Pub Date : 2022-04-26DOI: 10.20473/jisebi.8.1.42-50
A. Abdillah, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita, D. Purwitasari
Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering
{"title":"Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data","authors":"A. Abdillah, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita, D. Purwitasari","doi":"10.20473/jisebi.8.1.42-50","DOIUrl":"https://doi.org/10.20473/jisebi.8.1.42-50","url":null,"abstract":"Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking.\u0000Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions.\u0000Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making.\u0000Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%.\u0000Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions.\u0000Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87217519","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 : 2022-04-26DOI: 10.20473/jisebi.8.1.31-41
Sherly Rosa Anggraeni, Narandha Arya Ranggianto, I. Ghozali, C. Fatichah, D. Purwitasari
Background: Twitter is one of the most used social media, with 310 million active users monthly and 500 million tweets per day. Twitter is not only used to talk about trending topics but also to share information about accidents, fires, traffic jams, etc. People often find these updates useful to minimize the impact. Objective: The current study compares the effectiveness of three deep learning methods (CNN, RCNN, CLSTM) combined with neuroNER in classifying multi-label incidents. Methods: NeuroNER is paired with different deep learning classification methods (CNN, RCNN, CLSTM). Results: CNN paired with NeuroNER yield the best results for multi-label classification compared to CLSTM and RCNN. Conclusion: CNN was proven to be more effective with an average precision value of 88.54% for multi-label incidents classification. This is because the data we used for the classification resulted from NER, which was in the form of entity labels. CNN immediately distinguishes important information, namely the NER labels. CLSTM generates the worst result because it is more suitable for sequential data. Future research will benefit from changing the classification parameters and test scenarios on a different number of labels with more diverse data. Keywords: CLSTM, CNN, Incident Classification, Multi-label Classification, RCNN
{"title":"Deep Learning Approaches for Multi-Label Incidents Classification from Twitter Textual Information","authors":"Sherly Rosa Anggraeni, Narandha Arya Ranggianto, I. Ghozali, C. Fatichah, D. Purwitasari","doi":"10.20473/jisebi.8.1.31-41","DOIUrl":"https://doi.org/10.20473/jisebi.8.1.31-41","url":null,"abstract":"Background: Twitter is one of the most used social media, with 310 million active users monthly and 500 million tweets per day. Twitter is not only used to talk about trending topics but also to share information about accidents, fires, traffic jams, etc. People often find these updates useful to minimize the impact.\u0000Objective: The current study compares the effectiveness of three deep learning methods (CNN, RCNN, CLSTM) combined with neuroNER in classifying multi-label incidents.\u0000Methods: NeuroNER is paired with different deep learning classification methods (CNN, RCNN, CLSTM).\u0000Results: CNN paired with NeuroNER yield the best results for multi-label classification compared to CLSTM and RCNN.\u0000Conclusion: CNN was proven to be more effective with an average precision value of 88.54% for multi-label incidents classification. This is because the data we used for the classification resulted from NER, which was in the form of entity labels. CNN immediately distinguishes important information, namely the NER labels. CLSTM generates the worst result because it is more suitable for sequential data. Future research will benefit from changing the classification parameters and test scenarios on a different number of labels with more diverse data.\u0000Keywords: CLSTM, CNN, Incident Classification, Multi-label Classification, RCNN","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85502288","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 : 2022-04-26DOI: 10.20473/jisebi.8.1.21-30
Eka Alifia Kusnanti, D. C. R. Novitasari, F. Setiawan, Aris Fanani, M. Hafiyusholeh, Ghaluh Indah Permata Sari
Background: Ocean surface currents need to be monitored to minimize accidents at ship crossings. One way to predict ocean currents—and estimate the danger level of the sea—is by finding out the currents’ velocity and their future direction. Objective: This study aims to predict the velocity and direction of ocean surface currents. Methods: This research uses the Elman recurrent neural network (ERNN). This study used 3,750 long-term data and 72 short-term data. Results: The evaluation with Mean Absolute Percentage Error (MAPE) achieved the best results in short-term predictions. The best MAPE of the U currents (east to west) was 14.0279% with five inputs; the first and second hidden layers were 50 and 100, and the learning rate was 0.3. While the best MAPE of the V currents (north to south) was 3.1253% with five inputs, the first and second hidden layers were 20 and 50, and the learning rate was 0.1. The ocean surface currents’ prediction indicates that the current state is from east to south with a magnitude of around 169,5773°-175,7127° resulting in a MAPE of 0.0668%. Conclusion: ERNN is more effective than single exponential smoothing and RBFNN in ocean current prediction studies because it produces a smaller error value. In addition, the ERNN method is good for short-term ocean surface currents but is not optimal for long-term current predictions. Keywords: MAPE, ERNN, ocean currents, ocean currents’ velocity, ocean currents’ directions
