Peningkatan Performa Cluster Fuzzy C-Means pada Klastering Sentimen Menggunakan Particle Swarm Optimization

Penulis

  • Rimbun Siringoringo Universitas Methodist Indonesia
  • Jamaluddin Jamaluddin Universitas Methodist Indonesia

DOI:

https://doi.org/10.25126/jtiik.2019641090

Kata Kunci:

fuzzy c-means, particle swarm optimization, klastering sentimen, ulasan produk

Abstrak

Fuzzy C-Means (FCM) merupakan algoritma klastering  yang sangat baik dan lebih fleksibel dari algoritma klastering konvensional. Selain kelebihan tersebut, kelemahan utama algoritma ini adalah sensitif terhadap pusat klaster. Pusat klaster yang sensitif mengakibatkan hasil akhir sulit di kontrol dan FCM  mudah terjebak  pada optimum lokal. Untuk mengatasi masalah tersebut, penelitian ini memperbaiki kinerja FCM dengan menerapkan Particle Swarm Optimization (PSO) untuk menentukan pusat klaster yang lebih baik. Penelitian ini diterapkan pada klastering sentimen dengan menggunakan data berdimensi tinggi yaitu ulasan produk yang dikumpulkan dari beberapa situs toko online di Indonesia. Hasil penelitian menunjukkan bahwa penerapan PSO pada pembangkitan pusat klaster FCM dapat memperbaiki performa FCM serta memberikan luaran yang lebih sesuai. Performa klastering yang menjadi acuan  adalah Rand Index, F-Measure dan Objective Function Value (OFV). Untuk keseluruhan performa tersebut, FCM-PSO memberikan hasil yang lebih baik dari FCM. Nilai OFV yang lebih baik menunjukkan bahwa FCM-PSO tersebut membutuhkan waktu konvergensi yang lebih cepat serta penanganan noise yang lebih baik.


Abstract


Fuzzy C-Means (FCM) algorithm is one of the popular fuzzy clustering techniques. Compared with the hard clustering algorithm, FCM is more flexible and fair. However, FCM is significantly sensitive to the initial cluster center and easily trapped in a local optimum. To overcome this problem, this study proposes and improved FCM with Particle Swarm Optimization (PSO) algorithm to determine a better cluster center for high dimensional and unstructured sentiment clustering. This study uses product review data collected from several online shopping websites in Indonesia. Initial processing product review data consists of Case Folding, Non Alpha Numeric Removal, Stop Word Removal, and Stemming. PSO is applied for the determination of suite cluster center. Clustering performance criteria are Rand Index, F-Measure and Objective Function Value (OFV). The results showed that FCM-PSO can provide better performance compared to the conventional FCM in terms of Rand Index, F-measure and Objective Function Values (OFV). The better OFV value indicates that FCM-PSO requires faster convergence time and better noise handling.

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Referensi

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Diterbitkan

15-07-2019

Terbitan

Bagian

Ilmu Komputer

Cara Mengutip

Peningkatan Performa Cluster Fuzzy C-Means pada Klastering Sentimen Menggunakan Particle Swarm Optimization. (2019). Jurnal Teknologi Informasi Dan Ilmu Komputer, 6(4), 349-354. https://doi.org/10.25126/jtiik.2019641090