Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205620
Abdulaziz Almaleh
The management of mental health issues in the workplace has always been a significant and challenging task, especially for professionals. Despite the evidence of the detrimental effects of preventable mental health disorders and stress in the workplace, many organizations have not taken enough preventative measures. To address this issue, data were collected from the OSMI website, which aims to raise awareness of mental illness in the workplace. The collected data was label encoded to improve prediction accuracy. Various machine learning techniques were applied to the data to develop a model to help individuals with mental health issues create a healthier work environment. Our proposed approach involved the implementation of classification algorithms, including Random Forest, Logistic Regression, Support Vector Machine, Adaboost, and Gradient Boosting, to obtain the highest accuracy possible.
{"title":"Machine Learning-Based Forecasting of Mental Health Issues Among Employees in the Workplace","authors":"Abdulaziz Almaleh","doi":"10.1109/IAICT59002.2023.10205620","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205620","url":null,"abstract":"The management of mental health issues in the workplace has always been a significant and challenging task, especially for professionals. Despite the evidence of the detrimental effects of preventable mental health disorders and stress in the workplace, many organizations have not taken enough preventative measures. To address this issue, data were collected from the OSMI website, which aims to raise awareness of mental illness in the workplace. The collected data was label encoded to improve prediction accuracy. Various machine learning techniques were applied to the data to develop a model to help individuals with mental health issues create a healthier work environment. Our proposed approach involved the implementation of classification algorithms, including Random Forest, Logistic Regression, Support Vector Machine, Adaboost, and Gradient Boosting, to obtain the highest accuracy possible.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"17 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116065933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205752
Justam, Indrabayu, Z. Zainuddin, Basri
This research aims to recognize and count the number of cocoa pods on trees using an Unmanned Aerial Vehicle (UAV). The main challenge in this research is to estimate the yield of stacked cocoa fruits and count the number of fruits on the trees. Therefore, this research proposes an algorithm that accurately estimates cocoa fruit yield. Image processing is done through many stages: image segmentation, boundary determination, shape analysis, and overlap analysis. The proposed algorithm can recognize and calculate the number of cocoa pods on a tree accurately through overlap analysis between the pods found. The results show that the detection accuracy after applying overlap analysis reaches 94.5%.
{"title":"Detection and Counting of the Number of Cocoa Fruits on Trees Using UAV","authors":"Justam, Indrabayu, Z. Zainuddin, Basri","doi":"10.1109/IAICT59002.2023.10205752","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205752","url":null,"abstract":"This research aims to recognize and count the number of cocoa pods on trees using an Unmanned Aerial Vehicle (UAV). The main challenge in this research is to estimate the yield of stacked cocoa fruits and count the number of fruits on the trees. Therefore, this research proposes an algorithm that accurately estimates cocoa fruit yield. Image processing is done through many stages: image segmentation, boundary determination, shape analysis, and overlap analysis. The proposed algorithm can recognize and calculate the number of cocoa pods on a tree accurately through overlap analysis between the pods found. The results show that the detection accuracy after applying overlap analysis reaches 94.5%.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133605716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205954
Zulfadli, A. A. Ilham, Indrabayu
The accuracy of customer reviews is crucial for an e-commerce platform to assist buyers in selecting high-quality products from a vast array of options. This research aims to develop a sentiment analysis model for evaluating customer opinions expressed in e-commerce product reviews. The proposed approach utilizes the Soft Voting (SV) technique, which demonstrates superior accuracy compared to the conventional Sentiment Selector (SS) method. The sentiment analysis model’s accuracy is determined by gathering probability values from three classifiers (Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB)) for each sentiment category (positive, neutral, negative). The evaluation is conducted using the Tokopedia product review dataset. The findings indicate that the Soft Voting (SV) model outperforms the Sentiment Selector (SS) approach. The proposed SV model achieves an accuracy, precision, recall, and f1-score of 69%, 70%, 69%, and 69%, respectively.
