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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 246 Documents
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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
Text Stemming and Lemmatization of Regional Languages in Indonesia: A Systematic Literature Review Abidin, Zaenal; Junaidi, Akmal; Wamiliana
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.217-231

Abstract

Background: Stemming is significantly essential in natural language processing (NLP) due to the ability to minimize word variations to fundamental forms. This procedure facilitates the analysis of textual data and enhances the precision of classification and information retrieval. Objective: Previous related systematic literature review has not been conducted on stemming and lemmatization in regional languages in Indonesia. Therefore, this study aims to conduct a systematic literature review to capture the latest developments in stemming and lemmatization in regional languages in Indonesia. Methods: This study was carried out using Kitchenham method, analyzing 35 studies extracted from 740, which were obtained from Scopus, IEEE Xplore, and Google Scholar, and published between 2014 and 2023. Results: The results showed that study trends in stemming possessed the potential to continue developing every year. Additionally, the main element in stemming and lemmatization studies was found to be the availability of digital dictionaries in regional languages. This was because greater number of basic vocabularies contributed more positively to stemming or lemmatization. The availability of word morphology information in regional languages would be constructive for making rule-based stemmers. Meanwhile, corpus-based stemming and lemmatization studies could only be conducted for languages with a large corpus to ensure there were various affixed words to process. Conclusion: Based on SLR study, stemming and lemmatization in regional languages in Indonesia developed significantly from 2014 to 2023. The two main strategies applied included using available digital dictionaries and language morphology information. However, the main challenges encountered were the limited number of vocabulary words in the dictionaries and testing various rule-based methods.   Keywords: Lemmatization, Morphology, Rule-based, Stemming, Systematic Literature Review.  
Analysis of Factors Influencing Behavioral Intention to Use Cloud-Based Academic Information System Using Extended Technology Acceptance Model (TAM) and Expectation-Confirmation Model (ECM) Wandira, Raju; Fauzi, Ahmad; Nurahim, Faisal
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.179-190

Abstract

Background: Technology Acceptance Model (TAM) and Expectation-Confirmation Model (ECM) integration model are commonly used to analyze the intention to use technology in education. Moreover, the ease of implementation causes various external factors influencing technology acceptance to continue growing. However, limited research focuses on the use of TAM and ECM in the acceptance of cloud-based academic system. Objective: This research aims to identify factors influencing user perceptions of cloud-based academic information system and the relationships among different factors. Methods: The research integrated Extended TAM and ECM, subsequently processing data obtained from 261 respondents using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The perceptions proposed included Facilitating Condition (FC), Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Confirmation (CM), Satisfaction (SF), and Behavioral Intention to Use (BIU). Results: Based on the data processing carried out, the results were PEOU against BIU (H1, êžµ=0.256, p=0.001), PU against BIU (H2, êžµ=0.200, p=0.007), and SF against BIU (H3, êžµ=0.499, p= 0.000). Furthermore, it also comprised FC against PEOU (H4, êžµ=0.839, p=0.000), PU (H5, êžµ=0.849, p=0.000) and SF (H6, êžµ=0.294, p=0.000), as well as CM against SF (H7, êžµ=0.358, p=0.000) and PU against SF (H8, êžµ=0.325, p=0.000). These results showed that each proposed construct significantly influenced behavioral intentions to use cloud-based academic information system. Conclusion: The results showed that each factor proposed in the construct significantly influenced user intentions to use cloud-based academic system. Consequently, the most influential drivers in using cloud-based academic system were SF, PU, PEOU, and FC. Keywords: Acceptance, Behavioral Intention, Cloud-Based Academic System, Expectation
Ground Coverage Classification in UAV Image Using a Convolutional Neural Network Feature Map Maulidiya, Erika; Fatichah, Chastine; Suciati, Nanik; Sari, Yuslena
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.206-216

