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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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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
Melanoma Detection using Convolutional Neural Network with Transfer Learning on Dermoscopic and Macroscopic Images Jessica Millenia; Mohammad Farid Naufal; Joko Siswantoro
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.8.2.149-161

Abstract

Background: Melanoma is a skin cancer that starts when the melanocytes that produce the skin color pigment start to grow out of control and form a cancer. Detecting melanoma early before it spreads to the lymph nodes and other parts of the body is very important because it makes a big difference to the patient's 5-year life expectancy. Screening is the process of conducting a skin examination to suspect a mole is melanoma using dermoscopic or macroscopic images. However, manual screening takes a long time. Therefore, automatic melanoma detection is needed to speed up the melanoma detection process. The previous studies still have weakness because it has low precision or recall, which means the model cannot predict melanoma accurately. The distribution of melanoma and moles datasets is imbalanced where the number of melanomas is less than moles. In addition, in previous study, comparisons of several CNN transfer learning architectures have not been carried out on dermoscopic and macroscopic images. Objective: This study aims to detect melanoma using the Convolutional Neural Network (CNN) with transfer learning on dermoscopic and macroscopic melanoma images. CNN with Transfer learning is a popular method for classifying digital images with high accuracy. Methods: This study compares four CNN with transfer learning architectures, namely MobileNet, Xception, VGG16, and ResNet50 on dermoscopic and macroscopic image. This research also uses black-hat filtering and inpainting at the preprocessing stage to remove hair from the skin image. Results: MobileNet is the best model for classifying melanomas or moles in this experiment which has 83.86% of F1 score and 11 second of training time per epoch. Conclusion: MobileNet and Xception have high average F1 scores of 84.42% and 80.00%, so they can detect melanoma accurately even though the number of melanoma datasets is less than moles. Therefore, it can be concluded that MobileNet and Xception are suitable models for classifying melanomas and moles. However, MobileNet has the fastest training time per epoch which is 11 seconds. In the future, oversampling method can be implemented to balance the number of datasets to improve the performance of the classification model.
Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning Subroto Singha; Priyangka Roy
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.8.2.218-225

Abstract

Background: Skin cancer can quickly become fatal. An examination and biopsy of dermoscopic pictures are required to determine if skin cancer is malignant or benign. However, these examinations can be costly. Objective: In this research, we proposed deep learning (DL)-based approach to identify a melanoma, the most dangerous kind of skin cancer. DL is particularly excellent in learning traits and predicting cancer. However, DL requires a vast number of images. Method: We used image augmentation and transferring learning to categorise images into benign and malignant. We used the public ISIC 2020 database to train and test our models. The ISIC 2020 dataset classifies melanoma as malignant. Along with the categorization, the dataset was examined for variation. The training and validation accuracy of three of the best pre-trained models were compared. To minimise the loss, three optimizers were used: RMSProp, SGD, and ADAM. Results: We attained training accuracy of 98.73%, 99.12%, and 99.76% using ResNet, VGG16, and MobileNetV2, respectively. We achieved a validation accuracy of 98.39% using these three pre-trained models. Conclusion: The validation accuracy of 98.39% outperforms the prior pre-trained model. The findings of this study can be applied in medical science to help physicians diagnose skin cancer early and save lives.   Keywords: Deep Learning, ISIC 2020, Pre-trained Model, Skin Cancer, Transfer Learning
A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification Angona Biswas; Md. Saiful Islam
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.1-15

Abstract

Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature extraction process consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained. Objective: This paper proposes a hybrid model for classifying brain tumors more accurately and rapidly is a preferable choice for aggravating tasks. The main objective of this research is to classify brain tumors through Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM)-based hybrid model. Methods: The MRI images are firstly preprocessed to improve the feature extraction process through the following steps: resize, effective noise reduction, and contrast enhancement.  Noise reduction is done by anisotropic diffusion filter, and contrast enhancement is done by adaptive histogram equalization. Secondly, the implementation of augmentation enhances the data number and data variety. Thirdly, custom deep CNN is constructed for meaningful deep feature extraction. Finally, the superior machine learning classifier SVM is integrated for classification tasks. After that, this proposed hybrid model is compared with transfer learning models: AlexNet, GoogLeNet, and VGG16. Results: The proposed method uses the ‘Figshare’ dataset and obtains 96.0% accuracy, 98.0% specificity, and 95.71% sensitivity, higher than other transfer learning models. Also, the proposed model takes less time than others. Conclusion: The effectiveness of the proposed deep CNN-SVM model divulges by the performance, which manifests that it extracts features automatically without overfitting problems and improves the classification performance for hybrid structure, and is less time-consuming.   Keywords:  Adaptive histogram equalization, Anisotropic diffusion filter, Deep CNN, E-health, Machine learning, SVM, Transfer learning.
Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation Nur Alifiah; Dian Kurniasari; Amanto Amanto; Warsono Warsono
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.16-27

