PERAMALAN PENJUALAN VIDEO GAME GLOBAL MENGGUNAKAN MODEL DEEP LEARNING (LSTM) BERBASIS DERET WAKTU
Keywords:
Peramalan Deret Waktu, LSTM, Deep Learning, Penjualan Game, Prediksi Pasar GlobalAbstract
Industri game global mengalami dinamika penjualan yang sangat fluktuatif akibat perubahan tren pasar, siklus perilisan, dan preferensi pemain yang tidak stabil. Ketidakpastian ini menuntut pendekatan prediktif yang lebih adaptif dibandingkan metode statistik tradisional. Penelitian ini mengkaji efektivitas model Long Short-Term Memory (LSTM) sebagai arsitektur deep learning yang mampu menangkap ketergantungan jangka panjang dalam deret waktu penjualan gim global. Dataset yang digunakan merupakan data historis penjualan gim yang telah dibersihkan dan direstrukturisasi ke dalam format deret waktu dengan resolusi tahunan. Model LSTM dikembangkan melalui proses normalisasi data, pembentukan window sequence, dan pelatihan berbasis backpropagation through time. Kinerja model dievaluasi menggunakan metrik Mean Squared Error (MSE), Mean Absolute Error (MAE), dan Root Mean Squared Error (RMSE). Hasil eksperimen menunjukkan bahwa LSTM mampu mempelajari pola musiman dan tren multi-dekade dengan akurasi prediksi yang lebih tinggi dibandingkan baseline regresi linear dan ARIMA, dengan penurunan rata-rata RMSE sebesar 18,7%. Model juga menunjukkan stabilitas prediksi pada horizon jangka menengah, sehingga potensial digunakan untuk perencanaan strategis oleh penerbit gim dan analis industri. Temuan ini menegaskan bahwa pendekatan deep learning berbasis deret waktu memberikan peningkatan signifikan dalam pemodelan dinamika penjualan gim yang kompleks, serta membuka peluang penelitian lanjutan pada granularitas platform, genre, atau wilayah regional.
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