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Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 13 Documents
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Search results for , issue "Vol. 10 No. 1 (2024): February" : 13 Documents clear
Evaluation of Success and Failure Factors for Maternal and Child Health in Integrated Healthcare Center Information Systems (IHCIS) Using the HOT-Fit Method Hardiyanti, Cicin; Kusumadewi, Sri; Kurniawan, Rahadian
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.152-166

Abstract

Background: Maternal and child health in Integrated Healthcare Center Information Systems (IHCIS) has been implemented in several community health centers. Some have been implemented successfully, but others have failed. Many factors influence the success and failure of IHCIS implementation. Thus, knowing these factors can be used to assist the decision-making process in implementing IHCIS. Objective: This research aims to determine the factors affecting the success and failure of IHCIS for maternal and child health using the HOT-Fit (Human, Organization, Technology, and Fit) model. Methods: This research begins with preliminary research to identify the problem, determine research variables, and collect data. It uses quantitative and qualitative approaches. A quantitative approach is conducted at locations that have successfully implemented IHCIS. The data collection instrument uses a questionnaire. A qualitative approach was conducted in locations that were still experiencing failure in implementing IHCIS. Data collection techniques through direct interviews. Results: Organizational factors do not fully determine the success or failure of the information system. Factors supporting the success of IHCIS are human (user satisfaction and system use) and technological factors (quality of information and the quality of service). However, technology (system quality and information quality) is the main factor in the failure of IHCIS implementation. Problems with system quality include the system login, limited access to the internet, and an information system that is not in accordance with requirements. The perceived information obstacle is that the system is not yet integrated, and the information produced is incomplete. Conclusion: To satisfy requirements, the information and system qualities must be enhanced. Implementing IHCIS requires an appropriate strategy according to local circumstances and conditions. This approach involves human, organizational, and technological factors.   Keywords: Community Health Workers, Evaluation, HOT-Fit, Integrated Healthcare Center Information Systems, Success Factors
Leveraging Biotic Interaction Knowledge Graph and Network Analysis to Uncover Insect Vectors of Plant Virus Katili, Moh. Zulkifli; Yeni Herdiyeni; Hardhienata, Medria Kusuma Dewi
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.94-109

Abstract

Background: Insect vectors spread 80% of plant viruses, causing major agricultural production losses. Direct insect vector identification is difficult due to a wide range of hosts, limited detection methods, and high PCR costs and expertise. Currently, a biodiversity database named Global Biotic Interaction (GloBI) provides an opportunity to identify virus vectors using its data. Objective: This study aims to build an insect vector search engine that can construct an virus-insect-plant interaction knowledge graph, identify insect vectors using network analysis, and extend knowledge about identified insect vectors. Methods: We leverage GloBI data to construct a graph that shows the complex relationships between insects, viruses, and plants. We identify insect vectors using interaction analysis and taxonomy analysis, then combine them into a final score. In interaction analysis, we propose Targeted Node Centric-Degree Centrality (TNC-DC) which finds insects with many directly and indirectly connections to the virus. Finally, we integrate Wikidata, DBPedia, and NCBIOntology to provide comprehensive information about insect vectors in the knowledge extension stage. Results: The interaction graph for each test virus was created. At the test stage, interaction and taxonomic analysis achieved 0.80 precision. TNC-DC succeeded in overcoming the failure of the original degree centrality which always got bees in the prediction results. During knowledge extension stage, we succeeded in finding the natural enemy of the Bemisia Tabaci (an insect vector of Pepper Yellow Leaf Curl Virus). Furthermore, an insect vector search engine is developed. The search engine provides network analysis insights, insect vector common names, photos, descriptions, natural enemies, other species, and relevant publications about the predicted insect vector. Conclusion: An insect vector search engine correctly identified virus vectors using GloBI data, TNC-DC, and entity embedding. Average precision was 0.80 in precision tests. There is a note that some insects are best in the first-to-five order.   Keywords: Knowledge Graph, Network Analysis, Degree Centrality, Entity Embedding, Insect Vector
Analyzing Variances in User Story Characteristics: A Comparative Study of Stakeholders with Diverse Domain and Technical Knowledge in Software Requirements Elicitation Trisnawati, Ersalina; Raharjana, Indra Kharisma; Taufik, Taufik; Basori, Ahmad Hoirul; Alghanmi, Nouf Atiahallah; Mansur, Andi Besse Firdausiah
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.110-125

Abstract

Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories, Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories.   Keywords: User story, Agile Software Development, Requirements Elicitation, Stakeholder, Domain Knowledge, Process Innovation

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