Pub Date : 2023-04-28DOI: 10.20473/jisebi.9.1.70-83
S. Hasanah, Y. Herdiyeni, M. Hardhienata
Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks; each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869; 0.1513; and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area. Keywords: COVID-19, Facebook Prophet , Hierarchical clustering, Socioeconomic and demographic
{"title":"The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model","authors":"S. Hasanah, Y. Herdiyeni, M. Hardhienata","doi":"10.20473/jisebi.9.1.70-83","DOIUrl":"https://doi.org/10.20473/jisebi.9.1.70-83","url":null,"abstract":"Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks; each part has its disease transmission pattern.\u0000Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique.\u0000Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time.\u0000Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869; 0.1513; and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3.\u0000Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area.\u0000 \u0000Keywords: COVID-19, Facebook Prophet , Hierarchical clustering, Socioeconomic and demographic","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90321454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.162-174
Hafiza Akter Munira, Md. Saiful Islam
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
{"title":"Hybrid Deep Learning Models for Multi-classification of Tumour from Brain MRI","authors":"Hafiza Akter Munira, Md. Saiful Islam","doi":"10.20473/jisebi.8.2.162-174","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.162-174","url":null,"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.\u0000Objective: This study proposes hybrid deep learning approaches for classifying brain tumours using convolutional neural networks (CNN) and machine learning (ML) classifiers.\u0000Methods: 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.\u0000Results: 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).\u0000Conclusion: 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.\u0000 \u0000Keywords: Brain Tumour, Convolutional Neural Network, Feature Extraction, Multi-Classification, Machine Learning Classifiers","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88589983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.175-181
Nur Ghaniaviyanto Ramadhan, Adiwijaya Adiwijaya
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.
{"title":"Data Mining Techniques in Handling Personality Analysis for Ideal Customers","authors":"Nur Ghaniaviyanto Ramadhan, Adiwijaya Adiwijaya","doi":"10.20473/jisebi.8.2.175-181","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.175-181","url":null,"abstract":"Background: Personality distinguishes individuals from one another, guides their actions and reactions, and dictates their preferences in many aspects of life, including shopping.\u0000Objective: This study determines the characteristics of an ideal customer based on individual personality.\u0000Methods: 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.\u0000Results: 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.\u0000Conclusion: 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.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85253521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.109-118
Endra Yuliawan, Shofwatul Uyun
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
{"title":"Chest X-ray Image Classification for COVID-19 diagnoses","authors":"Endra Yuliawan, Shofwatul Uyun","doi":"10.20473/jisebi.8.2.109-118","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.109-118","url":null,"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.\u0000Objective: The current study compares the classification and accuracy of detection methods with two, three dan five classes.\u0000Methods: 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.\u0000Results: The results of the five-class method showed 98% accuracy, the three-class method showed 99.99%, and the two-class showed 99.99%.\u0000Conclusion: 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.\u0000 \u0000Keywords: COVID-19, CNN, Classification, Deep Learning","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80353728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.119-129
Evi Triandini, Gusti Ngurah, S. Wijaya, Riza Wulandari, Ni Wayan, Cahya Ayu, Pratami, Ketut Putu Suniantara, Candra Ahmadi, Wijaya Wulandari Pratami Suniantara Triandini, Ahmadi
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
{"title":"Identifying Messenger Platform Preferences using Multiple Linear Regression and Conjoint Analyses","authors":"Evi Triandini, Gusti Ngurah, S. Wijaya, Riza Wulandari, Ni Wayan, Cahya Ayu, Pratami, Ketut Putu Suniantara, Candra Ahmadi, Wijaya Wulandari Pratami Suniantara Triandini, Ahmadi","doi":"10.20473/jisebi.8.2.119-129","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.119-129","url":null,"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.\u0000Objective: This study aims to capture user preferences for messenger platforms and inform software development companies to improve their products based on user needs.\u0000Methods: 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.\u0000Results: 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.\u0000Conclusion: The research can identify the indicators to guide the creation of an ideal messenger platform based on customer and user preferences.\u0000 \u0000Keywords: Conjoint, Messenger Platform, Multiple Linear Regression, Preference","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88914412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.196-206
Yuniar Farida, Husna Nur Laili, Achmad Teguh Wibowo, L. N. Desinaini, Silvia Kartika Sari
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.
{"title":"Selecting the Best-Performing Low-Cost Carrier (LCC) Airlines Using Analytical Hierarchy Process (AHP) and Elimination et Choix Traduisant la Realite (ELECTRE)","authors":"Yuniar Farida, Husna Nur Laili, Achmad Teguh Wibowo, L. N. Desinaini, Silvia Kartika Sari","doi":"10.20473/jisebi.8.2.196-206","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.196-206","url":null,"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.\u0000Objective: This study aims to determine the best-performing LCC airlines, the criteria, and the sub-criteria to improve the performance.\u0000Methods: 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.\u0000Results: 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.\u0000Conclusion: 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.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76024560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.130-141
Radit Rahmadhan, Meditya Wasesa
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.
{"title":"Segmentation using Customers Lifetime Value: Hybrid K-means Clustering and Analytic Hierarchy Process","authors":"Radit Rahmadhan, Meditya Wasesa","doi":"10.20473/jisebi.8.2.130-141","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.130-141","url":null,"abstract":"Background: Understanding customers’ electricity consumption patterns is essential for developing predictive analytics, which is needed for effective supply and demand management.\u0000Objective: 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.\u0000Methods: 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.\u0000Results: 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.\u0000Conclusion: 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.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85969928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.218-225
Subroto Singha, Priyangka Roy
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
{"title":"Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning","authors":"Subroto Singha, Priyangka Roy","doi":"10.20473/jisebi.8.2.218-225","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.218-225","url":null,"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.\u0000Objective: 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.\u0000Method: 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.\u0000Results: 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.\u0000Conclusion: 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.\u0000 \u0000Keywords: Deep Learning, ISIC 2020, Pre-trained Model, Skin Cancer, Transfer Learning","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"23 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77947653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.149-161
Jessica Millenia, M. F. Naufal, J. Siswantoro
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.
{"title":"Melanoma Detection using Convolutional Neural Network with Transfer Learning on Dermoscopic and Macroscopic Images","authors":"Jessica Millenia, M. F. Naufal, J. Siswantoro","doi":"10.20473/jisebi.8.2.149-161","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.149-161","url":null,"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. \u0000Objective: 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. \u0000Methods: 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. \u0000Results: 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. \u0000Conclusion: 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.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74586813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-29DOI: 10.20473/jisebi.8.2.100-108
A. Abdurrahman, Aurik Gustomo, E. Prasetio, Sonny Rustiadi
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
{"title":"Designing an Open Innovation Framework for Digital Transformation Based on Systematic Literature Review","authors":"A. Abdurrahman, Aurik Gustomo, E. Prasetio, Sonny Rustiadi","doi":"10.20473/jisebi.8.2.100-108","DOIUrl":"https://doi.org/10.20473/jisebi.8.2.100-108","url":null,"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.\u0000Objective: 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. \u0000Methods: 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.\u0000Results: 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.\u0000Conclusion: 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.\u0000Keywords: DX, Innovation, Open Innovation, Open Innovation Framework","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73008306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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