{"title":"Sentiment Analysis with Soft-Voting Method on Customer Reviews for Purchasing Transactions of E-Commerce","authors":"Zulfadli, A. A. Ilham, Indrabayu","doi":"10.1109/IAICT59002.2023.10205954","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205954","url":null,"abstract":"The accuracy of customer reviews is crucial for an e-commerce platform to assist buyers in selecting high-quality products from a vast array of options. This research aims to develop a sentiment analysis model for evaluating customer opinions expressed in e-commerce product reviews. The proposed approach utilizes the Soft Voting (SV) technique, which demonstrates superior accuracy compared to the conventional Sentiment Selector (SS) method. The sentiment analysis model’s accuracy is determined by gathering probability values from three classifiers (Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB)) for each sentiment category (positive, neutral, negative). The evaluation is conducted using the Tokopedia product review dataset. The findings indicate that the Soft Voting (SV) model outperforms the Sentiment Selector (SS) approach. The proposed SV model achieves an accuracy, precision, recall, and f1-score of 69%, 70%, 69%, and 69%, respectively.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133856140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205792
Rika Favoria Gusa, Risanuri Hidayat, H. A. Nugroho
Ultrasound imaging is widely used in medical diagnosis because it is non-invasive and free from ionizing radiation. However, ultrasound images have low contrast and contain speckle noise, making diagnosis difficult. Therefore, speckle noise reduction and image contrast enhancement are important prerequisites in ultrasound image processing. Many methods can be used in the ultrasound image pre-processing stage. In this paper, fetal ultrasound images were enhanced in contrast and sharpness using four enhancement methods, namely histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), unsharp masking (UM), and maximum local variation-based unsharp masking (MLVUM). These methods were applied to ultrasound images in two ways. Those are without filtering them and by first filtering them using a speckle reducing anisotropic diffusion (SRAD) filter. A comparative analysis was carried out on the performance of the four enhancement methods using the absolute mean brightness error (AMBE), average local contrast (ALC), and average gradient (AG) parameters. The results show that UM and MLVUM work better in increasing the contrast of fetal ultrasound images than HE and CLAHE. Applying the HE, CLAHE, UM, and MLVUM methods without filtering produces ultrasound images with better sharpness and contrast than enhanced images involving filtering but causing speckle noise amplification.
{"title":"Performance Analysis of Enhancement Methods on Fetal Ultrasound Images","authors":"Rika Favoria Gusa, Risanuri Hidayat, H. A. Nugroho","doi":"10.1109/IAICT59002.2023.10205792","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205792","url":null,"abstract":"Ultrasound imaging is widely used in medical diagnosis because it is non-invasive and free from ionizing radiation. However, ultrasound images have low contrast and contain speckle noise, making diagnosis difficult. Therefore, speckle noise reduction and image contrast enhancement are important prerequisites in ultrasound image processing. Many methods can be used in the ultrasound image pre-processing stage. In this paper, fetal ultrasound images were enhanced in contrast and sharpness using four enhancement methods, namely histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), unsharp masking (UM), and maximum local variation-based unsharp masking (MLVUM). These methods were applied to ultrasound images in two ways. Those are without filtering them and by first filtering them using a speckle reducing anisotropic diffusion (SRAD) filter. A comparative analysis was carried out on the performance of the four enhancement methods using the absolute mean brightness error (AMBE), average local contrast (ALC), and average gradient (AG) parameters. The results show that UM and MLVUM work better in increasing the contrast of fetal ultrasound images than HE and CLAHE. Applying the HE, CLAHE, UM, and MLVUM methods without filtering produces ultrasound images with better sharpness and contrast than enhanced images involving filtering but causing speckle noise amplification.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131333231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205851
Jyoti S. Shukla, R. Pandya
The contemporary drought monitoring approaches are bounded by the need for greater visibility toward potentially hazardous scenarios. Hence, a temporal predictive analysis is aimed in this paper, which will be highly advantageous in subsequent planning for catastrophe mitigation and for presaging the vegetative health or probable drought event. Furthermore, the well-established Machine Learning (ML) models, comprising Random Forest and Ridge regressor, in addition to Deep Learning (DL) models, such as Multilayer Perceptron, 1D-CNN, and Pix2Pix Generative Adversarial Networks (P2P), are implemented across several timeframes of 1, 3, 6, 9, and 12 months. Also, the ML/DL models are trained by utilizing the Vegetative Health Index (VHI) values derived from NOAA/AVHRR satellite data from 1981 to 2022, with the Indian state of Karnataka conforming as the research region. In addition to generating temporal forecasts, the P2P model is further executed to perform an annual seasonal analysis that depicts the variations in dryness over time, Subsequently, the prediction performance is assessed through Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) scores. The pattern of prediction accuracy annotated demonstrates more accurate forecasts for durations of one month (short term) with the best R2 score, MSE and MAE notching up to 0.88, 0.009, and 0.055, respectively; consequently, as hypothesized, escorted by a decline with widening temporal gaps for future projections such as the yearly level (long term) where the R2 score, MSE, and MAE reduced up to 0.60, 0.030 and 0.114 respectively. Also, the seasonal analysis delivered valuable insights into the influences of various climatic factors on the dryness level of the landmass, which will act conducive to better future planning and preparation.