{"title":"Predicting Velocity and Direction of Ocean Surface Currents using Elman Recurrent Neural Network Method","authors":"Eka Alifia Kusnanti, D. C. R. Novitasari, F. Setiawan, Aris Fanani, M. Hafiyusholeh, Ghaluh Indah Permata Sari","doi":"10.20473/jisebi.8.1.21-30","DOIUrl":"https://doi.org/10.20473/jisebi.8.1.21-30","url":null,"abstract":"Background: Ocean surface currents need to be monitored to minimize accidents at ship crossings. One way to predict ocean currents—and estimate the danger level of the sea—is by finding out the currents’ velocity and their future direction.\u0000Objective: This study aims to predict the velocity and direction of ocean surface currents.\u0000Methods: This research uses the Elman recurrent neural network (ERNN). This study used 3,750 long-term data and 72 short-term data.\u0000Results: The evaluation with Mean Absolute Percentage Error (MAPE) achieved the best results in short-term predictions. The best MAPE of the U currents (east to west) was 14.0279% with five inputs; the first and second hidden layers were 50 and 100, and the learning rate was 0.3. While the best MAPE of the V currents (north to south) was 3.1253% with five inputs, the first and second hidden layers were 20 and 50, and the learning rate was 0.1. The ocean surface currents’ prediction indicates that the current state is from east to south with a magnitude of around 169,5773°-175,7127° resulting in a MAPE of 0.0668%.\u0000Conclusion: ERNN is more effective than single exponential smoothing and RBFNN in ocean current prediction studies because it produces a smaller error value. In addition, the ERNN method is good for short-term ocean surface currents but is not optimal for long-term current predictions.\u0000Keywords: MAPE, ERNN, ocean currents, ocean currents’ velocity, ocean currents’ directions","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82993551","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 : 2021-10-28DOI: 10.20473/jisebi.7.2.138-148
Ika Nurkasanah
Background: Inventory policy highly influences Supply Chain Management (SCM) process. Evidence suggests that almost half of SCM costs are set off by stock-related expenses.Objective: This paper aims to minimise total inventory cost in SCM by applying a multi-agent-based machine learning called Reinforcement Learning (RL).Methods: The ability of RL in finding a hidden pattern of inventory policy is run under various constraints which have not been addressed together or simultaneously in previous research. These include capacitated manufacturer and warehouse, limitation of order to suppliers, stochastic demand, lead time uncertainty and multi-sourcing supply. RL was run through Q-Learning with four experiments and 1,000 iterations to examine its result consistency. Then, RL was contrasted to the previous mathematical method to check its efficiency in reducing inventory costs.Results: After 1,000 trial-error simulations, the most striking finding is that RL can perform more efficiently than the mathematical approach by placing optimum order quantities at the right time. In addition, this result was achieved under complex constraints and assumptions which have not been simultaneously simulated in previous studies.Conclusion: Results confirm that the RL approach will be invaluable when implemented to comparable supply network environments expressed in this project. Since RL still leads to higher shortages in this research, combining RL with other machine learning algorithms is suggested to have more robust end-to-end SCM analysis. Keywords: Inventory Policy, Multi-Echelon, Reinforcement Learning, Supply Chain Management, Q-Learning
{"title":"Reinforcement Learning Approach for Efficient Inventory Policy in Multi-Echelon Supply Chain Under Various Assumptions and Constraints","authors":"Ika Nurkasanah","doi":"10.20473/jisebi.7.2.138-148","DOIUrl":"https://doi.org/10.20473/jisebi.7.2.138-148","url":null,"abstract":"Background: Inventory policy highly influences Supply Chain Management (SCM) process. Evidence suggests that almost half of SCM costs are set off by stock-related expenses.Objective: This paper aims to minimise total inventory cost in SCM by applying a multi-agent-based machine learning called Reinforcement Learning (RL).Methods: The ability of RL in finding a hidden pattern of inventory policy is run under various constraints which have not been addressed together or simultaneously in previous research. These include capacitated manufacturer and warehouse, limitation of order to suppliers, stochastic demand, lead time uncertainty and