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Journal of Emerging Information Systems and Business Intelligence (JEISBI)
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 27743993     DOI : 10.26740/jeisbi
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Library & Information Science
Journal of Emerging Information Systems and Business Intelligence (JEISBI) aims to provide scholarly literature focused on studies and research in the fields of Information Systems (IS) and Business Intelligence (BI). This journal also includes public reviews on the development of theories, methods, and applications relevant to these topics. All published works are presented exclusively in English to reach a global audience of readers and researchers. The journal’s scope includes but is not limited to the following fields: Data Mining Generative Artificial Intelligence Big Data Analytics Business Intelligence Enterprise Architecture UI/UX Business Process Management Enterprise System System Development Decision Support System IS/IT Strategy and Planning IT Investment and Productivity IT Project Governance IS Business Value Audit SI/TI Cybersecurity and Risk Management IS/IT Operations and Service Management IT Ethics Organizational and Human Behavior Technology Digital Sociology
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 5 Documents
 1 
Search results for , issue "Vol. 5 No. 4 (2024)" : 5 Documents clear
Web-Based Decision Support System for Best Laptop Selection Using MABAC Method Mochammad Rafi Diaz Ardhana; Aries Dwi Indriyanti
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.64267

Abstract

The advancement of technology in the modern era has made devices such as laptops essential in daily life. According to a report from Ministry of Communication and Information Technology of Indonesia that published in 2017, from a survey of 2,121 respondents showed that more than half percent respondent use laptop for work and study, while 34.94% use laptop for entertainment. However, selecting the right laptop often poses a challenge, especially for students in the Informatics Engineering Department at Universitas Negeri Surabaya, who frequently use outdated laptops. To address this issue, a Decision Support System (DSS) is needed, utilizing the Multi-Attributive Border Approximation Area Comparison (MABAC) method. In this study, the MABAC method was used to select laptops based on criteria such as price, CPU, RAM, and storage. By applying the MABAC method, the DSS is believed to effectively address the issue of selecting the most suitable laptop, thereby enhancing productivity and performance. This research successfully developed a web-based Decision Support System (DSS) for selecting the best laptops using the Multi-Attributive Border Approximation Area Comparison (MABAC) method, which simplifies the evaluation process for users. The DSS incorporates 10 criteria: price, processor, RAM, storage, storage type, screen size, graphics card, laptop weight, battery, operating system, and warranty. The MABAC calculations ranked the Asus Vivobook 14 A1400EA as the best laptop with a score of 0.15, followed by the HP 14s EP0022TU and Lenovo Ideapad Slim 3 14ITL6 with scores of 0.05, while the Dell Latitude 3420 ranked last with a score of -0.05.
Business Process Reengineering in the Training Service Business Process of CV. Maxindo Consulting to Improve Company Performance Efficiency Ruben Emanuel Widagdo; Suartana, I Made
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.64521

Abstract

In facing global competition, companies must ensure that employees can work effectively and efficiently, with both high-quality and high-quantity output. Employee training has become one of the crucial methods to meet these standards, considering the importance of competent human resources in supporting a company’s competitiveness. CV. Maxindo Consulting is a company that specializes in employee training services across various sectors. However, its current business processes, such as proposal requests and training evaluations, remain time-consuming, hindering work efficiency and potentially harming the company’s image. Therefore, Business Process Reengineering (BPR) is proposed as a solution. This BPR approach is expected to enhance service quality, operational efficiency, and the company’s positive image. The BPR method, as explained by Hammer and Champy (1994) and Davenport & Short (1990), represents a novel approach to business process improvements that can optimize company services and performance.
Sentiment Analysis of 2024 Election Fraud Using SVM and Naïve Bayes Algorithms Hilmi, Faalih Hibban; Indriyanti, Aries dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.64703

