Y. Purwati, F. S. Utomo, Nikmah Trinarsih, Hanif Hidayatulloh
The Al-Quran is the sacred book of Muslims, and it provides God's word in the form of orders, instructions, and guidelines for people to follow to have happy lives both here and in the afterlife. Several earlier research has used ontologies to store the knowledge found in the Quran. The previous study focused on extracting the relationship between classes and instances or the "is-a relation" by classifying instances based on the referenced class. Based on the performance testing of the instances classification framework, the test results show that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation had dropped the accuracy value to 65.41% when the test data size was increased to 30%. Likewise, with BPNN with TF-IDF and stemming operations. In the Indonesian Quran translation dataset with a test data size of 30%, the accuracy value drops to 57.86%. Instances classification based on the thematic topics of the Qur'an aims to connect verses (instances) to topics (classes) to get an overall picture of the topic and provide a better understanding to users. This study aims to apply the feature selection technique to the instances classification framework for the Al-Quran ontology and to analyze the impact of applying the feature selection technique to the framework with a small dataset and training data. The instances classification framework in this study consists of several stages: text-preprocessing, feature extraction, feature selection, and instances classification. We applied Chiq-Square as a technique to perform feature selection. SVM and BPNN as a classifier. Based on the experiment results, it can be concluded that the feature selection implementation using Chi-Square increases the value of precision, f-measure, and accuracy on the test data size from 40% to 60% in all datasets. The feature selection using Chi-Square and SVM classifier provides the highest precision value with a test data size of 60% on the Tafsir Quran dataset from the Ministry of Religious Affairs Indonesia: 64.36%. Furthermore, the feature selection implementation and BPNN classifier also increase the highest accuracy value with a test data size of 60% in the Quranic Tafsir dataset from the Ministry of Religion of the Republic of Indonesia: 63.09%.
{"title":"Feature Selection Technique to Improve the Instances Classification Framework Performance for Quran Ontology","authors":"Y. Purwati, F. S. Utomo, Nikmah Trinarsih, Hanif Hidayatulloh","doi":"10.30630/joiv.7.2.1195","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1195","url":null,"abstract":"The Al-Quran is the sacred book of Muslims, and it provides God's word in the form of orders, instructions, and guidelines for people to follow to have happy lives both here and in the afterlife. Several earlier research has used ontologies to store the knowledge found in the Quran. The previous study focused on extracting the relationship between classes and instances or the \"is-a relation\" by classifying instances based on the referenced class. Based on the performance testing of the instances classification framework, the test results show that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation had dropped the accuracy value to 65.41% when the test data size was increased to 30%. Likewise, with BPNN with TF-IDF and stemming operations. In the Indonesian Quran translation dataset with a test data size of 30%, the accuracy value drops to 57.86%. Instances classification based on the thematic topics of the Qur'an aims to connect verses (instances) to topics (classes) to get an overall picture of the topic and provide a better understanding to users. This study aims to apply the feature selection technique to the instances classification framework for the Al-Quran ontology and to analyze the impact of applying the feature selection technique to the framework with a small dataset and training data. The instances classification framework in this study consists of several stages: text-preprocessing, feature extraction, feature selection, and instances classification. We applied Chiq-Square as a technique to perform feature selection. SVM and BPNN as a classifier. Based on the experiment results, it can be concluded that the feature selection implementation using Chi-Square increases the value of precision, f-measure, and accuracy on the test data size from 40% to 60% in all datasets. The feature selection using Chi-Square and SVM classifier provides the highest precision value with a test data size of 60% on the Tafsir Quran dataset from the Ministry of Religious Affairs Indonesia: 64.36%. Furthermore, the feature selection implementation and BPNN classifier also increase the highest accuracy value with a test data size of 60% in the Quranic Tafsir dataset from the Ministry of Religion of the Republic of Indonesia: 63.09%.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90345613","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}