Abstract

Background: To understand land transformation at the local level, there is a need to develop new strategies appropriate for land management policies and practices. In various geographical research, ground coverage plays an important role particularly in planning, physical geography explorations, environmental analysis, and sustainable planning. Objective: The research aimed to analyze land cover using vegetation density data collected through remote sensing. Specifically, the data assisted in land processing and land cover classification based on vegetation density. Methods: Before classification, image was preprocessed using Convolutional Neural Network (CNN) architecture's ResNet 50 and DenseNet 121 feature extraction methods. Furthermore, several algorithm were used, namely Decision Tree, Naí¯ve Bayes, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Results: Classification comparison between methods showed that using CNN method obtained better results than machine learning. By using CNN architecture for feature extraction, SVM method, which adopted ResNet-50 for feature extraction, achieved an impressive accuracy of 85%. Similarly using SVM method with DenseNet121 feature extraction led to a performance of 81%. Conclusion: Based on results comparing CNN and machine learning, ResNet 50 architecture performed the best, achieving a result of 92%. Meanwhile, SVM performed better than other machine learning method, achieving an 84% accuracy rate with ResNet-50 feature extraction. XGBoost came next, with an 82% accuracy rate using the same ResNet-50 feature extraction. Finally, SVM and XGBoost produced the best results for feature extraction using DenseNet-121, with an accuracy rate of 81%.   Keywords: Classification, CNN Architecture, Feature Extraction, Ground Coverage, Vegetation Density.
A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach Yani , Nik Afiqah N. Ahmad; Fauzi , Shukor Sanim Mohd; Zaki , Nurul Ain Mohd; Ismail, Mohammad Hafiz
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.232-249

Abstract

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies.   Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review  
Motorcycle Taxi in Shared Mobility and Informal Transportation: A Bibliometric Analysis Herawatie, Dyah; Siswanto, Nurhadi; Widodo , Erwin
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.250-269

Abstract

Background: Motorcycle taxi (MCT) is a form of widely used informal transportation. Over the last few years, MCT has developed from conventional public transportation into a modern business using digital technology. In these services, digitalization has received a positive response from the public and the increasing number creates many challenges. However, there is a gap in the discussion of new service models for shared mobility transportation. Objective: This research aimed to analyze MCT, providing an overview of the services as informal transportation. Methods: Bibliometric analysis was used to evaluate 366 articles published in the Scopus database between 2011-2023. In addition, annual publications and citation topics, most productive sources and influential articles, relevant affiliations, productive countries, location research, main topics, and future research options were identified. Results: Frequently occurring topics were reported with past and present academic developments related to MCT services. Based on the publication themes, the main topics were arranged into five clusters, namely (a) the development of more sustainable transportation services, (b) environmental and health impact, (c) road safety, (d) risky behaviors or risk factors as MCT drivers, and (e) utilization of MCT for medical or health services. Meanwhile, the research topics comprised themes about travel behavior, health, safety-security, customer satisfaction, and advanced mobility topics. Conclusion: This research increased knowledge about main topics, trends and future analysis options in MCT. The academic developments served as a guide to future topics.   Keywords: Motorcycle taxi, public transportation, informal transportation, shared mobility, bibliometric analysis
The Art of Internet Mapping: A Comprehensive Guide to Regional Internet Topology Mapping at the Autonomous System Level Witono, Timotius; Yazid, Setiadi; Sucahyo, Yudho Giri
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.191-205

Abstract

Background: Internet topology is studied more by researchers on global internet coverage compared to limited regional coverage. However, some studies also see the importance of studying internet topology in certain countries or regions. The internet performance of a country or region can be influenced by the structure of its internet topology, and research on internet topology can contribute to improving internet topology in that region. Objective: This research initially carried out internet topology mapping in a limited region, then used experience from each step in conducting internet topology mapping to create a comprehensive guide on procedures for internet topology mapping at the autonomous system (AS) level in certain regional areas. Methods: Internet topology mapping is carried out by inferring relationships between ASes through an inference process against border gateway protocol (BGP) table dumps, while the internet topology mapping method chosen is passive mapping. Results: The entire series of steps involved in the regional internet topology mapping process have been successfully outlined in a detailed guide as a result of this research. Evaluation of the research results was carried out by implementing the application of this comprehensive guide and also through assessments from experts in related fields regarding the results of this research. The results of both evaluations showed that the research results were appropriate. Conclusion: This research provides a comprehensive guide for mapping internet topology in specific regional areas, consisting of nine sequential steps grouped into four major steps. This guide can be used to assist similar research efforts in other regional areas as well as provide further knowledge regarding studies in this field. This research is different from previous studies, because it provides a comprehensive guide to the internet topology mapping process, which has not been available in previous studies.   Keywords: Internet Topology Mapping, Regional Internet Topology, Autonomous System, Border Gateway Protocol
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
Motivations and Potential Solutions in Developing a Knowledge Management System for Organization at Higher Education: A Systematic Literature Review Maharani, Nandhita Zefania; Kurniawan, Shabrina Salsabila; Sensuse, Dana Indra; Eitiveni, Imairi; Hidayat, Deden Sumirat; Purwaningsih, Erisva Hakiki
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.270-289

