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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Tunas Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat BEES: Bulletin of Electrical and Electronics Engineering Bulletin of Data Science Journal of Artificial Intelligence and Engineering Applications (JAIEA) JOMLAI: Journal of Machine Learning and Artificial Intelligence Journal of Informatics, Electrical and Electronics Engineering Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Okprana, Harly
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Journal : Journal of Informatics, Electrical and Electronics Engineering

 1 
Rekomendasi Pemilihan Peserta Lomba Kompetensi Siswa (LKS) Tingkat Kejuruan Dengan Teknik Promethee Andi Sahputra; Eka Irawan; Harly Okprana
Journal of Informatics, Electrical and Electronics Engineering Vol. 1 No. 1 (2021): September 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Participants in the student competency competition (LKS) of the RK Bintang Timur Pematangsiantar Private Vocational School were conducted manually by collecting students, counting and comparing with supporting data such as competency report cards by productive teachers. Therefore the RK Bintang Timur Pematangsiantar Private Vocational School needs to overcome problems in selecting students as participants in the student competency competition to be more effective and on target. The Promethee algorithm is used in order of order or priority in an efficient and simple multi-criteria analysis. Based on the research data sample, the highest score was obtained in alternative A1 with a value of 0.406 with 6 criteria, namely academic value, number of absences, student council scores, attitude, craft and tidiness. Then proceed to the matrix normalization process which produces the outflow, inflow and net flow values ??used in the ranking process
Penerapan Jaringan Saraf Tiruan Backprogation Dalam Memprediksi Jumlah Pasien Rumah Sakit Dea Dwi Rizki Tampubolon; Irfan Sudahri Damanik; Harly Okprana
Journal of Informatics, Electrical and Electronics Engineering Vol. 1 No. 2 (2021): Desember 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Artificial Neural Network is one of the artificial representations of the human brain that always tries to simulate the learning process in the human brain. Artificial Neural Network (ANN) is defined as an information processing system that has characteristics similar to human neural networks. ANN is an information processing system that has similar characteristics to a biological neural network. The hospital is an integral part of a social and health organization with the function of providing services, healing disease and preventing disease to the community. Backpropagation network is one of the algorithms that are often used in solving problems. complicated problem. This algorithm is also used in regulatory applications because the training process is based on a simple relationship. The problems that occur at the Djasemen Saragih Pematangsiantar Hospital are the lack of doctors working at the hospital so that there is a density of patients that occur every year, and the absence of patient rooms that are placed at home. ill when there was an increase that was not recognized by the hospital. With the data available every year, it is expected that the use of artificial neural networks using the backprogation method is very useful for the hospital in determining the prediction of the number of hospital patients for the next year can be used as the basic material for changes or additional patient rooms when there is an excess of predicted patients.
Co-Authors Agus Perdana Windarto Ambariyanto Ambariyanto Andi Sahputra Annas Prasetio Aulanda, Lulu Bahrudi Efendi Damanik Cici Astria Dea Dwi Rizki Tampubolon Dedy Hartama Eka Irawan Eka Irawan F Fitriyani GS , Achmad Daengs Hadinata, Jaya Tata Halimatusakdiah Pohan Haryoto, Parawystia Prabasini Hasudungan Siahaan Hendro Sanjaya Heru Satria Tambunan Heru Satria Tambunan Ilham Syahputra Saragih Ina Syafitri Ingkana Devi Cahaya Irfan Sudahri Damanik Jaya Tata Hardinata Millah Sari Muhammad Zarlis Nasution, Rizki Alfadillah Nasution, Selvi Salsabillah Nasution, Siti Andry Yani Ni Luh Wiwik Sri Rahayu Ginantra Poningsih Poningsih Poningsih Poningsih, Poningsih Primatua Sitompul Rizky Khairunnisa Sormin Robiansyah, Wendi Sepridho, Jaka Sonia Tarigan Surya Darma Syahfitri, Retno Ayu Syahputra, Muhammad Riza Teuku Afriliansyah Tri Ayu Lestari Tri Novika Wanto, Anjar Widodo Saputra Winanjaya, Riki Yuegilion Pranayama