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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 8 Documents
 1 
Search results for , issue "Vol. 7 No. 2 (2021): October" : 8 Documents clear
Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel Belindha Ayu Ardhani; Nur Chamidah; Toha Saifudin
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.119-128

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 
Conformance Checking of Dwelling Time Using a Token-based Method Bambang Jokonowo; Nenden Siti Fatonah; Emelia Akashah Patah Akhir
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.129-137

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
Examining the Factors Contributing to Fintech Peer-to-peer Lending Adoption Rudy Sunardi; Usep Suhud; Dedi Purwana; Hamidah Hamidah
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.91-101

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
Reinforcement Learning Approach for Efficient Inventory Policy in Multi-Echelon Supply Chain Under Various Assumptions and Constraints Ika Nurkasanah
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.138-148

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
Optimising Outpatient Pharmacy Staffing to Minimise Patients Queue Time using Discrete Event Simulation Putri Amelia; Artya Lathifah; Muhammad Dliya'ul Haq; Christoph Lorenz Reimann; Yudi Setiawan
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.102-111

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
Comparison of Backpropagation and Kohonen Self Organising Map (KSOM) Methods in Face Image Recognition Lady Silk Moonlight; Fiqqih Faizah; Yuyun Suprapto; Nyaris Pambudiyatno
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.149-161

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 
Scenario Model to Mitigate Traffic Congestion and Improve Commuting Time Efficiency Shabrina Luthfiani Khanza; Erma Suryani; Rully Agus Hendrawan
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.112-118

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 
Technology Adoption in Small-Medium Enterprises based on Technology Acceptance Model: A Critical Review Adisthy Shabrina Nurqamarani; Eddy Sogiarto; Nurlaeli Nurlaeli
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 2 (2021): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.2.162-172

Abstract

Background: Technology acceptance model (TAM) has been extensively used to analyse user acceptance of technologies adopted by enterprises at different levels. Moreover, the technology adoption has drawn attention among practitioners and academic communities alike, leading to the development of approaches to understand the concept. However, there is a degree of inconsistency found in previous studies on different types of TAM models used in explaining user acceptance of technologies among small-medium enterprises (SMEs).Objective: This critical literature review aims to synthesise the technology adoption scholarly studies using TAM. It is expected to aid the identification of the most relevant factors influencing SMEs in adopting technology. Additionally, analysing the variations of TAM developed in previous studies could provide suggested variables specific to the type of technology industry.Methods: An integrated approach was used, and this involves a review of articles on the adoption of technologies in SMEs from 2011 to 2021, retrieved from popular databases using a mixture of keywords such as technology acceptance model (TAM), technology adoption, and technology adoption in SMEs.Results: An overview of TAM studies on user acceptance of technology in this review covers a wide range of research areas from financial technology to human resource management-related technology. Perceived usefulness and perceived ease of use were discovered to be the most common factors in TAM from the 21 articles reviewed. Meanwhile, some other variables were observed such as context, type of technology and level of user experience.Conclusion: The review highlights key trends in previous studies on IT adoption in SMEs, which assist researchers and developers in understanding the most relevant factors and suitable TAM models in determining user acceptance in a particular field. Keywords: Technology Acceptance Model, Technology Adoption, Small-medium Enterprises, Critical Review

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