Pemilihan Skill Terbaik Menggunakan TOPSIS Berbasis OWA Untuk Game
Keywords:
Ordered Weighted Averaging, Permainan, Technique for Order Performance by Similarity to Ideal Solution, OWA-TOPSIS, skillAbstract
Perkembangan game modern, khususnya pada genre Role Playing Game (RPG), menghadirkan tantangan baru bagi pemain dalam menentukan strategi yang efektif melalui pemilihan skill. Banyaknya pilihan skill dengan karakteristik beragam, ditambah dengan dinamika situasi pertempuran, sering kali menyulitkan pemain dalam memilih strategi yang paling optimal. Untuk mengatasi permasalahan tersebut, penelitian ini mengusulkan sistem rekomendasi skill pada game Pedjoeang dengan mengintegrasikan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) dan Ordered Weighted Averaging (OWA). Integrasi ini memanfaatkan keunggulan TOPSIS dalam menghitung kedekatan relatif terhadap solusi ideal, sekaligus fleksibilitas OWA dalam mendistribusikan bobot kriteria secara lebih adaptif. Sistem ini mengevaluasi enam alternatif skill terhadap enam kriteria, dengan bobot kriteria ditentukan melalui perhitungan OWA. Hasil pengujian menunjukkan bahwa Skill 5 memperoleh nilai closeness coefficient tertinggi (0,4846) dan menempati peringkat pertama, sedangkan Skill 4 mendapatkan nilai terendah (0,4538) dan berada pada peringkat terakhir. Selain itu, perbandingan antara TOPSIS konvensional dengan metode TOPSIS–OWA menunjukkan adanya perbedaan peringkat, khususnya pada Skill 3 dan Skill 4, yang membuktikan bahwa penerapan OWA meningkatkan sensitivitas sistem terhadap kriteria tertentu. Kontribusi utama penelitian ini adalah membuktikan efektivitas integrasi OWA pada metode TOPSIS dalam sistem rekomendasi berbasis game, yang mampu menghasilkan rekomendasi lebih adaptif dan kontekstual. Temuan ini memperkaya literatur mengenai pengambilan keputusan multikriteria di lingkungan interaktif serta membuka peluang penerapan pada genre game lain dengan kompleksitas lebih tinggi. Lebih jauh, pendekatan ini dapat meningkatkan pengalaman pemain melalui rekomendasi skill yang lebih cerdas, sehingga mendukung pengambilan keputusan strategis dalam gameplay yang dinamis.
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