Article Per Year (5 Year)
p-Index From 2020 - 2025
0.947
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Aplikasi Statistika & Komputasi Statistik Prosiding Seminar Nasional Official Statistics
Ridho, Farid
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Predicting Startup Success Using Machine Learning Approach Ningrum, Icha Wahyu Kusuma; Ridho, Farid; Wijayanto, Arie Wahyu
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8338

Abstract

Predicting startup success is important because it helps investors, entrepreneurs, and stakeholders allocate resources more efficiently, minimize risks, and enhance decision-making in an uncertain and competitive environment. Therefore, investors need to predict whether a startup will succeed or fail. Investors conduct this assessment to determine if a startup is worthy of funding. The company's founders mark success here by receiving a sum of money through the Initial Public Offering (IPO) or Merger and Acquisition (M&A) process. If the startup closes, we will consider it a failure. The data used consists of 923 startup companies in the United States. We carried out the classification using four methods: Random Forest, Support Vector Machines (SVM), Gradient Boosting, and K-Nearest Neighbor (KNN). We then compare the results from the four methods with and without feature selection. We determine the feature selection based on the relative importance of each method. The results of this study indicate that the Random Forest method with feature selection has the best accuracy, precision, recall, and F1 score than the other methods, respectively 81.85%, 80.19%, 87.09%, and 83.44%.
Co-Authors Arie Wahyu Wijayanto Maulidya, Luthfi Ningrum, Icha Wahyu Kusuma Pandudinata, Maulana Setiyawan, Arbi Suadaa, Lya Hulliyyatus Syibli, Muhammad Zuhdi Ali Hisyam