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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 10 Documents
 1 
Search results for , issue "Vol. 8 No. 1 (2022): April" : 10 Documents clear
Early Stopping Effectiveness for YOLOv4 Afif Rana Muhammad; Hamzah Prasetio Utomo; Priyanto Hidayatullah; Nurjannah Syakrani
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.11-20

Abstract

Background: YOLOv4 is one of the fastest algorithms for object detection. Its methods, i.e., bag of freebies and bag of specials, can prevent overfitting, but this can be combined with early stopping as it could also prevent overfitting. Objective: This study aims to identify the effectiveness of early stopping in preventing overfitting in the YOLOv4 training process. Methods: Four datasets were grouped based on the training data size and object class, These datasets were tested in the experiment, which was carried out using three patience hyperparameters: 2, 3, and 5. To assess the consistency, it was repeated eight times. Results: The experimental results show that early stopping is triggered more frequently in training with data below 2,000 images. Of the three patience hyperparameters used, patience 2 and 3 were able to halve the training duration without sacrificing accuracy. Patience 5 rarely triggers early stopping. There is no pattern of correlation between the number of object classes and early stopping. Conclusion: Early stopping is useful only in training with data below 2,000 images. Patience with a value of 2 or 3 are recommended. Keywords: Early Stopping, Overfitting, Training data, YOLOv4
Mask R-CNN and GrabCut Algorithm for an Image-based Calorie Estimation System Tiara Lestari Subaran; Transmissia Semiawan; Nurjannah Syakrani
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.1-10

Abstract

Background: A calorie estimation system based on food images uses computer vision technology to recognize and count calories. There are two key processes required in the system: detection and segmentation. Many algorithms can undertake both processes, each algorithm with different levels of accuracy. Objective: This study aims to improve the accuracy of calorie calculation and segmentation processes using a combination of Mask R-CNN and GrabCut algorithms. Methods: The segmentation mask generated from Mask R-CNN and GrabCut were combined to create a new mask, then used to calculate the calorie. By considering the image augmentation technique, the accuracy of the calorie calculation and segmentation processes were observed to evaluate the method’s performance. Results: The proposed method could achieve a satisfying result, with an average calculation error value of less than 10% and an F1 score above 90% in all scenarios. Conclusion: Compared to earlier studies, the combination of Mask R-CNN and GrabCut could obtain a more satisfying result in calculating food calories with different shapes. Keywords: Augmentation, Calorie Calculation, Detection
Predicting Velocity and Direction of Ocean Surface Currents using Elman Recurrent Neural Network Method Eka Alifia Kusnanti; Dian C. Rini Novitasari; Fajar Setiawan; Aris Fanani; Mohammad Hafiyusholeh; Ghaluh Indah Permata Sari
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.21-30

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. 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
Deep Learning Approaches for Multi-Label Incidents Classification from Twitter Textual Information Sherly Rosa Anggraeni; Narandha Arya Ranggianto; Imam Ghozali; Chastine Fatichah; Diana Purwitasari
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.31-41

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. 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
Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data Abid Famasya Abdillah; Cornelius Bagus Purnama Putra; Apriantoni Apriantoni; Safitri Juanita; Diana Purwitasari
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.42-50

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. 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
License Plate Character Recognition using Convolutional Neural Network Firman Maulana Adhari; Taufik Fuadi Abidin; Ridha Ferdhiana
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.51-60

Abstract

Background: In the last decade, the number of registered vehicles has grown exponentially. With more vehicles on the road, traffic jams, accidents, and violations also increase. A license plate plays a key role in solving such problems because it stores a vehicle’s historical information. Therefore, automated license-plate character recognition is needed. Objective: This study proposes a recognition system that uses convolutional neural network (CNN) architectures to recognize characters from a license plate’s images. We called it a modified LeNet-5 architecture. Methods: We used four different CNN architectures to recognize license plate characters: AlexNet, LeNet-5, modified LeNet-5, and ResNet-50 architectures. We evaluated the performance based on their accuracy and computation time. We compared the deep learning methods with the Freeman chain code (FCC) extraction with support vector machine (SVM). We also evaluated the Otsu and the threshold binarization performances when applied in the FCC extraction method. Results: The ResNet-50 and modified LeNet-5 produces the best accuracy during the training at 0.97. The precision and recall scores of the ResNet-50 are both 0.97, while the modified LeNet-5’s values are 0.98 and 0.96, respectively. The modified LeNet-5 shows a slightly higher precision score but a lower recall score. The modified LeNet-5 shows a slightly lower accuracy during the testing than ResNet-50. Meanwhile, the Otsu binarization’s FCC extraction is better than the threshold binarization. Overall, the FCC extraction technique performs less effectively than CNN. The modified LeNet-5 computes the fastest at 7 mins and 57 secs, while ResNet-50 needs 42 mins and 11 secs. Conclusion: We discovered that CNN is better than the FCC extraction method with SVM. Both ResNet-50 and the modified LeNet-5 perform best during the training, with F measure scoring 0.97. However, ResNet-50 outperforms the modified LeNet-5 during the testing, with F-measure at 0.97 and 1.00, respectively. In addition, the FCC extraction using the Otsu binarization is better than the threshold binarization. Otsu binarization reached 0.91, higher than the static threshold binarization at 127. In addition, Otsu binarization produces a dynamic threshold value depending on the images’ light intensity. Keywords: Convolutional Neural Network, Freeman Chain Code, License Plate Character Recognition, Support Vector Machine
Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study Kuncahyo Setyo Nugroho; Fitra A. Bachtiar; Wayan Firdaus Mahmudy
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.61-70

