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All Journal Jurnal Sistim Informasi dan Teknologi JTH: Journal of Technology and Health
Dicky Jhon Anderson Butarbutar
Bina Nusantara University
Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Medicine & Pharmacology Nursing

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Digital Bank User Acceptance Analysis Using The Extended Technology Acceptance Model Dicky Jhon Anderson Butarbutar; Asri Ady Bakri; Nurlaili Rahmi; Novrini Hasti; Aprih Santoso
Jurnal Sistim Informasi dan Teknologi 2023, Vol. 5, No. 3
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jsisfotek.v5i3.281

Abstract

The Covid-19 pandemic requires banks to provide electronic payments, carry out digital transformation and open up opportunities for financial technology-based companies. Digital transformation in banking requires reliable integration and security between systems. Otherwise, it can become a gap for cyber attacks. Cyber security in banking includes Confidentiality, Integrity, Availability, Non-repudiation, and Authentication. Jenius is the first digital bank in Indonesia that makes it easy for customers to complete transactions and manage finances digitally without going to the bank. This bank was once a victim of a cyber attack that attacked customers with material losses of around 50 billion and non-material losses in the form of decreased bank credibility. Digital Bank also has problems that often occur, such as failing to identify customers, differences in facts on proof of transfer, frequent force close applications, etc. This study aims to determine the effect of security on behavioral intention and other factors that influence behavioral choice in this digital bank using the modified Technology Acceptance Model (TAM) model. The variables used are Confidentiality, Integrity, Availability, Non-repudiation, Authentication, Perceived Security, Perceived usefulness (PU), Perceived Trust, Perceived Ease of Use (PEoU), and Behavioral Intentions. This research uses quantitative methodology by distributing questionnaires to 200 sample customers. The measurement results show that more than half variables are accepted. The four rejected variables are the Confidentiality, Availability, and Non-repudiation variables to perceived security and the Perceived Ease of Use (PEoU) variable for the trust variable. Future research can add several external variables and review the rejected variables.
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.
Co-Authors Aprih Santoso Asri Ady Bakri Dedy Irwan Handry Eldo Muhammad Lukman Hakim Novrini Hasti Nurlaili Rahmi Renita Selviana