ARTIFICIAL INTELLIGENCE DAN LLM DALAM MENGHASILKAN ANALISA STOCK MANAGEMENT

KUSUMA, ALIF NDARU (2025) ARTIFICIAL INTELLIGENCE DAN LLM DALAM MENGHASILKAN ANALISA STOCK MANAGEMENT. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Amid the rapid expansion of data in the digital era, the ability to analyze and forecast information has become essential for supporting strategic decision-making, particularly in commodity stock management. This study develops a system that integrates Generative Artificial Intelligence (AI) and Large Language Models (LLMs) to generate textual narratives from predictive data visualizations. The Autoregressive Integrated Moving Average (ARIMA) model is employed to forecast commodity prices based on historical price data, rainfall intensity, and solar radiation. The forecast results are visualized in graphs and interpreted by the LLM to produce human-readable summaries. The system also provides inter-provincial shipping recommendations by analyzing surplus and deficit conditions, geographic distances, and estimated delivery times. The findings demonstrate that the system simplifies the interpretation of complex forecasting outputs and enhances strategic insights for effective stock distribution management. This research highlights the potential of combining generative AI and predictive modeling to enrich data understanding and improve data-driven decision-making in agricultural and logistics sectors. Keyword : Artificial Intelligence, Large Language Models, Data Visualization, Text Analysis, Prompt Engineer Di tengah pesatnya pertumbuhan data di era digital, kemampuan untuk menganalisis dan memproyeksikan informasi menjadi elemen penting dalam mendukung pengambilan keputusan strategis, khususnya dalam pengelolaan stok komoditas. Penelitian ini mengembangkan sistem berbasis Generative Artificial Intelligence (AI) dan Large Language Model (LLM) untuk menghasilkan narasi teks dari visualisasi data prediktif. Model Autoregressive Integrated Moving Average (ARIMA) digunakan untuk memprediksi harga komoditas dengan mempertimbangkan variabel harga historis, curah hujan, dan penyinaran matahari. Hasil prediksi divisualisasikan dalam bentuk grafik yang kemudian dianalisis oleh LLM untuk menghasilkan deskripsi naratif yang mudah dipahami. Sistem juga dirancang untuk memberikan rekomendasi pengiriman antarprovinsi berdasarkan kondisi surplus dan defisit komoditas, jarak, dan estimasi waktu distribusi. Hasil penelitian menunjukkan bahwa sistem mampu menyederhanakan interpretasi visualisasi data kompleks dan memberikan informasi strategis yang mendukung manajemen distribusi stok secara efisien. Penelitian ini memperlihatkan potensi integrasi AI generatif dan model peramalan dalam memperkaya pemahaman terhadap data, sekaligus meningkatkan kualitas pengambilan keputusan berbasis data di sektor pertanian dan logistik. Keyword : Kecerdasan Buatan, Model Bahasa Besar, Visualisasi Data, Analisis Teks,Perancangan Prompt

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 25 048
NIM/NIDN Creators:	41821010010
Uncontrolled Keywords:	Kecerdasan Buatan, Model Bahasa Besar, Visualisasi Data, Analisis Teks,Perancangan Prompt
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.7 Multimedia Systems/Sistem-sistem Multimedia > 006.75 Social Multimedia/Multimedia Social > 006.754 Online Social Network/Situs Jejaring Sosial, Sosial Media
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	07 Aug 2025 04:22
Last Modified:	07 Aug 2025 04:22
URI:	http://repository.mercubuana.ac.id/id/eprint/96641

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