Journal of Information System Exploration and Research
Please be advised that starting from 2026, our official journal website has moved to: https://joiser.shmpublisher.com 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. However, 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 SINTA 3, Garuda, Google Scholar, Crossref, Copernicus, Dimensions, and BASE. The journal is a Gold Open Access journal, online readers don't have to pay any fee.
Analysis and Visualization of Purchasing Pattern in Retail Product Transaction using Apriori Algorithm
The rapid growth of the retail industry generates large volumes of transaction data that can be analyzed to support data-driven business decision making. This study aims to analyze and visualize purchasing patterns in retail product transactions by applying data mining techniques using the Apriori algorithm and business intelligence visualization through Microsoft Power BI. The dataset consists of 1 million retail transactions collected from an open retail transaction repository. The research stages include data collection, transaction data preprocessing, implementation of the Apriori algorithm with a minimum support threshold of 0.002 and a minimum confidence of 0.5, and visualization of the analysis results through interactive dashboards using Power BI and a Python-based application developed with the Streamlit framework. The results indicate that the Apriori algorithm successfully identifies frequent product associations and generates 12 association rules that meet the criteria of strong association rules. Power BI visualizations provide comprehensive insights into transaction trends based on customer categories, store types, payment methods, seasons, and transaction regions. These findings are expected to assist retail companies in formulating marketing strategies, developing product recommendations, and optimizing inventory management in a more effective and data-driven manner. This study contributes by integrating large-scale association rule mining with interactive business intelligence visualization for retail decision support.
Direitos de Autor (c) 2026 N. Nelis Febriani SM, Nuk Ghurroh Setyoningrum, Mae Lodana; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim https://creativecommons.org/licenses/by-sa/4.0
Optimization of Energy Consumption Prediction with Random Forest Regressor and XGBoost Feature Importance
Energy consumption is increasing as industry and technology advance. However, it will have a bad impact if its use is not properly controlled. Therefore, predicting energy consumption is needed to prevent energy waste and to streamline its use across several influencing factors. Predictions are made using the Random Forest Regressor method. Where regression and Random Forest techniques can produce accurate results for continuous values such as total energy consumption. The feature importance method is also used to select the most influential features. Where of the 40 features in the energy consumption dataset in Southern California, only 24 features were selected based on the average threshold of the gain value. The results showed that the use of XGBoost feature importance lowered the Mean Absolute Error (MAE) value of the Random Forest Regressor, which was 16.56 to 16.55. This value is the difference between the actual data and the predicted data. This proves that the model successfully predicts with a small error value. The application of feature importance in energy consumption prediction using Random Forest Regressor is expected to be more efficient in energy consumption, especially in the sectors that most affect the increase in energy consumption.
Direitos de Autor (c) 2026 Risma Moulidya Syafei, Tiara Lailatul Nikmah, Devi Nurul Anisa, Sidiq Noor Kharisma https://creativecommons.org/licenses/by-sa/4.0
Explainable Artificial Intelligence-Based Model for Student Academic Performance Prediction
This study focuses on predicting student academic performance while emphasizing model interpretability through Explainable Artificial Intelligence (XAI). The main objective is to identify potential academic risks using machine learning models and provide transparent explanations for their decisions. Historical student academic data were used to train and evaluate two classification models: Random Forest and XGBoost. The results show that both models exhibit strong predictive performance. Random Forest achieved an accuracy of 90.77% and a precision of 0.7500 for the risk class, while XGBoost attained a higher recall of 0.7000 with an accuracy of 89.23% and a precision of 0.6364. Both models achieved an identical F1-score of 0.6667 for the risk class. The application of XAI methods, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), revealed the main features influencing the predictions. Globally, G2 (previous period’s final grade), failures (number of failed courses), and absences were identified as the most critical factors. Local interpretations from SHAP and LIME also clarified individual predictions, both correct and misclassified. The study contributes to developing an accurate and transparent decision-support system to enable more personalized, effective, and data-driven academic interventions.