multi-sourcing supply. RL was run through Q-Learning with four experiments and 1,000 iterations to examine its result consistency. Then, RL was contrasted to the previous mathematical method to check its efficiency in reducing inventory costs.Results: After 1,000 trial-error simulations, the most striking finding is that RL can perform more efficiently than the mathematical approach by placing optimum order quantities at the right time. In addition, this result was achieved under complex constraints and assumptions which have not been simultaneously simulated in previous studies.Conclusion: Results confirm that the RL approach will be invaluable when implemented to comparable supply network environments expressed in this project. Since RL still leads to higher shortages in this research, combining RL with other machine learning algorithms is suggested to have more robust end-to-end SCM analysis. Keywords: Inventory Policy, Multi-Echelon, Reinforcement Learning, Supply Chain Management, Q-Learning","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75857085","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 : 2021-10-28DOI: 10.20473/jisebi.7.2.149-161
Lady Silk Moonlight, Fiqqih Faizah, Y. Suprapto, N. Pambudiyatno
Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning
{"title":"Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition","authors":"Lady Silk Moonlight, Fiqqih Faizah, Y. Suprapto, N. Pambudiyatno","doi":"10.20473/jisebi.7.2.149-161","DOIUrl":"https://doi.org/10.20473/jisebi.7.2.149-161","url":null,"abstract":"Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning ","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89779135","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 : 2021-10-28DOI: 10.20473/jisebi.7.2.112-118
Shabrina Luthfiani Khanza, E. Suryani, R. A. Hendrawan
Background: Commuting time is highly influenced by traffic congestion. System dynamics simulation can help identify the cause of traffic problems to improve travel time efficiency.Objective: This study aims to reduce traffic congestion and minimise commuting time efficiency using system dynamics simulation and scenarios. The developed scenarios implement the Bus Rapid Transit (BRT) and trams projects in the model.Methods: System dynamics simulation is used to analyse the transport system in Surabaya and the impact of BRT and trams project implementation in the model in order to improve commuting time and to reduce congestion.Results: From the simulation results, with the implementation of BRT and tram projects along with highway expansion, traffic congestion is predicted to decline by 24-44%. With the reduction of traffic congestion, travel time efficiency is predicted to improve by 11-28%. On the contrary, implementation of BRT and tram project without highway expansion is predicted to increase the traffic congestion by 5% in the initial year of implementation, then traffic congestion is predicted to decline by 2% in 2035.Conclusion: Based on the scenarios, transport project implementation such as BRT and trams should be accompanied with improvement of infrastructure. Further research is needed to develop a more comprehensive transportation system to capture a broader view of the problem. Keywords: Model, Simulation, System Dynamics, Traffic Congestion, Travel Time
{"title":"Scenario Model to Mitigate Traffic Congestion and Improve Commuting Time Efficiency","authors":"Shabrina Luthfiani Khanza, E. Suryani, R. A. Hendrawan","doi":"10.20473/jisebi.7.2.112-118","DOIUrl":"https://doi.org/10.20473/jisebi.7.2.112-118","url":null,"abstract":"Background: Commuting time is highly influenced by traffic congestion. System dynamics simulation can help identify the cause of traffic problems to improve travel time efficiency.Objective: This study aims to reduce traffic congestion and minimise commuting time efficiency using system dynamics simulation and scenarios. The developed scenarios implement the Bus Rapid Transit (BRT) and trams projects in the model.Methods: System dynamics simulation is used to analyse the transport system in Surabaya and the impact of BRT and trams project implementation in the model in order to improve commuting time and to reduce congestion.Results: From the simulation results, with the implementation of BRT and tram projects along with highway expansion, traffic congestion is predicted to decline by 24-44%. With the reduction of traffic congestion, travel time efficiency is predicted to improve by 11-28%. On the contrary, implementation of BRT and tram project without highway expansion is predicted to increase the traffic congestion by 5% in the initial year of implementation, then traffic congestion is predicted to decline by 2% in 2035.Conclusion: Based on the scenarios, transport project implementation such as BRT and trams should be accompanied with improvement of infrastructure. Further research is needed to develop a more comprehensive transportation system to capture a broader view of the problem. Keywords: Model, Simulation, System Dynamics, Traffic Congestion, Travel Time ","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78878270","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 : 2021-10-28DOI: 10.20473/jisebi.7.2.129-137