Lung Cancer is one of the cancer types with the most significant mortality rate, mainly because of the disease's slow detection. Therefore, the early identification of this disease is crucial. However, the primary issue of microarray is the curse of dimensionality. This problem is related to the characteristic of microarray data, which has a small sample size yet many attributes. Moreover, this problem could lower the accuracy of cancer detection systems. Various machines and deep learning techniques have been researched to solve this problem. This paper implemented a deep learning method named Convolutional Recurrent Neural Network (CRNN) to build the Lung Cancer detection system. Convolutional neural networks (CNN) are used to extract features, and recurrent neural networks (RNN) are used to summarize the derived features. CNN and RNN methods are combined in CRNN to derive the advantages of each of the methods. Several previous research uses CRNN to build a Lung Cancer detection system using medical image biomarkers (MRI or CT scan). Thus, the researchers concluded that CRNN achieved higher accuracy than CNN and RNN independently. Moreover, CRNN was implemented in this research by using a microarray-based Lung Cancer dataset. Furthermore, different drop-out values are compared to determine the best drop-out value for the system. Thus, the result shows that CRNN gave a higher accuracy than CNN and RNN. The CRNN method achieved the highest accuracy of 91%, while the CNN and RNN methods achieved 83% and 71% accuracy, respectively.
{"title":"Implementation of CRNN Method for Lung Cancer Detection based on Microarray Data","authors":"Azka Khoirunnisa, -. Adiwijaya, D. Adytia","doi":"10.30630/joiv.7.2.1339","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1339","url":null,"abstract":"Lung Cancer is one of the cancer types with the most significant mortality rate, mainly because of the disease's slow detection. Therefore, the early identification of this disease is crucial. However, the primary issue of microarray is the curse of dimensionality. This problem is related to the characteristic of microarray data, which has a small sample size yet many attributes. Moreover, this problem could lower the accuracy of cancer detection systems. Various machines and deep learning techniques have been researched to solve this problem. This paper implemented a deep learning method named Convolutional Recurrent Neural Network (CRNN) to build the Lung Cancer detection system. Convolutional neural networks (CNN) are used to extract features, and recurrent neural networks (RNN) are used to summarize the derived features. CNN and RNN methods are combined in CRNN to derive the advantages of each of the methods. Several previous research uses CRNN to build a Lung Cancer detection system using medical image biomarkers (MRI or CT scan). Thus, the researchers concluded that CRNN achieved higher accuracy than CNN and RNN independently. Moreover, CRNN was implemented in this research by using a microarray-based Lung Cancer dataset. Furthermore, different drop-out values are compared to determine the best drop-out value for the system. Thus, the result shows that CRNN gave a higher accuracy than CNN and RNN. The CRNN method achieved the highest accuracy of 91%, while the CNN and RNN methods achieved 83% and 71% accuracy, respectively.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"186 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73338369","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}
Tan Wen Yit, Rohayanti Hassan, N. Zakaria, S. Kasim, Sim Hiew Moi, A. R. Khairuddin, Hidra Amnur
The unstable properties and the advantages of the mRNA vaccine have encouraged many experts worldwide in tackling the degradation problem. Machine learning models have been highly implemented in bioinformatics and the healthcare fieldstone insights from biological data. Thus, machine learning plays an important role in predicting the degradation rate of mRNA vaccine candidates. Stanford University has held an OpenVaccine Challenge competition on Kaggle to gather top solutions in solving the mentioned problems, and a multi-column root means square error (MCRMSE) has been used as a main performance metric. The Nucleic Transformer has been proposed by different researchers as a deep learning solution that is able to utilize a self-attention mechanism and Convolutional Neural Network (CNN). Hence, this paper would like to enhance the existing Nucleic Transformer performance by utilizing the AdaBelief or RangerAdaBelief optimizer with a proposed decoder that consists of a normalization layer between two linear layers. Based on the experimental result, the performance of the enhanced Nucleic Transformer outperforms the existing solution. In this study, the AdaBelief optimizer performs better than the RangerAdaBelief optimizer, even though it possesses Ranger’s advantages. The advantages of the proposed decoder can only be shown when there is limited data. When the data is sufficient, the performance might be similar but still better than the linear decoder if and only if the AdaBelief optimizer is used. As a result, the combination of the AdaBelief optimizer with the proposed decoder performs the best with 2.79% and 1.38% performance boost in public and private MCRMSE, respectively.
