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IMPLEMENTASI PENGEMBANGAN METODE DIFFERENTIAL EVOLUTIONUNTUK CLUSTERING PIXEL Saikhu, Ahmad; Fahmi, Hisyam
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 9, No 2, Juli 2011
Publisher : Teknik Informatika, ITS Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (277.541 KB)

Abstract

Perkembangan metode komputasi telah mengalami percepatan yang luar biasa. Berbagai teknik komputasi untuk mendapatkan solusi dengan kinerja optimal terus berkembang. Sejumlah algoritma termasuk dalam rumpun Evolutionary Computation, diantaranya adalah Differential Evolution (DE) yang berhasil menyelesaikan masalah optimasi dalam berbagai bidang diantaranya masalah clustering. Keunggulan DE adalah karena implementasinya yangmudah dan kecepatan konvergensinya. Dalam clustering, DE menghadapi kendala penentuan jumlah cluster. Pada penelitian ini diimplementasikan sebuah algoritma Evolutionary Clustering (EC) yang merupakan pengembangan dari DE. EC diterapkan untuk melakukan pengelompokan pixel-pixel dari citra gray-scale atas beberapa area homogen yang berbeda satu dengan lainnya. EC tidak membutuhkan informasi awal tentang jumlah cluster yang akan terbentuk. EC menjadi salah satu solusi untuk menentukan jumlah cluster optimal dengan nilai validitas yang lebih baik. Kinerja dari EC akan dibandingkan dengan algoritma Fuzzy C-Means (FCM). Hasil dari EC dibanding FCM relatif sama dari segi nilai cluster validity index namun EC membutuhkan waktu relatif lebih singkat.
THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS Fitrianah, Devi; Fahmi, Hisyam
SINERGI Vol 23, No 3 (2019)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (217.061 KB) | DOI: 10.22441/sinergi.2019.3.002

Abstract

This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.
Pemanfaatan Posdaya Masjid Baitussalam sebagai Pusat Pengolahan Sari Buah Markisa di Dusun Robyong, Desa Wonomulyo, Kabupaten Malang Kusumastuti, Ari; Widayani, Heni; Mulyanto, Angga Dwi; Fahmi, Hisyam
Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat Vol 5, No 2 (2019): Agrokreatif Jurnal Ilmiah Pengabdian Kepada Masyarakat
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/agrokreatif.5.2.89-95

Abstract

THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS Fitrianah, Devi; Fahmi, Hisyam
SINERGI Vol 23, No 3 (2019)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2019.3.002

Abstract

This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.