The socio-technical architecture of constructing a community's traditional house is a zine-qua-non at the locus of developing tourism destinations in several areas worldwide. A socio-technical system is an old approach that is realigned with developing integrated tourism components, especially various tourist attractions based on local cultural treasures. The results of this qualitative research with a phenomenological approach analyze and explain the noumena (meaning) behind the phenomena (facts) regarding socio-technical architecture based on Tongkonan traditional houses in Tana Toraja, Indonesia. The study results found that architectural works are full of symbolic meaning in constructing Tongkonan traditional houses. The crystallization of basic values and value orientation as the noumena (meaning) behind the socio-technical architectural phenomenon of the Tongkonan traditional house that stands upright is because five pillars support it as a representation of 5A (Attractions, Accessibility, Accommodation, Amenity, Ansilarity) as a component of tourism development. The Tongkonan roof model, which at first glance looks like a person praying by raising their hands up or to God, the Creator of the universe, is proof of the basic values and orientation of the socio-cultural and spiritual values of the Toraja people. The image of a rooster, sun, and arrangement of horns mounted on the Tongkonan wall proves the rich treasures of local socio-cultural life (local wisdom, local genius) of the local community as a result of creativity and innovation that sustainably has value.
{"title":"Visualization Mapping of the Socio-Technical Architecture based on Tongkonan Traditional House","authors":"Taufiq Natsir, Bakhrani Rauf, Faisal Syafar, Ahmad Wahidiyat Haedar, Faisal Najamuddin","doi":"10.30630/joiv.7.3.1788","DOIUrl":"https://doi.org/10.30630/joiv.7.3.1788","url":null,"abstract":"The socio-technical architecture of constructing a community's traditional house is a zine-qua-non at the locus of developing tourism destinations in several areas worldwide. A socio-technical system is an old approach that is realigned with developing integrated tourism components, especially various tourist attractions based on local cultural treasures. The results of this qualitative research with a phenomenological approach analyze and explain the noumena (meaning) behind the phenomena (facts) regarding socio-technical architecture based on Tongkonan traditional houses in Tana Toraja, Indonesia. The study results found that architectural works are full of symbolic meaning in constructing Tongkonan traditional houses. The crystallization of basic values and value orientation as the noumena (meaning) behind the socio-technical architectural phenomenon of the Tongkonan traditional house that stands upright is because five pillars support it as a representation of 5A (Attractions, Accessibility, Accommodation, Amenity, Ansilarity) as a component of tourism development. The Tongkonan roof model, which at first glance looks like a person praying by raising their hands up or to God, the Creator of the universe, is proof of the basic values and orientation of the socio-cultural and spiritual values of the Toraja people. The image of a rooster, sun, and arrangement of horns mounted on the Tongkonan wall proves the rich treasures of local socio-cultural life (local wisdom, local genius) of the local community as a result of creativity and innovation that sustainably has value.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106125","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}
Based on Global Islamic Economy Indicator 2019/2020 report, Indonesia is in the fourth position globally as a country that uses a Sharia economic system. Seeing Indonesia's opportunities, it should be able to act as a regional and global halal hub. Efforts to encourage the halal industry through strengthening the halal value chain are one of the strategies to encourage Indonesia to become a global halal hub player. This study utilizes the structural equation modeling to examine relationships among key factors affecting readiness towards halal logistics in the food and beverages industry in Indonesia. 13 key factors are confirmed with measurement-model results, including (1) Cleanliness, (2) Safety, (3) Islamic Dietary Law, (4) Physical Segregation, (5) Material Handlings, (6) Storage and Transport, (7) Packaging and Labelling, (8) Ethical Practices, (9) Training and Personnel, (10) Resource Availability, (11) Innovative Capability, (12) Marketing Performance, (13) Financial Performance. The population in this study is in the food and beverage industries, especially in Semarang, Yogyakarta, Malang, and Surabaya. Cluster random sampling was used in this research with as many as 150 sample respondents. A survey with an online questionnaire was conducted in this research. The structural-model results reveal directions of relationships among key factors. Resource availability, training and personnel, and innovative capability are the most important factor in halal supply chain readiness. Further research can focus on other industrial sectors, such as fashion and tourism, as stated in the 2019-2024 Indonesian Sharia Economic Masterplan