Direitos de Autor (c) 2026 Wildan Hidayatulloh, Fathoni Mahardika, Dani Indra Junaedi https://creativecommons.org/licenses/by-sa/4.0
Ensemble Deep Learning: A State-Of-The-Art Comprehensive Review
Ensemble learning has been a cornerstone of machine learning, providing improved predictive performance and robustness by combining multiple models. However, in the era of deep learning, the landscape of ensemble techniques has rapidly evolved, influenced by advances in neural architectures, training models, and practical application requirements. This review provides a state-of-the-art survey of ensemble deep learning approaches, focusing on recent developments of ensemble methods. We introduce a classification of ensemble strategies based on model diversity, fusion mechanisms, and task alignment, and highlight emerging techniques such as attention-based ensemble fusion, neural architecture search-based ensembles, and large ensembles of language or vision models. The review also examines theoretical foundations, practical tradeoffs, and domain-specific adaptations in some fields. Compiling state-of-the-art benchmarks, we evaluate ensemble performance in terms of accuracy, efficiency, robustness, and interpretability. We also identify key challenges such as scalability, overfitting, and deployment limitations and present open research directions, including ensemble learning for continuous learning, federated learning, and learning from scratch. By connecting key insights with current trends, this review aims to guide researchers and practitioners in designing and implementing ensemble deep learning systems to address the next generation of AI challenges.
Direitos de Autor (c) 2026 Mahmoud Abdel Samie https://creativecommons.org/licenses/by-sa/4.0
Analysis of the Stacking Ensemble Learning Model of Categorical Boosting and Naïve Bayes Algorithms for Crop Selection Based on Soil Characteristics
This study aims to develop a machine learning model for selecting crop types based on soil characteristics, using the Categorical Boosting and Naïve Bayes algorithms as base learners. Next, an ensemble learning technique using a stacking approach was applied to improve the performance of the base model that was built. This was done to analyze and compare the performance results of each ensemble model that was carried out. Model performance was evaluated using evaluation metrics including precision, recall, f1-score, and accuracy. The results of this study indicate that the stacking ensemble model with Random Forest as the meta learner can provide better performance compared to other ensemble models. This model achieved a precision of 98.85337%, a recall of 99.84848%, an F1-score of 99.84844%, an accuracy of 99.84848%, and a model training time of 78.61110 seconds. Based on these results, this study is expected to provide tangible contributions and new knowledge in plant selection classification based on soil characteristics, thereby aiding in the precise and efficient determination of suitable plant types.
Direitos de Autor (c) 2025 Ilham Maulana, Budi Prasetiyo https://creativecommons.org/licenses/by-sa/4.0
Analyzing the Impact of Effort Expectancy and Cognitive Attitudes on The Willingness to Accept ChatGPT
This study aims to analyze the impact of Effort Expectancy (EE) adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT) and Cognitive Attitude (CA) from the Theory of Reasined Action (TRA) model on Willingness to Accept (WA) adapted from TAM on ChatGPT. By understanding the relationship between these factors, we can identify effective strategies to increase user acceptance of ChatGPT technology. The research method used is quantitative with multiple linear regression calculations in SPSS. This study obtained 50 respondents with a total of 10 variables but there were 3 main variables. With the final result, Effort Expectancy has no significant effect on Willingness to Accept while Cognitive Attitude has a significant effect on Willingness to Accept. This suggests that users’ perceptions of how easy or difficult it is to use ChatGPT do not influence their decision to accept and use the technology. In this context, users may feel that ease of use is not a major factor influencing their acceptance of ChatGPT. This means that users’ cognitive attitudes—including their beliefs, perceptions, and understanding of the technology—play an important role in their decision to accept and use ChatGPT.
Direitos de Autor (c) 2025 Andri Saputra; Oktafiyani Aisah Noraini, Dwika Ananda Agustina Pertiwi https://creativecommons.org/licenses/by-sa/4.0
Identifying Coconut Maturity Levels Using CNN and YOLOv8 Deep Learning Algorithms
To improve the efficiency and accuracy of determining coconut maturity levels in the processing industry, this study proposes an automated detection system employing Convolutional Neural Networks (CNN) and the You Only Look Once version 8 (YOLOv8) algorithm to classify maturity levels from image data. This study introduces an automated detection system using Convolutional Neural Networks (CNN) and the You Only Look Once version 8 (YOLOv8) algorithm to identify coconut maturity levels from image data. A dataset of 230 coconut images was utilized, classified into two categories: Young Coconut and Mature Coconut. The YOLOv8 model was trained and evaluated using standard object detection metrics, including mean Average Precision (mAP), precision, recall, and F1-score. The proposed model achieved a mAP of 90.5%, precision of 99.3%, recall of 94.2%, and F1-score of 96.6%, demonstrating high accuracy in detecting coconut maturity. This approach offers a practical and efficient alternative to manual assessment, contributing to improved accuracy and operational efficiency in agricultural practices.
