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All Journal Journal of Information System,Graphics, Hospitality and Technology Insyst : Journal of Intelligent System and Computation
Audrey Ayu Dianaris
Institut Sains Dan Teknologi Terpadu Surabaya
Computer Science & IT Control & Systems Engineering Education Engineering Other

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Faktor-Faktor yang Mempengaruhi Adopsi E-learning untuk Siswa SMA di Indonesia dengan Menggunakan Extended Technology Acceptance Model Audrey Ayu Dianaris; Edwin Pramana; Herman Budianto
Journal of Information System,Graphics, Hospitality and Technology Vol. 4 No. 01 (2022): Journal of Information System, Graphics, Hospitality and Technology
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37823/insight.v4i01.179

Abstract

Penggunaan berbagai macam aplikasi berbasis internet sudah meluas di Indonesia. Didukung dengan berbagai macam perangkat yang mampu mengaksesnya di kalangan remaja, terutama siswa sekolah pada bangku pendidikan di SMA. Gaya hidup serba mobile dan aktivitas penunjang akademis siswa di luar pendidikan formal, cukup menyita waktu. Sehingga waktu belajar secara tradisional pun semakin sedikit. Perkembangan teknologi yang pesat juga berdampak pada dunia pendidikan. Memanfaatkan teknologi, keterbatasan akses informasi dan materi belajar, terutama keterbatasan ruang dan waktu dapat dijembatani dengan menggunakan E-learning. Penelitian ini bertujuan untuk mencari tahu, faktor-faktor apa saja yang dapat mempengaruhi siswa SMA di Indonesia untuk mau mengadopsi E-learning. Sebuah model teoritis dibuat berdasarkan sejumlah penelitian sebelumnya dan memanfaatkan model dasar Technology Acceptance Model (TAM) dan konstruksi E-learning yang spesifik. Pengumpulan data dilakukan dengan menggunakan kuesioner berbasis online. Data akhir yang terkumpul berjumlah 517 data dari siswa SMA di Indonesia. Structural Equation Modeling (SEM) digunakan untuk menganalisis dan pengolahan model teoritis menggunakan software AMOS. Faktor-faktor dalam model teoritis adalah Self-Efficacy, Social Influence, Computer Anxiety, Prior Experience, Facilitating Conditions, dan Perceived Enjoyment. Bentuk dasar TAM yang digunakan meliputi Perceived Usefulness, Perceived Ease of Use, dan Behavioral Intention. Dalam proses Factor Analysis, Facilitating Conditions dihapus dari model teoritis, karena tidak mampu menunjukkan posisi konvergen dan diskriminan. Faktor Perceived Usefulness dan Perceived Enjoyment adalah dua faktor yang paling mempengaruhi Behavioral Intention di dalam proses adopsi E-learning. Hasil penelitian menunjukkan Perceived Enjoyment memiliki pengaruh secara langsung dan positif pada Perceived Usefulness yang tertinggi dibandingkan faktor lainnya. Self-Efficacy memiliki pengaruh secara langsung dan positif pada Perceived Ease of Use yang tertinggi dibandingkan faktor lainnya. Berdasarkan hasil penelitian ini, maka dapat ditekankan, bahwa untuk mencapai tujuan agar seseorang mau mengadopsi E-learning, instansi terkait harus menunjang kebutuhan penerapan E-learning dengan berfokus pada sisi manfaat dan kepuasan yang menyenangkan pengguna dalam pengalamannya menggunakan E-learning
A Hybrid Machine Learning and Deep Learning Approach for In-Game Assistance Dianaris, Audrey Ayu; Vincent; Setiono, Kevin; Setiawan, Mikhael; Pranoto, Yuliana Melita; Dewi, Grace Levina
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.430

Abstract

The rapid development of artificial intelligence (AI) has opened new possibilities for enhancing user interaction within video games. This study presents the design and implementation of a button-based assistant system for the simulation game Story of Seasons: Friends of Mineral Town, aimed at simplifying repetitive player tasks and improving the overall gameplay experience. The proposed system leverages a hybrid approach that combines Machine Learning and Deep Learning techniques, specifically Optical Character Recognition (OCR) with Tesseract, object detection using a custom-trained YOLOv7 model, the A* pathfinding algorithm for navigation, and automated input control through scripting. The assistant is capable of reading in-game time, weather, and events directly from screen captures, recognizing non-player characters (NPCs), and automatically directing the player’s character to desired locations or NPCs based on contextual data such as day, time, and weather conditions. A database-driven module stores key information such as NPC schedules, favorite gifts, and daily events to enable informed decision-making and interaction automation. Comprehensive testing was conducted, including comparisons of pathfinding algorithms, model accuracy assessments, and user experience evaluations involving volunteers. Results showed high detection accuracy with YOLOv7 and positive user feedback on the assistant's interface and usability. Users reported a more streamlined and enjoyable gaming experience, especially in managing daily tasks and character interactions. This research demonstrates how a hybrid AI-based approach can be effectively applied to traditional video games, offering a foundation for future development in intelligent game assistance systems. The proposed methodology not only improves convenience but also provides insights into the practical integration of AI in user-centric game design.
Co-Authors Dewi, Grace Levina Edwin Pramana Herman Budianto Mikhael Setiawan Setiono, Kevin Vincent Yuliana Melita Pranoto