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Journal of Information System Exploration and Research
Published by shm publisher
ISSN : 29641160     EISSN : 29636361     DOI : https://doi.org/10.52465/joiser
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Journal of Information System Exploration and Research (JOISER) (e-ISSN: 2963-6361, p-ISSN: 2964-1160) is a journal that publishes and disseminates scientific research papers on information systems to a wide audience, particularly within the information system society. Articles devoted to discussing any and all aspects of the most recent and noteworthy advancements in the fields of Decision Science, Computer Science, and Computer Science Applications will be considered for publication. Submit your paper now through Online submission ONLY. The JOISER publication period is carried out every six months, namely in January and July. But, authors can submit their work to JOISER at any time throughout the year, as the submission process is continuous. The JOISER has been indexed by Google Scholar, Crossref, Copernicus, and BASE. The Journal of Information Systems Exploration and Research aim publishes articles concerning the design and implementation information system, data models, process models, algorithms, and software for information systems. Subject areas include data management, data mining, machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. We welcome system papers that focus on decision science and machine learning, computer science application, pplication domains, Internet of Things, which present innovative, high-performance, and scalable solutions to data management problems for those domains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 25 Documents
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The Effect of Digitalization on Business Performance in the MSME Industry Context Umar, Fadhil; Septian, M. Rivaldi Ali; Pertiwi, Dwika Ananda Agustina
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.199

Abstract

The current digital era is increasingly developing in the use of new technology that creates value for companies and offers benefits. Digitalization is useful for increasing competitive advantage to improve business performance. The purpose of this study is to find out whether digitalization affects business performance and to find out whether competitive advantage can mediate digitalization on business performance. The sample of this research is 115 SMEs in Semarang. data were analyzed using the SEM approach with the smartPLS tool. The results of the study show that the digitalization variable has an influence on business performance, furthermore, competitive advantage also has a positive and significant effect on business performance. The results of the indirect effect test also show that competitive advantage can mediate the relationship between digitalization and business performance. The better the implementation of digitalization, the higher the competitive advantage MSMEs, consequently leading to an increase the business performance.
The Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques Rofik, Rofik; Aulia, Reza; Musaadah, Khalimah; Ardyani, Salma Shafira Fatya; Hakim, Ade Anggian
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.203

Abstract

Credit risk assessment plays an important role in efficient and safe banking decision-making. Many studies have been conducted to analyze credit scoring with a focus on achieving high accuracy. However, predicting credit scoring decisions also requires model construction that handles class imbalance and proper model implementation. This research aims to increase the accuracy of credit assessment by balancing data using Synthetic Minority Oversampling (SMOTE) and applying ensemble stacking learning techniques. The proposed model utilizes a base learner consisting of Random Forest, SVM, Extra-Tree Classifier, and XGboost as a meta-learner. Then to handle unbalanced classes using SMOTE. The research process was carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. The model was tested using the German Credit dataset by applying cross-validation. The evaluation results show that the stacking ensemble learning model developed has optimal performance, with an accuracy of 83.21%, precision of 79.29%, recall of 91.78%, and f1-score of 85.08%. This research shows that optimizing the stacking ensemble learning model with data balancing using SMOTE in credit scoring can improve performance in credit scoring.
Service Level Agreement Enforcement Model with Human Factor for Electronic Health Record Mohamed, Amir Mohamed Talib; Atan, Rodziah; Alshammari, Abdulaziz; Alsahli, Abdulaziz; Rozami, Mohammad Nasrollah
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.204

Abstract

Service Level Agreement (SLA) is a document contract between the service provider and service recipient which is the expected services to be delivered and received. SLA includes all the information about the services provided and their performance. The SLA identified the level of services performance such as penalties, priorities, compensation and resolution time. If the quality of service does not meet the SLA usage then the service provider need to pay penalties also known as SLA violation. SLA violation occurred might be from software or hardware but another factor such as human factor also involved. The performance of the system and the quality of services requires a human interference to enforce the SLA. In this research work, the human factor such as user willingness, skill/knowledge, information sharing, Staff adequacy was being investigated. The method survey was implemented to find the relationship between human factor and SLA usage. Respondents in IT department are selected to fill in survey form. 11 respondents are used for pilot study to find the reliability of instrument and 24 respondents are used for actual data. The result show there is positive significant value in relationship between human factor and SLA usage.
The Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm Damayanti, Nabila Putri; Prameswari, Della Egyta; Puspita, Wiyanda; Sundari, Putri Susi
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.229

Abstract

This research was conducted to increase accuracy in classifying sentences containing hate speech and non-hate speech on Twitter. This is important to do because, as technology develops, it also comes with negative impacts, one of which is hate speech. This classification is carried out using a combination of Logistic Regression (LR) and Support Vector Machine (SVM) methods. This combination is based on the ease of implementation and speed of LR as well as SVM's ability to handle more complex and non-linear data. In this context, LR is used to model the probability that a comment on Twitter contains hate elements or not. The model can then provide probability predictions for each class, and a threshold can be set to determine the final class. This research shows that combining these methods can build a good classification model with an accuracy of 96%.
Early Detection of Diabetes Using Random Forest Algorithm Noviyanti, Cindy Nabila; Alamsyah, Alamsyah
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.245

