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Untoro, Meida Cahyo
Teknik Informatika, Institut Teknologi Sumatera
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Journal : Journal of Intelligent Systems and Information Technology

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Optimizing Startup Success Prediction Through SMOTE Oversampling and Classification Najie, Muhammad; Sofian, Ahmad Alif; Sidabutar, Ribka Julyasih; Untoro, Meida Cahyo
Journal of Intelligent Systems and Information Technology Vol. 1 No. 2 (2024): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i2.33

Abstract

Rapid technological advancements have led to a surge in the number of startups competing with innovative ideas. Predicting the chances of a startup's future success becomes crucial for entrepreneurs in making informed decisions and strategizing their growth. This study investigates the effectiveness of the Gradient Boosting classification algorithm in predicting startup success. To address potential class imbalance within the dataset, a pre-processing step utilizing Synthetic Minority Oversampling Technique (SMOTE) was employed. The dataset itself encompassed a wide range of variables related to startup attributes and performance metrics. The F1-score metric was utilized to evaluate the model's accuracy while minimizing false positive predictions that could potentially mislead investors. Gradient Boosting algorithm was employed to analyze the dataset, which was pre-processed using SMOTE to handle potential class imbalance. This technique helps to create synthetic data points for the minority class, resulting in a more balanced dataset for the classification model. The dataset itself encompassed a wide range of variables related to startup attributes and performance. The F1-score metric was utilized to evaluate the model's accuracy while minimizing false positive predictions that could potentially mislead investors. Gradient Boosting algorithm achieved an F1-score of 86% for predicting successful startups and 85% for predicting unsuccessful ones. The low false positive prediction rate of 7.9% on the test data further validates the model's reliability. The findings demonstrate the effectiveness of Gradient Boosting in predicting startup success with high accuracy and minimal false positives
Co-Authors Afriansyah, Aidil Ahmad Agung Zefi Syahputra Aidil Afriasnyah Algifari, Muhammad Habib Amrulloh, Iqbal Anastasia Puteri Dewi Andika Setiawan Andika Setiawan, Andika Andini, Maria Anggraini, Leslie Annisa Dwi Atika Anugerah Perdana Aprilia Purwanto Aprilianda, Mohamad Meazza Arre Pangestu Athalla, Muhammad Nadhif Bagaskara, Radhinka Bangun, Natasya Ate Malem Ba’its, Alfian Kafilah Buliali, Joko Lianto Dani Al Mahkya Desi Budiarti Dharmawan, Benedictus Budhi Dian Anggraini Drantantiyas, Nike Dwi Grevika Eka Nur'azmi Yunira Eko Dwi Nugroho Eri Yuni Nilasari Faisal, Amir Faza Nur Fuadina Febrianto, Andre Feri Fahrianto Fery Widhiatmoko Fitrawan, Mhd. Kadar Gunawan, Rayhan Fatih Harmiansyah Hidayah, Fathan Rizki Ibn, Ferreyla Setara Ilham Firman Ashari Irawati, Febri Dwi Jerhi Wahyu Fernanda Kesuma, Alvin Kurniawansyah, Apri Laisya, Nashwa Putri Leo Viranda Millennium Leonard Rizta Listiani, Amalia M. Syamsuddin Wisnubroto Mahdia Nisrina Maharani M Mandiri, Tobyanto Putra Marbun, Rustian Afencius Maria Oktarise Natania Gultom Mastuti Widianingsih, Mastuti Muhammad Adam Aslamsyah Muhammad Affandi Muhammad Alfarizi Tazkia Muhammad Farhan Muhammad Muttaqin Muhammad Nadhif Athalla Muhammad Yusuf Muhammad Zulfarhan Najie, Muhammad Nasrulloh, M. Anas Nazla Andintya Wijaya Nestiawan Ferdiyanto Nur'azmi, Eka Nurul Fajrin Ariyani Oktaviana Rinda Sari Perdana, Agung Mahadi Putra Prabandari, Pungki Resti Praramadhana, Daffa Praseptiawan, Mugi Pungki Resti Prabandari Raidah Hanifah Raidah Hanifah Retnosari, Hesti Revangga, Dwi Arthur Riyanarto Sarno Samsu Bahri Sianturi, Elsa Elisa Yohana Sidabutar, Ribka Julyasih Sinaga, Nydia Renli Siregar, Abu Bakar Siddiq Sofian, Ahmad Alif Sophia Nouriska Suranta, Akmal Fauzan Tirta Setiawan Verdiana, Miranti Winda Yulita Wisnubroto, M. Syamsuddin Yulita, Winda Yunira, Eka Nur'azmi Yusuf, Muhammad Asyroful Nur Maulana