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All Journal Journal of Economic, Business and Engineering (JEBE) INOVTEK Polbeng - Seri Informatika
Hafizha Nurul Qolby
Universitas Pendidikan Indonesia
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mechanical Engineering

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Comparative Analysis of Machine Learning Models for BUMN Bank Stock Sentiment Classification During Danantara Formation Period Hafizha Nurul Qolby; Rangga Gelar Guntara; Syti Sarah Maesaroh
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/91z79392

Abstract

Discussions about state-owned bank stocks (BBRI, BBNI, and BMRI) on platform X intensified during the formation of Danantara. However, the correlation between social media sentiment and stock movements remains weak due to high noise levels and potential buzzer activity. This study combines sentiment and text similarity analyses (cosine similarity) to identify repeated communication patterns in discussions related to state-owned bank stocks. A total of 1,086 tweets were manually labeled and verified by two independent validators Text features were represented using TF–IDF and evaluated through four classical machine learning algorithms: Naïve Bayes, Logistic Regression, Support Vector Machine, and XGBoost. The model was validated using a hold-out scheme (80:20) and assessed with a confusion matrix. The sentiment distribution of the dataset shows 53% negative and 47% positive tweets Logistic Regression achieved the highest accuracy of 66%. The cosine similarity analysis identified 1.8% of tweets with similarity ≥0.90, indicating limited recurring communication patterns. These findings suggest that integrating sentiment and text similarity analyses can serve as an initial approach to detect indications of coordinated activity and to understand public opinion dynamics toward state-owned bank stocks.
PERBANDINGAN EFEKTIVITAS PLATFORM SHOPEE, TOKOPEDIA, LAZADA, DAN TIKTOK SHOP DALAM MENINGKATKAN PENJUALAN PRODUK UMKM MAKANAN RINGAN (STUDI KASUS PADA UMKM MAKANAN RINGAN DI TASIKMALAYA) Hafizha Nurul Qolby; Shiddiq Hafizhulmalik; Rejas Pratama; Dian M. Manufandu; Syti Sarah Maesaroh
Journal of Economic, Business and Engineering (JEBE) Vol. 6 No. 1 (2024): October
Publisher : Universitas Sains Al Qur'an

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32500/jebe.v6i1.7023

Abstract

Penelitian ini membandingkan efektivitas empat platform e-commerce, yakni Shopee, Tokopedia, Lazada, dan TikTok Shop, dalam meningkatkan penjualan produk UMKM makanan ringan di Tasikmalaya. Metode penelitian yang digunakan adalah metode kuantitatif dengan memanfaatkan data sekunder dari penjualan 40 produk makanan ringan UMKM di Tasikmalaya. Analisis dilakukan menggunakan uji Kruskal-Wallis dan analisis post-hoc dengan pendekatan metode komparatif. Hasil penelitian menunjukkan perbedaan signifikan dalam efektivitas keempat platform e-commerce dalam meningkatkan penjualan produk UMKM. Analisis post-hoc mengungkapkan bahwa Shopee dan TikTok Shop lebih efektif dalam meningkatkan penjualan dibandingkan Tokopedia dan Lazada. Temuan ini dapat membantu UMKM memilih platform e-commerce yang paling sesuai untuk mengoptimalkan penjualan produk makanan ringan mereka.
Co-Authors Dian M. Manufandu Maesaroh, Syti Sarah Rangga Gelar Guntara Rejas Pratama Shiddiq Hafizhulmalik