Bambang Jokonowo, Nenden Siti Fatonah, E. A. P. Akhir
Background: Standard operating procedure (SOP) is a series of business activities to achieve organisational goals, with each activity carried to be recorded and stored in the information system together with its location (e.g., SCM, ERP, LMS, CRM). The activity is known as event data and is stored in a database known as an event log.Objective: Based on the event log, we can calculate the fitness to determine whether the business process SOP is following the actual business process.Methods: This study obtains the event log from a terminal operating system (TOS), which records the dwelling time at the container port. The conformance checking using token-based replay method calculates fitness by comparing the event log with the process model.Results: The findings using the Alpha algorithm resulted in the most traversed traces (a, b, n, o, p). The fitness calculation returns 1.0 were produced, missing, and remaining tokens are replied to each of the other traces.Conclusion: Thus, if the process mining produces a fitness of more than 0.80, this shows that the process model is following the actual business process. Keywords: Conformance Checking, Dwelling time, Event log, Fitness, Process Discovery, Process Mining
背景:标准作业程序(SOP)是为实现组织目标而进行的一系列业务活动，每项活动都要记录并存储在信息系统中，并连同其位置(例如，SCM、ERP、LMS、CRM)。该活动称为事件数据，并存储在称为事件日志的数据库中。目的:根据事件日志计算适应度，判断业务流程SOP是否遵循实际业务流程。方法:本研究从终端操作系统(TOS)获取事件日志，记录集装箱港口的停留时间。使用基于令牌的重播方法的一致性检查通过将事件日志与流程模型进行比较来计算适合度。结果:使用Alpha算法的结果产生了遍历次数最多的轨迹(a, b, n, o, p)。产生了适应度计算返回值1.0，丢失了，剩余的标记被回复到每个其他轨迹。结论:因此，如果流程挖掘产生的适应度大于0.80，则表明流程模型遵循实际业务流程。关键词:一致性检查，停留时间，事件日志，适应度，过程发现，过程挖掘
{"title":"Conformance Checking of Dwelling Time Using a Token-based Method","authors":"Bambang Jokonowo, Nenden Siti Fatonah, E. A. P. Akhir","doi":"10.20473/jisebi.7.2.129-137","DOIUrl":"https://doi.org/10.20473/jisebi.7.2.129-137","url":null,"abstract":"Background: Standard operating procedure (SOP) is a series of business activities to achieve organisational goals, with each activity carried to be recorded and stored in the information system together with its location (e.g., SCM, ERP, LMS, CRM). The activity is known as event data and is stored in a database known as an event log.Objective: Based on the event log, we can calculate the fitness to determine whether the business process SOP is following the actual business process.Methods: This study obtains the event log from a terminal operating system (TOS), which records the dwelling time at the container port. The conformance checking using token-based replay method calculates fitness by comparing the event log with the process model.Results: The findings using the Alpha algorithm resulted in the most traversed traces (a, b, n, o, p). The fitness calculation returns 1.0 were produced, missing, and remaining tokens are replied to each of the other traces.Conclusion: Thus, if the process mining produces a fitness of more than 0.80, this shows that the process model is following the actual business process. Keywords: Conformance Checking, Dwelling time, Event log, Fitness, Process Discovery, Process Mining","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79945145","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 : 2021-10-28DOI: 10.20473/jisebi.7.2.102-111
P. Amelia, A. Lathifah, Muhammad Dliya'ul Haq, Christoph Lorenz Reimann, Yudi Setiawan
Background: To remain relevant in the customer-oriented market, hospitals must pay attention to the quality of services and meet customers' expectations from admission to discharge stage. For an outpatient customer, pharmacy is the last unit visited before discharge. It is likely to influence patient satisfaction and reflect the quality of hospital's service. However, at certain hospitals, the waiting time is long. Resources need to be deployed strategically to reduce queue time. Objective: This research aims to arrange the number of staff (pharmacists and workers) in each station in the pharmacy outpatient service to minimise the queue time.Methods: A discrete simulation method is used to observe the waiting time spent at the pharmacy. The simulation run is valid and effective to test the scenario. Results: It is recommended to add more personnel for the non-compounding medicine and packaging to reduce the waiting time by 22.41%Conclusion: By adding personnel to non-compounding and packaging stations, the system performance could be improved. Cost-effectiveness analysis should be done to corroborate the finding. Keywords: Discrete Event Simulation, Hospital, Outpatient Service, Pharmacy Unit, System AnalysisBackground: To remain relevant in the customer-oriented market, hospitals must pay attention to the quality of services and meet customers' expectations from admission to discharge stage. For an outpatient customer, pharmacy is the last unit visited before discharge. It is likely to influence patient satisfaction and reflect the quality of hospital's service. However, at certain hospitals, the waiting time is long. Resources need to be deployed strategically to reduce queue time. Objective: This research aims to arrange the number of staff (pharmacists and workers) in each station in the pharmacy outpatient service to minimise the queue time.Methods: A discrete simulation method is used to observe the waiting time spent at the pharmacy. The simulation run is valid and effective to test the scenario. Results: It is recommended to add more personnel for the non-compounding medicine and packaging to reduce the waiting time by 22.41%Conclusion: By adding personnel to non-compounding and packaging stations, the system performance could be improved. Cost-effectiveness analysis should be done to corroborate the finding. Keywords:Discrete Event Simulation, Hospital, Outpatient Service, Pharmacy Unit, System Analysis