Direitos de Autor (c) 2025 Alfaiz Alafi Luthfie, Alamsyah Alamsyah https://creativecommons.org/licenses/by-sa/4.0
Ensemble Learning-based Potato Leaf Disease Classification Using DenseNet201 and MobileNetV2
Early and late blight are major threats to potato crops and can cause significant losses for farmers. Early disease classification is essential for quick and appropriate treatment. This study proposes an ensemble learning approach by combining DenseNet201 and MobileNetV2 architectures to classify potato leaf diseases from digital images. The dataset used consists of 2,152 potato leaf images and is processed through normalization, augmentation, and image resizing stages. The ensemble model was trained with optimized parameters and evaluated using accuracy, precision, recall, and F1-score. The test results showed an accuracy of 99.56%, with precision, recall, and F1- score values of 99.56% each. Demonstrated improved performance compared to single CNN models on the evaluated dataset, and offers an accurate and efficient solution for disease detection in the agricultural sector.
Direitos de Autor (c) 2026 Burhan Ahmad, Alamsyah https://creativecommons.org/licenses/by-sa/4.0
Enhancing Abusive Language Detection on Twitter Using Stacking Ensemble Learning
Detecting abusive language on Twitter is an important step in reducing the prevalence of negative content and harassment. This study aims to improve the accuracy and effectiveness of abusive language detection on Twitter by addressing the limitations of the single model commonly used previously. The stacking method is employed by combining Term Frequency-Inverse Document Frequency (TF-IDF) as the feature extraction method, along with the Naive Bayes and XGBoost algorithms as classification models. Naive Bayes is known for its simplicity in handling text classification, while XGBoost excels in processing complex data and achieving high accuracy. The combination of these two models is expected to improve performance in detecting coarse language. The research results show that the proposed model outperforms the methods in previous studies, with an accuracy of 91.91% and an AUC of 96.76%. These findings demonstrate the effectiveness of the stacking approach in reducing classification errors in coarse language detection. Further research could explore the use of larger datasets or more complex models to improve detection accuracy.
Direitos de Autor (c) 2025 Putri Utami, Yulizchia Malica Pinkan Tanga, Jumanto Unjung, Much Aziz Muslim https://creativecommons.org/licenses/by-sa/4.0
Guava Disease Classification Using EfficientNet and Genetic Algorithm-Optimized XGBoost
Guava is an evergreen plant in the Myrtaceae family, is renowned for its adaptability and noteworthy nutritional benefits. However, guava production has experienced a substantial decline in recent years due to various diseases affecting the fruit. Farmers typically employ manual inspection to identify these diseases, a method that is time-consuming, labor-intensive, and susceptible to errors. This underscores the necessity for an automated classification model capable of accurately diagnosing guava fruit diseases. While numerous machine learning and deep learning models have been developed for agricultural disease detection, research on combining deep transfer learning as a feature extractor with machine learning classifiers remains relatively limited. Addressing this research gap, the proposed model integrates the strengths of both approaches, achieving an impressive accuracy of 98.62%, surpassing the performance reported in previous studies. This encouraging outcome underscores the potential of hybrid models in enhancing guava fruit disease classification, paving the way for more efficient and scalable agricultural management solutions.
Direitos de Autor (c) 2025 Aditya Yoga Darmawan, Bagus Al Qohar, Ahmad Ubai Dullah, Jumanto https://creativecommons.org/licenses/by-sa/4.0
An Implementation of Loyalty Program Theory Based on Recency Frequency Monetary Score in Information Systems to Increase Customer Loyalty
This study aims to help online retail stores find the right strategy for treating customers through customer segmentation based on Recency, Frequency, and Monetary (RFM) Score. With a quantitative approach, this study uses the K-Means Clustering algorithm to group customers based on their RFM values ​​and applies it within the Loyalty Program Theory framework. The results show that the Best Customers segment has the highest percentage at 26.3%, which emphasizes the importance of retaining high-value customers through exclusive loyalty programs such as VIP access and premium offers. In contrast, the Lost Customers segment at 24.8% requires attention through retargeting and discount programs to attract them back. This study proves that data-based customer segmentation and the implementation of relevant strategies can strengthen long-term relationships with customers, increase loyalty, and ultimately help the development of online retail businesses.