Abstract

Background: Amidst a rapidly evolving digital landscape that accelerates the flow of information, higher education institutions face the unique challenge of managing vast and dynamic knowledge resources. This research delves into the motivations and innovative solutions for developing Knowledge Management Systems (KMS), which is key to optimizing knowledge resource utilization and enhancing academic collaboration. Objective: This research provides a comprehensive mapping of problems and solutions for developing university knowledge management systems based on previous research. Not only that, but the results of this study also suggest three future research studies that can be adopted. Methods: This study used the Kitchenham systematic literature review method. The author uses literature in the form of journals and conference proceedings published from 2019 to 2023. Twenty-three articles were used for this study from 5 databases, such as ACM, ProQuest, Scopus, Taylor & Francis, and IEEE Xplore. Results: This study reveals research trends in knowledge management systems within higher education, examining aspects such as country, data collection methods, research methodologies, and theoretical frameworks. The main problems motivating the development of KMS are identified and categorized based on the people, process, and technology framework. In overcoming these problems in the university business process, there are several alternative solutions, both in the form of requirements and systems. Thus, the results of this study seek to provide guidelines for future research to adopt alternative solutions from this research and develop KMS to provide new solutions. Conclusion: This study advances knowledge about various trends, motivations, requirements, and system solutions to address KMS problems in higher education. The authors' research results can add valuable insights to improve our understanding of the development of KMS in universities in various countries. Future research can identify new potential in KMS in business processes currently running in a university with appropriate methodologies.   Keywords: Knowledge management system, higher education, systematic literature review, problem, solution
Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Estimating the Susceptible-Exposed-Infected-Recovered (SEIR) Model Parameter Values Sa'adah, Aminatus; Sasmito, Ayomi; Pasaribu, Asysta Amalia
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.290-301

Abstract

Background: The most commonly used mathematical model for analyzing disease spread is the Susceptible-Exposed-Infected-Recovered (SEIR) model. Moreover, the dynamics of the SEIR model depend on several factors, such as the parameter values. Objective: This study aimed to compare two optimization methods, namely genetic algorithm (GA) and particle swarm optimization (PSO), in estimating the SEIR model parameter values, such as the infection, transition, recovery, and death rates. Methods: GA and PSO algorithms were compared to estimate parameter values of the SEIR model. The fitness value was calculated from the error between the actual data of cumulative positive COVID-19 cases and the numerical data of cases from the solution of the SEIR COVID-19 model. Furthermore, the numerical solution of the COVID-19 model was calculated using the fourth-order Runge-Kutta algorithm (RK-4), while the actual data were obtained from the cumulative dataset of positive COVID-19 cases in the province of Jakarta, Indonesia. Two datasets were then used to compare the success of each algorithm, namely, Dataset 1, representing the initial interval for the spread of COVID-19, and Dataset 2, representing an interval where there was a high increase in COVID-19 cases. Results: Four parameters were estimated, namely the infection rate, transition rate, recovery rate, and death rate, due to disease. In Dataset 1, the smallest error of GA method, namely 8.9%, occurred when the value of , while the numerical error of PSO was 7.5%. In Dataset 2, the smallest error of GA method, namely 31.21%, occurred when , while the numerical error of PSO was 3.46%. Conclusion: Based on the parameter estimation results for Datasets 1 and 2, PSO had better fitting results than GA. This showed PSO was more robust to the provided datasets and could better adapt to the trends of the COVID-19 epidemic.   Keywords: Genetic algorithm, Particle swarm optimization, SEIR model, COVID-19, Parameter estimation.  

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