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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 9 Documents
 1 
Search results for , issue "Vol. 9 No. 1 (2023): April" : 9 Documents clear
A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification Angona Biswas; Md. Saiful Islam
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.1-15

Abstract

Background: Feature extraction process is noteworthy in order to categorize brain tumors. Handcrafted feature extraction process consists of profound limitations. Similarly, without appropriate classifier, the promising improved results can’t be obtained. Objective: This paper proposes a hybrid model for classifying brain tumors more accurately and rapidly is a preferable choice for aggravating tasks. The main objective of this research is to classify brain tumors through Deep Convolutional Neural Network (DCNN) and Support Vector Machine (SVM)-based hybrid model. Methods: The MRI images are firstly preprocessed to improve the feature extraction process through the following steps: resize, effective noise reduction, and contrast enhancement.  Noise reduction is done by anisotropic diffusion filter, and contrast enhancement is done by adaptive histogram equalization. Secondly, the implementation of augmentation enhances the data number and data variety. Thirdly, custom deep CNN is constructed for meaningful deep feature extraction. Finally, the superior machine learning classifier SVM is integrated for classification tasks. After that, this proposed hybrid model is compared with transfer learning models: AlexNet, GoogLeNet, and VGG16. Results: The proposed method uses the ‘Figshare’ dataset and obtains 96.0% accuracy, 98.0% specificity, and 95.71% sensitivity, higher than other transfer learning models. Also, the proposed model takes less time than others. Conclusion: The effectiveness of the proposed deep CNN-SVM model divulges by the performance, which manifests that it extracts features automatically without overfitting problems and improves the classification performance for hybrid structure, and is less time-consuming.   Keywords:  Adaptive histogram equalization, Anisotropic diffusion filter, Deep CNN, E-health, Machine learning, SVM, Transfer learning.
Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation Nur Alifiah; Dian Kurniasari; Amanto Amanto; Warsono Warsono
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.16-27

Abstract

Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022.   Keywords: Artificial Intelligence, Artificial Neural Network (ANN) K-Fold Cross Validation, COVID-19 Cases, Data Mining, Prediction.
Trends and Applications of Gamification in E-Commerce: A Systematic Literature Review Muhamad Adhytia Wana Putra Rahmadhan; Dana Indra Sensuse; Ryan Randy Suryono; Kautsarina Kautsarina
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.28-37

Abstract

Background: Gamification is a trend that has emerged with the growth of e-commerce. Given the wide range of human characteristics, determining which gamification elements perform well and what impact those gamification elements have can be challenging. Objective: This study aims to conduct a systematic literature review to broadly review the impact that can be caused by the application of gamification elements in e-commerce. This study also attempts to identify the current trends in using gamification elements. Methods: This study was carried out based on the Kitchenham approach and analyzes 25 research papers extracted from a total of 550 papers. The articles were gathered from ACM, Emerald, ScienceDirect, and Scopus and were published between 2016 and 2021. Results: This study found that the trend of research in the field of gamification in e-commerce continues to grow every year. Also, this study found that the most frequently used gamification elements are achievement-oriented (such as rewards, points, badges, and leaderboards). Meanwhile, immersion-related gamification elements (such as avatars, fantasy, etc.) are emerging as a new trend for new gamification elements to be incorporated in e-commerce. This study also found three major themes, namely consumer loyalty, consumer engagement, and user behavior, as a result of the application of gamification in e-commerce. Conclusion: This study helps to improve knowledge of various gamification elements, trends, and impacts on e-commerce. Future studies need to examine the challenges that may arise in the application of gamification elements to the three major themes found in this study and find potential solutions to overcome them.   Keywords: E-Commerce, Gamification, Gamification trends and applications, Kitchenham, Systematic literature review.  
Simulation of System Dynamics for Improving The Quality of Paddy Production in Supporting Food Security Mala Rosa Aprillya; Erma Suryani
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.38-46

Abstract

Background: The food security policy is an effort to ensure stable food availability and stable access of the community to food. As the population increases, this will affect the fulfillment of food needs in the future. Therefore, increase in rice production is needed to support food security. Objective: Conduct an analysis of the factors affecting the quality of rice production by using a dynamic system simulation that can be used as a basis for formulating policy strategies. Method: Simulation using System Dynamics (SD) is a method used to study and analyze complex systems by modeling non-linear behavior. Then several scenarios were carried out for the best decision-making using a computer. Result: The results of the scenario show that increasing the quality of paddy production in order to meet food needs in the future is doable by boosting the rendement of paddy as it will upgrade rice production which  will contribute greatly to rice production. Conclusion: From the simulation results, the  study can be used to increase the quality of rice production to maintain food security by improving the harvesting mechanism to increase yields. For further research, the use of Smart Agriculture can be considered to increase production of rice.   Keywords: Food security, Rice production, Rice production, System dynamics
Factors Affecting Adoption of Telemedicine for Virtual Healthcare Services in Indonesia Rima Alviani; Betty Purwandari; Imairi Eitiveni; Mardiana Purwaningsih
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.47-69

