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All Journal JTH: Journal of Technology and Health Jurnal Multidisiplin Indonesia JIPITI: Jurnal Pengabdian kepada Masyarakat Jurnal Ilmu Komputer dan Teknologi Informasi
Renita Selviana
Institut Teknologi dan Bisnis Yadika Pasuruan
Computer Science & IT Economics, Econometrics & Finance Education Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary 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): 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)

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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.
Analisis Ancaman Social Engineering Berbasis Agentic AI dan Strategi Mitigasi Menggunakan Pendekatan Zero Trust Teguh Pradana; Nur Amalia Hasma; Handry Eldo; Renita Selviana; Ayuliana Ayuliana
Jurnal Ilmu Komputer dan Teknologi Informasi Vol. 3 No. 2 (2026): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jikti.v3i2.2057

Abstract

The development of Agentic Artificial Intelligence (Agentic AI) presents new challenges in cybersecurity due to its ability to plan, adapt, and act autonomously. This study aims to analyze the threat of social engineering based on Agentic AI and to formulate mitigation strategies using the Zero Trust approach. The research method employs literature review and threat modeling to identify attack vectors, user vulnerabilities, and potential impacts. The analysis shows that Agentic AI can enhance the personalization, scale, speed, and effectiveness of social engineering attacks. This study develops a conceptual framework for mitigating social engineering attacks based on Agentic AI by applying Zero Trust principles as a layered defense approach that places identity, context, and risk at the center of security decision-making.
Sistem Monitoring Kualitas Air PT. Centralpertiwi Bahari Menggunakan Metode Prototype Berbasis Web Mokhamad Mukti Maulana; Renita Selviana; Alimin Alimin
Jurnal Multidisiplin Indonesia Vol. 5 No. 9 (2026): Jurnal Multidisiplin Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jmi.v5i9.2945

Abstract

Penelitian ini bertujuan merancang dan membangun sistem monitoring kualitas air berbasis web pada PT Centralpertiwi Bahari Situbondo untuk memperbaiki proses pencatatan, penyimpanan, penelusuran, dan penyajian data kualitas air. Nilai penelitian terletak pada penerapan metode Prototype yang melibatkan pengguna dalam evaluasi dan penyempurnaan sistem secara iteratif, dengan cakupan parameter pH, salinitas, suhu, amonia (NH?), dan oksigen terlarut (DO). Data dikumpulkan melalui observasi, wawancara, dan studi pustaka. Pengembangan dilakukan melalui komunikasi kebutuhan, perencanaan cepat, perancangan cepat, pembangunan prototype, evaluasi dan umpan balik, penyempurnaan prototype, pengembangan sistem final, dan pengujian fungsional. Sistem masih memiliki keterbatasan berupa penyimpanan lokal, belum adanya notifikasi otomatis, belum terintegrasi sensor IoT secara langsung, dan belum menerapkan manajemen multi-pengguna secara penuh. Prototype dievaluasi oleh dua petugas monitoring dan satu supervisor. Masukan utama berupa penambahan filter parameter grafik dan penyempurnaan penamaan file ekspor telah diimplementasikan. Pengujian Black Box mencakup 15 skenario dan seluruh skenario menghasilkan keluaran sesuai harapan sehingga tingkat keberhasilan mencapai 100%. Sistem membantu membuat pencatatan dan pengelolaan data lebih terstruktur, mempermudah pemantauan kondisi air, serta meningkatkan efisiensi penyampaian informasi antara petugas lapangan dan pihak terkait.
Co-Authors Alimin Alimin Ayuliana Ayuliana Cut Susan Octiva Dedy Irwan Dennis Lorens Dicky Jhon Anderson Butarbutar Handry Eldo Mokhamad Mukti Maulana Muhammad Lukman Hakim Nur Amalia Hasma Ratnawita Teguh Pradana