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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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Assessing User Satisfaction in E-Haj Systems: Insights from Bangladesh Alam, S. M. Ashraful; Islam, Md. Tariqul; Akter, Most. Sadia; Islam, Md. Kamrul; Bhuiyan, Mohammad Rakibul Islam
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.925

Abstract

This study examines how e-haj management systems in Bangladesh affect user satisfaction. Therefore, the authors presented the hypothesis using webQual 4.0 Model. Data was collected using a 5-point-lickert questionnaire. 347 valid data were collected from Dhaka city. SPSS 27 displayed descriptive statistics, and Smart PLS 3.3.3 was analysed for measurement and structural model. The study found the positive impact of the usability, information quality, and service information quality of e-hajj on users' satisfaction. Thus, e-government implementers can get benefits from the findings of the paper as they come to know what factors motivates individuals to use the government's e-haj management portal. This finding also suggested that government should focus on website’s easy to navigate option, updated information and 24/7 customer service. As a result, this tendency of the citizens towards e-government services will be increased day by day and motivated to accept these e-hajj system. This research will increase trust and improve the democratic process for all citizens including businesses, or different government agencies by enhancing service quality provided to them. Small sample size, data collection period, and location are the limitations of this study. Future researchers may combine more model’s items and reduce these limitations to improve practical application studies.
Sentiment Analysis and Trend Mapping of Hotel Reviews Using LSTM and GRU Singgalen, Yerik Afrianto
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.926

Abstract

This study explores applying Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for sentiment analysis and trend mapping of hotel reviews, specifically focusing on customer feedback from Hotel Vila Ombak in Lombok, Indonesia. The primary objective was to leverage these advanced deep learning models to capture nuanced sentiment patterns in unstructured textual data, enhancing insights into guest satisfaction. The analysis was conducted on a dataset of 326 reviews, achieving an overall model accuracy of 91% (0.91). The results showed that while the models excelled in identifying positive sentiments, with a precision of 0.94, recall of 0.98, and F1-score of 0.96, they struggled with minority classes. Both negative and neutral sentiments exhibited 0% accuracy, primarily due to the dataset’s imbalance, where positive reviews constituted 92.3% of the total entries. The macro average metrics (precision 0.31, recall 0.33, F1-score 0.32) highlighted the model's limitations in classifying sentiments less frequently despite high weighted averages driven by the dominant positive class. This research underscores the need to address data imbalance and suggests that future studies incorporate techniques like data augmentation or hybrid models to improve performance across all sentiment categories. By optimizing sentiment analysis models, hospitality businesses can gain deeper insights into customer feedback, ultimately enhancing service quality and customer satisfaction.
Evolution of AI in Information Systems: A Bibliometric Study Mimi, Afsana
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.928

Abstract

Significant challenges for traditional information systems are posed due to the ever-growing volume and complexity of data. Artificial intelligence has emerged as a powerful solution to address these challenges by adapting and making intelligent decisions. Valuable insights can be gained from data to automate repetitive tasks and optimize the operations. This study examines how the researchers are concentrating to explore multifaceted impact of AI on the design, implementation, and optimization of information systems. AI is transforming the landscape of information systems with progresses in machine learning, text mining, cognitive computing and other AI technologies by enhancing the efficiency and adaptability across various domains. This study delves into this emerging landscape by conducting a comprehensive bibliometric analysis of Artificial intelligence in Information systems research. This bibliometric study retrieved a dataset of publications from Scopus database spanning from 1960 to 2023 to find out the insights hidden within the scientific papers. The analysis encompasses key bibliometric indicators, such as citation patterns, co-authorship networks, and thematic clusters etc. to represent historical development of research in Artificial intelligence within the context of Information systems. This study fills a gap in AI and IS literature, drawing on 306 publications, with key contributions from the USA, China, UK, Germany, India and leading authors like OGIELA L (Lidia Ogiela) and CIMINO JJ ( James J. Cimino). Co-authorship networks highlight the dominance of collaborative research hubs in countries like USA, China, Canada, Australia, while citation patterns underscore the influence of seminal works and cross-disciplinary contributions. The findings presented in this paper offer valuable insights for researchers, practitioners, and policymakers seeking a deeper understanding of the growing AI-IS landscape. As this is the first paper which takes the attempt to conduct a bibliometric analysis on artificial intelligence in information systems, this paper serves as a roadmap for navigating the rich tapestry of research, fostering collaboration, and guiding future investigations in this rapidly evolving and interdisciplinary field.
Analysis of Labeling and Class-Balancing Effects on Clash of Champions Sentiment Using LSTM and BERT Atmaja, Audi Ilham; Maimunah, Maimunah; Sukmasetya, Pristi
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.929