Abstract

Diabetes is one of the most chronic and deadly diseases. According to data from WHO in 2021, there were approximately 422 million adults living with diabetes worldwide, and this number is expected to continue to increase in the future due to various factors. Many studies have been conducted for early detection of diabetes by focusing on improving accuracy. However, a big problem in diabetes prediction is the selection of the right classification algorithm. This study aims to improve the accuracy of early detection of diabetes by implementing the Random Forest algorithm model. This research was conducted with the stages of data collection, data preprocessing, split data, modeling, and evaluation. This research uses the Pima Indian Diabetes data set. The results showed that the diabetes early detection model using the Random Forest algorithm produced an accuracy of 87%. This research shows that by using the Random Forest algorithm model, the performance of early detection of diabetes can be improved. However, there is still room for optimization of this performance, which is recommended for further research to carry out feature selection, data balancing, more complex model building, and exploring larger data.
Breast Cancer Diagnosis Utilizing Artificial Neural Network (ANN) Algorithm for Integrating Multi-Omics Data and Clinical Features Rofik, Rofik; Artiyani, Fani; Pertiwi, Dwika Ananda Agustina
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.249

Abstract

Breast cancer is one of the most common diseases affecting women worldwide, with a significant impact on patient's health and quality of life. Despite advances in medical technology and research, breast cancer diagnosis remains a challenge due to its complexity involving various biological and clinical factors. Several previous studies have focused on detecting this disease with optimal accuracy, but the selection of appropriate algorithms and methods is key to achieving this goal. This study aims to improve the accuracy of breast cancer diagnosis by using the ANN algorithm and data balancing method, SMOTE. This research uses Multi-Omic data and Clinical Features obtained in general from Kaggle. The research process is carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. This research successfully obtained an increase in accuracy, which was able to achieve an accuracy of 99.30%.  This research shows that early detection of breast cancer with ANN algorithm and data balancing using SMOTE can improve accuracy performance in early detection of breast cancer. Given the use of data in this study is not too large, it is recommended for further research to use a larger dataset to validate the strength of the model that has been built on more varied data.
The Influence of Determining the K-Value on Improving the Diabetes Classification Model using the K-NN Algorithm Korina, Nanda Putri; Prasetiyo, Budi; Hakim, Ade Anggian; Septian, M Rivaldi Ali
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.344

Abstract

Diabetes mellitus is still an important health problem globally, so it requires an efficient classification model to help determine a patient's diagnosis. This study aims to determine the K-value on the accuracy performance of the diabetes classification model using the K-Nearest Neighbors (K-NN) algorithm. This research utilizes a simulated dataset generated through interaction with ChatGPT, we investigate various K-values ​​in the K-NN model and assess its accuracy using a confusion matrix. Based on experiments, we found that the K-NN classification model with a K=6 obtained an optimal accuracy of 97.62%. Thus, our findings highlight the important role of selecting optimal K-values ​​in improving the performance of diabetes classification models.
Digit and Mark Recognition Using Convolutional Neural Network for Voting Digitization in Indonesia Mandasari, Mandasari; Al Qohar, Bagus
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.365

Abstract

Digitization of voting results in Indonesia is essential to ensure the accuracy and integrity of the election process. This research introduces an innovative approach that uses Convolutional Neural Networks (CNN) for handwritten number recognition and tally mark recognition. This research uses a dataset obtained from Kaggle. The research process is conducted in several stages, namely Data Collection, Preprocessing, Data Sharing, Modelling, and Evaluation. The results showed that the proposed model achieved an accuracy of 98%. This research shows that using the CNN algorithm for handwritten number recognition and tally mark recognition can improve accuracy and efficiency in digitizing voting results. It is expected that this research can make a significant contribution to the development of a more reliable digital voting system. Future research is recommended to use a larger dataset to validate the strength of the model, which has been built on more varied data.
Implementation of Least Significant Bit Steganography to Secure Text Messages in Images Herviana, Wiyan Herra; Djuniadi, Djuniadi
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.438

Abstract

Steganography is a technique of securing secret messages in other messages that are not known. Simulation of the steganography method using the Least Significant Bit technique is used to change the last bit in one byte of data by using a text message as the container medium. This study aims to implement the security of text messages in images using the Least Significant Bit technique which is supported by the steganography method. Simulation techniques are used to conduct studies using Cryptool2 which can describe the concept of cryptography. The results obtained from this study regarding the security of text message insertion into an image in *.jpg and *.png format with 5 sampling trials are (1) the encrypted image cannot be distinguished directly through human eyes, (2) there is an increase in file size the image after being encrypted with an average for five trials is 0.31%, this increase depends on the length of the text message and a key to be inserted, the longer the insertion, the larger the resulting file size, (3) The higher the resolution of the image where the description encryption is inserted, the longer the process required, (4) The simulation time of steganographic decryption is faster than steganographic encryption. The decryption simulation process is the same as 50% of the encryption process.
Inception ResNet v2 for Early Detection of Breast Cancer in Ultrasound Images Nikmah, Tiara Lailatul; Syafei, Risma Moulidya; Anisa, Devi Nurul; Juanara, Elmo; Mahrus, Zohri
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.439

Abstract

Breast cancer is one of the leading causes of death in women. Early detection through breast ultrasound images is important and can be improved using machine learning models, which are more accurate and faster than manual methods. Previous research has shown that the use of the CNN (Convolutional Neural Network) algorithm in breast cancer detection still does not achieve high accuracy. This study aims to improve the accuracy of breast cancer detection using the Inception ResNet v2 transfer learning method and data augmentation. The data is divided into training, validation and testing data consisting of 3 classes, namely Benign, Malignant and Normal. The augmentation process includes rotation, zoom, and rescale. The model trained using CNN and Inception ResNet v2 showed good performance by producing the highest accuracy of 89.72% in the training data evaluation data and getting 90% accuracy in the prediction test stage with data testing. This study shows that the combination of data augmentation and the Inception ResNet v2 architecture can improve the accuracy of breast cancer detection in CNN models.

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