{"title":"Factors Influencing Readiness towards Halal Logistics among Food and Beverages Industry in the Era of E-Commerce in Indonesia","authors":"Prafajar Suksessanno Muttaqin, Erlangga Bayu Setyawan, Nia Novitasari","doi":"10.30630/joiv.7.3.2055","DOIUrl":"https://doi.org/10.30630/joiv.7.3.2055","url":null,"abstract":"Based on Global Islamic Economy Indicator 2019/2020 report, Indonesia is in the fourth position globally as a country that uses a Sharia economic system. Seeing Indonesia's opportunities, it should be able to act as a regional and global halal hub. Efforts to encourage the halal industry through strengthening the halal value chain are one of the strategies to encourage Indonesia to become a global halal hub player. This study utilizes the structural equation modeling to examine relationships among key factors affecting readiness towards halal logistics in the food and beverages industry in Indonesia. 13 key factors are confirmed with measurement-model results, including (1) Cleanliness, (2) Safety, (3) Islamic Dietary Law, (4) Physical Segregation, (5) Material Handlings, (6) Storage and Transport, (7) Packaging and Labelling, (8) Ethical Practices, (9) Training and Personnel, (10) Resource Availability, (11) Innovative Capability, (12) Marketing Performance, (13) Financial Performance. The population in this study is in the food and beverage industries, especially in Semarang, Yogyakarta, Malang, and Surabaya. Cluster random sampling was used in this research with as many as 150 sample respondents. A survey with an online questionnaire was conducted in this research. The structural-model results reveal directions of relationships among key factors. Resource availability, training and personnel, and innovative capability are the most important factor in halal supply chain readiness. Further research can focus on other industrial sectors, such as fashion and tourism, as stated in the 2019-2024 Indonesian Sharia Economic Masterplan","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106266","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}
Arizal Akbar Zikri, Hanni Defianti, Wahyu Hidayat, Acep Purqon
Many applications use symmetric or asymmetric airfoils, such as aircraft design, wind turbines, and heat transfer. Each airfoil has different aerodynamic coefficients. Obtaining the aerodynamic coefficients is a must to optimize the airfoil design. Engineers use various methods to get the airfoil aerodynamic coefficients. A prediction method is an approximation approach that effectively reduces time and cost. This article uses convolutional neural networks (CNN) to get approximation values of those coefficients. In CNN, we collect 8920 aerodynamic coefficients for 223 NACA 4 as labels in datasets by using XFOIL at and with varying angles of attacks starting to with increment of . The simulation results are compared to the experiment using E387 airfoil for validation. Then, airfoil geometries as part of input datasets were transformed into Grayscale and RGB images using the signed distance function (SDF) and mesh algorithm. Each airfoil representation was trained using an 80% dataset and tested using a 20% dataset with Adam as an optimizer to generate each prediction model using modified LeNet-5. We use three different layer depths in modified LeNet-5 to obtain the optimal layer number. There is no remarkable improvement when varying the depth layers, so four layers are used instead. Simulation results show that using an SDF with Fast Marching Method on CNN predicts the most effective for the airfoil’s lift, drag, and pitch moment coefficient with varying angles of attack simultaneously. One can extend the method by using SDF to recognize different flow conditions.
{"title":"Geometry Representation Effectiveness in Improving Airfoil Aerodynamic Coefficient Prediction with Convolutional Neural Network","authors":"Arizal Akbar Zikri, Hanni Defianti, Wahyu Hidayat, Acep Purqon","doi":"10.30630/joiv.7.3.1577","DOIUrl":"https://doi.org/10.30630/joiv.7.3.1577","url":null,"abstract":"Many applications use symmetric or asymmetric airfoils, such as aircraft design, wind turbines, and heat transfer. Each airfoil has different aerodynamic coefficients. Obtaining the aerodynamic coefficients is a must to optimize the airfoil design. Engineers use various methods to get the airfoil aerodynamic coefficients. A prediction method is an approximation approach that effectively reduces time and cost. This article uses convolutional neural networks (CNN) to get approximation values of those coefficients. In CNN, we collect 8920 aerodynamic coefficients for 223 NACA 4 as labels in datasets by using XFOIL at and with varying angles of attacks starting to with increment of . The simulation results are compared to the experiment using E387 airfoil for validation. Then, airfoil geometries as part of input datasets were transformed into Grayscale and RGB images using the signed distance function (SDF) and mesh algorithm. Each airfoil representation was trained using an 80% dataset and tested using a 20% dataset with Adam as an optimizer to generate each prediction model using modified LeNet-5. We use three different layer depths in modified LeNet-5 to obtain the optimal layer number. There is no remarkable improvement when varying the depth layers, so four layers are used instead. Simulation results show that using an SDF with Fast Marching Method on CNN predicts the most effective for the airfoil’s lift, drag, and pitch moment coefficient with varying angles of attack simultaneously. One can extend the method by using SDF to recognize different flow conditions.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106269","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}
More and more local governments in Indonesia are making their data available to the public. This benefits data scientists, researchers, business owners, and other potential users seeking datasets for empirical research and business innovation. However, just because Open Government Data (OGD) portals are accessible does not mean that they necessarily adhere to the established rules and principles of data openness. To evaluate the level of openness of 24 OGD portals in Indonesia, this study used the K-means Clustering algorithm to partition them into three levels: Leaders, Followers, and Beginners. A group of 30 participants, including researchers, data scientists, business enablers, and graduate students, rated the portals on 32 sub-questions related to the eight main principles of data disclosure, focusing on health, population, and education datasets. The study found that eight portals were categorized as Leaders, ten as Followers, and seven as Beginners regarding their level of openness. The study demonstrated that the K-means Clustering algorithm can be effectively used to assess the degree of openness of OGD portals in Indonesia based on eight main principles of data openness. The study recommends increasing the number of OGD portals in eastern territories to supplement the existing case studies in the western and central regions.