{"title":"Predictive Modeling of Vegetative Drought Using ML/DL Approach on Temporal Satellite Data","authors":"Jyoti S. Shukla, R. Pandya","doi":"10.1109/IAICT59002.2023.10205851","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205851","url":null,"abstract":"The contemporary drought monitoring approaches are bounded by the need for greater visibility toward potentially hazardous scenarios. Hence, a temporal predictive analysis is aimed in this paper, which will be highly advantageous in subsequent planning for catastrophe mitigation and for presaging the vegetative health or probable drought event. Furthermore, the well-established Machine Learning (ML) models, comprising Random Forest and Ridge regressor, in addition to Deep Learning (DL) models, such as Multilayer Perceptron, 1D-CNN, and Pix2Pix Generative Adversarial Networks (P2P), are implemented across several timeframes of 1, 3, 6, 9, and 12 months. Also, the ML/DL models are trained by utilizing the Vegetative Health Index (VHI) values derived from NOAA/AVHRR satellite data from 1981 to 2022, with the Indian state of Karnataka conforming as the research region. In addition to generating temporal forecasts, the P2P model is further executed to perform an annual seasonal analysis that depicts the variations in dryness over time, Subsequently, the prediction performance is assessed through Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) scores. The pattern of prediction accuracy annotated demonstrates more accurate forecasts for durations of one month (short term) with the best R2 score, MSE and MAE notching up to 0.88, 0.009, and 0.055, respectively; consequently, as hypothesized, escorted by a decline with widening temporal gaps for future projections such as the yearly level (long term) where the R2 score, MSE, and MAE reduced up to 0.60, 0.030 and 0.114 respectively. Also, the seasonal analysis delivered valuable insights into the influences of various climatic factors on the dryness level of the landmass, which will act conducive to better future planning and preparation.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124844035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205785
Antor Mahamudul Hashan, Kumar Avinash, Al-Saeedi Adnan, Subhankar Dey, Rizu, R. Islam
The automatic human facial emotion recognition (AHFER) system has its wide significant contribution in several disciplines, such as human-computer collaboration, human-robot interaction, and so on. Multiple research projects have been conducted regarding this topic because it is a challenging and interesting task, especially in the area of computer vision. The purpose of the work is to recognize facial emotions using a depthwise separable convolutional neural network (DS-CNN). Apart from that, a facial emotion dataset has been proposed, and splitting functions, intensity normalization, image cropping, and grayscale conversion have been used in data pre-processing. The AHFER system is capable of recognizing four types of emotions: happy, sad, angry, and neutral. The results of the experiment showed that the AHFER method is 99 percent accurate when training and 93 percent accurate when validating. Additionally, we determined the confusion matrix with precision, recall, and fl-score. A comparison between the DS-CNN and DNN models was performed. The DS-CNN model performed significantly better than the DNN model. The DS-CNN model could be improved in the future by including more facial emotion categories.
