Penerapan Metode K-MEANS-ACO Untuk Pengelompokan Biji Wijen Berdasarkan Sifat Warna Cangkang Biji

WIjaya, Pangestu Ari (2017) Penerapan Metode K-MEANS-ACO Untuk Pengelompokan Biji Wijen Berdasarkan Sifat Warna Cangkang Biji. Sarjana thesis, Universitas Brawijaya.

Abstract

Wijen merupakan salah satu bahan makanan yang menghasilkan minyak nabati. Wijen paling banyak dikonsumsi oleh masyarakat dunia. Kebutuhan wijen saat ini semakin meningkat sehingga perlu adanya kualitas yang baik dalam memproduksi wijen. Dalam mengembangkan kualitas wijen saat ini salah satunya diperlukan melakukan persilangan antar kultivar. Untuk melakukan proses persilangan tanaman wijen, warna cangkang biji wijen sangat berpengaruh pada kualitasnya. Dari beberapa penelitian sebelumnya telah dilakukan pengelompokan biji wijen dengan metode kualitatif dan kuantitatif. Pada metode kualitatif dilakukan dengan pengamatan langsung sedangkan pada metode kuantitatif dilakukan dengan mengolah data wijen dari hasil pengukuran menggunakan alat chromameter yang menghasilkan atribut warna L*, a* dan b*. Beberapa penelitian sebelumnya telah berhasil melakukan pengelompokan dengan metode kuantitatif yaitu metode IWOKM, PSOKM dan GAKM. Ketiga metode tersebut mampu menghasilkan pengelompokan data wijen dengan hasil yang baik. Penelitian ini akan mengelompokan dan membandingkan hasil data wijen menggunakan metode K-Means ACO dengan metode sebelumnya. Dari beberapa jurnal metode tersebut terbukti metode K-Means-ACO memiliki hasil yang optimal, karena dalam pengelompokkannya menggabungkan metode algoritma optimasi dan clustering. Berdasarkan hasil pengujian metode K-Means-ACO dibandingan dengan metode sebelumnya, hasilnya baik dalam mengelompokan biji wijen berdasarkan sifat warna cangkang biji. Hal ini dibuktikan dengan rata–rata nilai fitness pada metode K-Means-ACO mencapai 10,1469 dan rata–rata nilai kekompakan kelompok menggunakan nilai silhoutte coeffisientt mencapai 0,7715 dan hasil pengelompokan yaitu 233:58. Sehingga dapat disimpulkan bahwa metode K-Means-ACO dapat digunakan sebagai metode alternatif untuk pengelompokan biji wijen berdasarkan sifat warna cangkang biji.

English Abstract

Sesame is one kinds of the groceries that produce vegetable oil. Sesame is the most consumed foods by the world community. Nowadays, the needs of sesame is increasing so it is necessary to pick a good quality in producing sesame. In developing the quality of sesame today, one example is to cross the cultivars. To conduct sesame plants crossing, the color of sesame seed shell is very infuential on its quality. Several previous studies used in this research has been done to cluster sesame seed with qualitative and quantitative method. The qualitative method in this research is conducted by field observation while the quantitative method is conducted by processing the sesame data from measurement result by using chromameter which resulted of an L*, a* and b* color. Several previous studies has successfully done the clustering by using qualitative method namely IWOKM, PSOKM and GAKM method. Those three methods are capable of producing a clustering of sesame data with good results. This study will categorize and compare the result of sesame data by using K-Means-ACO method with the previous method. From several journals, the method is proved that K-Means-ACO method has optimal results because in the analysis step combined the optimization and clustering algorithm method. Based on the test results of the K-Means-ACO method compared with the previous method, the good result of clustering sesame seed based on the color of the seed shell. It is proven by the average fitness value on the K-Means-ACO method which had reached 10,1469 and the average of the group cohesiveness value using the silhouette coeficient value had reached 0,7715 with the grouping result is 233:58. After all, this research could be concluded that the K-Means-ACO method could be used as the alternative method to conduct the sesame seed classification based on its seed shell color.

Item Type: Thesis (Sarjana)
Identification Number: SKR/FTIK/2017/586/051708098
Uncontrolled Keywords: Wijen, Pengelompokan, Kualitatif, Kuantitatif, Chromameter, K-Means-Ant Colony OptimizationWijen, Pengelompokan, Kualitatif, Kuantitatif, Chromameter, K-Means-Ant Colony Optimization
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: 04 Oct 2017 04:17
Last Modified: 06 Dec 2021 06:47
URI: http://repository.ub.ac.id/id/eprint/3237
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