{"title":"K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals","authors":"Emigawaty Emigawaty, Kusworo Adi, Adian Fatchur Rochim, Budi Warsito, Adi Wibowo","doi":"10.30630/joiv.7.3.1761","DOIUrl":"https://doi.org/10.30630/joiv.7.3.1761","url":null,"abstract":"More and more local governments in Indonesia are making their data available to the public. This benefits data scientists, researchers, business owners, and other potential users seeking datasets for empirical research and business innovation. However, just because Open Government Data (OGD) portals are accessible does not mean that they necessarily adhere to the established rules and principles of data openness. To evaluate the level of openness of 24 OGD portals in Indonesia, this study used the K-means Clustering algorithm to partition them into three levels: Leaders, Followers, and Beginners. A group of 30 participants, including researchers, data scientists, business enablers, and graduate students, rated the portals on 32 sub-questions related to the eight main principles of data disclosure, focusing on health, population, and education datasets. The study found that eight portals were categorized as Leaders, ten as Followers, and seven as Beginners regarding their level of openness. The study demonstrated that the K-means Clustering algorithm can be effectively used to assess the degree of openness of OGD portals in Indonesia based on eight main principles of data openness. The study recommends increasing the number of OGD portals in eastern territories to supplement the existing case studies in the western and central regions.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136107229","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}
Learners' learning experiences diverge and undergo rapid shifts due to trends in technology, and the transfer of knowledge in varying formats and styles is ubiquitous. Infographics find its application in instructional design and technology that drives education with state-of-the-art tools and applied methods. Being a format of data, Infographics or visual images presents information or knowledge using visuals. This study aims to present the student perceptions of learning via infographics at Princess Nourah bint Abdurrahman University (PNU) in Saudi Arabia. The study employs a survey questionnaire consisting of 13 close-ended questions posed to assess the learners' responses at PNU. Applied Likert's scale-based questions present a conversion of user data input to quantitative figures, which present the level of understanding and the role of infographics in education. The survey involved 45 undergraduate female students pursuing undergraduate degree courses at PNU. Using survey research design methodology, the study investigated learners' perceptions of infographics to add to their learning experiences and provides a quantitative analysis of observed responses to the survey questionnaire. The study conducts an online survey and classifies participants of different age groups into five categories for assessment. The study findings reveal that PNU learners perceive a positive role of infographics in their learning. However, learners showcase varied perceptions of a) the acceptance of assignments based on infographics and b) the use of static versus animated infographics. The study guides research scholars toward the intuition of infographics in learning environments and reports the two research problems to be addressed in future works.
{"title":"Saudi Learners' Perception of Infographics in Education: A Survey","authors":"Suzan Alyahya","doi":"10.30630/joiv.7.3.1521","DOIUrl":"https://doi.org/10.30630/joiv.7.3.1521","url":null,"abstract":"Learners' learning experiences diverge and undergo rapid shifts due to trends in technology, and the transfer of knowledge in varying formats and styles is ubiquitous. Infographics find its application in instructional design and technology that drives education with state-of-the-art tools and applied methods. Being a format of data, Infographics or visual images presents information or knowledge using visuals. This study aims to present the student perceptions of learning via infographics at Princess Nourah bint Abdurrahman University (PNU) in Saudi Arabia. The study employs a survey questionnaire consisting of 13 close-ended questions posed to assess the learners' responses at PNU. Applied Likert's scale-based questions present a conversion of user data input to quantitative figures, which present the level of understanding and the role of infographics in education. The survey involved 45 undergraduate female students pursuing undergraduate degree courses at PNU. Using survey research design methodology, the study investigated learners' perceptions of infographics to add to their learning experiences and provides a quantitative analysis of observed responses to the survey questionnaire. The study conducts an online survey and classifies participants of different age groups into five categories for assessment. The study findings reveal that PNU learners perceive a positive role of infographics in their learning. However, learners showcase varied perceptions of a) the acceptance of assignments based on infographics and b) the use of static versus animated infographics. The study guides research scholars toward the intuition of infographics in learning environments and reports the two research problems to be addressed in future works.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136107396","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}
Antonius Bima Murti Wijaya, Desideria Cempaka Wijaya Murti, Victoria Sundari Handoko
This research aims to modify a CNN (Convolutional Neural Network) based on LeNet-5 to reduce overfitting in a Tinalah Tourism Village dataset object detection. Tinalah Tourism Village has many objects that can be identified for tourism education and enhanced tourist experience. While these objects, spread across the different sites of Tinalah do vary, some share similarities in their histogram patterns. Visually, if the size of a picture is reduced in the LeNet-5 ‘preferred size’ feature, it will inevitably lose some of its information, making pictures too similar reducing accuracy. In order to learn and classify objects, this research performs a modification on LeNet-5 architecture to provide a better performance geared toward larger input imaging. The previous state-of-the-art architecture showed an overfitting performance where the training accuracy performed too much better than the testing accuracy in our dataset. We brought in a dropout layer to reduce overfitting, increase the dense layer's size, and add a convolution layer. We then compared the modified LeNet-5 with other state-of-the art architecture, such as LeNet-5 and AlexNet. Results showed that a modified LeNet-5 outperformed other architectures, especially in performing accuracy for testing the Tinalah dataset, reaching 0.913 or (91,3 %). This research discusses the dataset, the modified LeNet-5 architecture, and performance comparison between state-of-the-art CNN architecture. Our CNN architecture can be developed by involving a transfer learning mechanism to provide greater accuracy for further research.
