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Predicting future blood glucose (BG) levels for diabetic patients will help them avoid potentially critical health issues. We demonstrate the use of machine learning models to predict future blood glucose levels given a history of blood glucose values as the single input parameter. We propose an Artificial Neural Network (ANN) model with time-domain attributes to predict blood glucose levels 15, 30, 45 and 60 min in the future. Initially, the model’s features are selected based on the previous 30 min of BG measurements before a trained model is generated for each patient. These features are combined with time-domain attributes to give additional inputs to the proposed ANN. The prediction model was tested on 12 patients with Type 1 diabetes (T1D) and the results were compared with other data-driven models including the Support Vector Regression (SVR), K-Nearest Neighbor (KNN), C4.5 Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) models. Our results show that the proposed BG prediction model that is based on an ANN outperformed all other models with an average Root Mean Square Error (RMSE) of 2.82, 6.31, 10.65 and 15.33 mg/dL for Prediction Horizons (PHs) of 15, 30, 45 and 60 min, respectively. Our testing showed that combining time-domain attributes into the input data resulted in enhanced performance of majority of prediction models. The implementation of proposed prediction model allows patients to obtain future blood glucose levels, so that the preventive alerts can be generated before critical hypoglycemic/ hyperglycemic events occur.
Ganjar Alfian; Muhammad Syafrudin; Muhammad Anshari; Filip Benes; Fransiskus Tatas Dwi Atmaji; Imam Fahrurrozi; Ahmad Fathan Hidayatullah; Jongtae Rhee. Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features. Biocybernetics and Biomedical Engineering 2020, 40, 1586 -1599.
AMA StyleGanjar Alfian, Muhammad Syafrudin, Muhammad Anshari, Filip Benes, Fransiskus Tatas Dwi Atmaji, Imam Fahrurrozi, Ahmad Fathan Hidayatullah, Jongtae Rhee. Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features. Biocybernetics and Biomedical Engineering. 2020; 40 (4):1586-1599.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Muhammad Anshari; Filip Benes; Fransiskus Tatas Dwi Atmaji; Imam Fahrurrozi; Ahmad Fathan Hidayatullah; Jongtae Rhee. 2020. "Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features." Biocybernetics and Biomedical Engineering 40, no. 4: 1586-1599.
Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.
Ganjar Alfian; Muhammad Syafrudin; Norma Latif Fitriyani; Muhammad Anshari; Pavel Stasa; Jiri Svub; Jongtae Rhee. Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors. Mathematics 2020, 8, 1620 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Norma Latif Fitriyani, Muhammad Anshari, Pavel Stasa, Jiri Svub, Jongtae Rhee. Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors. Mathematics. 2020; 8 (9):1620.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Norma Latif Fitriyani; Muhammad Anshari; Pavel Stasa; Jiri Svub; Jongtae Rhee. 2020. "Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors." Mathematics 8, no. 9: 1620.
Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.
Muhammad Syafrudin; Ganjar Alfian; Norma Latif Fitriyani; Muhammad Anshari; Tony Hadibarata; Agung Fatwanto; Jongtae Rhee. A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting. Mathematics 2020, 8, 1590 .
AMA StyleMuhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani, Muhammad Anshari, Tony Hadibarata, Agung Fatwanto, Jongtae Rhee. A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting. Mathematics. 2020; 8 (9):1590.
Chicago/Turabian StyleMuhammad Syafrudin; Ganjar Alfian; Norma Latif Fitriyani; Muhammad Anshari; Tony Hadibarata; Agung Fatwanto; Jongtae Rhee. 2020. "A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting." Mathematics 8, no. 9: 1590.
Heart disease, one of the major causes of mortality worldwide, can be mitigated by early heart disease diagnosis. A clinical decision support system (CDSS) can be used to diagnose the subjects’ heart disease status earlier. This study proposes an effective heart disease prediction model (HDPM) for a CDSS which consists of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and eliminate the outliers, a hybrid Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) to balance the training data distribution and XGBoost to predict heart disease. Two publicly available datasets (Statlog and Cleveland) were used to build the model and compare the results with those of other models (naive bayes (NB), logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF)) and of previous study results. The results revealed that the proposed model outperformed other models and previous study results by achieving accuracies of 95.90% and 98.40% for Statlog and Cleveland datasets, respectively. In addition, we designed and developed the prototype of the Heart Disease CDSS (HDCDSS) to help doctors/clinicians diagnose the patients’/subjects’ heart disease status based on their current condition. Therefore, early treatment could be conducted to prevent the deaths caused by late heart disease diagnosis.
