SI1922423419: Perbedaan revisi

OPTIMALISASI SISTEM REKOMENDASI STARTUP

MENGGUNAKAN METODE RANDOM FOREST

SKRIPSI

Disusun Oleh :

FAKULTAS SAINS DAN TEKNOLOGI

PROGRAM STUDI TEKNIK INFORMATIKA

KONSENTRASI SOFTWARE ENGINEERING

UNIVERSITAS RAHARJA

TANGERANG

TA. 2023/2024

ABSTRAK

Penelitian ini dilatarbelakangi oleh Peraturan Pemerintah berdasarkan Undang-Undang No. 11 Tahun 2020 tentang Cipta Kerja, yang memberikan dukungan sumber daya, termasuk fasilitas untuk mendukung digitalisasi. Dalam konteks ini, keberadaan platform Digital Library menjadi relevan mengingat peran teknologi di era digital. Penelitian ini fokus pada penggunaan metode Random Forest untuk meningkatkan efisiensi dan efektivitas sistem rekomendasi pada aplikasi AIMEE, sebuah platform yang mendukung pertumbuhan startupreneur di Indonesia. AIMEE memfasilitasi matchmaking antara startup, model bisnis, sektor industri, dan berbagai provinsi, serta talenta digital, dengan tujuan mewujudkan smart economy. Permasalahan utama yang dihadapi AIMEE adalah ketidaktersediaan fitur matchmaking antara startup dan investor. Penelitian ini bertujuan untuk mengatasi kekurangan ini dengan mengimplementasikan model machine learning, yaitu Random Forest. Pendekatan pemecahan masalah melibatkan langkah-langkah seperti menetapkan tujuan, pengumpulan data startup, analisis data dengan metode Random Forest, visualisasi hasil analisis, dan optimalisasi data startupreneur. Manfaatnya melibatkan efisiensi pengolahan data bisnis, kemudahan pemahaman data, kemampuan menganalisis data dari berbagai sumber, dan melaporkannya dalam bentuk visual bervariasi sebagai panduan dalam pengambilan keputusan bisnis. Penelitian ini memberikan kontribusi terhadap literatur dengan memadukan regulasi pemerintah, kebutuhan industri startup, dan penerapan teknologi machine learning untuk memperbaiki fitur matchmaking dalam platform AIMEE.

Kata Kunci: AIMEE, Random Forest, Machine Learning, Matchmaking, Optimalisasi Data

ABSTRACT

This research is motivated by the Government Regulation based on Law No. 11 of 2020 concerning Job Creation, which provides resource support, including facilities to support digitisation. In this context, the existence of a Digital Library platform is relevant given the role of technology in the digital era. This research focuses on using the Random Forest method to improve the efficiency and effectiveness of the recommendation system on the AIMEE application, a platform that supports the growth of startupreneurs in Indonesia. AIMEE facilitates matchmaking between startups, business models, industry sectors, and various provinces, as well as digital talents, with the aim of realising a smart economy. The main problem faced by AIMEE is the unavailability of matchmaking features between startups and investors. This research aims to address this shortcoming by implementing a machine learning model, namely Random Forest. The problem-solving approach involves steps such as goal setting, startup data collection, data analysis using Random Forest method, visualisation of analysis results, and optimisation of startupreneur data. The benefits involve the efficiency of business data processing, ease of data understanding, ability to analyse data from various sources, and report it in varied visual forms as a guide in business decision-making. This research contributes to the literature by combining government regulations, indus

Keywords : AIMEE, Random Forest, Machine Learning, Matching, Data Optimisation

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