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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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The Impact of Social Media Engagement on Market Share: A System Dynamics Model Erma Suryani; Rully Agus Hendrawan; Benyamin Limanto; Fatharani Wafda; Inayah Auliyah
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.71-79

Abstract

Background: Some studies have shown that Return on Total Assets is a strategy to increase market share. Other studies have also shown that social media like WeChat can increase market share. However, no studies have considered Instagram engagement in increasing market share. Objective: This study aims to identify variable linkage that increases market share through a dynamic system approach in small and medium-sized enterprises (SMEs). Methods: Using a System Dynamics approach, this study presents a model simulation with a proposed increase in market share by considering Instagram features. This approach creates a Causal Loop Diagram converted into a simulated Stock Flow Diagram. The value generated from the simulation is validated with the mean comparison and % error variance formulas. Results: Instagram engagement increases market share from 0.009 to 0.018. Such engagement can be increased by posting regularly and doing more activities, such as increasing post frequency, holding contests, and maximizing all features. Conclusion: This study has successfully modeled information technology, i.e., a promotion module on social media. However, this work has not yet demonstrated how the features can gain more market share, so future research is needed.  Keywords: Causal Loop Diagram, Engagement, Market Share, Stock Flow Diagram, System Dynamics
Academic Recommender System Using Engagement Advising and Backward Chaining Model Cut Fiarni; Arif Samuel Gunawan; Fredrick Victor
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.91-99

Abstract

Background: The goal of academic supervision is to help students plan their academic journey and graduate on time. An intelligent support system is needed to spot potentially struggling students and identify the issues as early as possible. Objective: This study aims to develop an academic advising recommender system that improves decision-making through system utility, ease of use, and clearly visualized information. The study also aims to find the best advising relationship model to be implemented in the proposed system. Methods: The system was modeled by following the hybrid approach to obtain information and suggest recommended actions. The recommendation was modeled by backward chaining to prevent students from dropping out. Results: To validate the recommendations given by the proposed system, we used conformity level, and the result was 94.45%. To evaluate the utility of the system, we used the backbox method, resulting in satisfactory responses. Lastly, to evaluate user acceptance, we used the technology acceptance model (TAM), resulting in 85% ease of use and 91.2% perceived usefulness for the four main features, study planning, graduate timeline simulation, progress report, and visualization of academic KPIs. Conclusion: We propose an academic recommender system with KPIs visualization and academic planning information. Keywords: Academic advising model, recommender system, backward chaining, goal-driven, technology acceptance model, certainty factor
The Drivers of a Digital Signature System Adoption: Evidence from Finance and Information System Departments Ahmad Arif Santosa; Firdaus Alamsjah
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.80-90

Abstract

Background: With the massive e-commerce transactions and document transfers, reliable system protection is needed. A digital signature is a tool that consists of encryption and decryption algorithms in a secret key to prevent data theft and online fraud. Objective: This research proposes an integrated technology-organization-environment (TOE) and the unified theory of acceptance and use of technology (UTAUT) to determine the factors affecting consumer intention to adopt the digital signature system. This research uses finance and information system departments’ perspectives in various industries. Methods: The analytical method is the Structural Equation Modeling (SEM) approach using the Smart Partial Least Square statistical version 3.0 software to examine the hypothesized connections between latent variables. Results: The results show that support from top management, size of the enterprise, and social influence have significant and positive effects on digital signature adoption. Meanwhile, user involvement and perceived simplicity have a negative effect on the adoption of a digital signature system in finance and information system departments. Conclusion: The current research suggests that executive levels in the finance and information system departments encourage the adoption of digital signature tools in doing daily tasks to increase efficiency. Keywords: Digital signature, consumer intention, finance and information system, structural equation modeling, TOE and UTAUT
Designing an Open Innovation Framework for Digital Transformation Based on Systematic Literature Review Abdurrahman Abdurrahman; Aurik Gustomo; Eko Agus Prasetio; Sonny Rustiadi
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.100-108