{"title":"Transformer in mRNA Degradation Prediction","authors":"Tan Wen Yit, Rohayanti Hassan, N. Zakaria, S. Kasim, Sim Hiew Moi, A. R. Khairuddin, Hidra Amnur","doi":"10.30630/joiv.7.2.1165","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1165","url":null,"abstract":"The unstable properties and the advantages of the mRNA vaccine have encouraged many experts worldwide in tackling the degradation problem. Machine learning models have been highly implemented in bioinformatics and the healthcare fieldstone insights from biological data. Thus, machine learning plays an important role in predicting the degradation rate of mRNA vaccine candidates. Stanford University has held an OpenVaccine Challenge competition on Kaggle to gather top solutions in solving the mentioned problems, and a multi-column root means square error (MCRMSE) has been used as a main performance metric. The Nucleic Transformer has been proposed by different researchers as a deep learning solution that is able to utilize a self-attention mechanism and Convolutional Neural Network (CNN). Hence, this paper would like to enhance the existing Nucleic Transformer performance by utilizing the AdaBelief or RangerAdaBelief optimizer with a proposed decoder that consists of a normalization layer between two linear layers. Based on the experimental result, the performance of the enhanced Nucleic Transformer outperforms the existing solution. In this study, the AdaBelief optimizer performs better than the RangerAdaBelief optimizer, even though it possesses Ranger’s advantages. The advantages of the proposed decoder can only be shown when there is limited data. When the data is sufficient, the performance might be similar but still better than the linear decoder if and only if the AdaBelief optimizer is used. As a result, the combination of the AdaBelief optimizer with the proposed decoder performs the best with 2.79% and 1.38% performance boost in public and private MCRMSE, respectively.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79707930","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}
Puja Putri Abdullah, T. Raharjo, B. Hardian, Tiarma Simanungkalit
Applying Agile methodologies in the public sector is nothing new. In recent years, governments worldwide have moved towards Agile development, especially with the Pandemic that requires governments to move and make decisions quickly. However, the difference between the government system and the private sector, such as holding the principle of a hierarchy of authority, still challenges Agile application. This study aims to explore challenges and provide solutions for applying Agile project management in the public sector by conducting a systematic literature review (SLR) using the PRISMA method. The literature used in the SLR was obtained from four paper databases, namely Scopus, IEEE Xplore, ACM, and Emerald Insight. Five hundred ninety-five papers were found, and 18 suitable papers were obtained, which were then analyzed and obtained a total of 43 challenging issues. Each of these issues is grouped based on eight project performance domains of PMBOK 7th edition, and the solution for each challenge is obtained from the mapping results from the SLR papers and PMBOK 7th edition Guide. The results showed that the most issues were in the Development Approach and Lifecycle and Project Work domain categories, with 8 issues each. Followed by Team with 7 issues, Stakeholder with 6 issues, Delivery with 5 issues, Measurement with 4 issues, Planning with 3 issues, and Uncertainty with 2 issues. This research can be useful for academics or practitioners as a reference in facing the challenges of implementing Agile project management in the public sector
{"title":"Challenges and Best Practices Solution of Agile Project Management in Public Sector: A Systematic Literature Review","authors":"Puja Putri Abdullah, T. Raharjo, B. Hardian, Tiarma Simanungkalit","doi":"10.30630/joiv.7.2.1098","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1098","url":null,"abstract":"Applying Agile methodologies in the public sector is nothing new. In recent years, governments worldwide have moved towards Agile development, especially with the Pandemic that requires governments to move and make decisions quickly. However, the difference between the government system and the private sector, such as holding the principle of a hierarchy of authority, still challenges Agile application. This study aims to explore challenges and provide solutions for applying Agile project management in the public sector by conducting a systematic literature review (SLR) using the PRISMA method. The literature used in the SLR was obtained from four paper databases, namely Scopus, IEEE Xplore, ACM, and Emerald Insight. Five hundred ninety-five papers were found, and 18 suitable papers were obtained, which were then analyzed and obtained a total of 43 challenging issues. Each of these issues is grouped based on eight project performance domains of PMBOK 7th edition, and the solution for each challenge is obtained from the mapping results from the SLR papers and PMBOK 7th edition Guide. The results showed that the most issues were in the Development Approach and Lifecycle and Project Work domain categories, with 8 issues each. Followed by Team with 7 issues, Stakeholder with 6 issues, Delivery with 5 issues, Measurement with 4 issues, Planning with 3 issues, and Uncertainty with 2 issues. This research can be useful for academics or practitioners as a reference in facing the challenges of implementing Agile project management in the public sector","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"71 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83612080","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}
