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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 13 Documents
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Search results for , issue "Vol. 10 No. 1 (2024): February" : 13 Documents clear
Medical Image Fusion for Brain Tumor Diagnosis Using Effective Discrete Wavelet Transform Methods Ramaraj, Vijayan; Venkatachalaappaswamy, Mareeswari; Sankar , Manoj Kumar
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.70-80

Abstract

Background: The field of clinical or medical imaging is beginning to experience significant advancements in recent years. Various medical imaging methods such as computed tomography (CT), X-radiation (X-ray), and magnetic resonance imaging (MRI) produce images with distinct resolution differences, goals, and noise levels, making it challenging for medical experts to diagnose diseases. Objective: The limitations of a single medical image modality have increased the necessity for medical image fusion. The proposed solution is to create a fusion method of merging two types of medical images, such as MRI and CT. Therefore, this study aimed to develop a software solution that swiftly identifies the precise region of a brain tumor, speeding up the diagnosis and treatment planning. Methods: The proposed methodology combined clinical images by using discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). This strategy depended on a multi-goal decay of the image information using DWT, and high-frequency sub-bands of the disintegrated images were combined using a weighted averaging method. Meanwhile, the low-frequency sub-bands were straight-forwardly replicated in the resulting image. The combined high-quality image was recreated using the IDWT. This method can handle images with various modalities and resolutions without the need for previous data. Results: The results showed that the outcomes of the proposed method were assessed by different metrics such as accuracy, recall, F1-score, and visual quality. The method showed a high accuracy of 98% over the familiar neural network techniques. Conclusion: The proposed method was found to be computationally effective and produced high-quality medical images to assist professionals. Furthermore, the method can be stretched out to other image modalities and exercised by hybrid techniques of wavelet transform and neural networks and used for different clinical image analysis tasks.   Keywords: CT and MRI, Image fusion, brain tumor, wavelet transform methods, medical images, machine learning, CNN  
Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases Hayat, Cynthia; Soenandi, Iwan Aang
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.1-12

Abstract

Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin   Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization
The Performance Comparison of DBSCAN and K-Means Clustering for MSMEs Grouping based on Asset Value and Turnover Sutramiani, Ni Putu; Arthana, I Made Teguh; Lampung, Pramayota Fane'a; Aurelia, Shana; Fauzi, Muhammad; Darma, I Wayan Agus Surya
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.13-24

Abstract

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country's economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability.   Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover
The Role of Brand Image and Trust in the Adoption of FinTech Digital Payment for Online Transportation Winanti, Winanti; Fernando, Erick
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.126-138

Abstract

Background: The widespread use of financial technology (FinTech) is a popular aspect across various fields, particularly in online transportation. However, the usage has led to an increase in illegal FinTech, causing significant problems for public. Issues related to account security, such as hacks leading to the loss of user balances and misuse of data, contribute to the erosion of brand image and public trust. Despite the growing prominence of FinTech, explorations on the application in the context of online transportation remain limited. Previous studies have not discussed the impact of brand image on perceived usefulness and ease of use. Therefore, this current study explores the importance of combining brand image and trust factors to increase user intention. This process is achieved by investigating brand image and trust as crucial factors influencing increased perceived ease and benefits during the integration of FinTech in online transportation services. Objective: This study aimed to measure the impact of brand image and trust factors on the adoption of FinTech in online transportation. Methods: The investigation was carried out with a quantitative analysis approach using Partial Least Squares–Structural Equation Modeling (PLS–SEM). Furthermore, it focused on understanding FinTech services in online transportation, incorporating factors such as trust, brand image, perceived ease of use, perceived usefulness, and user intention. Data were collected by using a purposive sampling method through online questionnaire distribution. PLS-SEM was adapted for analyzing variable relationships, hypotheses, and models. Results: The results showed that factors including trust, perceived ease of use, and perceived usefulness significantly influenced the willingness to use FinTech in online transportation services. However, it was observed that brand image factors did not impact user intentions. Conclusion: This study showed a critical aspect in understanding the value of FinTech services by explaining the importance of establishing trust and building a good brand image as precursors. These factors indirectly contributed to increased perceived benefits and ease of use. Therefore, the insights offered valuable input for companies aiming to develop trusted FinTech platforms with a positive product image.   Keywords: Brand Image, Trust, FinTech, Online Transportation
A Practical Approach to Enhance Data Quality Management in Government: Case Study of Indonesian Customs and Excise Office Nugraha, Tito Febrian; Wibowo, Wahyu Setiawan; Genia, Venera; Fadhil, Ahmad; Ruldeviyani, Yova
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.51-69

