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Memprediksi Jumlah Siswa Baru Menggunakan Metode Backpropagation (STUDI KASUS: SMK HARAPAN BANGSA KUALA) Kris Jayanti; Katen Lumbanbatu; Suci Ramadani
JUKI : Jurnal Komputer dan Informatika Vol. 3 No. 1 (2021): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2021
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v3i1.40

Abstract

Artificial Neural Network (ANN) and time series data can be used for forecasting methods well. Artificial Neural Network is a method whose working principle is adapted from a mathematical model in humans or biological nerves. Neural networks are characterized by; (1) the pattern of connections between neurons (called architecture), (2) determining the weight of the connection (called training or learning), and (3) the activation function. The research objective was to obtain the best artificial neural network architecture, comparing the two methods of Backpropogation Neural Networks with the Radial Base Function Artificial Neural Network (RBF) method. This research is a research using real data (true experimental). This research was conducted at SMK Harapan Bangsa Kuala, which was obtained from 2015 to 2019. The results showed that for one iteration using the backpropagation method the result was 0,378197657 with a squared error 0.143033468, then the results achieved were not in accordance with the target.
JARINGAN SYARAF TIRUAN DENGAN ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI NILAI UJIAN KOMPETENSI SISWA (STUDI KASUS SMKS JABAL RAHMAH STABAT) Kiki Sri Handayani; Katen Lumbanbatu; Magdalena Simanjuntak
Jurnal Abdi Ilmu Vol 14 No 1 (2021): Jurnal Abdi Ilmu
Publisher : UNIVERSITAS PEMBANGUNAN PANCA BUDI

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Abstract

Competency testing is a process of assessment (assessment) both technical and non-technical through the collection of relevant evidence to determine whether a person is competent or not yet competent in a certain competency unit or job qualification. The implementation of the series of "tests" is basically to determine the level of knowledge, skills and personality of students. To find out the passing standards of student competence in facing exams, a method is needed to process the old student grade data to predict the value of students who will take the national exam, namely by using the artificial neural network method with Backpropagation, the results obtained are 0.55178871 with the number of squared errors. 0.004595309, then the result has reached the target, then the iteration stops.
SISTEM PAKAR DIAGNOSA PENYAKIT PADA IKAN LELE DENGAN MENGGUNAKAN METODE CERTAINTY FACTOR Yudha Ryansyah; Katen Lumbanbatu; Marto Sihombing
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 3, No 2 (2022): Desember 2022
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v3i2.2356

Abstract

Abstrak— Ikan lele merupakan salah satu ikan air tawar yang sangat banyak dan diminati oleh masyarakat. Banyaknya minat masyarakat akan ikan lele, tidak jarang masyarakat yang membudidayakan ikan lele, mulai dari kolam kecil rumahan hingga peternak besar. Pembudidayaan ikan lele harus dilakukan dengan perawatan yang maksimal untuk mendapatkan keuntungan yang maksimal. Tapi dalam membudidayakan ikan lele pasti ada masalah dalam proses pembudidayaannya, salah satunya adalah faktor penyakit. Munculnya gangguan penyakit pada ikan lele merupakan resiko yang harus selalu diantisipasi oleh pembudidaya. Setiap pembudidaya ikan lele harus memilki pengetahuan akan pencegahan dan pengobatan terhadap penyakit ikan lele. Namun tidak semua pembudidaya memilki pengetahuan yang cukup untuk mengatasi penyakit pada ikan lele. Tidak adanya pakar atau dokter yang selalu siap siaga juga menjadi masalah penyakit ikan lele ini. Diperlukan sebuah sistem yang dapat menyerupai seorang pakar yang dapat mendiagnosa penyakit ikan lele. Sistem pakar merupakan salah satu cabang ilmu dari AI yang membuat penggunaan secara luas mengenai pengetahuan khusus untuk penyelesaian masalah tingkat manusia (seorang pakar). Metode Certainty Factor merupakan suatu metode untuk membuktikan apakah suatu fakta itu pasti ataukah tidak pasti yang biasanya digunakan dalam sistem pakar. Metode ini sangat cocok untuk sistem pakar yang mendiagnosis sesuatu yang belum pasti. Sistem pakar yang dibangun mampu untuk mendiagnosa penyakit ikan lele secara tepat dan cepat, serta dapat mengatasi masalah penyakit ikan lele secara efektif, sehingga para pembudidaya atau peternak dapat menghasilkan hasil panen sesuai dengan yang diharapkan.Kata Kunci: Certainty Factor, Ikan Lele, Sistem Pakar.Abstract— Catfish is one of the freshwater fish that is very much in demand by the public. There is a lot of public interest in catfish, it is not uncommon for people who cultivate catfish, ranging from small home ponds to large breeders. Cultivation of catfish must be done with maximum care to get maximum profit. But in cultivating catfish, there must be problems in the cultivation process, one of which is the disease factor. The emergence of disease disorders in catfish is a risk that must always be anticipated by farmers. Every catfish cultivator must have knowledge of the prevention and treatment of catfish diseases. However, not all farmers have sufficient knowledge to treat catfish disease. The absence of experts or doctors who are always on standby is also a problem for this catfish disease. We need a system that can resemble an expert who can diagnose catfish disease. An expert system is a branch of AI that makes extensive use of specialized knowledge for solving human-level problems (an expert). The Certainty Factor method is a method to prove whether a fact is certain or uncertain which is usually used in expert systems. This method is very suitable for expert systems that diagnose something that is not certain. The expert system built is able to diagnose catfish diseases accurately and quickly, and can effectively deal with catfish disease problems, so that cultivators or breeders can produce harvests as expected.Keywords - Catfish, Certainty Factor, Expert System.