S. S. Hidayat, Dwi Rahmawati, Muhamad Cahyo Ardi Prabowo, L. Triyono, Farika T. Putri
Seed inspection is crucial for plant nurseries and farmers as it ensures seed quality when growing seedlings. It is traditionally accomplished by expert inspectors filtering samples manually, but there are some challenges, such as cost, accuracy, and large numbers. Speed and accuracy were the main conditions for increasing agricultural productivity. Machine learning is a sub-science of Artificial Intelligence that can be applied in research on the classification of rice seed quality. The pipeline of a machine learning system is dataset collection, training, validation, and testing. Model making begins with taking data on the characteristics of rice seeds based on physical parameters in the form of seed shape and color. The dataset used is two thousand images divided into two categories, namely superior seeds and non-superior seeds. Training and Validation was conducted using the Convolutional Neural Network (CNN) algorithm with the concept of cross-validation on Google Collaboratory notebooks. The ratio split of train data and validation data in modeling from a dataset is 80:20. The result of the model formed is a model with the development of a Deep Convolutional Neural Network (Deep CNN) that can classify the digital image data of rice seeds from the results of data calls uploaded into the system. The results of the experiment conducted on 30 test data can be analyzed so that the system can classify superior and non-superior seeds with a precision value of 93% and a recall of 95%.
{"title":"Determining the Rice Seeds Quality Using Convolutional Neural Network","authors":"S. S. Hidayat, Dwi Rahmawati, Muhamad Cahyo Ardi Prabowo, L. Triyono, Farika T. Putri","doi":"10.30630/joiv.7.2.1175","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1175","url":null,"abstract":"Seed inspection is crucial for plant nurseries and farmers as it ensures seed quality when growing seedlings. It is traditionally accomplished by expert inspectors filtering samples manually, but there are some challenges, such as cost, accuracy, and large numbers. Speed and accuracy were the main conditions for increasing agricultural productivity. Machine learning is a sub-science of Artificial Intelligence that can be applied in research on the classification of rice seed quality. The pipeline of a machine learning system is dataset collection, training, validation, and testing. Model making begins with taking data on the characteristics of rice seeds based on physical parameters in the form of seed shape and color. The dataset used is two thousand images divided into two categories, namely superior seeds and non-superior seeds. Training and Validation was conducted using the Convolutional Neural Network (CNN) algorithm with the concept of cross-validation on Google Collaboratory notebooks. The ratio split of train data and validation data in modeling from a dataset is 80:20. The result of the model formed is a model with the development of a Deep Convolutional Neural Network (Deep CNN) that can classify the digital image data of rice seeds from the results of data calls uploaded into the system. The results of the experiment conducted on 30 test data can be analyzed so that the system can classify superior and non-superior seeds with a precision value of 93% and a recall of 95%.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"164 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73130812","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}
Advanced technologies such as Big Data, the Internet of Things, artificial intelligence, robotics, cloud computing, and additive manufacturing are enablers of the industry 4.0 revolution and signify intense transformations in socio-economic systems. This work investigates the enabling nature of certain technologies in the emergence and development of different quality paradigms. Each enabling technology is related to a certain industrial revolution; consequently, a certain quality paradigm has been developed. Where is quality management now, in which direction its development is going, and what can be expected in the future is discussed in this paper. The research focuses on the most important factors discussed in the literature that influenced quality development throughout history. Results are presented in written and graphical form and include newly established theories based on recent innovations. Since this is a cumulative overview of different quality methods, it only briefly discusses the most important theories. It was observed that with Industry 4.0 enabling technologies, we are currently experiencing a transformation in this discipline, reaching a higher level in the competition for market positioning. Particularly, meeting explicit customer needs is upgraded with latent customer needs - linked to the customer's emotional responses (delight) to products/services. This paper contributes to a new field of research that is becoming increasingly popular.
