Predicting Startup Success Using Machine Learning Approach
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 fai...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2024-10-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8338 |
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| author | Icha Wahyu Kusuma Ningrum Farid Ridho Arie Wahyu Wijayanto |
| author_facet | Icha Wahyu Kusuma Ningrum Farid Ridho Arie Wahyu Wijayanto |
| author_sort | Icha Wahyu Kusuma Ningrum |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-1739b7b2ac29485aa18a1ab51e22ed80 |
| institution | OA Journals |
| issn | 2548-6861 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-1739b7b2ac29485aa18a1ab51e22ed802025-08-20T01:55:19ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612024-10-018228029010.30871/jaic.v8i2.83388338Predicting Startup Success Using Machine Learning ApproachIcha Wahyu Kusuma Ningrum0Farid Ridho1Arie Wahyu Wijayanto2Politeknik Statistika STISPoliteknik Statistika STISPoliteknik Statistika STISPredicting 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%.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8338gradient boostingk-nearest neighborrandom foreststartupsupport vector machines |
| spellingShingle | Icha Wahyu Kusuma Ningrum Farid Ridho Arie Wahyu Wijayanto Predicting Startup Success Using Machine Learning Approach Journal of Applied Informatics and Computing gradient boosting k-nearest neighbor random forest startup support vector machines |
| title | Predicting Startup Success Using Machine Learning Approach |
| title_full | Predicting Startup Success Using Machine Learning Approach |
| title_fullStr | Predicting Startup Success Using Machine Learning Approach |
| title_full_unstemmed | Predicting Startup Success Using Machine Learning Approach |
| title_short | Predicting Startup Success Using Machine Learning Approach |
| title_sort | predicting startup success using machine learning approach |
| topic | gradient boosting k-nearest neighbor random forest startup support vector machines |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8338 |
| work_keys_str_mv | AT ichawahyukusumaningrum predictingstartupsuccessusingmachinelearningapproach AT faridridho predictingstartupsuccessusingmachinelearningapproach AT ariewahyuwijayanto predictingstartupsuccessusingmachinelearningapproach |