Analisis dan Implementasi Teknologi Kecerdasan Buatan untuk Beragam Use Case di Telkom Indonesia

Ajiputra, Valentino Reswara (2025) Analisis dan Implementasi Teknologi Kecerdasan Buatan untuk Beragam Use Case di Telkom Indonesia. Project Report. [s.n.], [s.l.]. (Unpublished)

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Abstract

Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) telah menjadi salah satu pendorong utama transformasi digital di berbagai sektor industri, termasuk pada bidang telekomunikasi. Selama pelaksanaan kerja praktik di PT Telkom Indonesia (Persero) Tbk, khususnya di Chapter Data Science & AI (DSC), penulis berkesempatan untuk menganalisis, mengimplementasikan, dan mengevaluasi berbagai proyek berbasis AI yang digunakan dalam konteks operasional perusahaan. Beberapa proyek yang dikerjakan meliputi analisis RAG Chatbot dan YOLO Object Detection, evaluasi performa Large Language Model (LLM) untuk pembuatan judul percakapan (title generation) pada sistem TelkomGPT, deteksi aplikasi pengubah metadata gambar untuk kebutuhan keamanan sistem, pengembangan sistem Face Spoofing Detection menggunakan model MobileNet, ViT_B16, dan Autoencoder, serta pengembangan dan deployment sebuah aplikasi Streamlit.
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The development of Artificial Intelligence (AI) technology has become one of the main drivers of digital transformation across various industrial sectors, including telecommunications. During the internship at PT Telkom Indonesia (Persero) Tbk, particularly in the Chapter of Data Science & AI (DSC), the author had the opportunity to analyze, implement, and evaluate several AI-based projects applied in the company’s operational context. The projects undertaken included the analysis of RAG Chatbot and YOLO Object Detection, performance evaluation of Large Language Models (LLM) for conversation title generation in the TelkomGPT system, detection of image metadata modification applications for system security purposes, development of a Face Spoofing Detection system using MobileNet, ViT_B16, and Autoencoder models, as well as the development and deployment of a Streamlit application.

Item Type:	Monograph (Project Report)
Uncontrolled Keywords:	Artificial Intelligence, RAG Chatbot, YOLO, Large Language Model, Face Spoofing Detection, ONNX, Streamlit
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science)
Divisions:	Faculty of Industrial Technology > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Valentino Reswara Ajiputra
Date Deposited:	04 Nov 2025 06:16
Last Modified:	04 Nov 2025 06:16
URI:	http://repository.its.ac.id/id/eprint/128717

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