{"title":"Industry 4.0: The New Quality Management Paradigm in Era of Industrial Internet of Things","authors":"Benjamin Duraković, Maida Halilovic","doi":"10.30630/joiv.7.2.1738","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1738","url":null,"abstract":"Advanced technologies such as Big Data, the Internet of Things, artificial intelligence, robotics, cloud computing, and additive manufacturing are enablers of the industry 4.0 revolution and signify intense transformations in socio-economic systems. This work investigates the enabling nature of certain technologies in the emergence and development of different quality paradigms. Each enabling technology is related to a certain industrial revolution; consequently, a certain quality paradigm has been developed. Where is quality management now, in which direction its development is going, and what can be expected in the future is discussed in this paper. The research focuses on the most important factors discussed in the literature that influenced quality development throughout history. Results are presented in written and graphical form and include newly established theories based on recent innovations. Since this is a cumulative overview of different quality methods, it only briefly discusses the most important theories. It was observed that with Industry 4.0 enabling technologies, we are currently experiencing a transformation in this discipline, reaching a higher level in the competition for market positioning. Particularly, meeting explicit customer needs is upgraded with latent customer needs - linked to the customer's emotional responses (delight) to products/services. This paper contributes to a new field of research that is becoming increasingly popular.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135903200","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}
There has been a considerable rise in the amount of research and development focused on computer vision over the previous two decades. One of the most critical processes in computer vision is "visual tracking," which involves following objects with a camera. Tracking objects is the practice of following an individual moving object or group of moving things over time. Identifying or connecting target elements in consecutive video frames of a badminton match requires visual object tracking. The aim of this study is to identify badminton players using the You Only Look Once (YOLO) technique in conjunction with a variety of training heuristics. This methodology has a few advantages over other approaches to detecting objects. The convolutional neural network and Fast convolutional neural network are two examples of the many algorithmic approaches that are available. In this study, a neural network is used to produce predictions about the bounding boxes and the class probabilities for these boxes.. The results demonstrated that it was far faster than other methods in terms of its ability to recognize the image. The performance of image classification networks significantly improved as a result of the implementation of a variety of training strategies for the detection of objects. The mean average precision score for YOLOv3 with various training heuristics increased from 32.0 to 36.0 as a direct result of these adjustments. In comparison to YOLOv3, our future study might examine the performance of alternative models like Faster R-CNN or RetinaNet.
