Sani, Askia (2017) Kelompok Penyakit Tanaman Apel Menggunakan Metode K-Meanss Berbasis Web. Sarjana thesis, Universitas Brawijaya.
Abstract
Keadaan tanaman apel yang baik bisa dideteksi dengan mengidentifikasi penyakit pada apel. Salah satu metode untuk mendeteksi penyakit apel bisa dilakukan dengan K-meanss. Aplikasi pengelompokan penyakit tanaman apel menggunakan metode K-meanss berbasis android ini dapat mempermudah mendeteksi penyakit tanaman apel, aplikasi ini berbasis android sehingga pengguna dapat dengan mudah menjalankan aplikasi ini dimana saja dan kapan saja dengan handphone pengguna. Yang mana pembahasan ini tentang membangun aplikasi sistem pendeteksi yang dapat digunakan pengguna untuk mendeteksi terdapatnya hama dalam perkebunan apel. Aplikasi sistem diganosa penyakit tanaman apel dapat menampilkan hasil diagnosis dengan cepat dan tepat berdasarkan gejala-gejala yang dimasukan oleh user. Untuk membuat hasil diagnosis menjadi sangat valid, maka data gejala yang dimasukan ke dalam sistem pendukung keputusan adalah suatu data gejala, harus lengkap artinya gejala-gejala yang bisa mengarah ke suatu penyakit tersebut harus dimasukan secara lengkap, karena kesimpulan hasil diagnosis (persentase) yang ditampilkan dihitung secara otomatis oleh sistem dengan menggunakan K-meanss.
English Abstract
The state of a good apple plant can be detected by identifying the disease in apples. One method to detect apple disease can be done with K-meanss. Application of apple plant disease grouping using this android based K-meanss method can make it easier to detect the apple plant penyakit, this application is based on android so that users can easily run this application anywhere and anytime with the user's mobile phone. This is where the discussion is about building a detection sistem application that users can use to detect the presence of pests in apple plantations. Application of diganosa sistem of apple plant disease can display diagnosis result quickly and precisely based on the symptoms entered by user. To make the diagnosis become very valid, then the data of symptoms included into decision support sistem is a symptom data, must be complete meaning of symptoms that can lead to a disease must be entered in full, because conclusion of diagnosis result (percentage) shown counted Automatically by the sistem using K-meanss.
Item Type: | Thesis (Sarjana) |
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Identification Number: | SKR/FTIK/2017/739/051708552 |
Uncontrolled Keywords: | K-meanss, Data Mining, Clustering, Diagnosis Tanaman Apel |
Subjects: | 000 Computer science, information and general works > 004 Computer science > 004.015 1 Finite mathematic |
Divisions: | Fakultas Ilmu Komputer > Teknik Informatika |
Depositing User: | Yusuf Dwi N. |
Date Deposited: | 02 Oct 2017 07:14 |
Last Modified: | 30 Nov 2020 15:41 |
URI: | http://repository.ub.ac.id/id/eprint/3094 |
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