Abstract

Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022.   Keywords: Artificial Intelligence, Artificial Neural Network (ANN) K-Fold Cross Validation, COVID-19 Cases, Data Mining, Prediction.
Trends and Applications of Gamification in E-Commerce: A Systematic Literature Review Muhamad Adhytia Wana Putra Rahmadhan; Dana Indra Sensuse; Ryan Randy Suryono; Kautsarina Kautsarina
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.28-37

Abstract

Background: Gamification is a trend that has emerged with the growth of e-commerce. Given the wide range of human characteristics, determining which gamification elements perform well and what impact those gamification elements have can be challenging. Objective: This study aims to conduct a systematic literature review to broadly review the impact that can be caused by the application of gamification elements in e-commerce. This study also attempts to identify the current trends in using gamification elements. Methods: This study was carried out based on the Kitchenham approach and analyzes 25 research papers extracted from a total of 550 papers. The articles were gathered from ACM, Emerald, ScienceDirect, and Scopus and were published between 2016 and 2021. Results: This study found that the trend of research in the field of gamification in e-commerce continues to grow every year. Also, this study found that the most frequently used gamification elements are achievement-oriented (such as rewards, points, badges, and leaderboards). Meanwhile, immersion-related gamification elements (such as avatars, fantasy, etc.) are emerging as a new trend for new gamification elements to be incorporated in e-commerce. This study also found three major themes, namely consumer loyalty, consumer engagement, and user behavior, as a result of the application of gamification in e-commerce. Conclusion: This study helps to improve knowledge of various gamification elements, trends, and impacts on e-commerce. Future studies need to examine the challenges that may arise in the application of gamification elements to the three major themes found in this study and find potential solutions to overcome them.   Keywords: E-Commerce, Gamification, Gamification trends and applications, Kitchenham, Systematic literature review.  
Simulation of System Dynamics for Improving The Quality of Paddy Production in Supporting Food Security Mala Rosa Aprillya; Erma Suryani
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.38-46

Abstract

Background: The food security policy is an effort to ensure stable food availability and stable access of the community to food. As the population increases, this will affect the fulfillment of food needs in the future. Therefore, increase in rice production is needed to support food security. Objective: Conduct an analysis of the factors affecting the quality of rice production by using a dynamic system simulation that can be used as a basis for formulating policy strategies. Method: Simulation using System Dynamics (SD) is a method used to study and analyze complex systems by modeling non-linear behavior. Then several scenarios were carried out for the best decision-making using a computer. Result: The results of the scenario show that increasing the quality of paddy production in order to meet food needs in the future is doable by boosting the rendement of paddy as it will upgrade rice production which  will contribute greatly to rice production. Conclusion: From the simulation results, the  study can be used to increase the quality of rice production to maintain food security by improving the harvesting mechanism to increase yields. For further research, the use of Smart Agriculture can be considered to increase production of rice.   Keywords: Food security, Rice production, Rice production, System dynamics
Factors Affecting Adoption of Telemedicine for Virtual Healthcare Services in Indonesia Rima Alviani; Betty Purwandari; Imairi Eitiveni; Mardiana Purwaningsih
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.47-69

Abstract

Background: The utilization of virtual healthcare services, particularly telemedicine, has been accelerated by the COVID-19 pandemic. Although the pandemic is no longer the primary concern, telemedicine still holds potential for long-term adoption. However, implementing telemedicine in Indonesia as an online platform for remote healthcare delivery still faces issues, despite its potential. Further investigation is required to identify the factors that affect its adoption and develop strategies to surmount implementation challenges. Objective: This study aims to examine and enrich knowledge about the adoption of telemedicine in Indonesia. Methods: A cross-sectional survey was conducted through an online questionnaire to collect data. Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) was employed by integrating with several factors, such as eHealth Literacy, Privacy Concerns, and Trust. Gender and age were considered as moderating variables. Data samples were analyzed using Partial Least Square – Structural Equation Modeling (PLS–SEM). Results: The findings suggest that performance expectancy, effort expectancy, social influence, eHealth literacy, and trust have a significant impact on adults’ behavioral intention to use telemedicine. However, facilitating condition, price value, and privacy concern do not show any significant effects on adults’ Behavioral Intention to Use Telemedicine.   Conclusion: This study highlights the importance of understanding adoption factors to develop effective strategies. Results show performance expectancy, effort expectancy, social influence, eHealth literacy, and trust are significant factors, while facilitating condition, price value, and privacy concern are not. The UTAUT2 model is a good predictive tool for healthcare adoption. To increase usage intention, several aspects must be considered in the implementation of telemedicine.   Keywords: Adoption, Behavioral Intention to Use, Telemedicine, UTAUT2, Virtual Healthcare.

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