Abstract

Advances in digital technology have changed the way people interact and access information, including in education. One educational event that has caught the public's attention is Clash of Champions by Ruangguru, designed to increase young people's interest in learning through an interactively presented competition. The purpose of this study is to use posts on X social media to examine public opinion on the event. Using TweetHarvest, 1,891 tweets were gathered and preprocessed (cleaning, case folding, normalization, tokenization, stopword removal, stemming, and English translation). A total of 12 experimental scenarios were created by combining VADER and TextBlob labeling strategies with class balancing techniques (undersampling and SMOTE), and the LSTM and BERT models were evaluated for each scenario. The best results were achieved by combining VADER, SMOTE, and BERT, yielding an accuracy of 97.73%, with precision, recall, and F1-scores of 98%, 98%, and 96% (positive), 99% (neutral), and 98% (negative), respectively. These findings highlight the efficacy of transformer-based models like BERT in addressing class imbalance and improving sentiment classification. The integration of SMOTE effectively mitigated class imbalance, providing consistent and accurate performance across all sentiment categories.
Optimizing Aspect-Based Sentiment Analysis for Kyai Langgeng Park Using PSO and SVM Saputra, Dio Raka Venda; Maimunah, Maimunah; Arumi, Endah Ratna
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.930

Abstract

This study aims to analyze aspect-based sentiment on Taman Kyai Langgeng tourism reviews, focusing on three main aspects: price, service, and facilities. This study combines Particle Swarm Optimization (PSO) method for feature selection and Synthetic Minority Over-sampling Technique (SMOTE) to handle data imbalance, which is a novel approach in aspect-based sentiment analysis. A total of 827 review data were retrieved from the Google Maps platform and manually labeled. This method resulted in significantly improved sentiment classification accuracy over the model without optimization. After the application of PSO and SMOTE, the model accuracy for the price aspect increased from 91.56% to 94.28%, the service aspect from 89.75% to 92.85%, and the facility aspect from 79.51% to 88.88%. The results of this study show that the combined PSO and SMOTE approach not only improves the accuracy, but also the consistency of sentiment classification on various aspects. These findings provide deep insights for tourism managers in identifying strengths and weaknesses based on visitor reviews.
Misinformation Detection: A Review for High and Low-Resource Languages Rananga, Seani; Isong, Bassey; Modupe, Abiodun; Marivate, Vukosi
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.931

Abstract

The rapid spread of misinformation on platforms like Twitter, and Facebook, and in news headlines highlights the urgent need for effective ways to detect it. Currently, researchers are increasingly using machine learning (ML) and deep learning (DL) techniques to tackle misinformation detection (MID) because of their proven success. However, this task is still challenging due to the complexity of deceptive language, digital editing tools, and the lack of reliable linguistic resources for non-English languages. This paper provides a comprehensive analysis of relevant research, providing insights into advanced techniques for MID. It covers dataset assessments, the importance of using multiple forms of data (multimodality), and different language representations. By applying the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) methodology, the study identified and analyzed literature from 2019 to 2024 across five databases: Google Scholar, Springer, Elsevier, ACM, and IEEE Xplore. The study selected thirty-one papers and examined the effectiveness of various ML and DL approaches with a focal point on performance metrics, datasets, and false or misleading information detection challenges. The findings indicate that most current MID models are heavily dependent on DL techniques, with approximately 81% of studies preferring these over traditional ML methods. In addition, most studies are text-based, with much less attention given to audio, speech, images, and videos. The most effective models are mainly designed for high-resource languages, with English datasets being the most used (67%), followed by Arabic (14%), Chinese (11%), and others. Less than 10% of the studies focus on low-resource languages (LRLs). Therefore, the study highlighted the need for robust datasets and interpretable, scalable MID models for LRLs. It emphasizes the critical need to prioritize and advance MID research for LRLs across all data types, including text, audio, speech, images, videos, and multimodal approaches. This study aims to support ongoing efforts to combat misinformation and promote a more informed understanding of under-resourced African languages.
Clustering Sugar Content in Children's Snacks for Diabetes Prevention Using Unsupervised Learning Darmayanti, Irma; Saputra, Dhanar Intan Surya; Saputri, Inka; Hidayati, Nurul; Hermanto, Nandang
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.932