{"title":"Improving Badminton Player Detection Using YOLOv3 with Different Training Heuristic","authors":"Muhammad Abdul Haq, N. Tagawa","doi":"10.30630/joiv.7.2.1166","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1166","url":null,"abstract":"There has been a considerable rise in the amount of research and development focused on computer vision over the previous two decades. One of the most critical processes in computer vision is \"visual tracking,\" which involves following objects with a camera. Tracking objects is the practice of following an individual moving object or group of moving things over time. Identifying or connecting target elements in consecutive video frames of a badminton match requires visual object tracking. The aim of this study is to identify badminton players using the You Only Look Once (YOLO) technique in conjunction with a variety of training heuristics. This methodology has a few advantages over other approaches to detecting objects. The convolutional neural network and Fast convolutional neural network are two examples of the many algorithmic approaches that are available. In this study, a neural network is used to produce predictions about the bounding boxes and the class probabilities for these boxes.. The results demonstrated that it was far faster than other methods in terms of its ability to recognize the image. The performance of image classification networks significantly improved as a result of the implementation of a variety of training strategies for the detection of objects. The mean average precision score for YOLOv3 with various training heuristics increased from 32.0 to 36.0 as a direct result of these adjustments. In comparison to YOLOv3, our future study might examine the performance of alternative models like Faster R-CNN or RetinaNet.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86699894","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}
Faisal Dharma Adhinata, Nur Ghaniaviyanto Ramadhan, Nia Annisa Ferani Tanjung, Muhammad Dzulfikar Fauzi
Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies
农业是印尼满足人们日常粮食需求的主要部门。土豆是代替大米的农产品之一。马铃薯的生长需要防止杂草争夺营养。喷洒农药会造成环境污染，影响栽培植物。目前，正在开发利用人工智能(AI)方法对作物进行分类的农业技术。使用人工智能的分类过程取决于获得的数据集的数量。本研究获得的数据集数量不是很大，所以对于使用的人工智能方法有特殊的要求。本研究旨在结合局部特征提取方法和深度特征方法以及监督机器学习对小数据集进行分类。本研究使用的局部特征方法是局部二值模式(local Binary Pattern, LBP)和定向梯度直方图(Histogram of Oriented Gradients, HOG)，深层特征方法是MobileNet和MobileNetV2。著名的支持向量机(SVM)使用分类方法来分离两个数据类。实验结果表明，局部特征HOG方法在训练过程中速度最快。然而，最准确的结果是使用MobileNetV2深度特征方法，准确率为98%。由于特征提取过程需要经过许多神经网络层，因此深度特征产生了最好的精度。这项研究可以提供如何通过结合几种策略来分析少量数据集的见解
{"title":"A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets","authors":"Faisal Dharma Adhinata, Nur Ghaniaviyanto Ramadhan, Nia Annisa Ferani Tanjung, Muhammad Dzulfikar Fauzi","doi":"10.30630/joiv.7.2.1164","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1164","url":null,"abstract":"Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"427 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75902863","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}
R. Andreswari, R. Fauzi, Berlian Maulidya Izzati, Vandha Widartha, Dita Pramesti
Independent Campus, Freedom to Learn (ICFL) Program is one of the manifestations of student-centered learning. This program can help students reach their full potential by allowing them to pursue their passions and talents. This study aims to see how the segmentation of students participating in the ICFL program is based on demographic data. This research is based on survey responses from students participating in the ICFL program. The method used in this study is input data preparation, pre-processing, data cleansing, and data analysis. The information will be pre-processed before being utilized and evaluated. To help produce better outcomes in data clustering, the K-Means clustering approach is used, which is processed using the Python computer language. The data is clustered using the K-Means clustering approach based on gender characteristics, Grade Point Average (GPA), university entrance selection, ICFL category, and year or semester when participating in ICFL. This study resulted in three clusters with each of its criteria. The dominant gender is found in clusters 2 (100% female) and 3 (100% male). Software Development was the most popular ICFL category among students in cluster 1, accounting for 67%, while Design and Analysis Information Systems was the most popular in clusters 2 and 3. The most dominant ICFL program is found in three clusters. ICFL - Internship program in which at least 40% of participants come from each cluster. The research results are expected to assist stakeholders in evaluating the implementation of the ICFL program.