Norma Latif Fitriyani; Muhammad Syafrudin; Ganjar Alfian; Jongtae Rhee. HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System. IEEE Access 2020, 8, 133034 -133050.
AMA StyleNorma Latif Fitriyani, Muhammad Syafrudin, Ganjar Alfian, Jongtae Rhee. HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System. IEEE Access. 2020; 8 (99):133034-133050.
Chicago/Turabian StyleNorma Latif Fitriyani; Muhammad Syafrudin; Ganjar Alfian; Jongtae Rhee. 2020. "HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System." IEEE Access 8, no. 99: 133034-133050.
Understanding customer shopping behavior in retail store is important to improve the customers' relationship with the retailer, which can help to lift the revenue of the business. However, compared to online store, the customer browsing activities in the retail store is difficult to be analysed. Therefore, in this study the customer shopping behavior analysis (i.e., browsing activity) in retail store by utilizing radio frequency identification (RFID)-enabled shelf and machine learning model is proposed. First, the RFID technology is installed in the store shelf to monitor the movement tagged products. The dataset was gathered from receive signal strength (RSS) of the tags for different customer behavior scenario. The statistical features were extracted from RSS of tags. Finally, machine learning models were utilized to classify different customer shopping activities. The experiment result showed that the proposed model based on Multilayer Perceptron (MLP) outperformed other models by as much as 97.00%, 96.67%, 97.50%, and 96.57% for accuracy, precision, recall, and f-score, respectively. The proposed model can help the managers better understand what products customer interested in, so that can be utilized for product placement, promotion as well as relevant product recommendations to the customers.
Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee; Pavel Stasa; Agus Mulyanto; Agung Fatwanto. In-store Customer Shopping Behavior Analysis by Utilizing RFID-enabled Shelf and Multilayer Perceptron Model. IOP Conference Series: Materials Science and Engineering 2020, 803, 1 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Jongtae Rhee, Pavel Stasa, Agus Mulyanto, Agung Fatwanto. In-store Customer Shopping Behavior Analysis by Utilizing RFID-enabled Shelf and Multilayer Perceptron Model. IOP Conference Series: Materials Science and Engineering. 2020; 803 ():1.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Jongtae Rhee; Pavel Stasa; Agus Mulyanto; Agung Fatwanto. 2020. "In-store Customer Shopping Behavior Analysis by Utilizing RFID-enabled Shelf and Multilayer Perceptron Model." IOP Conference Series: Materials Science and Engineering 803, no. : 1.
Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee; Muhammad Anshari; M. Mustakim; Imam Fahrurrozi. Blood Glucose Prediction Model for Type 1 Diabetes based on Extreme Gradient Boosting. IOP Conference Series: Materials Science and Engineering 2020, 803, 1 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Jongtae Rhee, Muhammad Anshari, M. Mustakim, Imam Fahrurrozi. Blood Glucose Prediction Model for Type 1 Diabetes based on Extreme Gradient Boosting. IOP Conference Series: Materials Science and Engineering. 2020; 803 ():1.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Jongtae Rhee; Muhammad Anshari; M. Mustakim; Imam Fahrurrozi. 2020. "Blood Glucose Prediction Model for Type 1 Diabetes based on Extreme Gradient Boosting." IOP Conference Series: Materials Science and Engineering 803, no. : 1.
Ganjar Alfian; Muhammad Syafrudin; Umar Farooq; Muhammad Rifqi Ma'Arif; M. Alex Syaekhoni; Norma Latif Fitriyani; Jaeho Lee; Jongtae Rhee. Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model. Food Control 2020, 110, 1 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Umar Farooq, Muhammad Rifqi Ma'Arif, M. Alex Syaekhoni, Norma Latif Fitriyani, Jaeho Lee, Jongtae Rhee. Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model. Food Control. 2020; 110 ():1.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Umar Farooq; Muhammad Rifqi Ma'Arif; M. Alex Syaekhoni; Norma Latif Fitriyani; Jaeho Lee; Jongtae Rhee. 2020. "Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model." Food Control 110, no. : 1.
