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All Journal International Journal of Engineering, Science and Information Technology Insyst : Journal of Intelligent System and Computation Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK)
Mikhael Setiawan
Institut Sains Dan Teknologi Terpadu Surabaya
Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Sistem Konsultasi dan Booking Service Mobil Berbasis Web Setiawan, Mikhael; Surya Pratama, Eric; Irawati Setiawan, Esther; Putera Gunawan, Tjwanda
Prosiding TAU SNARS-TEK Seminar Nasional Rekayasa dan Teknologi Vol. 2 No. 1 (2023): Prosiding TAU SNARS-TEK Seminar Nasional Rekayasa dan Teknologi 2023
Publisher : Fakultas Teknik dan Teknologi - TANRI ABENG UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/snarstek.v2i1.603

Abstract

Penggunaan kendaraan bermotor sebagai alat transportasi sudah tidak dapat terlepas dari kehidupan manusia saat ini. Namun tidak jarang kendaraan bermotor, khususnya mobil, mengalami banyak kendala. Kendala yang terjadi memiliki alasan yang bermacam-macam dengan penanganan yang berbeda-beda pula. Kendala atau kerusakan kendaraan bermotor ini, tidak jarang membutuhkan konsultasi mendalam dan service kendaraan di bengkel. Berdasarkan permasalahan tersebut, dibangunlah sebuah sistem konsultasi dan booking service mobil. Sistem ini menyediakan berbagai fitur bagi customer seperti untuk melakukan konsultasi permasalahan kendaraan, booking layanan service kendaraan di bengkel maupun di rumah, booking emergency service, reminder jadwal melakukan service, dan mereview hasil service yang diterima. Sistem ini juga menyediakan fitur bagi pengelola jasa service kendaraan yaitu admin dan teknisi. Admin dapat mendaftarkan teknisi, mengatur paket service, melayani konsultasi, konfirmasi booking service, dan melihat laporan rating, laporan hasil service, serta laporan hasil konsultasi. Teknisi dapat melihat daftar pekerjaan dan menerima reminder jadwal service yang telah diberikan. Sistem ini dikembangkan dengan menggunakan metodologi Waterfall, menggunakan Bootstrap sebagai CSS Framework, Laravel sebagai fullstack PHP framework, MySQL sebagai basis data, Google Maps API untuk membantu dalam penandaan lokasi service, dan Midtrans sebagai payment gateway. Uji coba dilakukan pada 20 responden sebagai customer, 2 responden sebagai teknisi, dan 1 responden sebagai admin. Hasil ujicoba pada customer menunjukkan bahwa pada fitur booking service sebanyak 65% customer menyatakan telah berjalan dengan baik sekali dan 30% menyatakan baik, pada fitur lokasi emergency service sebanyak 70% customer menyatakan akurat dan 20% menyatakan cukup akurat, pada fitur konsultasi 100% customer menyatakan terbantu. Hasil ujicoba pada teknisi menunjukkan bahwa sebanyak 100% responden menyatakan bahwa fitur daftar pekerjaan, reminder pekerjaan, dan notifikasi email telah berjalan baik. Hasil ujicoba pada admin menunjukkan bahwa fitur konsultasi dan laporan telah berjalan dengan baik, serta membantu admin dalam menemukan customer baru
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.
Hybrid Graph Attention Networks for Influencer Ranking in Student Activity Networks Setiawan, Mikhael; Santoso, Ong Hansel; Chandra, Iwan
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1474

Abstract

Detecting influencers in a social network of massive student activities is vital for universities because it will help them understand potential leaders and social behavior. This paper mitigates the issues of classical topology-based metrics by presenting volume calculation through Graph Attention Networks (GATs) applied to a real network with 2,520 students and about 282,000 interactions. A new hybrid method of influencer ranking proposed, which combines the node embeddings obtained by GAT with a structural influence signal from PageRank. The evaluation system includes two main parts. First, qualitative evaluation of the hybrid ranking method against PageRank-only. This assessment learns from a ground truth dataset of 993 formal leaders. Second, evaluate the communities found by GNNs against those discovered by classical methods using internal quality criteria, including modularity and conductance. From the observation, PageRank baseline does slightly better than the hybrid method in ranking and both methods are significantly better from a random rank with their Spearman’s Rank Correlation equal to 0.513 for PageRank based and 0.451 of the hybrid variant, respectively. Yet, in the task of community detection, GNNs have greater representational capacity. Even though the resulting modularity score was also very competitive, communities had much lower (and hence better) average conductance than Louvain and Walktrap methods (0.137 vs 0.198 and 0.302). These paired results shows that: the success of a PageRank baseline is tied to our formal-role-based ground truth which is structural. The GNN’s increased ability to discriminate such well-delineated, socially close communities implies that the embeddings it learns better represent the network’s true social structure. In conclusion, while PageRank effectively reveals the formal leaders in a community, our hybrid GAT technique acts as complement to shed light on emerging influencers.
Disjoint Community Detection pada Network Kegiatan Kemahasiswaan di ISTTS Menggunakan Fast Greedy dan Walktrap Setiawan, Mikhael; Gunawan; F.X.Ferdinandus
Intelligent System and Computation Vol 3 No 1 (2021): 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.v3i1.175

Abstract

Disjoint community detection bertujuan untuk menemukan sebuah komunitas pada network dengan melakukan pemisahan. Pada penelitian ini, disjoint akan dilakukan pada network kegiatan kemahasiswaan di ISTTS. Metode disjoint community detection yang digunakan adalah fast greedy dan walktrap algorithm.  Data kegiatan kemahasiswaan berisi mengenai mahasiswa bersama-sama dengan mahasiswa lainnya mengikuti kegiatan kemahasiswaan apa saja. Setelah disjoint berhasil dilakukan, maka akan dihitung nilai closeness centrality dari setiap mahasiswa, dimana pada akhirnya akan dihitung correlation coefficient dengan IPK mahasiswa tersebut untuk mencari hubungan antara centrality mahasiswa dengan IPK mereka. Hasil closeness centrality ini selanjutnya di rata-rata untuk semua hasil algoritma untuk melihat bagaimana korelasi closeness centrality dengan ipk mahasiswa tersebut. Uji coba dilakukan dengan membentuk gml dari kombinasi filter, yang menghasilkan sekitar 2527 gml dengan nilai akhir korelasi adalah 62 - 63% weak positif dengan diikuti 16-18% moderate positif, dan 14-16% tidak berkorelasi sama sekali. Akhirnya dapat disimpulkan bahwa closeness centrality dalam sebuah komunitasnya, hanya berpengaruh secara weak positif dengan ipk mahasiswa tersebut.
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%.
Co-Authors Dewi, Grace Levina Dhaniswara, Erwin Dianaris, Audrey Ayu F.X. Ferdinandus Gunawan Irawati Setiawan, Esther Iwan Chandra Santoso, Ong Hansel Setiono, Kevin Surya Pratama, Eric Tjwanda Putera Gunawan Vincent Yuliana Melita Pranoto