Abstract

Background: The exponential data growth emphasises the importance of efficient information flow in organisations, especially in the financial sector. Data quality significantly influences decision-making, necessitating reliable Data Quality Management (DQM) frameworks. Previous studies propose DQM to maintain data quality through regulation, technology, measurement, evaluation, and improvement. Researchers highlight high-quality data benefits in private organisations but note the lack of improvement in data utilisation in public organisations. In Indonesia, data accuracy and quality are crucial for financial policies, with frequent reports of data inaccuracies in the Directorate General of Customs and Excise (DJBC), demanding standardised DQM practices. However, However, prior studies have yet to provide comprehensive and practical solutions to improve DQM practices. This study therefore aims to measure the DQM maturity, provide recommendations based on best practices, and formulate a practical strategy for improvements along with indicators tailored to the organisation, a topic that previous research has not explored. Methods: This study falls under a mixed method approach (a quantitative study followed by a qualitative study) and employs a three-stage methodology. The authors conduct maturity assessment using Loshin model through an assisted enumeration from 5 key stakeholders followed by recommendations based on the Data Management Body of Knowledge (DMBOK) and strategy formulation from internal documents and interview. Results: The data analysis yielded a DQM maturity score of 3.10, indicating a "defined to managed" level of maturity. Among eight components, only one receives a Managed level, two components are in the Defined level and the rest belongs to a Repeatable level. This study also proposes three strategies to bolster DQM by targeting 49 weak points, which will be progressively and sequentially implemented over a three-year period, using twelve possible solutions. Conclusion: The study highlights the importance of efficient data flow, particularly in the financial sector, and suggests DQM for maintaining data quality. DJBC's import DQM level is assessed using Loshin's measurements, revealing areas for improvement through key DMBOK activities. Recommendations include data governance, strategic planning, and sequential DQM implementation. The study concludes by formulating a practical approach to be applied in a three-year span with ten indicators to measure success.   Keywords: Data Quality Management, Data Quality Maturity Model, Data Quality Strategy, Loshin, DMBOK
Patients' Acceptance of Telemedicine Technology: The Influence of User Behavior and Socio-Cultural Dimensions Tri Aji, Purno; Ramadani , Luthfi
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.81-93

Abstract

Background: Over the years, the role of startups has experienced a significant increase in healthcare delivery, particularly in telemedicine. However, there are still some inherent challenges, including cultural factors, lack of digital literacy, and uneven internet network infrastructure that must be considered during implementation. Previous reports also showed that there was a knowledge gap regarding the factors influencing acceptance of telemedicine. Objective: This study aimed to introduce and investigate an adjusted model based on Technology Acceptance Model (TAM) to assess the influence of user dimensions, technological aspects, and socio-cultural elements on the intention to adopt telemedicine services. Methods: The hypothesized relationships between latent variables were examined through Structural Equation Modeling (SEM). In addition, data analysis was carried out using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results: Self-efficacy (β=-0.272, P=0.013), perceived usefulness (β=0.355, P=0.000), facilitating conditions (β=0.425, P=0.000), and cultural factors (β=0.421, P=0.001) were found to exert a significant influence on the intention to adopt telemedicine services. Meanwhile, trust, the variables of perceived ease of use, and social influence had no significant influences. Conclusion: This study emphasized the significance of comprehending the factors influencing the adoption of telemedicine services. In addition, the results showed that the extended TAM was applicable in assessing acceptance of telemedicine services.   Keywords: acceptance, telemedicine, TAM, SEM, intention to use
Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction Jumanto, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.38-50

Abstract

Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics.  Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability.   Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech
Sentiment Analysis on a Large Indonesian Product Review Dataset Romadhony, Ade; Al Faraby, Said; Rismala, Rita; Wisesty, Untari Novia; Arifianto, Anditya
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.167-178

