PERANCANGAN AGEN MUSUH BERBASIS KECERDASAN BUATAN PADA PLATFORM SCRATCH
DESIGN AND IMPLEMENTATION OF AN AI-BASED ENEMY AGENT IN THE SCRATCH PROGRAMMING ENVIRONMENT
DOI:
https://doi.org/10.52972/hoaq.vol17no2.p259-270Keywords:
Kecerdasan Buatan, Agen Cerdas, Scratch, Jarak Euclidean, GimAbstract
Penelitian ini bertujuan untuk merancang dan mengimplementasikan agen musuh berbasis kecerdasan buatan pada platform Scratch menggunakan mekanisme pemilihan target terdekat berdasarkan jarak Euclidean. Agen dirancang menggunakan pendekatan goal-based agent dengan tujuan memilih dan mengumpulkan objek terdekat untuk meningkatkan skor. Penelitian menggunakan model pengembangan 4D yang terdiri atas Define, Design, Development, dan Disseminate. Subjek penelitian berupa gim Wayang War yang melibatkan satu Player dan satu agen AI yang berkompetisi mengumpulkan tiga jenis objek selama 30 detik. Pengujian dilakukan melalui black-box testing terhadap 19 skenario fungsional dan 50 percobaan gameplay. Data dianalisis menggunakan statistik deskriptif dan paired-samples t-test. Hasil black-box testing menunjukkan tingkat keberhasilan sebesar 100%. Dari 50 percobaan gameplay, Player memperoleh win rate sebesar 54%, AI sebesar 40%, dan draw rate sebesar 6%. Rata-rata skor Player sebesar 52,90 (SD = 8,72), sedangkan AI sebesar 52,64 (SD = 9,01). Hasil paired-samples t-test menunjukkan tidak terdapat perbedaan yang signifikan secara statistik antara skor Player dan AI (t(49) = 0,233; p = 0,817), dengan ukuran efek Cohen's dz sebesar 0,0329. Hasil tersebut menunjukkan bahwa agen mampu melakukan pemilihan target secara otonom berdasarkan kondisi permainan dan menjalankan perilaku musuh berbasis tujuan.
This study aims to design and implement an artificial intelligence-based enemy agent on the Scratch platform using a nearest-target selection mechanism based on Euclidean distance. The agent was designed using a goal-based agent approach to select and collect the nearest available object to increase its score. The research employed the 4D development model consisting of Define, Design, Development, and Disseminate. The research subject was a game called Wayang War, featuring one Player and one AI agent competing to collect three types of objects within a 30-second gameplay session. Testing consisted of black-box testing involving 19 functional scenarios and 50 gameplay trials. The data were analyzed using descriptive statistics and a paired-samples t-test. Black-box testing achieved a 100% success rate. Across the 50 gameplay trials, the Player achieved a 54% win rate, while the AI achieved a 40% win rate and a 6% draw rate. The Player obtained a mean score of 52.90 (SD = 8.72), while the AI obtained a mean score of 52.64 (SD = 9.01). The paired-samples t-test indicated no statistically significant difference between the Player and AI scores (t(49) = 0.233; p = 0.817), with a Cohen's dz effect size of 0,0329. These findings indicate that the agent was able to autonomously select targets based on the game state and perform goal-based enemy behavior.
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