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All Journal Jurnal Ekonomi Syariah Teori dan Terapan Riau Journal of Computer Science Script Journal: Journal of Linguistic and English Teaching MAJALAH ILMIAH GLOBE Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Inotera El-Barka: Journal of Islamic Economics and Business Jurnal Hutan Tropika Balanca : Jurnal Ekonomi dan Bisnis Islam Journal of Law, Poliitic and Humanities Revenue : Jurnal Ekonomi Pembangunan dan Ekonomi Islam Seminar Nasional Insinyur Profesional (SNIP) SATIN - Sains dan Teknologi Informasi Applied Science and Technology Research Journal RJOCS (Riau Journal of Computer Science) LOGIC : Jurnal Ilmu Komputer dan Pendidikan Al-Zayn: Jurnal Ilmu Sosial & Hukum Jurnal Magister Ekonomi Syariah The Indonesian Journal of Computer Science Jeptira TIFDA : Journal Technology Information and Data Analytic MADINA : Journal of Islamic Studies Vocational: Journal of Educational Technology Papsel Economic Journal
Herianto
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Journal : The Indonesian Journal of Computer Science

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Analisis Perbandingan Algoritma Machine Learning dengan SMOTE dan Teknik Boosting dalam Peningkatan Akurasi Yuda Irawan; Refni Wahyuni; Rian Ordila; Herianto
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4368

Abstract

This research explores and enhances accuracy in sentiment classification related to Indonesia's Capital City relocation by combining Naive Bayes (NB), Random Forest (RF), SMOTE, and XGBoost. The study addresses challenges of unbalanced data and complexity in social media sentiment analysis. The combination of RF with SMOTE achieved the highest accuracy at 91.25%, demonstrating SMOTE's effectiveness in balancing the dataset and improving minority class detection. While adding XGBoost slightly reduced accuracy (90.92%), it increased the NB model's accuracy from 77.45% to 85.97% when combined with SMOTE. RF alone reached 87.46% and improved to 88.78% with XGBoost. The study underscores the importance of selecting and combining techniques to maximize sentiment prediction accuracy. Future research could explore deep learning or transformer models for even better results, offering deeper insights into public sentiment and aiding effective policy strategy development.
Co-Authors Adam Arif Budiman, Adam Arif Adrian Axel Yohannes Ahmad Makbul Ahmad Mujaffar Ahmad Syawaluddin Aji Setiawan Alva Edy Tontowi Alva Edy Tontowi Amar Ahmad Andi Ajeng Tenri Lala Andi Susilo Astuti, Eka Yuni Ayu Susilawati, Endang Baba Barus Banu Santoso Dhananjaya Yama Hudha Kumarajati Dikpride Despa Elysia, Shafa Febriana Sesunan, Mira Gusnawati, Rina Hasbi Haslindah Irnandas, Muh Jaman, Najma Jusmiana, Andi Kurnianto, Atik Kusmono M. Ali Latif Amri Mabanja, Ashiraf MARDIANA Mira Harimurti Mohammad Ardyansah Muhammad Kusumawan Herliansyah MUHAMMAD YUSUF Nababan, Mangatur Rudolf Nadia Shalehah Nasution, Saddam Husein Nasyiya Nirwan Nur Hasanah Nur Syamsiyah Nurhidaya Nurpasila Nuwa Pramesti, Gita Prasetyo, Arya Adhari Purnamasari, Dian Septi Raden Rosnawati Raditya Fadhil Arva Ratna Widyawati Refni Wahyuni Renhart Jemi Rian Ordila Ritonga, Syahirul Alim Riyadi Santosa Romatua, Pittor Rustanto Sahmiar Pulungan Sarah Iftin Atsani Sari , Andira Sarlin Siti Aisyah Sugianto, Didik Suryani, Okta Susanto, Herry  Suzuki Syofian Syahirul Alim Ritonga Tenri Lala, Andi Ajeng Tenri Lala Timor Setiyaningsih, Timor Tri Mahardika, Bagus Vincentius P Siregar Wangi Pandan Sari Wardani, Tri Ulfa Yahya Yuda Irawan Yudha, Afri YULISMAN