Abstract

Background: Term-weighting plays a key role in detecting emotion in texts. Studies in term-weighting schemes aim to improve short text classification by distinguishing terms accurately. Objective: This study aims to formulate the best term-weighting schemes and discover the relationship between n-gram combinations and different classification algorithms in detecting emotion in Twitter texts. Methods: The data used was the Indonesian Twitter Emotion Dataset, with features generated through different n-gram combinations. Two approaches assign weights to the features. Tests were carried out using ten-fold cross-validation on three classification algorithms. The performance of the model was measured using accuracy and F1 score. Results: The term-weighting schemes with the highest performance are Term Frequency-Inverse Category Frequency (TF-ICF) and Term Frequency-Relevance Frequency (TF-RF). The scheme with a supervised approach performed better than the unsupervised one. However, we did not find a consistent advantage as some of the experiments found that Term Frequency-Inverse Document Frequency (TF-IDF) also performed exceptionally well. The traditional TF-IDF method remains worth considering as a term-weighting scheme. Conclusion: This study provides recommendations for emotion detection in texts. Future studies can benefit from dealing with imbalances in the dataset to provide better performance. Keywords: Emotion Detection, Feature Engineering, Term-Weighting, Text Mining
The Impact of Social Media Engagement on Market Share: A System Dynamics Model Erma Suryani; Rully Agus Hendrawan; Benyamin Limanto; Fatharani Wafda; Inayah Auliyah
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.71-79

Abstract

Background: Some studies have shown that Return on Total Assets is a strategy to increase market share. Other studies have also shown that social media like WeChat can increase market share. However, no studies have considered Instagram engagement in increasing market share. Objective: This study aims to identify variable linkage that increases market share through a dynamic system approach in small and medium-sized enterprises (SMEs). Methods: Using a System Dynamics approach, this study presents a model simulation with a proposed increase in market share by considering Instagram features. This approach creates a Causal Loop Diagram converted into a simulated Stock Flow Diagram. The value generated from the simulation is validated with the mean comparison and % error variance formulas. Results: Instagram engagement increases market share from 0.009 to 0.018. Such engagement can be increased by posting regularly and doing more activities, such as increasing post frequency, holding contests, and maximizing all features. Conclusion: This study has successfully modeled information technology, i.e., a promotion module on social media. However, this work has not yet demonstrated how the features can gain more market share, so future research is needed.  Keywords: Causal Loop Diagram, Engagement, Market Share, Stock Flow Diagram, System Dynamics
Academic Recommender System Using Engagement Advising and Backward Chaining Model Cut Fiarni; Arif Samuel Gunawan; Fredrick Victor
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.91-99

Abstract

Background: The goal of academic supervision is to help students plan their academic journey and graduate on time. An intelligent support system is needed to spot potentially struggling students and identify the issues as early as possible. Objective: This study aims to develop an academic advising recommender system that improves decision-making through system utility, ease of use, and clearly visualized information. The study also aims to find the best advising relationship model to be implemented in the proposed system. Methods: The system was modeled by following the hybrid approach to obtain information and suggest recommended actions. The recommendation was modeled by backward chaining to prevent students from dropping out. Results: To validate the recommendations given by the proposed system, we used conformity level, and the result was 94.45%. To evaluate the utility of the system, we used the backbox method, resulting in satisfactory responses. Lastly, to evaluate user acceptance, we used the technology acceptance model (TAM), resulting in 85% ease of use and 91.2% perceived usefulness for the four main features, study planning, graduate timeline simulation, progress report, and visualization of academic KPIs. Conclusion: We propose an academic recommender system with KPIs visualization and academic planning information. Keywords: Academic advising model, recommender system, backward chaining, goal-driven, technology acceptance model, certainty factor
The Drivers of a Digital Signature System Adoption: Evidence from Finance and Information System Departments Ahmad Arif Santosa; Firdaus Alamsjah
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.80-90

Abstract

Background: With the massive e-commerce transactions and document transfers, reliable system protection is needed. A digital signature is a tool that consists of encryption and decryption algorithms in a secret key to prevent data theft and online fraud. Objective: This research proposes an integrated technology-organization-environment (TOE) and the unified theory of acceptance and use of technology (UTAUT) to determine the factors affecting consumer intention to adopt the digital signature system. This research uses finance and information system departments’ perspectives in various industries. Methods: The analytical method is the Structural Equation Modeling (SEM) approach using the Smart Partial Least Square statistical version 3.0 software to examine the hypothesized connections between latent variables. Results: The results show that support from top management, size of the enterprise, and social influence have significant and positive effects on digital signature adoption. Meanwhile, user involvement and perceived simplicity have a negative effect on the adoption of a digital signature system in finance and information system departments. Conclusion: The current research suggests that executive levels in the finance and information system departments encourage the adoption of digital signature tools in doing daily tasks to increase efficiency. Keywords: Digital signature, consumer intention, finance and information system, structural equation modeling, TOE and UTAUT

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