{"title":"Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network","authors":"Antor Mahamudul Hashan, Kumar Avinash, Al-Saeedi Adnan, Subhankar Dey, Rizu, R. Islam","doi":"10.1109/IAICT59002.2023.10205785","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205785","url":null,"abstract":"The automatic human facial emotion recognition (AHFER) system has its wide significant contribution in several disciplines, such as human-computer collaboration, human-robot interaction, and so on. Multiple research projects have been conducted regarding this topic because it is a challenging and interesting task, especially in the area of computer vision. The purpose of the work is to recognize facial emotions using a depthwise separable convolutional neural network (DS-CNN). Apart from that, a facial emotion dataset has been proposed, and splitting functions, intensity normalization, image cropping, and grayscale conversion have been used in data pre-processing. The AHFER system is capable of recognizing four types of emotions: happy, sad, angry, and neutral. The results of the experiment showed that the AHFER method is 99 percent accurate when training and 93 percent accurate when validating. Additionally, we determined the confusion matrix with precision, recall, and fl-score. A comparison between the DS-CNN and DNN models was performed. The DS-CNN model performed significantly better than the DNN model. The DS-CNN model could be improved in the future by including more facial emotion categories.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114426725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205553
Anjli Varghese, M. Jawahar, A. Prince
This paper introduces a new set of large-scale leather image data. Unlike the existing dataset, it comprises 7600 images with varied non-ideal behavior. The aim is to develop a versatile identification model that can efficiently determine the species from complex/practically challenging leather images. Hence, this research proposed to train a convolutional neural network (CNN) on a large-scale dataset. It performs a comparative study on five CNNs: ResNet50, MobileNet, DenseNet201, InceptionNetV3, and InceptionResNetV2. The analysis reveals that InceptionNetV3 outperforms with 98.23% accuracy and 1.71% negligible error. It also evaluates the generalization power of the trained InceptionNetV3 on the existing small-scale dataset (1200 images). Although the model is trained on non-ideal leather images, it results in 94.07% accuracy. However, learning from present and existing datasets improves the prediction rate to 98.5% accuracy. Thus, this work efficiently models a deeper CNN to predict species from leather images with ideal and non-ideal behavior. Contrary to the previous machine learning-based species prediction methods, the present deep learning method designs a fully-automated model with accurate and robust results.
{"title":"A Study on Deep Learning Models for Automatic Species Identification from Novel Leather Images","authors":"Anjli Varghese, M. Jawahar, A. Prince","doi":"10.1109/IAICT59002.2023.10205553","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205553","url":null,"abstract":"This paper introduces a new set of large-scale leather image data. Unlike the existing dataset, it comprises 7600 images with varied non-ideal behavior. The aim is to develop a versatile identification model that can efficiently determine the species from complex/practically challenging leather images. Hence, this research proposed to train a convolutional neural network (CNN) on a large-scale dataset. It performs a comparative study on five CNNs: ResNet50, MobileNet, DenseNet201, InceptionNetV3, and InceptionResNetV2. The analysis reveals that InceptionNetV3 outperforms with 98.23% accuracy and 1.71% negligible error. It also evaluates the generalization power of the trained InceptionNetV3 on the existing small-scale dataset (1200 images). Although the model is trained on non-ideal leather images, it results in 94.07% accuracy. However, learning from present and existing datasets improves the prediction rate to 98.5% accuracy. Thus, this work efficiently models a deeper CNN to predict species from leather images with ideal and non-ideal behavior. Contrary to the previous machine learning-based species prediction methods, the present deep learning method designs a fully-automated model with accurate and robust results.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117133035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205625
Muhammad Ilham Alhari, M. Lubis
Internet facilities in Dormitory Hall Telkom University use a 2.4Ghz wireless network. This facility is used by staff, employees, educators, and students. Therefore, bandwidth management is needed to maintain stability, distribute traffic evenly, and maintain connectivity. The method used is the Network Development Life Cycle (NDLC). The purpose of this study is to stabilize bandwidth usage in the Simple Network Management Protocol (SNMP) protocol, distribute upload and download speeds evenly, improve Quality of Service (QoS) through throughput, delay, jitter, and packet loss parameters. The results of this study provide quantitative output for each objective and QoS parameters that can be used as a reference for determining the distribution of bandwidth in accordance with the bandwidth capacity that is owned without disturbing QoS on wireless networks.