{"title":"Optimising Outpatient Pharmacy Staffing to Minimise Patients Queue Time using Discrete Event Simulation","authors":"P. Amelia, A. Lathifah, Muhammad Dliya'ul Haq, Christoph Lorenz Reimann, Yudi Setiawan","doi":"10.20473/jisebi.7.2.102-111","DOIUrl":"https://doi.org/10.20473/jisebi.7.2.102-111","url":null,"abstract":"Background: To remain relevant in the customer-oriented market, hospitals must pay attention to the quality of services and meet customers' expectations from admission to discharge stage. For an outpatient customer, pharmacy is the last unit visited before discharge. It is likely to influence patient satisfaction and reflect the quality of hospital's service. However, at certain hospitals, the waiting time is long. Resources need to be deployed strategically to reduce queue time. Objective: This research aims to arrange the number of staff (pharmacists and workers) in each station in the pharmacy outpatient service to minimise the queue time.Methods: A discrete simulation method is used to observe the waiting time spent at the pharmacy. The simulation run is valid and effective to test the scenario. Results: It is recommended to add more personnel for the non-compounding medicine and packaging to reduce the waiting time by 22.41%Conclusion: By adding personnel to non-compounding and packaging stations, the system performance could be improved. Cost-effectiveness analysis should be done to corroborate the finding. Keywords: Discrete Event Simulation, Hospital, Outpatient Service, Pharmacy Unit, System AnalysisBackground: To remain relevant in the customer-oriented market, hospitals must pay attention to the quality of services and meet customers' expectations from admission to discharge stage. For an outpatient customer, pharmacy is the last unit visited before discharge. It is likely to influence patient satisfaction and reflect the quality of hospital's service. However, at certain hospitals, the waiting time is long. Resources need to be deployed strategically to reduce queue time. Objective: This research aims to arrange the number of staff (pharmacists and workers) in each station in the pharmacy outpatient service to minimise the queue time.Methods: A discrete simulation method is used to observe the waiting time spent at the pharmacy. The simulation run is valid and effective to test the scenario. Results: It is recommended to add more personnel for the non-compounding medicine and packaging to reduce the waiting time by 22.41%Conclusion: By adding personnel to non-compounding and packaging stations, the system performance could be improved. Cost-effectiveness analysis should be done to corroborate the finding. Keywords:Discrete Event Simulation, Hospital, Outpatient Service, Pharmacy Unit, System Analysis","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81567019","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 : 2021-10-28DOI: 10.20473/jisebi.7.2.91-101
Rudy Sunardi, U. Suhud, Dedi Purwana, H. Hamidah
Background: Peer-to-peer (P2P) lending platform is one of key disruptive business models in financial technology. It bridges lenders and borrowers directly. Researchers have studied the leverage mechanism behind the P2P lending platform.Objective: This research proposes an enhanced technology acceptance model (TAM) to investigate how consumers embrace P2P lending platforms using quality of service and perceived risk as drivers of trust.Methods: This research uses structural equation modeling (SEM) to test the hypothesised connections between the latent variables.Results: The findings show that users' trust, perceived usefulness, and perceived ease of use in P2P lending platforms significantly influence attitudes towards adoption. Meanwhile, consumers' perceived risk in using P2P lending platforms is unaffected by the quality of service.Conclusion: The estimated model is consistent with the results shown in previous studies. The findings of the current research are useful for fine-tuning platform marketing plans and putting strategic goals into actions. For future research, we suggest including more variables to better understand the adoption intention of P2P lending platforms.Keywords: Adoption intention, Peer-to-peer lending, Structural equation modeling, Technology acceptance model