Direitos de Autor (c) 2025 Ricky Rajagukguk https://creativecommons.org/licenses/by-sa/4.0
Classification of Student Grading Using Naïve Bayes Method with Under-sampling Approach to Handle Imbalance
This study explores the application of the Naive Bayes classification method to predict student grades based on important attributes such as timeliness of assignment submission, attendance rate, and quality of work. This research uses a dataset that includes three attributes, namely timeliness of submission, attendance level in learning, and evaluation of the quality of assignments collected by students. The pre-processing is performed to clean the data, followed by an under-sampling stage to balance the class distribution. Then, the classification model is evaluated and tested using specific data samples to measure prediction accuracy. The results showed a significant improvement in model accuracy after applying under-sampling, highlighting the importance of handling data imbalance in predictive analysis. The implications of these findings are not only relevant in the context of higher education, but also offer opportunities for further development in data-driven decision-making in various fields.
Direitos de Autor (c) 2025 Alif Abdul Aziz, Budi Prasetiyo https://creativecommons.org/licenses/by-sa/4.0
Prototyping Disaster Preparedness Information System: A Case of Pandeglang District, Indonesia
In December 2018, a tsunami triggered by the eruption of Anak Krakatau Volcano (AKV) devastated the coastal area of Pandeglang, Indonesia, claiming hundreds of lives and leaving thousands missing. This tragedy underscores the critical importance of enhancing tsunami awareness through disaster preparedness and education. However, the lack of disaster preparedness in vulnerable areas, such as Pandeglang, remains a significant challenge. This is evident from the absence of early warning systems and evacuation initiatives at the time of the tsunami, highlighting the urgent need for improved disaster resilience in at-risk communities. This research aims to develop the disaster preparedness information system to equip society with sufficient knowledge and skill in case of the next disaster. The method this research uses is Soft Systems Methodology (SSM) to obtaining system requirements to the development of prototype. The prototype of a disaster preparedness information system was developed as a result. The system can be accessed using a smartphone or computer. This study introduces a novel approach by proposing a new prototype of disaster preparedness information specifically tailored for vulnerable areas in developing countries.
Direitos de Autor (c) 2025 Elmo Juanara, Ade Anggian Hakim, Yasunobu Maeda https://creativecommons.org/licenses/by-sa/4.0
Machine Learning Techniques for Classifying Indonesian Foods and Drinks by Nutritional Profiles
Local ingredients and Indonesia's diverse culinary traditions play an important role in shaping people's health and eating habits. Understanding the nutritional profile of Indonesian food is crucial to promoting healthier food choices. This study aims to classify Indonesian food and beverages based on their nutritional content, with a focus on calories, protein, fat, and carbohydrates. To achieve this, a dataset of 1,346 food items was preprocessed using normalization techniques to improve model performance. Each food item was categorized as High Protein, High Fat, or High Carbohydrate based on its dominant macronutrient content. Five machine learning models which are K-Nearest Neighbors, Decision Trees, Support Vector Machines, Random Forest, and Multilayer Perceptron-were used and compared. Among these models, the Support Vector Machine achieved the highest classification accuracy of 99.1%. These findings demonstrate the potential of machine learning in nutrition research, providing a basis for developing data-driven dietary recommendations tailored to individual nutritional needs. This research bridges traditional dietary research with modern computational approaches, offering insights for public health initiatives and personalized nutrition planning.
Direitos de Autor (c) 2025 Bagus Al Qohar, Yulizchia Malica Pinkan Tanga, Aditya Yoga Darmawan, Jumanto Unjung https://creativecommons.org/licenses/by-sa/4.0
The Asthma Classification Using an Adaptive Boosting Model with SVM-SMOTE Sampling
Asthma is a disease that affects the human respiratory tract, characterized by inflammation and narrowing of the respiratory tract such as wheezing, coughing, and shortness of breath. The causes of asthma can come from genetics, lifestyle, and a bad environment. Diagnosis made to asthma patients is very influential on the severity and treatment carried out. However, the diagnosis process may not be able to precisely determine asthma patients because the diagnosis is influenced by the classification of asthma based on the symptoms that appear. Therefore, this study proposes an asthma disease classification model that is optimized using a sampling method to balance the data. The proposed classification model uses the Adaptive Boosting algorithm with a sampling technique using SVM-SMOTE to help balance the data. The results obtained from the experiment achieved an accuracy of 98.60%. This result shows that the proposed model is more accurate and optimal in performing classification when compared to previous research.
Direitos de Autor (c) 2025 Ahmad Ubai Dullah, Aditya Yoga Darmawan, Putri Utami, Jumanto Unjung https://creativecommons.org/licenses/by-sa/4.0
Inception ResNet v2 for Early Detection of Breast Cancer in Ultrasound Images
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.
