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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi dan Manajemen Informatika
yunifa miftakhul arif
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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PERGANTIAN SKENARIO OTOMATIS PADA GAME TAJWID MENGGUNAKAN FUZZY SUGENO arif, yunifa miftakhul
MATICS Vol 6, No 2 (2014): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (725.335 KB) | DOI: 10.18860/mat.v6i2.2604

Abstract

Game akan menarik dan menyenangkan untuk dimainkan apabila cerita dalam game tersebut dapat memberikan tantangan dengan cerita yang dapat berubah sesuai dengan kondisi pemainnya. Perubahan cerita tersebut dapat membuat rasa penasaran pemain, sehingga dapat menumbuhkan rasa penasaran pemain terhadap game tersebut. Dalam penelitian ini dibahas tentang penerapan fuzzy sugeno dalam perubahan skenario game Tajwid agar pemian di setiap level bisa memainkan skenario yang berbeda. Dalam setiap level terdapat 3 skenario yaitu skenario mudah, menengah dan sulit. Skenario itu didapat dari inputan pemain pada level sebelumnya. Inputan untuk perubahan skenario berupa jumlah score dan jumlah nyawa player. Jadi setiap pemain akan mendapat skenario yang berbeda di level berikutnya dari hasil jumlah score dan jumlah nyawa di level sebelumnya. Fuzzy sugeno ini berfungsi untuk mengatur skenario yang cocok yang akan pemain dapatkan di level berikutnya sesuai inputan yang didapat di level sebelumnya. Genre yang di gunakan dalam penelitian ini adalah RPG (Role Playing Game). Kata kunci : Game, Skenario Game, Fuzzy Sugen, RPG
Optimizing Endless Runner Game Player Performance Using a Hybrid GMF-MLP Recommendation System Based on Neural Collaborative Filtering Hendry Cahyo Gunawan; Fresy Nugroho; Muhammad Ainul Yaqin; Suhartono; Yunifa Miftakhul Arif
Jurnal Teknologi dan Manajemen Informatika Vol. 12 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v12i1.16821

Abstract

Endless Runner games feature exponentially increasing difficulty as distance grows, often causing character failure and player frustration, largely because players struggle to select power-ups suited to their current difficulty context. While prior recommender-system research has mostly focused on purchase prediction for monetization, this study instead builds a personalized item recommendation system aimed at reducing failure and maximizing scores. We propose a hybrid Neural Collaborative Filtering (NCF) architecture combining General Matrix Factorization (GMF), which captures linear preferences, with a Multi-Layer Perceptron (MLP), which models non-linear interactions between playstyle and failure context (cause of death). The model was trained on 10,000 gameplay activity logs containing features such as jump count, obstacles avoided, and death cause. Over 20 training epochs, both training and validation accuracy converged to approximately 0.88–0.90, with a negligible gap between the two curves, indicating minimal overfitting. These results demonstrate that integrating GMF and MLP effectively produces recommendations adaptive to dynamic gameplay conditions.
Co-Authors Ainul Yaqin Fresy Nugroho Hendry Cahyo Gunawan Suhartono