Abstract

Diabetes is a chronic health problem with increasing prevalence, especially among children, due to the consumption of sugary foods/beverages. This study aims to cluster children's snack products based on sugar content using unsupervised learning by combining Hierarchical Clustering and K-Means algorithms optimized using Silhouette Score. This combined approach utilizes Hierarchical Clustering to determine the optimal value (????) of K-Means, ensuring the efficiency and accuracy of data clustering. A total of 157 sample data were effectively clustered with K-means. The test results with Silhouette Score yielded the highest value of 0.380 for 2 clusters, while 3 clusters scored 0.350 and 0.277 for 4 clusters. For this reason, 2 clusters better represent the homogeneity of the data in the cluster, although it has not reached the ideal condition. Further analysis showed that high sugar and calorie content in sugary drinks, including milk, could increase blood glucose levels. These findings can be the basis for the development of consumer-friendly nutrition labels. However, support is needed from the government to create a labelling policy to ensure the sustainability of implementation in the community as an educational effort to prevent the risk of diabetes in children.
A Readiness Assessment Tool for Smart City Implementation in Small and Rural Municipalities Mashau, Nkhangweni Lawrence; Kroeze, Jan Hendrik
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.937

Abstract

Small and rural municipalities are lagging in terms of implementing a smart city. These municipalities have limited resources to provide basic services to the citizens. There is a need for these municipalities to implement a smart city to manage resources effectively. However, an assessment tool to assess small and rural municipalities’ readiness for smart city implementation is lacking. This article offers such an assessment tool tailored specifically to assess small and rural municipalities’ readiness for smart city implementation. Design science research methodology (DSR) was used in a wider study to develop a related smart city readiness framework. In the preceding cycles of the DSR study, a literature review was used to provide relevant data for the construction of a conceptual framework, which was validated and improved using semi-structured interviews in a second and third cycle. The last cycle of the research developed and validated an assessment tool as an artefact that could be used to address critical issues, including limited resources and governance complexities that are unique to these municipalities. The findings showed that the proposed tool covered all the salient aspects, except for the aspect of smart buildings that are capable of collecting data without human intervention. This element was added to the final assessment tool. The tool can be used by personnel and consultants who are responsible for developing or implementing a smart city in small and rural municipalities. Furthermore, what makes this assessment tool unique is its alignment with the needs of small and rural municipalities. It was validated through participatory and expert reviews, providing a reliable instrument for policymakers and municipality managers in making an informed decision toward the readiness assessment of a smart city. A formula to calculate a municipality’s readiness level quantitively as a percentage, as well as a proposed evaluation heuristic, is also provided. The final, revised assessment tool prompts actionable insights informing the implementation of a smart city in small and rural municipalities.
Design and Build a Notary MIS Using the AES 256 Algorithm at a Web-Based Notary Office Damanik, Arya Pratama; Suendri, Suendri
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.940

Abstract

This research aims to design and develop a web-based Notary Management Information System (MIS) incorporating the AES 256 encryption algorithm to enhance data management and security in notary offices. Utilizing the Rapid Application Development (RAD) methodology, the system was developed through iterative collaboration between developers and users to meet both functional and non-functional requirements. Key features of the system include the management of order data, client records, notarial protocols, and user activity logs. The innovative application of the AES 256 algorithm ensures high-level data security, with validation tests confirming its effectiveness in protecting sensitive information. Performance testing demonstrated significant improvements, including a 40% reduction in data retrieval time and seamless encryption processes, compared to previous manual methods. The system also enhances accessibility and work efficiency through its web-based architecture. This research not only provides a practical solution for notary offices but also serves as a scalable model for secure MIS development in other industries.
Optimizing Zakat Distribution with GIS and Data Mining in Community Empowerment at BAZNAS Deli Serdang Efita, Sinta Dara; Triase, Triase
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.942

Abstract

This research aims to develop an information system that utilizes Geographic Information System technology to map and analyze the distribution of zakat effectively in Deli Serdang Regency. The methods used include collecting data on zakat recipients and applying the K-means grouping method to identify distribution patterns in 22 districts. The results of the study show that there are three groups of zakat recipients: High Recipients, Medium Recipients, and Low Recipients. Tanjung Morawa was identified as the district with the highest number of zakat recipients, namely 239 people, which shows a significant need. The K-Means algorithm plays an important role in identifying areas that need help, so this Geographic Information System-based grouping is proven to improve the efficiency of zakat resource allocation. This allows BAZNAS to target assistance more precisely and strategically. This study recommends the use of this approach to optimize zakat management and support community economic empowerment, as well as reduce social inequality in the region.

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