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Wildan Bakti Nugroho
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PENGATURAN MENU MAKAN HARIAN BAGI KESEHATAN BALITA MENGGUNAKAN ALGORITMA GENETIKA Vini Yolanda Putri; Dayu Agastya Rani; Dyan Anugerah Ramadani; Al Rizal Fikri Sulthoni Arrahman; Wildan Bakti Nugroho; Nur Hidayatul Afidah; Moh. Ziyaul Haq Ramadhani; Trismayanti Dwi Puspitasari
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 2 (2019): JURNAL SIMETRIS VOLUME 10 NO 2 TAHUN 2019
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (811.467 KB) | DOI: 10.24176/simet.v10i2.3392

Abstract

Balita  merupakan  masa  terjadinya  proses  pertumbuhan  dan  perkembangan dengan  cepat.  Jika kebutuhan gizi balita tidak terpenuhi, maka dikhawatirkan tidak tercapainya pertumbuhan dan perkembangan yang optimal. Hal tersebut dapat menyebabkan masalah kekurangan gizi, yang selanjutnya dapat beresiko menurunkan derajat kesehatan. Berdasarkan hal tersebut, perlu dirancang sebuah sistem informasi untuk mengatur kebutuhan gizi pada kesehatan balita. Metode yang digunakan ialah algoritma genetika, algoritma ini bekerja dengan sebuah populasi yang terdiri dari individu-individu. Dalam menu makan yang akan disusun, kromosom hanya akan mengkodekan jenis karbohidrat, protein, dan lemak. Pada   metode   ini,   seleksi   yang   digunakan   menggunakan  metode   elitism   selection   dan   mutasi menggunakan reciprocal exchange mutation. Diperoleh kebutuhan total kalori sebesar 115.76 kalori, kebutuhan karbohidrat sebesar 209 gram, kebutuhan protein sebesar 28.7 gram dan kebutuhan lemak sebesar 25,32 gram berdasarkan hasil dari proses crossover, mutasi dan seleksi pada generasi ke-2 dengan nilai rata-rata fitness 0,0879.
Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca) Zilvanhisna Emka Fitri; Wildan Bakti Nugroho; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.849 KB) | DOI: 10.17529/jre.v17i2.20806

Abstract

Every region in Indonesia has a very large diversity of banana species, but no system records information about the characteristics of banana varieties. The purpose of this research is to make an encyclopedia of banana types that can be used for learning by classifying banana varieties using banana images. This banana variety classification system uses image processing techniques and artificial neural network methods as classification methods.The varieties of bananas used are pisang merah, pisang pisang mas kirana, pisang klutuk, pisang raja and pisang cavendis. The parameters used are color features (Red, Green, and Blue) and shape features (area, perimeter, diameter, and length of fruit). The intelligent system used is the Backpropagation method and the Radial Basis Function Neural Network. The results showed that both methods were able to classify banana varieties with an accuracy rate of 98% for Backpropagation and 100% for the Radial Basis Function Neural Network.
Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca) Zilvanhisna Emka Fitri; Wildan Bakti Nugroho; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v17i2.20806

Abstract

Every region in Indonesia has a very large diversity of banana species, but no system records information about the characteristics of banana varieties. The purpose of this research is to make an encyclopedia of banana types that can be used for learning by classifying banana varieties using banana images. This banana variety classification system uses image processing techniques and artificial neural network methods as classification methods.The varieties of bananas used are pisang merah, pisang pisang mas kirana, pisang klutuk, pisang raja and pisang cavendis. The parameters used are color features (Red, Green, and Blue) and shape features (area, perimeter, diameter, and length of fruit). The intelligent system used is the Backpropagation method and the Radial Basis Function Neural Network. The results showed that both methods were able to classify banana varieties with an accuracy rate of 98% for Backpropagation and 100% for the Radial Basis Function Neural Network.
Co-Authors Abdul Madjid Al Rizal Fikri Sulthoni Arrahman Dayu Agastya Rani Dyan Anugerah Ramadani Imron, Arizal Mujibtamala Nanda Moh. Ziyaul Haq Ramadhani Nur Hidayatul Afidah Puspitasari, Trismayanti Dwi Vini Yolanda Putri Zilvanhisna Emka Fitri