Fhira Nhita, Fhira
Telkom University

Published : 3 Documents

Found 3 Documents

The Optimal High Performance Computing Infrastructure for Solving High Complexity Problem Sibaroni, Yuliant; Fitriyani, Fitriyani; Nhita, Fhira
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Original Source | Check in Google Scholar | Full PDF (207.647 KB)


The high complexity of the problems today requires increasingly powerful hardware performance. Corresponding economic laws, the more reliable the performance of the hardware, it will be comparable to the higher price. Associated with the high-performance computing (HPC) infrastructures, there are three hardware architecture that can be used, i.e. Computer Cluster, Graphical Processing Unit (GPU), and Super Computer. The goal of this research is to determine the most optimal of HPC infrastructure to solve high complexity problem. For this reason, we chose Travelling Salesman Problem (TSP) as a case study and Genetic Algorithm as a method to solve TSP. Travelling Salesman Problem is belong often the case in real life and has a high computational complexity. While the Genetic Algorithm (GA) is belong a reliable algorithm to solve complex cases, but has the disadvantage that the time complexity level is very high. In some research related to HPC infrastructure comparison, the performance of multi-core CPU single node for data computation has not been done. Whereas the current development trend leads to the development of PCs with higher specifications like this. Based on the experiments results, we conclude that the use of GA is very effective to solve TSP. the use of multi-core single-node in parallel for solving high complexity problems as far as this is still better than the two other infrastructure but slightly below compare to multi-core single-node serially, while GPU deliver the worst performance compared to others infrastructure. The utilization of a super computer PC for data computation is still quite promising considering the ease of implementation, while the GPU utilization for the purposes of data computing is profitable if we only utilize GPU to support CPU for data computing.
Comparative Study between Parallel K-Means and Parallel K-Medoids with Message Passing Interface (MPI) Nhita, Fhira
International Journal on Information and Communication Technology (IJoICT) Vol 2, No 2 (2016): December 2016
Publisher : School of Computing, Telkom University

Show Abstract | Original Source | Check in Google Scholar | Full PDF (658.482 KB)


Data mining is a combination technology for analyze a useful information from dataset using some technique such as classification, clustering, and etc. Clustering is one of the most used data mining technique these day. K-Means and K-Medoids is one of clustering algorithms that mostly used because it’s easy implementation, efficient, and also present good results. Besides mining important information, the needs of time spent when mining data is also a concern in today era considering the real world applications produce huge volume of data. This research analyzed the result from K-Means and K-Medoids algorithm and time performance using High Performance Computing (HPC) Cluster to parallelize K-Means and K-Medoids algorithms and using Message Passing Interface (MPI) library. The results shown that K-Means algorithm gives smaller SSE than K-Medoids. And also parallel algorithm that used MPI gives faster computation time than sequential algorithm.
Indonesian Journal on Computing (Indo-JC) Vol 1, No 1 (2016): March, 2016
Publisher : School of Computing, Telkom University

Show Abstract | Original Source | Check in Google Scholar | Full PDF (2025.842 KB)


Peramalan merupakan proses memperkirakan sesuatu secara sistematis berdasarkan keadaan atau fakta sebelumnya. Peramalan bisa dilakukan melalui serangkaian metode ilmiah atau dengan subjektif belaka. Soft computing (SC) merupakan salah satu metode ilmiah yang dapat digunakan untuk kasus peramalan atau prediksi, Soft Computing (SC) memiliki Algoritma dasar yakni Fuzzy System, Artificial Neural Network  (ANN), dan Evolutionary Alghorithms (EAs). Pada Tugas akhir ini dilakukan penelitian mengenai peramalan kalender masa tanam tanaman jagung yang berbasis curah hujan di wilayah Soreang Kabupaten Bandung menggunakan salah satu jenis algoritma dasar Soft computing (SC) yakni Evolutionary Alghorithms (EAs). Data yang digunakan adalah data curah hujan wilayah Soreang Kabupaten Bandung selama 10 tahun terakhir (2006-2015), data ini akan melalui preprocessing terlebih dahulu dengan Weighted Moving Average (WMA). Pada representasi individu, EAs memiliki empat algoritma yang bisa digunakan, salah satunya Grammatical Evolution (GE) yang akan digunakan pada penelitian ini. Selanjutnya, dalam tugas akhir ini digunakan logika Fuzzy untuk pengoptimasian GE, dengan cara mendefinisikan beberapa parameter pada awal running , agar proses dapat berjalan dengan baik. Hasil akhir yang didapat menunjukkan bahwa logika Fuzzy membantu meningkatkan performansi Eas dan Fuzzy EAs menghasilkan performansi peramalan kalender masa tanam sebesar 76,93%. Hasil peramalan akan digunakan untuk pembuatan kalender masa tanam di Kabupaten Bandung selama 13 (tiga belas) bulan yang dimulai pada Oktober 2014 sampai Oktober 2015.