Mengidentifikasi Tanaman Beracun pada Pola Daun dengan Jaringan Syaraf Tiruan Learning Vector Quantification

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Published: Jun 30, 2019
DOI: https://doi.org/10.35870/jtik.v3i1.47
Keywords:
The plant is poisonous pattern recognition Learning Vector Quantification

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Muhammad Jurnalies Habibie
Universitas Trilogi

Abstract

Technology nowadays is starting to go very fast, so that all people can use it. Toxic plants are very dangerous if consumed. Therefore to avoid undesirable events, an introduction to the community is needed to find out which plants are poisonous. Plants have many different types to recognize poisonous plants can be seen from the recognition of leaf patterns in these plants. For this reason, in order to determine the use of Learning Vector Quantification artificial neural networks. In this study, the use of input photos obtained from the camera. Photos will be processed later to extract the characteristics. Next, the process of pattern recognition can get the features in the photo. So that later it gets its characteristics. then the classification process uses the Learning Vector Quantification artificial neural network method. This research was conducted to be able to distinguish poisonous plants from those that are not. Which later the data is collected for grouping in accordance with the same data, so that information can be set about the plant.

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How to Cite
Habibie, M. J. (2019). Mengidentifikasi Tanaman Beracun pada Pola Daun dengan Jaringan Syaraf Tiruan Learning Vector Quantification. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 3(1), 7–12. https://doi.org/10.35870/jtik.v3i1.47
Issue
Vol. 3 No. 1 (2019): JANUARY-JUNE 2019
Section
Computer & Communication Science
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References

Asriani, F., & Nugraha, A. W. W. (2009). Pengenalan Pola Aksara Jawa Tulisan Tangan dengan Jaringan Syaraf Tiruan Perambatan-Balik. Dinamika Rekayasa, 5(2), 34–36.

Backpropagation, D. A. (n.d.). Pengenalan pola huruf tulisan tangan menggunakan jaringan syaraf tiruan dengan algoritma, 100.

Firmansyah, R. A., & Odinanto, T. (n.d.). JARINGAN SYARAF TIRUAN PADA APLIKASI SISTEM, 85–90.

Mantala, R. (2014). Pengenalan Pola Karakter Alfabet Menggunakan Artificial Neural Network Dengan Adaptive Linear Neuron, (1), 1–4.

Nugroho, S., & Harjoko, A. (2005). Penerapan Jaringan Syaraf Tiruan Untuk Mendeteksi Posisi Wajah Manusia Pada Citra Digital. Seminar Nasional Aplikasi Teknologi Informasi, 2005(Snati), 1–6.

Pattiserlihun, A., Setiawan, A., Trihandaru, S., Fisika, P. S., Sains, F., Kristen, U., … Indonesia, T. (n.d.). Pola Tulisan Dengan Metode Backpropagation, 1–9.

Prabowo, A., Sarwoko, E. A., & Riyanto, D. E. (2006). Perbandingan Antara Metode Kohonen Neural Network dengan Metode Learning Vector Quantization Pada Pengenalan Pola Tandatangan. Journal Sains & Matematika (JSM), 14, 147–153.

Pratama, A., Tritoasmoro, I. I., Susatio, E., & Kunci, K. (2013). diteliti sebagai Tugas Akhir oleh Isha Nindya Hastari , menggunakan metode adalah JST LVQ ( Jaringan Syaraf Tiruan Learning Vector Quantization ) dan untuk.

Qur’ani, D. Y., & Rosmalinda, S. (2010). Jaringan Syaraf Tiruan Learning Vector Quantization untuk Aplikasi Pengenalan Tanda Tangan (Artificial Neural Network Learning Vector Quantization for Introduction Application Signatures). Seminar Nasional Aplikasi Teknologi Informasi 2010 (SNATI 2010), 1(Snati), 1–5.

Wahyono, E. S., Informatika, J. T., Gunadarma, U., Pola, P., & Polisi, N. (n.d.). IDENTIFIKASI NOMOR POLISI MOBIL MENGGUNAKAN METODE JARINGAN SARAF BUATAN LEARNING VECTOR QUANTIZATION, 1–13.