Abstract

Elections are one of the main pillars of democracy, where the people's voice is the main determinant in government formation. Election fraud not only harms political competitors but also undermines public trust in democracy. The role of social media Twitter in widely disseminating information and disinformation adds to the challenge of maintaining election integrity. Sentiment analysis is the process of collecting and understanding individual opinions related to an event. Support Vector Machine (SVM) and Naïve Bayes algorithms are often used in this analysis due to their effectiveness and efficiency in text classification. This research aims to analyze public sentiment related to the 2024 presidential election fraud and compare the effectiveness of SVM and Naïve Bayes in sentiment classification. The study was conducted quantitatively, involving the stages of data collection, preprocessing, labeling, TF-IDF weighting, classification, and evaluation. The results of the sentiment analysis of public opinion on the 2024 presidential election fraud showed 42.5% negative sentiment, 38.6% neutral, and 18.9% positive. The dominance of negative sentiments reflects the public's concerns about election integrity. The high neutral sentiment indicates public doubt. To overcome this, transparency, strengthening supervisory institutions, electronic election technology, and strict law enforcement are needed. The SVM algorithm with RBF kernel produces 58% accuracy, better than Naïve Bayes with 51%.
Analysis of User Satisfaction of the "Lalamove" Application Using the SERVQUAL and EUCS Method Dewi, Anisa Tri Puspa; Bisma, Rahadian
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.65340

Abstract

Lalamove is a logistics platform connecting customers with trained drivers and couriers, offering secure, rapid, and convenient delivery solutions, focusing on quality, safety, and customer satisfaction. This study analyzes user satisfaction with the Lalamove application using twovice Quality (SERVQUAL) and End User  Computing Satisfaction (EUCS). The SERVQUAL method assesses service quality across five dimensions: tangible ty, dimensions, responsiveness, assurance, and empathy. Meanwhile, EUCS evaluates information system user satisfaction based on five dimensions: content, accuracy, Format, ease of use, and timeliness. Employing a quantitative approach with a survey method, this research involved 309 respondents comprising Lalamove application users, including customers and drivers. The results indicate that reliability and responsiveness (SERVQUAL), Format, and insourcing support (EUCS) significantly influence customer satisfaction. Additionally, the overall EUCS variables positively impact customer satisfaction. These findings suggest that combining SERVQUAL and EUCS provides a holistic understanding of service quality and user satisfaction, encompassing aspects of operational and user experience. Primary recommendations include enhancing system performance, improving delivery timeliness, and optimizing the application's interface design and usability.
Analysis of Factors Influencing Acceptance of the Online Population Administration Information System in Mojokerto Regency Using Technology Acceptance Model (TAM 3) AYER, FIDIANTI RAMADANI SUHADI; Indriyanti, Aries Dwi
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 5 No. 4 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v5i4.65853

Abstract

This research investigates factors influencing the acceptance of the POSKeTanMu online population administration system in Mojokerto Regency using the Technology Acceptance Model (TAM 3). POSKeTanMu (Pelayanan Online Sistem Kependudukan Tanpa Ketemu) is a self-service online population administration system for residents of Mojokerto Regency. A quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied, analyzing data from 159 respondents through questionnaires. Findings indicate that Behavioral Intention (BI) significantly influences user independence and responsibility. Key factors affecting technology acceptance include perceived usefulness, ease of use, social influence, and experience, while computer anxiety and system usability showed no significant impact. Additionally, the study explores the implementation of the Double Track program using a qualitative case study approach, guided by Thomas Lickona’s character-building theory. Data collection involved interviews, observations, and documentation, analyzed using Miles and Huberman’s framework. Results highlight ease of use, perceived benefits, and social perceptions as major drivers of technology adoption. Positive user experiences and social support play crucial roles in enhancing e-government adoption. This research contributes to the development of POSKeTanMu and provides strategic recommendations for the government to strengthen digital service implementation. Findings offer valuable insights for policy formulation to improve e-government services and promote broader technology adoption in society.

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