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INDONESIA
Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 246 Documents
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Dynamic Steganography Least Significant Bit with Stretch on Pixels Neighborhood Muhammad Khoiruddin Harahap; Nurul Khairina
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 2 (2020): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.2.151-158

Abstract

Background: The confidentiality of a message may at times be compromised. Steganography can hide such a message in certain media. Steganographic media such as digital images have many pixels that can accommodate secret messages. However, the length of secret messages may not match with the number of image pixels so the messages cannot be inserted into the digital images.Objective: This research aims to see the dynamics between an image size and a secret message’s length in order to prevent out of range messages entered in an image.Methods: This research will combine the Least Significant Bit (LSB) method and the Stretch technique in hiding secret messages. The LSB method uses the 8th bit to hide secret messages. The Stretch technique dynamically enlarges the image size according to the length of the secret messages. Images will be enlarged horizontally on the rightmost image pixel block until n blocks of image pixels.Results: This study compares an original image size and a stego image size and examines a secret message’s length that can be accommodated by the stego image, as well as the Mean Square Error and Structure Similarity Index. The test is done by comparing the size change of the original image with the stego image from the Stretch results, where each original image tested always changes dynamically according to the increasing number of secret message characters. From the MSE and SSIM test results, the success was only with the first image, while the second image to the fourth image remained erroneous because they also did not have the same resolution.Conclusion:The combination of LSB steganography and the Stretch technique can enlarge an image automatically according to the number of secret messages to be inserted. For further research development, image stretch must not only be done horizontally but also vertically. 
Investment Modelling Using Value at Risk Bayesian Mixture Modelling Approach and Backtesting to Assess Stock Risk Brina Miftahurrohmah; Catur Wulandari; Yogantara Setya Dharmawan
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.11-21

Abstract

Background: Stock investment has been gaining momentum in the past years due to the development of technology. During the pandemic lockdown, people have invested more. One the one hand, stock investment has high potential profitability, but on the other, it is equally risky. Therefore, a value at risk (VaR) analysis is needed. One approach to calculate VaR is by using the Bayesian mixture model, which has been proven to be able to overcome heavy-tailed cases. Then, the VaR’s accuracy needs to be tested, and one of the ways is by using backtesting, such as the Kupiec test.Objective: This study aims to determine the VaR model of PT NFC Indonesia Tbk (NFCX) return data using Bayesian mixture modelling and backtesting. On a practical level, this study can provide information about the potential risks of investing that is grounded in empirical evidence.Methods: The data used was NFCX data retrieved from Yahoo Finance, which was then modelled with a mixture model based on the normal and Laplace distributions. After that, the VaR accuracy was calculated and then tested by using backtesting.Results: The test results showed that the VaR with the mixture Laplace autoregressive (MLAR) approach (2;[2],[4]) was accurate at 5% and 1% quantiles while mixture normal autoregressive MNAR (2;[2],[2,4]) was only accurate at 5% quantiles.Conclusion: The better performing NFCX VaR model for this study based on backtesting using Kupiec test is MLAR(2;[2],[4]).
Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel Pulung Hendro Prastyo; Amin Siddiq Sumi; Ade Widyatama Dian; Adhistya Erna Permanasari
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 2 (2020): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.2.112-122

Abstract

Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately. 
Predicting Students Graduate on Time Using C4.5 Algorithm Herman Yuliansyah; Rahmasari Adi Putri Imaniati; Anggit Wirasto; Merlinda Wibowo
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.67-73

Abstract

Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students’ on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students’ graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.
Information Privacy Concerns Among Instagram Users: The Case of Indonesian College Students Eko Wahyu Tyas Darmaningrat; Hanim Maria Astuti; Fadhila Alfi
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 2 (2020): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.2.159-168