Direitos de Autor (c) 2024 Tiara Lailatul Nikmah, Risma Moulidya Syafei, Devi Nurul Anisa https://creativecommons.org/licenses/by-sa/4.0
Implementation of Least Significant Bit Steganography to Secure Text Messages in Images
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.
Direitos de Autor (c) 2024 Wiyan Herra Herviana, Djuniadi Djuniadi https://creativecommons.org/licenses/by-sa/4.0
Digit and Mark Recognition Using Convolutional Neural Network for Voting Digitization in Indonesia
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.
Direitos de Autor (c) 2024 Mandasari Mandasari, Bagus Al Qohar https://creativecommons.org/licenses/by-sa/4.0
The Influence of Determining the K-Value on Improving the Diabetes Classification Model using the K-NN Algorithm
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.
Direitos de Autor (c) 2024 Nanda Putri Korina Putri Korina, Budi Prasetiyo https://creativecommons.org/licenses/by-sa/4.0
The Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm
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%.
Direitos de Autor (c) 2023 Nabila Putri Damayanti, Della Egyta Prameswari, Wiyanda Puspita, Putri Susi Sundari https://creativecommons.org/licenses/by-sa/4.0
Breast Cancer Diagnosis Utilizing Artificial Neural Network (ANN) Algorithm for Integrating Multi-Omics Data and Clinical Features
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.
Direitos de Autor (c) 2024 Rofik Rofik, Fani Artiyani; Dwika Ananda Agustina Pertiwi https://creativecommons.org/licenses/by-sa/4.0
Early Detection of Diabetes Using Random Forest Algorithm
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.
Direitos de Autor (c) 2023 Cindy Nabila Noviyanti, Alamsyah Alamsyah https://creativecommons.org/licenses/by-sa/4.0
Service Level Agreement Enforcement Model with Human Factor for Electronic Health Record
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.
Direitos de Autor (c) 2023 Amir Mohamed Talib Mohamed, Rodziah Atan, Abdulaziz Alshammari, Abdulaziz Alsahli, Mohammad Nasrollah Rozami https://creativecommons.org/licenses/by-sa/4.0
The Effect of Digitalization on Business Performance in the MSME Industry Context
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.
Direitos de Autor (c) 2023 Fadhil Umar, M. Rivaldi Ali Septian, Dwika Ananda Agustina Pertiwi https://creativecommons.org/licenses/by-sa/4.0
The Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques
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.
Direitos de Autor (c) 2023 Rofik Rofik, Reza Aulia, Khalimah Musaadah, Salma Shafira Fatya Ardyani, Ade Anggian Hakim https://creativecommons.org/licenses/by-sa/4.0
Bankruptcy Prediction Using Genetic Algorithm-Support Vector Machine (GA-SVM) Feature Selection and Stacking
Bankruptcy is an impact caused by a company's financial failure. Financial failure in the company must be avoided so as not to cause losses to the company. In the research that was carried out utilizing a data set from the Taiwan Economic Journal as many as 6,819 to be trained using machine learning algorithms using classification techniques. The goal obtained from the research conducted is to obtain a classification technique with the best accuracy results. The method used in this research is preprocessing using the synthetic minority over-sampling technique to handling unbalanced data sets. Then, the results of the balanced data set will be processed using a genetic algorithm-support vector machine feature selection algorithm to reduce the attributes of the data set. Data sets that have experienced reduced attributes will be trained using the stacking method with a single classifier base learner in the form of k-nearest neighbors, naïve bayes, decision trees with classification and regression tree models, gradient boosting decision trees, and light gradient boosting. The meta-learner used in the stacking method is extreme gradient boosting. The results of the accuracy obtained from the research conducted were 99.22%.
Direitos de Autor (c) 2023 Wiena Faqih Abror, Alamsyah Alamsyah; Muhammad Aziz https://creativecommons.org/licenses/by-sa/4.0
Portfolio Selection Strategies in Bursa Malaysia Based on Quadratic Programming
The study aims to select the efficient portfolio on stock listed in Bursa Malaysia by using the quadratic programming method. It can help the investors to gain expected returns from the diversification portfolio. However, there are some problems that should be considered such as the measurement of inputs for Mean-Variance Models (MVM), use of portfolio models through time and consistency with management objectives in the portfolio. These problems will affect the performance of selected portfolio and cause the loss problem. Therefore, this study implements a quadratic programming approach to select an efficient portfolio on stocks listed in Bursa Malaysia. The study will choose 15 potential companies which have the best performance in the Bursa Malaysia. Quadratic programming (QP) model can solve any type of mathematical optimisation problem in the study. Therefore, investors can optimise the investment portfolio returns by using QP methods. However, we can observe the efficient frontier which is a graph that representing a list of portfolios that optimising expected return for a different level of portfolio risk so can help the investors make a good decision. The findings of this study will give important inputs, especially to the investors to maximise their portfolio return at different level of risks.