Abstract

Background: Innovation is a critical success factor of digital transformation (DX). Previous research has shown that open innovation (OI) can help companies accelerate DX and improve their business performance. Objective: This study develops a conceptual OI framework to support DX (OIDX) and provides an overview of the dimensions. OI in this study refers to Open Innovation 2.0.  Methods: We review previous research on OI dimensions, identify the activities, and map them along with the challenges that lead to failure. With this, we develop a framework to meet the needs and solve problems of OI implementation. Results: The OIDX framework has a comprehensive dimensional scope consisting of three perspectives, eight dimensions, and 26 sub-dimensions. The perspectives are enablers, activities, and output, and the dimensions are OI governance, external environment, internal climate, digital technology, importing mechanisms, collaboration, protection mechanisms, and export mechanisms. Conclusion: This study highlights the importance of defining dimensions to establish General System Theory. The practical application of this framework is to build an OI ecosystem that can increase the internal and external values of an organisation. The OI framework provides OI success parameters and criteria for building the OI maturity framework in future research. Keywords: DX, Innovation, Open Innovation, Open Innovation Framework
Selecting the Best-Performing Low-Cost Carrier (LCC) Airlines Using Analytical Hierarchy Process (AHP) and Elimination et Choix Traduisant la Realite (ELECTRE) Yuniar Farida; Husna Nur Laili; Achmad Teguh Wibowo; Latifatun Nadya Desinaini; Silvia Kartika Sari
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.196-206

Abstract

Background: Low-cost carrier (LCC) is a popular air transportation service as it offers affordable fares. Many airlines have adopted the LCC system because they need to adapt to the changes in the airline industry. The competition is tight. Despite the low cost, consumers demand quality services. Therefore, LCC airlines need to find their competitive edge. Objective: This study aims to determine the best-performing LCC airlines, the criteria, and the sub-criteria to improve the performance. Methods: This study uses two methods from multi-criteria decision-making (MCDM), namely the analytical hierarchy process (AHP) and elimination et choix traduisant la realite (ELECTRE) II. The MCDM is selected for this study because there are four criteria and 21 sub-criteria to evaluate airline performance. The AHP method selects subcriteria that affect airline customer satisfaction. It solves complex problems by establishing a hierarchy. After being assessed by relevant parties, weights or priorities are developed. The results are used to determine the best-performing airline. Meanwhile, the ELECTRE II method ranks the airline’s alternatives. This method is straightforward and widely used in the MCDM. Results: The results indicate that four criteria and 18 sub-criteria affect the performance of LCC airlines in Indonesia. The LCC airline with the best performance is AirAsia, followed by Citilink, Wings Air, and Lion Air. Conclusion: This research integrates the AHP and ELECTRE II methods in evaluating the performance of LCC airlines. This research also provides information about the criteria and sub-criteria to improve airline performance, hence, the customer experience.
Data Mining Techniques in Handling Personality Analysis for Ideal Customers Nur Ghaniaviyanto Ramadhan; Adiwijaya Adiwijaya
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.175-181

Abstract

Background: Personality distinguishes individuals from one another, guides their actions and reactions, and dictates their preferences in many aspects of life, including shopping. Objective: This study determines the characteristics of an ideal customer based on individual personality. Methods: Data mining techniques used in this study are K-nearest neighbour (KNN), linear support vector machine (SVM), and random forest. This study also applies the synthetic minority oversampling technique (SMOTE) to overcome the imbalance in the amount of data. Results: This study shows that the application of the SMOTE and random forest models resulted in 88% accuracy, 79% precision, and 70% recall, which are the highest compared to other models. Conclusion: SMOTE in this research is unsuitable for use in the KNN and linear SVM classification models. Ensemble-based models such as random forest can produce high accuracy when SMOTE is applied for data pre-processing.
Comparing Fuzzy Logic Mamdani and Naïve Bayes for Dental Disease Detection Linda Perdana Wanti; Oman Somantri
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.182-195

Abstract

Background: Dental disease detection is essential for the diagnosis of dental diseases. Objective: This research compares the Mamdani fuzzy logic and Naïve Bayes in detecting dental diseases. Methods: The first is to process data on dental disease symptoms and dental support tissues based on complaints of toothache consulted with experts at a community health centre (puskesmas). The second is to apply the Mamdani fuzzy logic and the Naïve Bayes to the proposed expert system. The third is to provide recommended decisions about dental diseases based on the symptom data inputted into the expert system. Patient data were collected at the North Cilacap puskesmas between July and December 2021. Results: The Mamdani fuzzy logic converts uncertain values into definite values, and the Naïve  Bayes method classifies the type of dental disease by calculating the weight of patients’ answers. The methods were tested on 67 patients with dental disease complaints. The accuracy rate of the Mamdani fuzzy logic was 85.1%, and the Naïve Bayes method was 82.1%. Conclusion: The prediction accuracy was compared to the expert diagnoses to determine whether the Mamdani fuzzy logic method is better than the Naïve Bayes method.   Keywords: Dental Disease, Expert System, Mamdani Fuzzy Logic, Naïve Bayes, Prediction
Segmentation using Customers Lifetime Value: Hybrid K-means Clustering and Analytic Hierarchy Process Radit Rahmadhan; Meditya Wasesa
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.130-141