{"title":"Students Demography Clustering Based on The ICFL Program Using K-Means Algorithm","authors":"R. Andreswari, R. Fauzi, Berlian Maulidya Izzati, Vandha Widartha, Dita Pramesti","doi":"10.30630/joiv.7.2.1916","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1916","url":null,"abstract":"Independent Campus, Freedom to Learn (ICFL) Program is one of the manifestations of student-centered learning. This program can help students reach their full potential by allowing them to pursue their passions and talents. This study aims to see how the segmentation of students participating in the ICFL program is based on demographic data. This research is based on survey responses from students participating in the ICFL program. The method used in this study is input data preparation, pre-processing, data cleansing, and data analysis. The information will be pre-processed before being utilized and evaluated. To help produce better outcomes in data clustering, the K-Means clustering approach is used, which is processed using the Python computer language. The data is clustered using the K-Means clustering approach based on gender characteristics, Grade Point Average (GPA), university entrance selection, ICFL category, and year or semester when participating in ICFL. This study resulted in three clusters with each of its criteria. The dominant gender is found in clusters 2 (100% female) and 3 (100% male). Software Development was the most popular ICFL category among students in cluster 1, accounting for 67%, while Design and Analysis Information Systems was the most popular in clusters 2 and 3. The most dominant ICFL program is found in three clusters. ICFL - Internship program in which at least 40% of participants come from each cluster. The research results are expected to assist stakeholders in evaluating the implementation of the ICFL program. ","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90231695","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}
The increasing utilization of IoT technology in various fields creates opportunities and risks for investigating all cybercrimes. At the same time, many research studies have concentrated on security and forensic investigations to collect digital evidence on IoT devices. However, until now, the IoT platform has not fully evolved to adjust the tools, methods, and procedures of IoT forensic investigations. The main reasons for investigators are the characteristics and infrastructure of IoT devices. For example, device number variations, heterogeneity, distribution of protocols used, data duplication, complexity, limited memory, etc. As a result, resulting is a tough challenge to identify, collect, examine, analyze, and present potential IoT digital evidence for forensic investigative processes effectively and efficiently. Indeed, there is not fully used and adapted international standard for the perfect IoT forensic investigation framework. In the research method, a literature review has been carried out by producing previous research studies that have contributed to further facing challenges. To keep the quality of the literature review, research questions (RQ) were conducted for all studies related to the IoT forensic investigation framework between 2015-2022. This research results highlight and provides a comprehensive overview of the twenty current IoT forensic investigation framework that has been proposed. Then, a summary or contribution is presented focusing on the latest research, grouping the forensic phases, and evaluating essential frameworks in the IoT forensic investigation process to obtain digital evidence. Finally, open research issues are presented for further research in developing IoT forensic investigative framework.
{"title":"An Overview Diversity Framework for Internet of Things (IoT) Forensic Investigation","authors":"Randi Rizal, S. R. Selamat, M. Z. Mas'ud","doi":"10.30630/joiv.7.2.1520","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1520","url":null,"abstract":"The increasing utilization of IoT technology in various fields creates opportunities and risks for investigating all cybercrimes. At the same time, many research studies have concentrated on security and forensic investigations to collect digital evidence on IoT devices. However, until now, the IoT platform has not fully evolved to adjust the tools, methods, and procedures of IoT forensic investigations. The main reasons for investigators are the characteristics and infrastructure of IoT devices. For example, device number variations, heterogeneity, distribution of protocols used, data duplication, complexity, limited memory, etc. As a result, resulting is a tough challenge to identify, collect, examine, analyze, and present potential IoT digital evidence for forensic investigative processes effectively and efficiently. Indeed, there is not fully used and adapted international standard for the perfect IoT forensic investigation framework. In the research method, a literature review has been carried out by producing previous research studies that have contributed to further facing challenges. To keep the quality of the literature review, research questions (RQ) were conducted for all studies related to the IoT forensic investigation framework between 2015-2022. This research results highlight and provides a comprehensive overview of the twenty current IoT forensic investigation framework that has been proposed. Then, a summary or contribution is presented focusing on the latest research, grouping the forensic phases, and evaluating essential frameworks in the IoT forensic investigation process to obtain digital evidence. Finally, open research issues are presented for further research in developing IoT forensic investigative framework.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83487914","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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