Early diseases prediction plays an important role for improving healthcare quality and can help individuals avoid dangerous health situations before it is too late. This paper proposes a disease prediction model (DPM) to provide an early prediction for type 2 diabetes and hypertension based on individual’s risk factors data. The proposed DPM consists of isolation forest (iForest) based outlier detection method to remove outlier data, synthetic minority oversampling technique tomek link (SMOTETomek) to balance data distribution, and ensemble approach to predict the diseases. Four datasets were utilized to build the model and extract the most significant risks factors. The results showed that the proposed DPM achieved highest accuracy when compared to other models and previous studies. We also developed a mobile application to provide the practical application of the proposed DPM. The developed mobile application gathers risk factor data and send it to a remote server, so that an individual’s current condition can be diagnosed with the proposed DPM. The prediction result is then sent back to the mobile application; thus, immediate and appropriate action can be taken to reduce and prevent individual’s risks once unexpected health situations occur (i.e., type 2 diabetes and/or hypertension) at early stages.
Norma Latif Fitriyani; Muhammad Syafrudin; Ganjar Alfian; Jongtae Rhee. Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension. IEEE Access 2019, 7, 144777 -144789.
AMA StyleNorma Latif Fitriyani, Muhammad Syafrudin, Ganjar Alfian, Jongtae Rhee. Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension. IEEE Access. 2019; 7 (99):144777-144789.
Chicago/Turabian StyleNorma Latif Fitriyani; Muhammad Syafrudin; Ganjar Alfian; Jongtae Rhee. 2019. "Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension." IEEE Access 7, no. 99: 144777-144789.
Radio frequency identification (RFID) technology can be utilized to monitor tagged product movements and directions for the purpose of inventory management. It is important for RFID gate to identify the several RFID readings such as movement type and direction as well as the static tags (tags that accidentally read by the reader). In this study, random forest (RF) method is utilized to detect the movement type and direction of RFID passive tags. The input features are derived from received signal strength (RSS) and timestamp of tags. The result showed that machine learning models successfully distinguish direction and movement type of tag. In addition, proposed model based on random forest generated accuracy as much as 98.39% and was significantly superior to the other models considered.
Ganjar Alfian; Muhammad Syafrudin; Jaeho Lee; Jongtae Rhee. Detecting Movement and Direction of Tags for RFID Gate. 2019 5th International Conference on Science and Technology (ICST) 2019, 1, 1 -5.
AMA StyleGanjar Alfian, Muhammad Syafrudin, Jaeho Lee, Jongtae Rhee. Detecting Movement and Direction of Tags for RFID Gate. 2019 5th International Conference on Science and Technology (ICST). 2019; 1 ():1-5.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Jaeho Lee; Jongtae Rhee. 2019. "Detecting Movement and Direction of Tags for RFID Gate." 2019 5th International Conference on Science and Technology (ICST) 1, no. : 1-5.
Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.
Ganjar Alfian; Muhammad Syafrudin; Bohan Yoon; Jongtae Rhee. False Positive RFID Detection Using Classification Models. Applied Sciences 2019, 9, 1154 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Bohan Yoon, Jongtae Rhee. False Positive RFID Detection Using Classification Models. Applied Sciences. 2019; 9 (6):1154.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Bohan Yoon; Jongtae Rhee. 2019. "False Positive RFID Detection Using Classification Models." Applied Sciences 9, no. 6: 1154.
PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.
Ganjar Alfian; Muhammad Fazal Ijaz; Muhammad Syafrudin; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. Customer behavior analysis using real-time data processing. Asia Pacific Journal of Marketing and Logistics 2019, 31, 265 -290.
AMA StyleGanjar Alfian, Muhammad Fazal Ijaz, Muhammad Syafrudin, M. Alex Syaekhoni, Norma Latif Fitriyani, Jongtae Rhee. Customer behavior analysis using real-time data processing. Asia Pacific Journal of Marketing and Logistics. 2019; 31 (1):265-290.
Chicago/Turabian StyleGanjar Alfian; Muhammad Fazal Ijaz; Muhammad Syafrudin; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. 2019. "Customer behavior analysis using real-time data processing." Asia Pacific Journal of Marketing and Logistics 31, no. 1: 265-290.
Maintaining product quality is essential for smart factories, hence detecting abnormal events in assembly line is important for timely decision-making. This study proposes an affordable fast early warning system based on edge computing to detect abnormal events during assembly line. The proposed model obtains environmental data from various sensors including gyroscopes, accelerometers, temperature, humidity, ambient light, and air quality. The fault model is installed close to the facilities, so abnormal events can be timely detected. Several performance evaluations are conducted to obtain the optimal scenario for utilizing edge devices to improve data processing and analysis speed, and the final proposed model provides the highest accuracy in terms of detecting abnormal events compared to other classification models. The proposed model was tested over four months of operation in a Korean automobile parts factory, and provided significant benefits from monitoring assembly line, as well as classifying abnormal events. The model helped improve decision-making by reducing or preventing unexpected losses due to abnormal events.