Abstract

Background: The publicly available large dataset plays an important role in the development of the natural language processing/computational linguistic research field. However, up to now, there are only a few large Indonesian language datasets accessible for research purposes, including sentiment analysis datasets, where sentiment analysis is considered the most popular task. Objective: The objective of this work is to present sentiment analysis on a large Indonesian product review dataset, employing various features and methods. Two tasks have been implemented: classifying reviews into three classes (positive, negative, neutral), and predicting ratings. Methods: Sentiment analysis was conducted on the FDReview dataset, comprising over 700,000 reviews. The analysis treated sentiment as a classification problem, employing the following methods: Multinomial Naí¯ve Bayes (MNB), Support Vector Machine (SVM), LSTM, and BiLSTM. Result: The experimental results indicate that in the comparison of performance using conventional methods, MNB outperformed SVM in rating prediction, whereas SVM exhibited better performance in the review classification task. Additionally, the results demonstrate that the BiLSTM method outperformed all other methods in both tasks. Furthermore, this study includes experiments conducted on balanced and unbalanced small-sized sample datasets. Conclusion: Analysis of the experimental results revealed that the deep learning-based method performed better only in the large dataset setting. Results from the small balanced dataset indicate that conventional machine learning methods exhibit competitive performance compared to deep learning approaches.   Keywords: Indonesian review dataset, Large dataset, Rating prediction, Sentiment analysis
Factors Influencing the Use of Mobile Social Commerce Application with UTAUT2 Extended Model Hakim, Muhammad Malik; Sonia, Putrisya Novatiara; Aryotejo, Guruh; Adhy, Satriyo; Ashari, Yeva Fadhilah; Alfarisi, Salman
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.25-37

Abstract

Background: Mobile social commerce is a collection of e-commerce activities accessed via mobile devices and supported by users actively engaging in commercial activities on social media. As a country with a substantial number of social media users, Indonesia has sufficient opportunities to implement mobile social commerce as application for online shopping. Objective: This study aimed to identify the factors influencing the use of mobile social commerce for online shopping, using Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). In this context, some variables were excluded, namely user behavior, price value, and moderating variables (age, gender, and experience). Additional variables considered included price saving orientation (PSO), privacy concerns (PC), social commerce construct (SCC), social support (SS), and trust (TR). Methods: Data were collected by distributing questionnaires to respondents who had used mobile social commerce for shopping, resulting in 320 collected responses. Furthermore, the collected data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM) method through SmartPLS 3.3.3 application. Results: The results showed that among the 17 proposed hypotheses, 6 were rejected, while 11 were accepted. Conclusion: In conclusion, the factors influencing the use of mobile social commerce consisted of effort expectancy, habit, hedonic motivation, SCC, SS, and PC. Therefore, future studies should concentrate on exploring the continued intention of users towards mobile social commerce application.   Keywords: Mobile Social Commerce, Privacy Concern, Social Construct, Social Support, UTAUT
Model-based Decision Support System Using a System Dynamics Approach to Increase Corn Productivity Suryani, Erma; Rafi, Haris; Utamima, Amalia
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.139-151

Abstract

Background: As the population increases, the need for corn products also increases. Corn is needed for various purposes, such as food consumption, industry, and animal feed. Therefore, increasing corn production is crucial to support food availability and the food industry. Objective: The objective of this project is to create a model to increase corn farming productivity using scenarios from drip irrigation systems and farmer field school programs. Methods: A system dynamics approach is utilized to model the complexity and nonlinear behaviour of the corn farming system. In addition, several scenarios are formulated to achieve the objective of increasing corn productivity. Results: Simulation results showed that adopting a drip irrigation system and operating a farmer field school program would increase corn productivity. Conclusion: The corn farming system model was successfully developed in this research. The scenario of implementing a drip irrigation system and the farmer field school program allowed farmers to increase corn productivity. Through the scenario of implementing a drip irrigation system, farmers can save water use, thereby reducing the impact of drought. Meanwhile, the scenario of the farmer field school program enables farmers to manage agriculture effectively. This study suggests that further research could consider the byproducts of corn production to increase the profits of corn farmers.   Keywords: Corn Farming, Decision Support System, Modeling, Simulation, System Dynamics

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