Implementasi Algoritma K-MEANS Clustering Dalam Pembangkitan Aturan Fuzzy Pada Perencanaan Konsumsi Pangan Harian

Caesandria, NovindaFiqih (2016) Implementasi Algoritma K-MEANS Clustering Dalam Pembangkitan Aturan Fuzzy Pada Perencanaan Konsumsi Pangan Harian. Sarjana thesis, Universitas Brawijaya.

Abstract

Pemerintah saat ini sedang mengupayakan peningkatan derajat kesehatan dan status gizi masyarakat Indonesia. Gizi seimbang dapat dicapai melalui perencanaan konsumsi pangan harian yang dapat dilakukan oleh seorang ahli gizi namun saat ini keberadaan tenaga ahli gizi masih belum merata. Guna mencari solusi permasalahan tersebut banyak penelitian yang dilakukan, bila diteliti studi yang bisa mengatasi permasalahan tersebut adalah studi sistem pakar penalaran fuzzy. Pembuatan sistem pakar tidak lepas dari pengetahuan pakar yang dalam sistem berupa aturan. Aturan sendiri saat ini sulit diakuisisi dengan baik karena terdapat perbedaan pengetahuan pada setiap pakar sehingga memungkinkan aturan yang diakuisisi tidak lengkap. Aturan dapat dibangkitkan secara otomatis dengan metode clustering. Pada penelitian ini diimplementasikan kecerdasan buatan untuk perancangan konsumsi pangan harian. Metode yang digunakan untuk membangkitkan aturan fuzzy adalah K-means clustering. K-means dalam sistem menjadi proses pelatihan untuk membentuk aturan fuzzy sedangkan Fuzzy Takagi Sugeno Kang menjadi mesin inferensi. Hasil Mean Absolute Persentage Error (MAPE) terkecil pada penelitian ini adalah pada laki-laki 22,55% sedangkan pada perempuan sebesar 11,49%. Hasil penelitian ini menunjukkan bahwa cluster ideal atau cluster dengan nilai varian terkecil belum tentu menghasilkan nilai terbaik.

English Abstract

Nowdays The government is currently working on the improvement of health and nutritional status of the people of Indonesia. Balanced nutrition can be achieved through planning daily food intake and can be done by a nutritionist, but now the existence of nutrition experts are still not evenly distributed. In order to find a solution to these problems much people do reasearches. There is study that can overcome these problem and the study is expert system fuzzy reasoning. Making expert systems can not be separated from the knowledge of experts and in the system it represent by rules. Rule itself is currently rather difficult acquired well because there are differences in each expert knowledge so as to enable the rules of the acquired incomplete. Therefore, the current rules can be generated automatically with a clustering method. So this study implements artificial intelligence to design daily food intake. The method that used to generate rule is a K-means clustering. In the process, K-means used to be the training process to establish rules while Fuzzy Takagi Sugeno Kang became inference engine. In this study the smallest Mean Absolute Percentage Error (MAPE) results are male 22,55%, while in women by 11,49%. Result of this study shows that cluster ideal or cluster with smallest varians value not necessarily result a best value.

Item Type: Thesis (Sarjana)
Identification Number: SKR/FTIK/2017/2/051700345
Subjects: 000 Computer science, information and general works > 005 Computer programming, programs, data
Divisions: Fakultas Ilmu Komputer > Teknik Informatika
Depositing User: Kustati
Date Deposited: 16 Feb 2017 11:07
Last Modified: 22 Oct 2021 04:24
URI: http://repository.ub.ac.id/id/eprint/147422
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