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Machine Learning Enabled Social Media Competitive Intelligence System Hendri Handoko; Yulina Ismiyanti; Omar Arif Al-Kamari
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/480er062

Abstract

Social media platforms generate massive volumes of publicly accessible digital data that reflect organizational competitive strategies, yet most existing competitor analyses remain manual, descriptive, and limited to surface-level engagement metrics, resulting in low scalability and weak strategic intelligence. This study proposes a Machine Learning Enabled Social Media Competitive Intelligence System designed to automate competitor strategy extraction through artificial intelligence and big data analytics. The objective is to develop a computational framework capable of identifying strategic content patterns, communication objectives, audience positioning, and paid advertising behaviors using data-driven techniques. Large-scale public data from social media posts, engagement indicators, and advertising transparency libraries are collected and processed through data preprocessing pipelines, including text normalization, tokenization, and feature extraction using TF-IDF and word embedding representations. Supervised machine learning algorithms are implemented to classify content themes, detect strategic clusters, and model competitive positioning patterns, while performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics to ensure robustness and reliability. Experimental findings demonstrate that the proposed system significantly enhances analytical consistency, scalability, and strategic insight generation compared to traditional mixed method approaches. This research contributes to the advancement of AI-driven social media analytics and establishes a computational foundation for scalable big data-based competitive intelligence systems aligned with Artificial Intelligence and Big Data domains.
PENGARUH MODEL PROBLEM BASED LEARNING BERBANTUAN MEDIA EDUCANDY TERHADAP HIGH ORDER THINKING SKILLS MATA PELAJARAN IPAS KELAS III SEKOLAH DASAR Yulina Ismiyanti; Firdhaus Layla Dewandharu
Jurnal Cahaya Edukasi Vol 3 No 1 (2026): Januari 2026
Publisher : Cahaya Smart Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63863/jce.v3i1.333

Abstract

Pendidikan dasar memiliki peran penting dalam membentuk kemampuan intelektual dan karakter peserta didik, termasuk kemampuan berpikir tingkat tinggi atau Higher Order Thinking Skills (HOTS). Penelitian ini bertujuan untuk mengetahui pengaruh penerapan model Problem Based Learning (PBL) berbantuan media Educandy terhadap kemampuan HOTS siswa kelas III pada mata pelajaran IPAS di SD Negeri 1 Mrisi. Penelitian menggunakan pendekatan kuantitatif dengan desain Pre-Experimental One-Group Pretest-Posttest, melibatkan 20 siswa sebagai sampel. Data dikumpulkan melalui tes HOTS berupa pretest dan posttest, kemudian dianalisis menggunakan uji normalitas Shapiro–Wilk dan Paired Sample T-Test. Hasil penelitian menunjukkan adanya peningkatan signifikan kemampuan HOTS siswa setelah penerapan PBL berbantuan media Educandy , dengan nilai rata-rata pretest 55,05 meningkat menjadi 80,95 pada posttest, serta nilai signifikansi uji T < 0,001. Analisis indikator HOTS menunjukkan peningkatan terbesar pada kemampuan menganalisis, sedangkan kemampuan mencipta memerlukan penguatan lebih lanjut. Temuan ini menunjukkan bahwa PBL yang dikombinasikan dengan media interaktif berbasis permainan mampu meningkatkan motivasi, keterlibatan, dan kemampuan berpikir tingkat tinggi siswa, sehingga pembelajaran menjadi lebih aktif, bermakna, dan kontekstual. Penelitian ini memberikan bukti empiris bahwa pemanfaatan model PBL berbantuan media Educandy efektif dalam mengembangkan HOTS peserta didik pada mata pelajaran IPAS di sekolah dasar.