Direitos de Autor (c) 2023 Liang Pei Ling; Yosza Dasril https://creativecommons.org/licenses/by-sa/4.0
Mask Detection System with Computer Vision-Based on CNN and YOLO Method Using Nvidia Jetson Nano
Health is an essential aspect of life. The World Health Organization (WHO) has officially declared the Corona Virus (Covid-19) a global pandemic that has spread to Indonesia. For preventive measures against Covid-19, the Indonesian government is trying to deal with the Covid-19 pandemic with 3M health protocol aimed at community activities, such as Memakai Masker (wearing masks), Mencuci Tangan (washing hands), and Menjaga Jarak (maintaining distance). In this study, software and hardware design was carried out to detect mask users and immediately warn violators who do not use masks automatically and can function automatically offline by utilizing digital image processing using NVIDIA Jetson Nano using the YOLO (You Only Look Once) method. The CNN YOLOv4-tiny model is chosen to obtain measurement results for mask user detection accuracy because it has a relatively minor computational value and is faster. The best camera detection angle is obtained at a vulnerable angle of 45O-90O or in the range of 90O-135O with value confidence that the average is 99.94% and the best accuracy is at a lux value greater than 70, and a minimum camera height of 1 meter and a maximum of 3 meters. Under conditions of lux 96 (bright), the maximum distance for detecting a face object is 12 meters, and the ability of the system to output a warning sound has been successfully integrated with a relay to run the mp3 module separately from the system, so as not to interfere with the Jetson Nano computation process and the model is successfully run on the Jetson Nano with an average computation of 13 frames per second.
Direitos de Autor (c) 2023 Ade Anggian Hakim, Elmo Juanara; Rispandi Rispandi https://creativecommons.org/licenses/by-sa/4.0
Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis
Air transportation is vital due to technological advancements and globalization. It is affordable and accessible worldwide, providing efficient services to reach destinations globally. This discussion focuses on full-service airlines that offer online-based services. Previous research indicates that available facilities and services influence passenger satisfaction. Previous research on customer satisfaction showed a correlation between satisfaction and services without accurate figures. In the present study, the customer satisfaction figure is measured using the Naive Bayes and K-Nearest Neighbour (K-NN) algorithm to obtain a tested level of accuracy. In this analysis, we will compare the effectiveness of Naive Bayes and K-NN algorithms in classifying airline passenger satisfaction. The results show that the accuracy of the Naive Bayes method of the two algorithms is higher than the K-NN method. The accuracy value of the Naive Bayes method is 84.48%, while the accuracy value of the K-NN method is 65.38%. From the test results, the precision value for Naive Bayes is 82.25%, and K-NN is 67.35%. Furthermore, the recall value for Naive Bayes is 82.43%, and K-NN is 74.33%.
Direitos de Autor (c) 2023 Annisa Nurdina, Audita Bella Intan Puspita https://creativecommons.org/licenses/by-sa/4.0
Decision Support System for Program Indonesia Pintar Recipients Using the Fuzzy Multi-Criteria Decision-Making Method
Program Indonesia Pintar (PIP) is the development of Bantuan Siswa Miskin (BSM) program, which covers students from the learning stages of SD or MI, SMP or MTs, SMA or Sekolah Menengah Kejuruan (SMK), the PIP Program is a National Program that aims to eliminate barriers to poor students participating in studying by helping poor students get access to appropriate learning services, avoiding dropping out of school, attracting poor students to return to study, helping students fulfill their desires in upgrading activities. Through the Program Indonesia Pintar (PIP), school-age children from poor households or families can continue to study, do not drop out of school. No recipients are on the wrong target for assistance from the Smart Indonesia Program at SMP Negri 39 Pekanbaru City. The method used in the decision support system is Fuzzy Multi-Criteria Decision Making (FMCDM) which assesses alternative determinants so that they can be used in policy analysis in decision-making. The results of this decision support will help decide the best choice of several substitutes based on the selected criteria.
Direitos de Autor (c) 2023 Abdul Hamid, Muhammad Sandi Rais, Muhammad Idris Rois, Salamun Salamun, Yonhendri Yonhendri, Ahmad Zulfan, Lasmi Oyong https://creativecommons.org/licenses/by-sa/4.0