishal Fachruddin Febianto, Aldi Kristiawan Firdhani Faujiyah Fitrah, La Ode Muhammad Fredricka, Jhoanne Gerinata Ginting Heldi Akbar Kosasih Hidayah, Hamimah Nur Himawan, Aldi Iqna Aulia Iskandar, Rachmat Juniardi, Trianda Kosasih, Heldi Akbar Lela Kesmasari Maulana, Firdi Rizky Melani Nur Astita Mohamed Doheir Muhammad Ridwan Nadya Selvi Pujiyanita Nafea, Mohammed Mansoor Nandhita Ayusari Nia Ekawati, Nia Novita Lestari Anggreini Pebriana, Sela Perdana, Rangga Satria Pujiyanita, Nadya Selvi Rahmawan, Septiadi Ramdan, Dadan Saepul Riansyah Ramadhan, Gery Riyan Riyan Rizqy Dimas Monica Rosdianti, Elfi Safira, Rifa Sari, Putri Nurlaela Shandy Tresnawati Sukmawati, Dini Syaefuloh, Herman Syams, Fawwaz Muhammad Syifa Fauzia Tahir Tahir, Syifa Fauzia Tati Ernawati Taufik Fajar Mustafa Tresnawati, Shandy Tri Herdiawan Apandi Tri Pratiwi Olivia Riska Bokings Wanaziana, Keny Kirana Zulkipli ., Zulkipli Zundi, Teguh Muhammad