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All Journal Nuansa Informatika Jurnal Ilmiah Manajemen dan Bisnis (JIMB) Jurnal Teknologi Terpadu Jurnal Teknologi Informasi dan Pendidikan Jurnal Teknologi Informatika dan Komputer Indonesian Journal of Digital Business Jurnal Pengabdian Masyarakat Bhinneka Jurnal Informatika Journal of Scientific Research, Education, and Technology Jurnal Sistem Informasi Galuh Jurnal Pengabdian Nasional (JPN) Indonesia Galuh International Journal of Community Service and Development
Herdiana, Oding
Universitas Pendidikan Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Nuansa Informatika

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STRATEGI PEMASARAN PRODUK INDUSTRI KREATIF MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING BERBASIS PARTICLE SWARM OPTIMIZATION Oding Herdiana; Shanti Maulani; Eryan Ahmad Firdaus
NUANSA INFORMATIKA Vol 15, No 2 (2021)
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (114.679 KB) | DOI: 10.25134/nuansa.v15i2.4394

Abstract

The existence of abundant UMKM data sources can be used to dig up information. Classification is one of the techniques to explore hidden data owned by data mining. Data mining classification methods, one of which is the Support Vector Machine (SVM) algorithm. The SVM algorithm has proven better results than the KKN, Decision Tree and Linear Regression algorithms. In the classification process, the accuracy and time efficiency results obtained are very important. So optimization is needed in order to increase accuracy and time efficiency during the classification process. The optimization of the SVM algorithm was carried out using the K-Means algorithm for the clustering and continuous process on UMKM data and the feature selection process using Particle Swarm Optimization (PSO). This paper aims to optimize the accuracy of the data in the form of type of business, business and turnover. From the results of the discussion of the SVM method using K-Means and PSO, it gives an average accuracy of 55% but 0.12% lower than SVM just using PSO. Keywords: UMKM, Clustering, K-Means, SVM, PSO
Co-Authors Abdullah, Muhammad Mufti Adam Hermawan Adi Prehanto Agung Suburdjati, Bacilius Alif Ridha Ramadhani Alifia Fatimatun Nazya Andini Ismayanti, Rosa Asep Nuryadin Btari Mariska Purwaamijaya Dadan Mulyana Didih Faridah, Didih Eryan Ahmad Firdaus Fauzi , Mochamad Ardan Fitriani, Vivi Indah Guntara, Rangga Gelar Kenneth Setiadi, Benedictus Maesaroh, Syti Sarah Maulana, Haisyam Moch Gan-gan Sidiq Mochamad Ardan Fauzi Muhammad Dzikri Ar Ridlo Muhammad Mufti Abdullah Muhammad Naufal Nurfirmansyah Ramadhani, Alif Ridha Rangga Gelar Guntara Risma Rahatuningtyas Shanti Maulani Sitepu, Kris Adam Gunanta Siti Helmyati Swardana, Ardli Varinia Azkarin