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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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Exploring the Digital Narratives in Tourism and Culture through The Case of Rambu Solo: Sentiment, Toxicity, and Content Analysis Singgalen, Yerik Afrianto
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.836

Abstract

This research urgently addresses the need to understand and manage viewer interactions with culturally significant video content, particularly the Rambu Solo ritual. By integrating the Digital Content Reviews and Analysis Framework with sentiment classification performance, toxicity score evaluation, and content analysis, the study systematically analyzed 21,562 posts across four videos, revealing critical themes related to cultural preservation and tourism impact that shaped viewer perceptions. Sentiment and toxicity evaluations of 15,762 posts showed an average toxicity score of 0.068, with a peak of 0.85174. Sentiment classification, using algorithms like SVM, k-NN, NBC, and DT, highlighted the superior performance of SVM enhanced by SMOTE, with an accuracy of 81.97%. However, the study identified limitations in automated sentiment analysis tools, noting that they may not fully capture the complexities of human expression. This research recommends incorporating advanced natural language processing techniques and multimodal analysis within the framework. This comprehensive methodology offers essential insights into the intersection of culture, tourism, and digital media, emphasizing the importance of creating and managing content that respects and promotes cultural heritage in the digital age. The findings are crucial for developing more effective strategies for digital content creation and community engagement, ensuring that cultural narratives are presented thoughtfully and respectfully to global audiences.
Unveiling Indonesia's New Capital: A Digital Content Analysis of Tourism Narratives Tabuni, Gasper; Singgalen, Yerik Afrianto
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.837

Abstract

This research investigates the role of digital narratives in promoting emerging destinations, with a focus on Indonesia's new capital (IKN). Utilizing the Digital Content Reviews and Analysis Framework, this study analyzed 248 digital posts, including social media posts and videos, to evaluate the effectiveness of tourism strategies that emphasize authentic cultural elements and unique regional attractions. The findings demonstrate that strategically crafted digital content significantly increases public awareness and interest in IKN. The analysis of 194 posts, through sentiment classification and toxicity scoring, reveals a predominantly positive public discourse, with an average toxicity score of 0.05541 and a maximum score of 0.90611. The sentiment classification model exhibited high accuracy (97.46% ± 3.00%) and precision (96.78% ± 4.17%), with a micro-average accuracy of 97.48%, and a notable AUC score of up to 0.999, indicating robust differentiation between positive and negative sentiments. These results underscore the practical implications of leveraging digital media to enhance tourism promotion strategies, suggesting that effective digital narratives, supported by comprehensive analytical frameworks and minimal toxicity, are crucial for converting interest into actual tourism activity. This approach positions IKN as a competitive entity in the global tourism market, emphasizing the importance of digital narratives in shaping international perceptions of new destinations.
Indonesian Health Question Multi-Class Classification Based on Deep Learning Vihikan, Wayan Oger; Trisna, I Nyoman Prayana
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.838

Abstract

The health online forum is commonly used by Indonesian to ask questions related to diseases. A well-known example, Alodokter, has hundreds of thousands of health questions which are assigned to certain topics. Building a model to classify questions into a topic is important for better organization and faster response by relevant health professionals. This research experimented on 20 deep learning methods from RNN, CNN, and IndoBERT with different configurations to see the performance of each model when classifying questions into six different most common diseases that cause death in Indonesia. The results show the majority of the model can outperform the SVM as baseline. Bidirectional RNN such BiLSTM and BiGRU combined with CNN show a good metric score even though a certain version of the IndoBERT model generally outperforms all the other models.
Manhattan Metric Technique in K-Means Clustering for Data Grouping Sari, Mila; Armansyah, Armansyah
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.841

Abstract

Clustering can be defined as a method commonly applied in data mining to group objects into clusters. Clusters consist of data objects that are similar to each other in a group but different from objects in other clusters. In this study, the data used is the data of KIP scholarship recipients for the 2016-2023 period. Various clustering metric measurement techniques have been frequently used by researchers, especially those focusing on distance and similarity metrics, such as Euclidean Distance, Manhattan, and Minkowski. In general, K-Means is an unsupervised learning method used in the clustering process to group data based on similarity. The elbow method is used to determine the optimal number of clusters, so that the clustering results obtained can be maximized to achieve better results. This study aims to analyze the use of Manhattan technique in K-Means clustering for data grouping. The research problem is how to analyze the Manhattan metric technique in K-Means clustering for effective data grouping. Applying the K-Means method shows that the existing data is successfully divided into four specified clusters. After determining the correct number of clusters, the K-Means method is used to sort the data in the dataset. From 3172 data, the final results obtained cluster 0 as many as 774 data, cluster 1 as many as 417 data, cluster 2 as many as 1244 data, and cluster 3 as many as 737 data. The results of the clustering process obtained a davies-bouldin index value of 1.4568.
Enhancing Organizational Learning through Social Media: Insights from Social Learning Theory Marcel, Marcel; Kristiani, Evelline; Mudita, Damida Shu
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.843

Abstract

This qualitative case study investigates social media and its effect on organizational learning within a technology manufacturing company. Seven participants, including a general manager and IT specialist, team leaders, frontline managers, and an HR coordinator, were interviewed through semi-structured interviews to get insights on the use of social media for organizational learning. The finding indicated that social media learning effectiveness is constrained by poor governance, lack of consistent leadership support, and technological enablers. There also are cultural challenges to overcome, such as generational differences and differing levels of digital literacy. By outlining the significant factors that need to be addressed for technology manufacturers to incorporate social media into their learning strategies fully, this study provides valuable practical advice on using social media for best organizational learning. For successful integration, the study indicates that strategic alignment and better digital literacy should exist. Future research should explore how these barriers might be overcome and test different social media approaches in various organizational contexts.
IoT-Based Smart Door Lock System with Fingerprint and Keypad Access Permana, Ketut Ananta Kevin; Piarsa, I Nyoman; Wiranatha, Anak Agung Ketut Agung Cahyawan
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.844