Abstract

Background: The utilization of virtual healthcare services, particularly telemedicine, has been accelerated by the COVID-19 pandemic. Although the pandemic is no longer the primary concern, telemedicine still holds potential for long-term adoption. However, implementing telemedicine in Indonesia as an online platform for remote healthcare delivery still faces issues, despite its potential. Further investigation is required to identify the factors that affect its adoption and develop strategies to surmount implementation challenges. Objective: This study aims to examine and enrich knowledge about the adoption of telemedicine in Indonesia. Methods: A cross-sectional survey was conducted through an online questionnaire to collect data. Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) was employed by integrating with several factors, such as eHealth Literacy, Privacy Concerns, and Trust. Gender and age were considered as moderating variables. Data samples were analyzed using Partial Least Square – Structural Equation Modeling (PLS–SEM). Results: The findings suggest that performance expectancy, effort expectancy, social influence, eHealth literacy, and trust have a significant impact on adults’ behavioral intention to use telemedicine. However, facilitating condition, price value, and privacy concern do not show any significant effects on adults’ Behavioral Intention to Use Telemedicine.   Conclusion: This study highlights the importance of understanding adoption factors to develop effective strategies. Results show performance expectancy, effort expectancy, social influence, eHealth literacy, and trust are significant factors, while facilitating condition, price value, and privacy concern are not. The UTAUT2 model is a good predictive tool for healthcare adoption. To increase usage intention, several aspects must be considered in the implementation of telemedicine.   Keywords: Adoption, Behavioral Intention to Use, Telemedicine, UTAUT2, Virtual Healthcare.
The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model Siti Nur Hasanah; Yeni Herdiyeni; Medria Kusuma Dewi Hardhienata
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.70-83

Abstract

Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks; each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869; 0.1513; and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area.   Keywords: COVID-19, Facebook Prophet , Hierarchical clustering, Socioeconomic and demographic
Aspect-based Sentiment and Correlation-based Emotion Detection on Tweets for Understanding Public Opinion of Covid-19 Salsabila Salsabila; Salsabila Mazya Permataning Tyas; Yasinta Romadhona; Diana Purwitasari
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.84-94

Abstract

Background: During the Covid-19 period, the government made policies dealing with it. Policies issued by the government invited public opinion as a form of public reaction to these policies. The easiest way to find out the public’s response is through Twitter’s social media. However, Twitter data have limitations. There is a mix between facts and personal opinions. It is necessary to distinguish between these. Opinions expressed by the public can be both positive and negative, so correlation is needed to link opinions and their emotions. Objective: This study discusses sentiment and emotion detection to understand public opinion accurately. Sentiment and emotion are analyzed using Pearson correlation to determine the correlation. Methods: The datasets were about public opinion of Covid-19 retrieved from Twitter. The data were annotated into sentiment and emotion using Pearson correlation. After the annotation process, the data were preprocessed. Afterward, single model classification was carried out using machine learning methods (Support Vector Machine, Random Forest, Naïve Bayes) and deep learning method (Bidirectional Encoder Representation from Transformers). The classification process was focused on accuracy and F1-score evaluation. Results: There were three scenarios for determining sentiment and emotion, namely the factor of aspect-based and correlation-based, without those factors, and aspect-based sentiment only. The scenario using the two aforementioned factors obtained an accuracy value of 97%, while an accuracy of 96% was acquired without them. Conclusion: The use of aspect and correlation with Pearson correlation has helped better understand public opinion regarding sentiment and emotion more accurately.   Keywords: Aspect-based sentiment, Deep learning, Emotion detection, Machine learning, Pearson correlation, Public opinion.
Security Aspect in Software Testing Perspective: A Systematic Literature Review Halim Wildan Awalurahman; Ibrahim Hafizhan Witsqa; Indra Kharisma Raharjana; Ahmad Hoirul Basori
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.95-107

Abstract

Background: Software testing and software security have become one of the most important parts of an application. Many studies have explored each of these topics but there is a gap wherein the relation of software security and software testing in general has not been explored. Objective: This study aims to conduct a systematic literature review to capture the current state-of-the-art in software testing related to security. Methods: The search strategy obtains relevant papers from IEEE Xplore and ScienceDirect. The results of the search are filtered by applying inclusion and exclusion criteria. Results: The search results identified 50 papers. After applying the inclusion/exclusion criteria, we identified 15 primary studies that discuss software security and software testing. We found approaches, aspects, references, and domains that are used in software security and software testing. Conclusion: We found certain approach, aspect, references, and domain are used more often in software security testing   Keywords: Software security, Software testing, Security testing approach, Security threats, Systematic literature review
Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation Vivine Nurcahyawati; Zuriani Mustaffa
Journal of Information Systems Engineering and Business Intelligence Vol. 9 No. 1 (2023): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.9.1.108-118

Abstract

Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world. Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation. Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed. Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance.   Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon,

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