KLASIFIKASI SEKOLAH SLTP BANJARMASIN BERBASIS TIK BERDASARKAN SARANA DAN PRASARANA MENGGUNAKAN K-NEAREST NEIGHBOR BERBASIS PARTICLE SWARM OPTIMIZATION

Dua faktor yang dapat menjelaskan mengapa upaya perbaikan mutu pendidikan selama ini kurang atau tidak berhasil. Pertama sifat pembangunan selama ini lebih bersifat input oriented. Strategi yang demikian lebih bersandar kepada asumsi bahwa bilamana semua input pendidikan telah terpenuhi seperti penyediaan sarana prasarana berbasis TIK, pelatihan guru dan tenaga kependidikan lainnya, maka secara otomatis lembaga pendidikan (sekolah) akan menghasilkan output (keluaran) yang bermutu sebagaimana yang diharapkan. Standar kompetensi berbasis TIK di klasifikasikan dalam label non berbatik, perintis, menengah dan lanjut, dalam pengambilan keputusan tersebut memerlukan waktu yang lama untuk menganalisa dalam mengklasifikasi sekolah berbasis TIK sehingga hasilnya menjadi kurang akurat, dari permasalahan yang ada tersebut digunakan metode klasifikasi pada data mining yang dapat mengklasifikasi SLTP berbasis TIK. Dalam penelitian ini diterapkan algoritma yang cukup baik dalam mengklasifikasi SLTP berbasis TIK yaitu metode K-Nearest Neighbor (KNN) dan Particle Swarm Optimization (PSO) yang digunakan untuk menghitung bobot setiap attributnya. Dari hasil pengujian dengan model tersebut maka hasil yang didapat algoritma KNN saja sudah menghasilkan akurasi sebesar 90% dan klasifikasi error sebesar 10% kemudian setelah dilakukan pembobotan berbasis PSO nilai akurasinya meningkat menjadi 97.14% dan klasifikasi error turun menjadi 2.86%. Hasil klasifikasi target perintis lebih banyak dari pada non berbatik, menengah dan lanjut, dengan adanya peningkatan tersebut model yang diperoleh pun menjadi lebih akurat dalam mengklasifikasi SLTP berbasis TIK.Kata kunci : Data Mining, Algoritma K-Nearest Neighbor, Particle Swarm Optimization

A Collection-Distribution Center Location and Allocation Optimization Model in Closed-Loop Supply Chain for Chinese Beer Industry

Recycling waste products is an environmental-friendly activity that can result in manufacturing cost saving and economic efficiency improving. In the beer industry, recycling bottles can reduce manufacturing cost and the industry’s carbon footprint. This paper presents a model for a collection-distribution center location and allocation problem in a closed-loop supply chain for the beer industry under a fuzzy random environment, in which the objectives are to minimize total costs and transportation pollution. Both random and fuzzy uncertainties, for which return rate and disposal rate are considered fuzzy random variables, are jointly handled in this paper to ensure a more practical problem solution. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A beer company case study is given to illustrate the application of the proposed model and to demonstrate the priority-based global-local-neighbor particle swarm optimization.

A Multi-Objective Collection-Distribution Center Location and Allocation Problem in a Closed-Loop Supply Chain for the Chinese Beer Industry

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

Reinforcement and Nonlinear Viscoelasticity of Polymer Melts Containing Mixtures of Nanofillers

The reinforcement and concomitant nonlinear viscoelastic behavior have been investigated for several composites of linear polymer melt with various binary mixtures of nanofillers having different surface chemistries and particle sizes. The dependence of storage modulus and loss factor on dynamic shear strain amplitude has been obtained for several compositions of each binary filler pair. Composites with mixed fillers display nonlinear interactions that are filler pair/matrix specific. Total filler concentration appears to be a major factor, suggesting that nearest neighbor particle spacing is crucial even if the neighbors are of a different filler type. The results are consistent with the theory recently proposed by Sternstein and Zhu in which the high reinforcement by nanofillers is due to the trapping of entanglements and the resultant effects on matrix chain mobility and entropic elasticity. At high strain amplitudes, the storage modulus and loss factor of the composites with binary filler mixtures are found to be nearly independent of the filler mixture ratio, and dependent only on the total filler concentration. A “partial molar” storage modulus for mixed filler composites is defined for future considerations, and specific interactions are presented for two binary filler systems.