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All Journal JTH: Journal of Technology and Health JIPITI: Jurnal Pengabdian kepada Masyarakat
Renita Selviana
Institut Teknologi dan Bisnis Yadika Pasuruan
Computer Science & IT Economics, Econometrics & Finance Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Other

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TRANSFORMER-BASED SENTIMENT ANALYSIS FOR PUBLIC OPINION CLASSIFICATION ON ELECTRIC VEHICLE ADOPTION USING NATURAL LANGUAGE PROCESSING Dicky Jhon Anderson Butarbutar; Muhammad Lukman Hakim; Renita Selviana; Dedy Irwan; Handry Eldo
JTH: Journal of Technology and Health Vol. 4 No. 1 (2026): July: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v4i1.840

Abstract

The growth of electric vehicle (EV) adoption has generated extensive public discussions on digital platforms, reflecting diverse perceptions of environmental benefits, economic feasibility, technological readiness, and government policies. This study aims to develop a Transformer-based Natural Language Processing (NLP) framework for classifying public sentiment toward EV adoption using textual data from social media and digital platforms. A quantitative experimental approach was applied through data collection, preprocessing, sentiment and emotion labeling, Transformer-based modeling, and performance evaluation. The dataset consisted of 5,000 public opinion texts, of which 3,785 records were retained after data cleaning and selection. Sentiment classification included three categories: positive, negative, and neutral, while emotion classification consisted of happy, trust, angry, fear, disappointed, and surprise. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results showed that positive sentiment was dominant, accounting for 43.46% of the analyzed opinions, followed by negative sentiment at 31.97% and neutral sentiment at 24.57%. Positive opinions were mainly related to environmental benefits and energy efficiency, whereas negative opinions reflected concerns about vehicle prices, charging infrastructure, charging time, and battery replacement costs. These findings indicate that Transformer-based NLP can capture contextual semantic information from large-scale public opinion data and support reliable sentiment classification. The proposed framework provides practical value for policymakers, researchers, and industry stakeholders in developing data-driven strategies to promote EV adoption and sustainable transportation.
Pelatihan Pemanfaatan Artificial Intelligence untuk Optimalisasi Pemasaran Produk UMKM pada Platform E-Commerce Ratnawita Ratnawita; Renita Selviana; Cut Susan Octiva; Handry Eldo; Dennis Lorens
JIPITI: Jurnal Pengabdian kepada Masyarakat Vol. 3 No. 2 (2026): Mei 2026 - JIPITI: Jurnal Pengabdian kepada Masyarakat
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi Artificial Intelligence (AI) telah memberikan peluang signifikan dalam meningkatkan efektivitas strategi pemasaran digital, khususnya bagi pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) yang memanfaatkan platform e-commerce. Namun, rendahnya literasi digital dan keterbatasan pemahaman terkait pemanfaatan AI menjadi kendala utama dalam optimalisasi pemasaran berbasis teknologi. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kompetensi pelaku UMKM dalam mengimplementasikan teknologi AI guna mendukung strategi pemasaran produk secara lebih efektif dan adaptif. Metode pelaksanaan kegiatan dilakukan melalui beberapa tahapan, yaitu sosialisasi, pelatihan penggunaan tools AI (seperti pembuatan konten otomatis, analisis tren pasar, dan optimasi deskripsi produk), pendampingan praktik langsung pada platform e-commerce, serta evaluasi melalui pre-test dan post-test. Hasil kegiatan menunjukkan adanya peningkatan signifikan pada pemahaman dan keterampilan peserta, dengan rata-rata nilai post-test meningkat sebesar 32% dibandingkan pre-test. Selain itu, peserta mampu menghasilkan konten pemasaran yang lebih menarik, terstruktur, dan berbasis data, sehingga berpotensi meningkatkan visibilitas dan konversi penjualan produk. Dengan demikian, pemanfaatan AI dalam pemasaran e-commerce terbukti efektif dalam mendukung transformasi digital UMKM. Kegiatan ini diharapkan dapat memberikan kontribusi berkelanjutan dalam peningkatan daya saing UMKM di era ekonomi digital.
Co-Authors Cut Susan Octiva Dedy Irwan Dennis Lorens Dicky Jhon Anderson Butarbutar Handry Eldo Muhammad Lukman Hakim Ratnawita Ratnawita