Abstract

Background: Teenagers in Indonesia have an open nature and satisfy their desire to exist by uploading photos or videos and writing posts on Instagram. The habit of uploading photos, videos, or writings containing their personal information can be dangerous and potentially cause user privacy problems. Several criminal cases caused by information misuse have occurred in Indonesia.Objective: This paper investigates information privacy concerns among Instagram users in Indonesia, more specifically amongst college students, the largest user group of Instagram in Indonesia.Methods: This study referred to the Internet Users' Information Privacy Concerns (IUIPC) method by collecting data through the distribution of online questionnaires and analyzed the data by using Structural Equation Modelling (SEM).Results: The research finding showed that even though students are mindful of the potential danger of information misuse in Instagram, it does not affect their intention to use Instagram. Other factors that influence Indonesian college students' trust are Instagram's reputation, the number of users who use Instagram, the ease of using Instagram, the skills and knowledge of Indonesian students about Instagram, and the privacy settings that Instagram has.Conclusion: The awareness and concern of Indonesian college students for information privacy will significantly influence the increased risk awareness of information privacy. However, the increase in risk awareness does not directly affect Indonesian college students' behavior to post their private information on Instagram.
Fatigue Detection on Face Image Using FaceNet Algorithm and K-Nearest Neighbor Classifier Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Danur Wijayanto
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.22-30

Abstract

Background: The COVID-19 pandemic has made people spend more time on online meetings more than ever. The prolonged time looking at the monitor may cause fatigue, which can subsequently impact the mental and physical health. A fatigue detection system is needed to monitor the Internet users well-being. Previous research related to the fatigue detection system used a fuzzy system, but the accuracy was below 85%. In this research, machine learning is used to improve accuracy.Objective: This research examines the combination of the FaceNet algorithm with either k-nearest neighbor (K-NN) or multiclass support vector machine (SVM) to improve the accuracy.Methods: In this study, we used the UTA-RLDD dataset. The features used for fatigue detection come from the face, so the dataset is segmented using the Haar Cascades method, which is then resized. The feature extraction process uses FaceNet's pre-trained algorithm. The extracted features are classified into three classes—focused, unfocused, and fatigue—using the K-NN or multiclass SVM method.Results: The combination between the FaceNet algorithm and K-NN, with a value of  resulted in a better accuracy than the FaceNet algorithm with multiclass SVM with the polynomial kernel (at 94.68% and 89.87% respectively). The processing speed of both combinations of methods has allowed for real-time data processing.Conclusion: This research provides an overview of methods for early fatigue detection while working at the computer so that we can limit staring at the computer screen too long and switch places to maintain the health of our eyes. 
Booking Prediction Models for Peer-to-peer Accommodation Listings using Logistics Regression, Decision Tree, K-Nearest Neighbor, and Random Forest Classifiers Mochammad Agus Afrianto; Meditya Wasesa
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 2 (2020): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.2.123-132

Abstract

Background: Literature in the peer-to-peer accommodation has put a substantial focus on accommodation listings' price determinants. Developing prediction models related to the demand for accommodation listings is vital in revenue management because accurate price and demand forecasts will help determine the best revenue management responses.Objective: This study aims to develop prediction models to determine the booking likelihood of accommodation listings.Methods: Using an Airbnb dataset, we developed four machine learning models, namely Logistics Regression, Decision Tree, K-Nearest Neighbor (KNN), and Random Forest Classifiers. We assessed the models using the AUC-ROC score and the model development time by using the ten-fold three-way split and the ten-fold cross-validation procedures.Results: In terms of average AUC-ROC score, the Random Forest Classifiers outperformed other evaluated models. In three-ways split procedure, it had a 15.03% higher AUC-ROC score than Decision Tree, 2.93 % higher than KNN, and 2.38% higher than Logistics Regression. In the cross-validation procedure, it has a 26,99% higher AUC-ROC score than Decision Tree, 4.41 % higher than KNN, and 3.31% higher than Logistics Regression.  It should be noted that the Decision Tree model has the lowest AUC-ROC score, but it has the smallest model development time.Conclusion: The performance of random forest models in predicting booking likelihood of accommodation listings is the most superior. The model can be used by peer-to-peer accommodation owners to improve their revenue management responses. 
Causal Modeling Between Factors on Quality of Life in Cancer Patients Using S3C-Latent Algorithm Yohani Setiya Rafika Nur; Ridho Rahmadi; Christantie Effendy
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.74-83

