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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) JiTEKH (Jurnal Ilmiah Teknologi Harapan) IJISTECH Journal of Social Responsibility Projects by Higher Education Forum Bulletin of Multi-Disciplinary Science and Applied Technology Jurnal Lentera Pengabdian Jurnal Kajian Ilmiah Teknologi Informasi dan Komputer Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi
Annisa Fadillah Siregar
Universitas Budi Darma, Medan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : Pendas : Jurnah Ilmiah Pendidikan Dasar

 1 
PENGENALAN POLA BUNGA BERBASIS CITRA MENGGUNAKAN JARINGAN SARAF TIRUAN DENGAN ALGORITMA PERCEPTRON Fahrezi, Azrial; Saputra, Imam; Siregar, Annisa Fadillah
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, Nomor 04, Desember 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i04.18128

Abstract

Flowers are transformations of buds, including stems and leaves, with shapes and colors adapted to the plant's functions. They also serve as sites for fertilization and pollination. Flowers come in various shapes and colors, with over 250,000 flowering plant species known and classified into 350 families. Therefore, employing technology for flower pattern recognition is crucial for enhancing accuracy and efficiency. One effective method involves using Artificial Neural Networks (ANN) in conjunction with the perceptron algorithm. This algorithm has proven effective in image-based pattern recognition due to its ability to learn complex and linear patterns from image data. This study explores the use of neural networks, specifically the perceptron method, in recognizing flower patterns. The test utilizes sunflower image samples, with the perceptron algorithm applied to produce accurate and effective data in flower pattern recognition.
IMPLEMENTASI JARINGAN SARAF TIRUAN UNTUK MEMPREDIKSI TINGKAT PRODUKSI JAGUNG GILING MENGGUNAKAN METODE BACKPROPAGATION (STUDI KASUS: MIKRA MAKMUR BERSAMA) Aritonang, Reza Sri Rezeki; Saputra, Imam; Siregar, Annisa Fadillah
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 9 No. 04 (2024): Volume 09, Nomor 04, Desember 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v9i04.18160

Abstract

This study aims to implement Artificial Neural Networks (ANN) to predict corn flour production levels at the agricultural company Mikra Makmur Bersama using the backpropagation learning method. As a machine learning technique, ANN has the potential to enhance prediction accuracy by effectively analyzing historical data. Data on corn flour production from 2021 to 2023 was collected from the company and used to train the ANN model with a backpropagation architecture. This process involves feedforward and backward propagation to optimize neuron weights, aiming to produce accurate and reliable predictions. The backpropagation algorithm updates weights based on prediction errors and can adapt to complex patterns in the data. The results show that the implemented ANN model successfully predicted corn flour production levels with significant accuracy, as tested with data from 2021 to 2023. This study is expected to serve as a reference for applying ANN technology in other agricultural sectors and encourage the use of advanced methods to enhance efficiency and productivity.
Co-Authors Ade Rizka Aritonang, Reza Sri Rezeki Devi, Wanda Tofani Fahrezi, Azrial Farid Akbar Siregar Imam Saputra Imam Saputra Kurnia Ulfa Manik, Jens Presisken Mesran, Mesran Miya Andriyani Nelly Astuti Hasibuan Nola Dita Puspa Rizka, Ade Rizkah Fadilllah Sari, Arini Vika Setiadi, Eka Widya Ningsih Simangunsong, Evi O Sinurat, Sinar