cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota surabaya,
Jawa timur
INDONESIA
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 12 Documents
 1   2 
Search results for , issue "Vol. 8 No. 2 (2022): October" : 12 Documents clear
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
Information Security Risk Assessment (ISRA): A Systematic Literature Review Rias Kumalasari Devi; Dana Indra Sensuse; Kautsarina; Ryan Randy Suryono
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.8.2.207-217

Abstract

Background: Information security is essential for organisations, hence the risk assessment. Information security risk assessment (ISRA) identifies, assesses, and prioritizes risks according to organisational goals. Previous studies have analysed and discussed information security risk assessment. Therefore, it is necessary to understand the models more systematically. Objective: This study aims to determine types of ISRA and fill a gap in literature review research by categorizing existing frameworks, models, and methods. Methods: The systematic literature review (SLR) approach developed by Kitchenham is applied in this research. A total of 25 studies were selected, classified, and analysed according to defined criteria. Results: Most selected studies focus on implementing and developing new models for risk assessment. In addition, most are related to information systems in general. Conclusion: The findings show that there is no single best framework or model because the best framework needs to be tailored according to organisational goals. Previous researchers have developed several new ISRA models, but empirical evaluation research is needed. Future research needs to develop more robust models for risk assessments for cloud computing systems.   Keywords: Information Security Risk Assessment, ISRA, Security Risk
Lexicon and Naive Bayes Algorithms to Detect Mental Health Situations from Twitter Data Sheila Shevira; I Made Agus Dwi Suarjaya; Putu Wira Buana
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.8.2.142-148

Abstract

Background: Twitter is a popular social media where users express emotions, thoughts, and opinions that cannot be channelled in the real world. They do this by tweeting short, concise, and clear messages. Since users often express themselves, Twitter data can detect mental health trends. Objective: This study aims to detect suicidal messages through tweets written by users with mental health issues. Methods: These tweets are analysed and classified using the lexicon-based and Naive Bayes algorithms to determine whether it contains suicidal messages. Results: The classification results show that the ‘normal’ classification is predominant at 52.3% of the total 3,034,826 tweets, which indicates an increase from September to December 2021. Conclusion: Most tweets are categorised as ‘normal’, therefore the mental health status appears secure. However, this finding needs to be re-examined in the future, especially in DKI Jakarta Province, which has the most cases of mental disorders. This study found that the Naive Bayes algorithm is more accurate (85.5%) than the lexicon-based algorithm. This can be improved in future studies by increasing performance at the pre-processing stage.   Keywords: Lexicon Based, Mental Disorder, Mental Health, Naïve Bayes, Twitter
Chest X-ray Image Classification for COVID-19 diagnoses Endra Yuliawan; Shofwatul ‘Uyun
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.8.2.109-118

Abstract

Background: Radiologists used chest radiographs to detect coronavirus disease 2019 (COVID-19) in patients and determine the severity levels. The COVID-19 cases were grouped into five classes, each receiving different treatments. An intelligent system is needed to advance the detection and identify vector features of X-ray images with a quality that is too poor to be read by radiologists. Deep learning is an intelligent system that can be used in this case. Objective: The current study compares the classification and accuracy of detection methods with two, three dan five classes. Methods: Deep learning can classify visual geometry group VGG 19 architectures with 1000 classes. The classification of the five classes' convolutional neural network (CNN) underwent model validation with a confusion matrix to produce accuracy and class values. The system could then diagnose patients’ examinations by radiology specialists. Results: The results of the five-class method showed 98% accuracy, the three-class method showed 99.99%, and the two-class showed 99.99%. Conclusion: It can be concluded that using the VGG 19 model is effective. This system can classify and diagnose viruses in patients to assist radiologists by reading the images.   Keywords: COVID-19, CNN, Classification, Deep Learning

Page 1 of 2 | Total Record : 12

 1   2 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol. 9 No. 2 (2023): October Vol. 9 No. 1 (2023): April Vol. 8 No. 2 (2022): October Vol. 8 No. 1 (2022): April Vol. 7 No. 2 (2021): October Vol. 7 No. 1 (2021): April Vol. 6 No. 2 (2020): October Vol. 6 No. 1 (2020): April Vol. 5 No. 2 (2019): October Vol. 5 No. 1 (2019): April Vol. 4 No. 2 (2018): October Vol. 4 No. 1 (2018): April Vol. 3 No. 2 (2017): October Vol. 3 No. 1 (2017): April Vol. 2 No. 2 (2016): October Vol 2, No 1 (2016): April Vol. 2 No. 1 (2016): April Vol 1, No 2 (2015): October Vol. 1 No. 2 (2015): October Vol 1, No 1 (2015): April Vol. 1 No. 1 (2015): April More Issue