Abstract

Background: Cancer patients can experience both physical and non-physical problems such as psychosocial, spiritual, and emotional problems, which impact the quality of life. Previous studies on quality of life mostly have employed multivariate analyses. To our knowledge, no studies have focused yet on the underlying causal relationship between factors representing the quality of life of cancer patients, which is very important when attempting to improve the quality of life.  Objective: The study aims to model the causal relationships between the factors that represent cancer and quality of life.Methods: This study uses the S3C-Latent method to estimate the causal model relationships between the factors. The S3C-Latent method combines Structural Equation Model (SEM), a multi objective optimization method, and the stability selection approach, to estimate a stable and parsimonious causal model.Results: There are nine causal relations that have been found, i.e., from physical to global health with a reliability score of 0.73, to performance status with a reliability score of 1, from emotional to global health with a reliability score of 0.71, to performance status with a reliability score of 0.82, from nausea, loss of appetite, dyspnea, insomnia, loss of appetite and from constipation to performance status with reliability scores of 0.76; 1; 0.61; 0.76; 0.72; 0.70, respectively. Moreover, this study found that 15 associations (strong relation where the causal direction cannot be determined from the data alone) between factors with reliability scores range from 0.65 to 1.Conclusion: The estimated model is consistent with the results shown in previous studies. The model is expected to provide evidence-based recommendation for health care providers in designing strategies to increase cancer patients’ life quality. For future research, we suggest studies to include more variables in the model to capture a broader view to the problem.
The Impact of Mobility Patterns on the Spread of the COVID-19 in Indonesia Syafira Fitri Auliya; Nurcahyani Wulandari
Journal of Information Systems Engineering and Business Intelligence Vol. 7 No. 1 (2021): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.7.1.31-41

Abstract

Background: The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly across the world and infected millions of people, many of whom died. As part of the response plans, many countries have been attempting to restrict people’s mobility by launching social distancing protocol, including in Indonesia. It is then necessary to identify the campaign’s impact and analyze the influence of mobility patterns on the pandemic’s transmission rate.Objective: Using mobility data from Google and Apple, this research discovers that COVID-19 daily new cases in Indonesia are mostly related to the mobility trends in the previous eight days.Methods: We generate ten-day predictions of COVID-19 daily new cases and Indonesians’ mobility by using Long-Short Term Memory (LSTM) algorithm to provide insight for future implementation of social distancing policies.Results: We found that all eight-mobility categories result in the highest accumulation correlation values between COVID-19 daily new cases and the mobility eight days before. We forecast of the pandemic daily new cases in Indonesia, DKI Jakarta and worldwide (with error on MAPE 6.2% - 9.4%) as well as the mobility trends in Indonesia and DKI Jakarta (with error on MAPE 6.4 - 287.3%).Conclusion: We discover that the driver behind the rapid transmission in Indonesia is the number of visits to retail and recreation, groceries and pharmacies, and parks. In contrast, the mobility to the workplaces negatively correlates with the pandemic spread rate.
Smart Dissemination by Using Natural Language Processing Technology Tora Fahrudin; Kastaman Kastaman; Sherin Nadya Meideni; Padma Edhitya Chairunnisafa Priyono; Muhammad Galang Fathirkina; Samira Samira
Journal of Information Systems Engineering and Business Intelligence Vol. 6 No. 2 (2020): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.6.2.133-142

Abstract

Background: Recently, WhatsApp has become the world's most popular text and voice messaging application with 1.5 billion users. A lot of WhatsApp Application Programming Interface (API) has also been established to be connected to other applications. On the other hand, the development of natural language processing (NLP) for WhatsApp messages has snowballed. There are extensive studies on the dissemination information using WhatsApp but the study on NLP involving data from WhatsApp is lacking.Objective: This study aims to implement NLP in smart dissemination applications by using WhatsApp API.Methods: We build a framework that embeds an intelligent system based on the NLP in WhatsApp API to disseminate a dynamic message. Some of the sentences are used to evaluate the accuracy of this application.Results: Smart dissemination consists of dynamic filter and dynamic content. Dynamic filter was conducted by using the POS tagger and clause statement. Meanwhile, dynamic content was built by using the replace MySQL function. There are twofold limitation: the application could not transform a message that matches rule <3> with conjunction “dan”; has the same attribute before and after <CC> tag; and the maximum of the logical operator is one type for coordinating conjunction (AND/OR) in one sentence.Conclusion: Our framework can be used for dynamic dissemination of messages using dynamic message content and dynamic message recipient with an accuracy of 95% from twenty sample messages.

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