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All Journal International Journal of Information Technology and Business
Alaudin Maulana Hirzan
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Computer Science & IT

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Classification of Investment Opportunities in Semarang City Using the K-Nearest Neighbor Data Mining Method Bernadus Very Christioko; Daru, April Firman; Dyan Sinung Prabowo; Alaudin Maulana Hirzan
International Journal of Information Technology and Business Vol. 7 No. 2 (2025): April : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.722025.01-08

Abstract

Investment is an activity undertaken to allocate funds with the expectation of generating future returns. In a dynamic economic environment, identifying profitable investment opportunities can be a complex task. This study aims to determine potential investment opportunities in Semarang City using a classification method that facilitates business actors or investors in selecting appropriate business sectors. The study utilizes valid data to help investors make informed decisions when establishing a business in the region. Data collection was conducted through research at the Investment and One-Stop Integrated Services Agency (DPMPTSP) of Semarang City, employing a quantitative approach with the K-Nearest Neighbor (K-NN) method. The dataset was divided into training and testing sets with an 80:20 ratio. The experimental results show that the implementation of the K-NN algorithm, conducted using Google Colab, achieved an accuracy of 86% based on 60 testing data points. This demonstrates that the K-NN classification algorithm is effective and produces accurate predictions. Therefore, applying data mining classification techniques to identify investment opportunities can serve as a viable solution to support strategic decision-making for investors.their business development strategies with sector-specific prospects in Semarang City.
Co-Authors April Firman Daru Dyan Sinung Prabowo