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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Insyst : Journal of Intelligent System and Computation
Dewi, Grace Levina
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Computer Science & IT

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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.
Thesis Defense Scheduling Optimization Using Harris Hawk Optimization Setiono, Kevin; Setiawan, Mikhael; Dewi, Grace Levina; Dhaniswara, Erwin
Intelligent System and Computation Vol 6 No 2 (2024): 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.v6i2.361

Abstract

This research discusses how the Harris Hawk Optimization (HHO) algorithm handles scheduling problems. The scheduling of thesis defenses at the Institut Sains dan Teknologi Terpadu Surabaya (ISTTS) is a complex issue because it involves the availability of lecturers, teaching/exam schedules, lecturer preferences, and limited room and time availability. The scheduling constraints in this research are divided into two categories: Hard Constraints and Soft Constraints. Hard constraints must not be violated, including each lecturer's unique availability, conflicts, and existing exam or teaching schedules. Soft constraints, on the other hand, include preferences for specific days or rooms for the defense. The complexity of scheduling due to these two types of constraints leads to longer scheduling times and an increased likelihood of human error. To automate and optimize this process, the author employs the HHO algorithm. HHO is inspired by the behavior of the Harris Hawk, known for its intelligence and ability to coordinate while hunting. The results of the HHO algorithm are translated into a slot meter, which helps to map the solution to available time slots. The HHO algorithm can generate schedules that comply with 90% of the hard constraints at ISTTS. Evolutionary algorithms typically have high complexity and computational time; in this case, the researcher experimented with multiprocessing. Multiprocessing improved the computational time by up to 39%.
Model Aplikasi Sistem E-Commerce untuk Rekomendasi dan Pemantauan Harga Produk Penjual Dewi, Grace Levina; Angeline, Brigitta; Hadikusuma, Andrew Jonathan; Setiono, Kevin; Setiawan, Mikhael; Tjandra, Suhatati; Armanto, Hendrawan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025 (Naskah Accepted)
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3293

Abstract

Sellers on e-commerce platforms face difficulties in determining strategic pricing due to dynamic competition. To address this problem, an integrated system application model was developed as a solution to assist sellers. This research utilized the Research and Development (R&D) methodology with a waterfall model, which included the stages of needs analysis, system design, implementation, performance verification and data analysis, and final evaluation and revision. The result is a functional application capable of providing features such as search, strategic price recommendations at three levels (lowest, average/middle, highest), viewing more detailed information about products sold in graphical form, and other features. Usability testing conducted on target users yielded average scores of 95.54% for free-type customers, 91.26% for premium-type customers, and 96.8% for buyer-type cutomers. This indicates excellent ease of use and a high level of acceptance. This website model proved to be an effective and practical tool for sellers to establish competitive pricing strategies.Keywords: E-commerce; Price Monitoring; Strategic Pricing; Research and Development; Recommender System AbstrakPenjual pada platform e-commerce menghadapi kesulitan dalam menentukan harga strategis akibat persaingan dinamis. Untuk mengatasi masalah ini, sebuah model aplikasi sistem terintegrasi dikembangkan sebagai solusi untuk membantu penjual. Penelitian ini menggunakan metodologi Research and Development (R&D) dengan waterfall yang mencakup tahap analisis kebutuhan; perancangan sistem; implementasi; verifikasi kinerja dan analisis data; dan evaluasi akhir dan revisi. Hasilnya adalah sebuah aplikasi fungsional yang mampu menyediakan fitur pencarian, rekomendasi harga strategis pada tiga level (terendah, rata-rata/menengah, tertinggi), melihat informasi lebih detail mengenai produk yang dijual dalam bentuk grafik, dan fitur lainnya. Pengujian usabilitas menggunakan usability testing terhadap target pengguna menghasilkan skor rata-rata 95,54% pada customer yang bertipe gratis, 91,26% untuk pengguna bertipe premium, dan 96,8% pengguna yang bertipe pembeli. Hal ini yang mengindikasikan kemudahan penggunaan dan tingkat penerimaan sangat baik. Model website ini terbukti menjadi alat bantu yang efektif dan praktis bagi penjual untuk menetapkan strategi harga kompetitif. 
Co-Authors Angeline, Brigitta Armanto, Hendrawan Dhaniswara, Erwin Dianaris, Audrey Ayu Hadikusuma, Andrew Jonathan Mikhael Setiawan Setiawan, Mikhael Setiono, Kevin Setiono, Kevin Tjandra, Suhatati Vincent Yuliana Melita Pranoto