Muhammad Syafrudin; Norma Latif Fitriyani; Ganjar Alfian; Jongtae Rhee. An Affordable Fast Early Warning System for Edge Computing in Assembly Line. Applied Sciences 2018, 9, 84 .
AMA StyleMuhammad Syafrudin, Norma Latif Fitriyani, Ganjar Alfian, Jongtae Rhee. An Affordable Fast Early Warning System for Edge Computing in Assembly Line. Applied Sciences. 2018; 9 (1):84.
Chicago/Turabian StyleMuhammad Syafrudin; Norma Latif Fitriyani; Ganjar Alfian; Jongtae Rhee. 2018. "An Affordable Fast Early Warning System for Edge Computing in Assembly Line." Applied Sciences 9, no. 1: 84.
With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.
Muhammad Syafrudin; Ganjar Alfian; Norma Latif Fitriyani; Jongtae Rhee. Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors 2018, 18, 2946 .
AMA StyleMuhammad Syafrudin, Ganjar Alfian, Norma Latif Fitriyani, Jongtae Rhee. Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors. 2018; 18 (9):2946.
Chicago/Turabian StyleMuhammad Syafrudin; Ganjar Alfian; Norma Latif Fitriyani; Jongtae Rhee. 2018. "Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing." Sensors 18, no. 9: 2946.
As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.
Muhammad Fazal Ijaz; Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee. Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Applied Sciences 2018, 8, 1325 .
AMA StyleMuhammad Fazal Ijaz, Ganjar Alfian, Muhammad Syafrudin, Jongtae Rhee. Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest. Applied Sciences. 2018; 8 (8):1325.
Chicago/Turabian StyleMuhammad Fazal Ijaz; Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee. 2018. "Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest." Applied Sciences 8, no. 8: 1325.
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.
Ganjar Alfian; Muhammad Syafrudin; Muhammad Fazal Ijaz; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors 2018, 18, 2183 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Muhammad Fazal Ijaz, M. Alex Syaekhoni, Norma Latif Fitriyani, Jongtae Rhee. A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing. Sensors. 2018; 18 (7):2183.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Muhammad Fazal Ijaz; M. Alex Syaekhoni; Norma Latif Fitriyani; Jongtae Rhee. 2018. "A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing." Sensors 18, no. 7: 2183.
Muhammad Syafrudin; Keeeun Lee; Ganjar Alfian; Jaeho Lee; Jongtae Rhee. Application of Bluetooth Low Energy-Based Real-Time Location System for Indoor Environments. Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things - BDIOT 2018 2018, 167 -171.
AMA StyleMuhammad Syafrudin, Keeeun Lee, Ganjar Alfian, Jaeho Lee, Jongtae Rhee. Application of Bluetooth Low Energy-Based Real-Time Location System for Indoor Environments. Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things - BDIOT 2018. 2018; ():167-171.
Chicago/Turabian StyleMuhammad Syafrudin; Keeeun Lee; Ganjar Alfian; Jaeho Lee; Jongtae Rhee. 2018. "Application of Bluetooth Low Energy-Based Real-Time Location System for Indoor Environments." Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things - BDIOT 2018 , no. : 167-171.
Currently, the manufacturing industry is experiencing a data-driven revolution. There are multiple processes in the manufacturing industry and will eventually generate a large amount of data. Collecting, analyzing and storing a large amount of data are one of key elements of the smart manufacturing industry. To ensure that all processes within the manufacturing industry are functioning smoothly, the big data processing is needed. Thus, in this study an open source-based real-time data processing (OSRDP) architecture framework was proposed. OSRDP architecture framework consists of several open sources technologies, including Apache Kafka, Apache Storm and NoSQL MongoDB that are effective and cost efficient for real-time data processing. Several experiments and impact analysis for manufacturing sustainability are provided. The results showed that the proposed system is capable of processing a massive sensor data efficiently when the number of sensors data and devices increases. In addition, the data mining based on Random Forest is presented to predict the quality of products given the sensor data as the input. The Random Forest successfully classifies the defect and non-defect products, and generates high accuracy compared to other data mining algorithms. This study is expected to support the management in their decision-making for product quality inspection and support manufacturing sustainability.