{"title":"Quality of Service (QoS) Wifi Network Study Case: Telkom University Dormitory Hall","authors":"Muhammad Ilham Alhari, M. Lubis","doi":"10.1109/IAICT59002.2023.10205625","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205625","url":null,"abstract":"Internet facilities in Dormitory Hall Telkom University use a 2.4Ghz wireless network. This facility is used by staff, employees, educators, and students. Therefore, bandwidth management is needed to maintain stability, distribute traffic evenly, and maintain connectivity. The method used is the Network Development Life Cycle (NDLC). The purpose of this study is to stabilize bandwidth usage in the Simple Network Management Protocol (SNMP) protocol, distribute upload and download speeds evenly, improve Quality of Service (QoS) through throughput, delay, jitter, and packet loss parameters. The results of this study provide quantitative output for each objective and QoS parameters that can be used as a reference for determining the distribution of bandwidth in accordance with the bandwidth capacity that is owned without disturbing QoS on wireless networks.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134132567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205809
Fadiah Nadhila, A. Alamsyah
The study establishes utilizing the Big Five Personality framework and a Personality Measurement Platform (PMP) for personality analysis. Moreover, Customer Complaint Ontology (CCOntology) framework implements a Naive Bayes machine learning methodology to evaluate and scrutinize customer complaints. The algorithm works by calculating the probability of each complaint category. This association is measured in percentages, enabling the identification of specific personality traits related to customer complaints through identifying complaint characteristics and areas of concern. The study has found that individuals with neurotic personality traits who encounter customer complaints are often associated with problem categories such as Non-Contract, Privacy, and Contract and are more likely to express strong emotional dissatisfaction with a product or service. Linking customer complaints with their corresponding personalities can be an incredibly effective and innovative strategy for personalized customer service businesses in anticipating their needs and providing tailored recommendations that can improve the likelihood of customers making purchases. This approach involves educating employees on the importance of actively listening to customers, asking relevant questions, and anticipating their needs, ensuring that businesses can enhance customer satisfaction while building a loyal customer base.
{"title":"Mapping Personality Traits to Customer Complaints: Framework for Personalized Customer Service","authors":"Fadiah Nadhila, A. Alamsyah","doi":"10.1109/IAICT59002.2023.10205809","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205809","url":null,"abstract":"The study establishes utilizing the Big Five Personality framework and a Personality Measurement Platform (PMP) for personality analysis. Moreover, Customer Complaint Ontology (CCOntology) framework implements a Naive Bayes machine learning methodology to evaluate and scrutinize customer complaints. The algorithm works by calculating the probability of each complaint category. This association is measured in percentages, enabling the identification of specific personality traits related to customer complaints through identifying complaint characteristics and areas of concern. The study has found that individuals with neurotic personality traits who encounter customer complaints are often associated with problem categories such as Non-Contract, Privacy, and Contract and are more likely to express strong emotional dissatisfaction with a product or service. Linking customer complaints with their corresponding personalities can be an incredibly effective and innovative strategy for personalized customer service businesses in anticipating their needs and providing tailored recommendations that can improve the likelihood of customers making purchases. This approach involves educating employees on the importance of actively listening to customers, asking relevant questions, and anticipating their needs, ensuring that businesses can enhance customer satisfaction while building a loyal customer base.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115435644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205529
Musrinah, M. A. Murti, Faisal Budiman
This study designed an identification electrical loads/devices system using 3 machine learning models which are Support Vector Machine, Random Forest, and Decision Tree algorithms. The system was applied for monitoring the use of electrical devices that are operating in order to find out the indications of waste. Data collection and testing of electrical devices was carried out using 7 electronic devices, namely rice cookers, laptops, lamps, hair dryers, fans, dispensers, and phone chargers. This study integrated EMG25, Current Transformer MSQ-30, electrical devices, USB Module RS-485 and Raspberry Pi3 for data processing, forming system models by algorithms and testing system identification. This research produced a system model of three algorithm Support Vector Machine, Random Forest, and Decision Tree with an accuracy of 93.5%, 95,5% and 92.5% respectively and wall time 0.489, 0.337, and 0.0278 second it was proven to be able to identify electrical devices that were operating correctly based on data characteristics.
{"title":"Identification of Household Electric Load Based on Harmonic Parameters Using SVM, RF, and DT Algorithms","authors":"Musrinah, M. A. Murti, Faisal Budiman","doi":"10.1109/IAICT59002.2023.10205529","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205529","url":null,"abstract":"This study designed an identification electrical loads/devices system using 3 machine learning models which are Support Vector Machine, Random Forest, and Decision Tree algorithms. The system was applied for monitoring the use of electrical devices that are operating in order to find out the indications of waste. Data collection and testing of electrical devices was carried out using 7 electronic devices, namely rice cookers, laptops, lamps, hair dryers, fans, dispensers, and phone chargers. This study integrated EMG25, Current Transformer MSQ-30, electrical devices, USB Module RS-485 and Raspberry Pi3 for data processing, forming system models by algorithms and testing system identification. This research produced a system model of three algorithm Support Vector Machine, Random Forest, and Decision Tree with an accuracy of 93.5%, 95,5% and 92.5% respectively and wall time 0.489, 0.337, and 0.0278 second it was proven to be able to identify electrical devices that were operating correctly based on data characteristics.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"47 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121000776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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