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All Journal Jurnal Sains dan Seni ITS Jurnal Kedokteran Brawijaya Journal of the Medical Sciences (Berkala Ilmu Kedokteran) Jurnal Kesehatan Reproduksi Universa Medicina International Journal of Computing Science and Applied Mathematics Indonesian Journal of Obstetrics and Gynecology (Majalah Obstetri dan Ginekologi Indonesia)
Soetrisno Soetrisno
Institut Teknologi Sepuluh Nopember
Arts Humanities Computer Science & IT Education Health Professions Immunology & microbiology Mathematics Medicine & Pharmacology Neuroscience Public Health

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Implementasi Algoritma Genetika untuk Optimalisasi Random Forest dalam Proses Klasifikasi Penerimaan Tenaga Kerja Baru : Studi Kasus PT.XYZ Laras Binarwati; Imam Mukhlash; Soetrisno Soetrisno
Jurnal Sains dan Seni ITS Vol 6, No 2 (2017)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (225.42 KB) | DOI: 10.12962/j23373520.v6i2.26887

Abstract

Kualitas sumber daya manusia sangat penting bagi suatu perusahaan untuk mempertahankan keunggulan kompetitifnya agar mampu bersaing dengan perusahaan lainnya maupun untuk meningkatan kualitas dari perusahaan itu sendiri. Oleh karena itu, menggali pola penerimaan tenaga kerja baru sangat diperlukan. Pada penelitian ini, metode random forest digunakan untuk menggali pola penerimaan tenaga kerja  baru. Adapun  algoritma genetika (GA) digunakan untuk mengoptimalkan akurasi berdasarkan pola yang didapat. Hasil pengujian program ini menunjukkan keakuratan pola yang dihasilkan oleh random forest yang dioptimalkan dengan algoritma genetika lebih tinggi dengan hasil keakuratan berkisar antara 91%-95% dibanding dengan hanya menggunakan random forest saja yang hanya berkisar 40-95%.
A Genetic Algorithm with Best Combination Operator for the Traveling Salesman Problem Muhammad Luthfi Shahab; Titin J. Ambarwati; Soetrisno Soetrisno; Mohammad Isa Irawan
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol 5, No 2 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (390.627 KB) | DOI: 10.12962/j24775401.v5i2.5830

Abstract

In this research, we propose a genetic algorithm with best combination operator (BC(x,y)O) for the traveling salesman problem. The idea of best combination operator is to find the best combination of some disjoint sub-solutions (also the reverse of sub-solutions) from some known solutions. We use BC(2,1)O together with a genetic algorithm. The proposed genetic algorithm uses the swap mutation operator and elitism replacement with filtration for faster computational time. We compare the performances of GA (genetic algorithm without BC(2,1)O), IABC(2,1)O (iterative approach of BC(2,1)O), and GABC(2,1)O (genetic algorithm with BC(2,1)O). We have tested GA, IABC(2,1)O, and GABC(2,1)O three times and pick the best solution on 50 problems from TSPLIB. From those 50 problems, the average of the accuracy from GA, IABC(2,1)O, and GABC(2,1)O are 65.12%, 94.21%, and 99.82% respectively.
Co-Authors Abd. Rasyid Syamsuri Anwar Sandi Wibowo, Anwar Sandi Bambang Eko Wiyono, Bambang Eko Cahyanto, Erindra Budi Didon Muhammad Trimulya Donny Irawan, Donny Eddy Tiro Eric Edwin Yuliantara Fadhlan Septianto Eka Putra Faris, Abdul Hari Wujoso Imam Mukhlash John Arianto Sondakh, John Arianto Laqif, Abdurahman Laras Binarwati Mappaware, Nasrudin Andi Mohammad Hakimi Mohammad Isa Irawan Nizar Hero K, Nizar Hero Nurinasari, Hafi Ova Emilia Rabrageri, Alberthzon Kris Silo Rifdy Fachry Risanto Siswosudarmo Shahab, Muhammad Luthfi Slamet Riyanto Soerjo Hadijono Sri Sulistyowati Sulistyawati, Sri Sulistyawati, Sri Supriyadi H Respati Supriyadi Hari Respati Suryoningrat, Dewanto Titin J. Ambarwati Tjiang, Rubin Enhui Trimulya, Didon M Umar Malinta Zulfa Afiq Fikriya