Muhammad Syafrudin; Norma Latif Fitriyani; Donglai Li; Ganjar Alfian; Jongtae Rhee; Yong-Shin Kang. An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability. Sustainability 2017, 9, 2139 .
AMA StyleMuhammad Syafrudin, Norma Latif Fitriyani, Donglai Li, Ganjar Alfian, Jongtae Rhee, Yong-Shin Kang. An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability. Sustainability. 2017; 9 (11):2139.
Chicago/Turabian StyleMuhammad Syafrudin; Norma Latif Fitriyani; Donglai Li; Ganjar Alfian; Jongtae Rhee; Yong-Shin Kang. 2017. "An Open Source-Based Real-Time Data Processing Architecture Framework for Manufacturing Sustainability." Sustainability 9, no. 11: 2139.
Since customer attention is increasing due to growing customer health awareness, it is important for the perishable food supply chain to monitor food quality and safety. This study proposes a real-time monitoring system that utilizes smartphone-based sensors and a big data platform. Firstly, we develop a smartphone-based sensor to gather temperature, humidity, GPS, and image data. The IoT-generated sensor on the smartphone has characteristics such as a large amount of storage, an unstructured format, and continuous data generation. Thus, in this study, we propose an effective big data platform design to handle IoT-generated sensor data. Furthermore, the abnormal sensor data generated by failed sensors is called outliers and may arise in real cases. The proposed system utilizes outlier detection based on statistical and clustering approaches to filter out the outlier data. The proposed system was evaluated for system and gateway performance and tested on the kimchi supply chain in Korea. The results showed that the proposed system is capable of processing a massive input/output of sensor data efficiently when the number of sensors and clients increases. The current commercial smartphones are sufficiently capable of combining their normal operations with simultaneous performance as gateways for transmitting sensor data to the server. In addition, the outlier detection based on the 3-sigma and DBSCAN were used to successfully detect/classify outlier data as separate from normal sensor data. This study is expected to help those who are responsible for developing the real-time monitoring system and implementing critical strategies related to the perishable supply chain.
Ganjar Alfian; Muhammad Syafrudin; Jongtae Rhee. Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain. Sustainability 2017, 9, 2073 .
AMA StyleGanjar Alfian, Muhammad Syafrudin, Jongtae Rhee. Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain. Sustainability. 2017; 9 (11):2073.
Chicago/Turabian StyleGanjar Alfian; Muhammad Syafrudin; Jongtae Rhee. 2017. "Real-Time Monitoring System Using Smartphone-Based Sensors and NoSQL Database for Perishable Supply Chain." Sustainability 9, no. 11: 2073.
Due to increasing concerns about environmental protection, the environmental sustainability of businesses has been widely considered in the manufacturing and supply chain context. Further, its adoption has been implemented in the retail industry for marketing field, including green product promotion. This study aimed to propose a customer purchasing behavior analysis as an alternative for supporting decision-making in order to promote green products in retail stores. Hence, right-on-target marketing strategies can be implemented appropriately. The study was carried out using shopping path data collected by radio frequency identification (RFID) from a large retail store in Seoul, South Korea. In addition, the store layout and its traffic were also analyzed. This method is expected to help experts providing appropriate decision alternatives. In addition, it can help retailers in order to increase product sales and achieve high levels of customer satisfaction.
M. Alex Syaekhoni; Ganjar Alfian; Young S. Kwon. Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making. Sustainability 2017, 9, 2008 .
AMA StyleM. Alex Syaekhoni, Ganjar Alfian, Young S. Kwon. Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making. Sustainability. 2017; 9 (11):2008.
Chicago/Turabian StyleM. Alex Syaekhoni; Ganjar Alfian; Young S. Kwon. 2017. "Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making." Sustainability 9, no. 11: 2008.
Ganjar Alfian; Jongtae Rhee; Hyejung Ahn; Jaeho Lee; Umar Farooq; Muhammad Fazal Ijaz; M. Alex Syaekhoni. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering 2017, 212, 65 -75.
AMA StyleGanjar Alfian, Jongtae Rhee, Hyejung Ahn, Jaeho Lee, Umar Farooq, Muhammad Fazal Ijaz, M. Alex Syaekhoni. Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering. 2017; 212 ():65-75.
Chicago/Turabian StyleGanjar Alfian; Jongtae Rhee; Hyejung Ahn; Jaeho Lee; Umar Farooq; Muhammad Fazal Ijaz; M. Alex Syaekhoni. 2017. "Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system." Journal of Food Engineering 212, no. : 65-75.