Khofifah, Nur (2024) "Perancangan Prototipe Pembaca Model Kausatik Sistem Transportasi Berbasis Rekayasa Instruksi GPT untuk Mendukung Pemahaman Variabel dan Polaritas Variabel Sistem. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Latar Belakang: Diagram kausatik atau Causal Loop Diagram pada sistem transportasi telah banyak dimodelkan pada beberapa penelitian, antara lain digunakan untuk memodelkan hubungan antara variabel yang menjadi faktor – faktor penting dalam komponen sistem, seperti tingkat penggunaan transportasi, kapasitas jalan, volume kendaraan, total kapasitas kendaraan. Variabel merupakan faktor dalam sistem yang dapat mengalami perubahan seiring berjalannya waktu. Interaksi antar variabel atau hubungan antar variabel direpresentasikan dengan notasi link polarity atau nilai polaritas.
Permasalahan: Memahami diagram kausatik sebagai model konseptual yang merepresentasikan informasi dari hubungan kompleks antara variabel merupakan tantangan bagi pembaca model dan bagaimana LLM dapat digunakan untuk mengidentifikasi variabel beserta polaritas dalam diagram kausatik dengan lebih efektif melalui rekayasa instruksi GPT serta bagaimana menyajikan informasi pembaca model yang terstruktur dan mudah diakses.
Tujuan: Dari kondisi ini, terdapat peluang untuk mengidentifikasi kebutuhan informasi pembaca, menyusun instruksi sistematis untuk LLM, dan merancang prototipe yang menyajikan informasi diagram kausatik dengan interaktif.
Data dan Metode: Pengambilan data pada penelitian ini dilakukan melalui studi literatur dan wawancara untuk penggalian kebutuhan dan mengumpulkan data berupa gambar model diagram kausatik sistem transportasi.
Hasil: Hasil dari penelitian ini berupa rancangan prototipe penyajian informasi model diagram kausatik dibidang sistem transportasi pada level low fidelity berbasis website dengan informasi yang dihasilkan LLM melalui perancangan instruksi yang efektif.
Nilai Tambah/Manfaat: Penelitian ini memberikan wawasan baru tentang bagaimana LLM dapat digunakan dalam bidang transportasi untuk memahami sistem yang kompleks dan bagaimana informasi model diagram kausatik sistem transportasi disajikan melalui prototipe untuk mendukung pemahaman sistem.
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Background: Causal Loop Diagrams for transport systems have been modelled in many studies, including to model the relationship between variables that are important factors in system components, such as transport utilization rate, road capacity, vehicle volume, and total vehicle capacity. Variables are factors in the system that can change over time. Interactions between variables or relationships between variables are represented with the notation link polarity or polarity value.
Problem: Understanding diagram kausatik as a conceptual model that represents information from complex relationships between variables is a challenge for model readers and how LLM can be used to identify variables along with polarity in diagram kausatik more effectively through engineering GPT instructions as well as how to present structured and accessible model reader information.
Purpose: From this condition, there is an opportunity to identify readers' information needs, develop systematic instructions for LLM, and design a prototype that presents diagram kausatik information interactively.
Data and Methods: Data collection in this study was conducted through literature review and interviews to explore the needs and collect data in the form of images of the transport system diagram kausatik model.
Results: The result of this research is a prototype design of presenting diagram kausatik model information in the field of transportation systems at a low fidelity level based on a website with information generated by LLM through effective instruksi design.
Value added/Benefits: This research provides new insights into how LLM can be used in the field of transportation to understand complex systems and how information on diagram kausatik models of transportation systems is presented through prototypes to support system understanding.
Item Type: | Thesis (Other) |
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Uncontrolled Keywords: | Causal Loop Diagram, Instruction Engineering, Large Language Model, Prototype, Transport System, Causal Loop Diagram, Large Language Model, Prototipe, Rekayasa Instruksi, Sistem Transportasi |
Subjects: | T Technology > T Technology (General) T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T58.8 Productivity. Efficiency T Technology > T Technology (General) > T59.7 Human-machine systems. |
Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis |
Depositing User: | Nur Khofifah |
Date Deposited: | 31 Jul 2024 06:22 |
Last Modified: | 31 Jul 2024 06:22 |
URI: | http://repository.its.ac.id/id/eprint/109846 |
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