{"title":"Examining the Factors Contributing to Fintech Peer-to-peer Lending Adoption","authors":"Rudy Sunardi, U. Suhud, Dedi Purwana, H. Hamidah","doi":"10.20473/jisebi.7.2.91-101","DOIUrl":"https://doi.org/10.20473/jisebi.7.2.91-101","url":null,"abstract":"Background: Peer-to-peer (P2P) lending platform is one of key disruptive business models in financial technology. It bridges lenders and borrowers directly. Researchers have studied the leverage mechanism behind the P2P lending platform.Objective: This research proposes an enhanced technology acceptance model (TAM) to investigate how consumers embrace P2P lending platforms using quality of service and perceived risk as drivers of trust.Methods: This research uses structural equation modeling (SEM) to test the hypothesised connections between the latent variables.Results: The findings show that users' trust, perceived usefulness, and perceived ease of use in P2P lending platforms significantly influence attitudes towards adoption. Meanwhile, consumers' perceived risk in using P2P lending platforms is unaffected by the quality of service.Conclusion: The estimated model is consistent with the results shown in previous studies. The findings of the current research are useful for fine-tuning platform marketing plans and putting strategic goals into actions. For future research, we suggest including more variables to better understand the adoption intention of P2P lending platforms.Keywords: Adoption intention, Peer-to-peer lending, Structural equation modeling, Technology acceptance model","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88007436","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 : 2021-10-28DOI: 10.20473/jisebi.7.2.119-128
B. A. Ardhani, N. Chamidah, T. Saifudin
Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order to improve the quality of workforce, spurred controversy among members of the public. The discussion covered the amount of budget, the training materials and the operations brought out various reactions. Opinions could be largely divided into groups: the positive and the negative sentiments.Objective: This research aims to propose an automated sentiment analysis that focuses on KPP. The findings are expected to be useful in evaluating the services and facilities provided.Methods: In the sentiment analysis, Support Vector Machine (SVM) in text mining was used with Radial Basis Function (RBF) kernel. The data consisted of 500 tweets from July to October 2020, which were divided into two sets: 80% data for training and 20% data for testing with five-fold cross validation.Results: The results of descriptive analysis show that from the total 500 tweets, 60% were negative sentiments and 40% were positive sentiments. The classification in the testing data show that the average accuracy, sensitivity, specificity, negative sentiment prediction and positive sentiment prediction values were 85.20%; 91.68%; 75.75%; 85.03%; and 86.04%, respectively.Conclusion: The classification results show that SVM with RBF kernel performs well in the opinion classification. This method can be used to understand similar sentiment analysis in the future. In KPP case, the findings can inform the stakeholders to improve the programmes in the future. Keywords: Kartu Prakerja, Sentiment Analysis, Support Vector Machine, Text Mining, Radial Basis Function
{"title":"Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel","authors":"B. A. Ardhani, N. Chamidah, T. Saifudin","doi":"10.20473/jisebi.7.2.119-128","DOIUrl":"https://doi.org/10.20473/jisebi.7.2.119-128","url":null,"abstract":"Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order to improve the quality of workforce, spurred controversy among members of the public. The discussion covered the amount of budget, the training materials and the operations brought out various reactions. Opinions could be largely divided into groups: the positive and the negative sentiments.Objective: This research aims to propose an automated sentiment analysis that focuses on KPP. The findings are expected to be useful in evaluating the services and facilities provided.Methods: In the sentiment analysis, Support Vector Machine (SVM) in text mining was used with Radial Basis Function (RBF) kernel. The data consisted of 500 tweets from July to October 2020, which were divided into two sets: 80% data for training and 20% data for testing with five-fold cross validation.Results: The results of descriptive analysis show that from the total 500 tweets, 60% were negative sentiments and 40% were positive sentiments. The classification in the testing data show that the average accuracy, sensitivity, specificity, negative sentiment prediction and positive sentiment prediction values were 85.20%; 91.68%; 75.75%; 85.03%; and 86.04%, respectively.Conclusion: The classification results show that SVM with RBF kernel performs well in the opinion classification. This method can be used to understand similar sentiment analysis in the future. In KPP case, the findings can inform the stakeholders to improve the programmes in the future. Keywords: Kartu Prakerja, Sentiment Analysis, Support Vector Machine, Text Mining, Radial Basis Function ","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84524623","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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