Andriansyah, Mochamad Rafli (2018) Klasifikasi Risiko Hipertensi Menggunakan Fuzzy Decision Tree Iterative Dichotomiser 3 (ID3). Sarjana thesis, Universitas Brawijaya.
Abstract
Hipertensi merupakan penyakit kelainan jantung dan pembuluh darah yang ditandai dengan peningkatan tekanan darah. Hipertensi dapat dikendalikan jika ditangani sejak dini, tetapi beberapa pasien baru menyadarinya setelah terjadi komplikasi kerusakan organ. Mengingat hipertensi adalah salah satu penyakit yang berbahaya, para peneliti sudah melakukan penelitian terkait klasifikasi hipertensi, salah satunya dengan menggunakan Fuzzy Decision Tree menggunakan algoritme ID3. Untuk menyelesaikan masalah hipertensi berdasarkan faktor-faktor yang ada, penulis menggunakan metode Fuzzy Decision Tree ID3 untuk klasifikasi risiko hipertensi yang memiliki tahapan inisialisasi nilai fuzzy, perhitungan nilai fuzzy entropy, nilai information gain, dan defuzzifikasi untuk menentukan hasil klasifikasi. Dari hasil pengujian yang telah dilakukan dapat menghasilkan nilai akurasi terbesar yaitu 80% yang didapatkan dari pengujian 30 data latih dengan 20 data uji, serta kombinasi nilai FCT dan LDT. Kesimpulan dari penelitian yang dibuat bahwa Fuzzy Decision Tree ID3 dapat menyelesaikan permasalahan klasifikasi risiko hipertensi dengan cukup baik.
English Abstract
Hypertension is a disease where the heart and the arteries have abnormalities which is indicated by the increase of blood pressure. Hypertension can be controlled if it’s handled from the early stage, however, several number of patients only earn the knowledge right after there’s a complication of failures of the organs. Considering that hypertension is one of the very lethal diseases, the researchers have done researches about the classification of hypertension, one of them used Fuzzy Decision Tree with ID3 algorithm. To solve the problem about hypertension based on the available factors, the study use Fuzzy Decision Tree ID3 method to classify the risks of hypertension that have initialization stages of Fuzzy values, the calculation of Fuzzy enthropy values, and the values of information gain, as well as defuzzification to determine the result of the classification. The testing that has been carried out could result in the highest accuration value, which is 80%, derived from the testing of 30 training data dan 20 testing data, as well as the combination of the FCT and LDT value. The conclusion of the research that has been accomplished is that Fuzzy Decision Tree ID3 can solve the problems in the classification of hypertension risks quite well.
| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | SKR/FTIK/2018/659/051808572 |
| Uncontrolled Keywords: | id3, fuzzy id3, fuzzy decision tree, risiko hipertensi, hipertensi, klasifikasi id3, fuzzy id3, fuzzy decision tree, hypertension risk, hypertension, classification |
| Subjects: | 500 Natural sciences and mathematics > 511 General principles of mathematics > 511.3 Mathematical logic (Symbolic logic) > 511.31 Nonclassical logic > 511.313 Fuzzy logic |
| Divisions: | Fakultas Ilmu Komputer > Teknik Informatika |
| Depositing User: | Budi Wahyono Wahyono |
| Date Deposited: | 04 Feb 2019 01:49 |
| Last Modified: | 21 Oct 2021 07:14 |
| URI: | http://repository.ub.ac.id/id/eprint/161722 |
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