Abstract

Doors are important components in a home, serving as entry points, room dividers, and security barriers. Door locks have evolved from manual mechanisms to automatic systems using technologies such as passwords, face sensors, and fingerprint sensors. To enhance practicality and efficiency, an Internet of Things (IoT)-based Smart Door Lock system using keypads and fingerprint sensors was developed in this research. The system was built using the Waterfall model Software Development Life Cycle (SDLC) and utilizes Firebase for real-time data communication and control through an Android application. Black box testing was conducted to verify the system’s functionality, achieving a 100% success rate across 20 trials. The system offers enhanced security and remote access control, with potential applications in both residential and commercial settings.
Enhancing Automated Vehicle License Plate Recognition with YOLOv8 and EasyOCR Salsabila, Nurul; Sriani, Sriani
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.848

Abstract

This research focuses on the development of an automatic system for vehicle license plate recognition using YOLOv8, EasyOCR, and CNN methods for object classification. The main issue raised is the need for an accurate and efficient system for recognizing vehicle license plates in real-time in dynamic environments, especially in urban areas with high traffic levels. The method used in this study involves resizing the input image to 416x416 pixels to standardize the data, analyzing the YOLO architecture that divides the image into a 7x7 grid, and using the Convolutional Neural Network (CNN) algorithm for feature extraction and object classification. Object detection uses the YOLOv8 method which is tasked with recognizing license plates using a previously trained YOLO (pretrained model) model then implemented and tested using video with 4k quality to ensure its effectiveness in detecting vehicle license plate objects, followed by the Optical Character Recognition (OCR) process with the EasyOCR method to read text on license plates and tested to ensure its effectiveness in reading characters on license plates vehicle number. The purpose of this research is to develop a system that can improve accuracy and efficiency in vehicle license plate recognition. The results show that the accuracy, precision, recall and F1-Score for object detection reach 100% and the average percentage of detected text conformity is 74.66%, which shows that this system is reliable in real applications and contributes to the development of automatic license plate recognition technology.
Article Recommendations with Item-Based Collaborative Filtering on Online News Portals Bravo, Bram; Indra, Indra
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.851

Abstract

News portals generate additional traffic or traffic visits from the article recommendation widget. However, it is unfortunate that the traffic visits obtained from the widget are still relatively small. The article recommendation widget is rarely clicked by readers because the available recommended articles are less relevant to readers, resulting in one reader only reading no more than 2 articles obtained from the article recommendation widget. The purpose of this study is to further optimize the currently available article recommendation widget feature by adding reader interest data so that the number of articles read by one user will increase and will directly have an impact on increasing traffic visits. The method used in this study is Item Based Collaborative Filtering. After using the item-based filtering method by calculating the set of items x read and the duration of the reader's time in reading item x. In this study, a simulation was given to one of the reader samples and it was found that the highest interest of the reader sample was in reading sports news with a calculation score is 0.743210. The results of this study are article recommendations that match the reader's interests. The results of the study are expected to help users find articles that match their interests and preferences, so that they can increase the level of interaction and engagement with online media.
Barriers to Business Process Innovation in Public Service Organizations Qiyamullaily, Arista; Susanto, Tony Dwi; Mahendrawathi, ER
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.858

Abstract

This study aims to identify the main barriers in implementing business process innovation in government organizations using the Systematic Literature Review (SLR) method. The barriers were categorized into four aspects: people, technology, structure, and process, in accordance with the Socio-Technical Theory approach. The results show that a lack of knowledge and training related to innovation, limited funding, and inadequate technological infrastructure are the dominant barriers. In addition, complex bureaucracy and lack of structured processes are also significant barriers. The research recommends a holistic approach that includes improved communication, training, technology investment, as well as bureaucratic reform to foster more effective innovation. The findings provide a basis for better policy-making and emphasize the importance of further research to understand and address barriers to innovation in different countries.
Classifying Legendary Pokémon with SF-Random Forest Algorithm Prayoga, Aji; Via, Yisti Vita; Diyasa, I Gede Susrama Mas
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.859

Abstract

Here’s an improved version of the abstract with better articulation: Accurate classification of legendary Pokémon is essential due to their distinct characteristics compared to regular Pokémon, impacting various domains such as research, gaming, and strategy development. This study employs the SF-Random Forest algorithm, an advanced variant of Random Forest, designed to effectively handle data heterogeneity and complexity. The dataset comprises 800 Pokémon samples, including attributes like type, base stats (HP, Attack, Defense, etc.), and other relevant features. To address the inherent imbalance between legendary and non-legendary Pokémon, the data preprocessing phase includes outlier removal, handling of missing values, normalization through Min-Max Scaling, and class balancing using the SMOTE (Synthetic Minority Over-sampling Technique) method. The preprocessed data is then used to train the SF-Random Forest model, with performance evaluated using metrics such as accuracy, precision, recall, and F1-score. The results reveal that SF-Random Forest achieves perfect scores across all metrics, demonstrating 100% accuracy, precision, recall, and F1-score. This highlights the algorithm's superior ability to identify key features and manage data imbalance compared to traditional classification methods. The study underscores the efficiency and robustness of SF-Random Forest as a classification tool, paving the way for the development of more advanced classification systems applicable to various fields requiring complex pattern recognition.

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