Abstract

Background: Understanding customers’ electricity consumption patterns is essential for developing predictive analytics, which is needed for effective supply and demand management. Objective: This study aims to understand customers’ segmentation and consumption behaviour using a hybrid approach combining the K-Means clustering, customer lifetime value concept, and analytic hierarchy process. Methods: This study uses more than 16 million records of customers’ electricity consumption data from January 2019 to December 2020. The K-Means clustering identifies the initial market segments. The results were then evaluated and validated using the customer lifetime value concept and analytical hierarchy process. Results: Three customer segments were identified. Segment 1 has 282 business customers with a total capacity of 938,837 kWh, peak load usage of 27,827 kWh, and non-peak load usage of 115,194 kWh. Segment 2 has 508,615 business customers with a total capacity of 4,260 kWh, a peak load of 35 kWh, and a non-peak load of 544 kWh. Segment 3 has 37 business customers with a total capacity of 2,226,351 kWh, a peak load of 123.297 kWh, and a non-peak load of 390,803. Conclusion: A business strategy that could be taken is to base customer relationship management (CRM) on the three-customer segmentation. For the least profitable segment, aside from retail account marketing, a continuous partnership program is needed to increase electricity consumption during the non-peak period. For the highly and moderately profitable segments, a premium business-to-business approach can be applied to accommodate their increasing energy consumption without excessive electricity use in the peak period. Special account executives need to be deployed to handle these customers.
Identifying Messenger Platform Preferences using Multiple Linear Regression and Conjoint Analyses Evi Triandini; I Gusti Ngurah Satria Wijaya; Riza Wulandari; Ni Wayan Cahya Ayu Pratami; I Ketut Putu Suniantara; Candra Ahmadi
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.119-129

Abstract

Background: The rapid development of telecommunication technology has prompted the creation of various messenger applications. The competition among social messengers to gain market share is becoming tighter. Objective: This study aims to capture user preferences for messenger platforms and inform software development companies to improve their products based on user needs. Methods: This research uses quantitative methods, i.e., categorical analysis and multiple linear regression analysis, to extend the results from qualitative methods that identify the preferences in past studies. The data were obtained through a questionnaire. Results: The results show that customers are influenced by accessibility, flexibility, effectiveness and chat history. Meanwhile, users are influenced by responsiveness, user-friendly interface, performance, personal needs, privacy and security, and customer services. Conclusion: The research can identify the indicators to guide the creation of an ideal messenger platform based on customer and user preferences.   Keywords: Conjoint, Messenger Platform, Multiple Linear Regression, Preference
Hybrid Deep Learning Models for Multi-classification of Tumour from Brain MRI Hafiza Akter Munira; Md Saiful Islam
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 2 (2022): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.2.162-174

Abstract

Background: Brain tumour categorisation can be assisted with computer-aided diagnostic (CAD) for medical applications. Biopsies to classify brain tumours can be costly and time-consuming. Radiologists may also misclassify brain tumour types when handling large amounts of data with multiple classes. In this case, technological advancements and machine learning can help. Objective: This study proposes hybrid deep learning approaches for classifying brain tumours using convolutional neural networks (CNN) and machine learning (ML) classifiers. Methods: A new 23-layer CNN architecture is developed for brain deep feature extraction from magnetic resonance imaging (MRI). Random forest (RF) and support vector machine (SVM) classifiers are then used to evaluate the extracted in-depth features from the flattened layer of the CNN model. This study is unique because it employs CNN, CNN-RF, CNN-SVM, and tuned Inception V3 deep learning models on multi-class brain MRI datasets. The proposed hybrid method is run on two publicly available datasets. Results: Among the four models, the CNN-RF model achieves 96.52% accuracy on the Fig share 3c dataset, while the CNN-SVM model achieves 95.41% accuracy on the large Kaggle 4c dataset with four classes (glioma, meningioma, normal, pituitary). Conclusion: Experimental outcomes show that the hybrid techniques can significantly enhance the classification performance, especially on multi-class datasets (glioma, meningioma, normal, pituitary). This study also examines the various weight strategies for dealing with overfitting analytics.   Keywords: Brain Tumour, Convolutional Neural Network, Feature Extraction, Multi-Classification, Machine Learning Classifiers

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