{"title":"Modified LeNet-5 Architecture to Classify High Variety of Tourism Object: A Case Study of Tourism Object for Education in Tinalah Village","authors":"Antonius Bima Murti Wijaya, Desideria Cempaka Wijaya Murti, Victoria Sundari Handoko","doi":"10.30630/joiv.7.3.2095","DOIUrl":"https://doi.org/10.30630/joiv.7.3.2095","url":null,"abstract":"This research aims to modify a CNN (Convolutional Neural Network) based on LeNet-5 to reduce overfitting in a Tinalah Tourism Village dataset object detection. Tinalah Tourism Village has many objects that can be identified for tourism education and enhanced tourist experience. While these objects, spread across the different sites of Tinalah do vary, some share similarities in their histogram patterns. Visually, if the size of a picture is reduced in the LeNet-5 ‘preferred size’ feature, it will inevitably lose some of its information, making pictures too similar reducing accuracy. In order to learn and classify objects, this research performs a modification on LeNet-5 architecture to provide a better performance geared toward larger input imaging. The previous state-of-the-art architecture showed an overfitting performance where the training accuracy performed too much better than the testing accuracy in our dataset. We brought in a dropout layer to reduce overfitting, increase the dense layer's size, and add a convolution layer. We then compared the modified LeNet-5 with other state-of-the art architecture, such as LeNet-5 and AlexNet. Results showed that a modified LeNet-5 outperformed other architectures, especially in performing accuracy for testing the Tinalah dataset, reaching 0.913 or (91,3 %). This research discusses the dataset, the modified LeNet-5 architecture, and performance comparison between state-of-the-art CNN architecture. Our CNN architecture can be developed by involving a transfer learning mechanism to provide greater accuracy for further research.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106265","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}
Robbi Hendriyanto, Anak Agung Gde Agung, Rizza Indah Mega Mandasari, Sri Widaningsih, Retno Setyorini
With a population of 273.5 million in 2020, the people's economy is very important for Indonesia. The populist economic model has existed in Indonesia for a long time, locally known as “Koperasi”. However, Koperasi could not keep up with information and technology in the industrial 4.0 era. When other industrial models adopt information technology massively, Koperasi seems to struggle to shift from the conventional model. Slowly but surely, Koperasi become less popular, especially among Generation Z, who considers information technology part of their daily needs and lifestyle. The scope of Koperasi’sbusiness shrinks and becomes limited, along with their business capital. On the other hand, information technology provides opportunities for small and medium-sized entrepreneurs. The data shows that this sector contributes to 60.5% of the national GDP, absorbs 96.9% of the workforce, and provides 99.9% of total employment. Unfortunately, many Micro, Small, and Medium Enterprises (MSME) have limited capital and tend to prefer fintech services that offer easier accessibility than Koperasi. This paper aims to propose an E-Koperasi and the synergy of Koperasi and MSMEs model platform, specifically the digital marketplace platform. We design the platform requirements using the Software Requirement Specification with the User Acceptance Test. As a result, an application is developed as a model platform available for Koperasi and MSMEs. The platform is proposed to support the Government’s program to digitalize the Koperasi and the MSMEs and increase their competitiveness in Industrial 4.0.
{"title":"GoEkopz: An E-Koperasi and Marketplace Synergy of Koperasi MSMEs Model Platform - Case Study: Koperasi Giat, eKopz Startup, PPKM Community","authors":"Robbi Hendriyanto, Anak Agung Gde Agung, Rizza Indah Mega Mandasari, Sri Widaningsih, Retno Setyorini","doi":"10.30630/joiv.7.3.2048","DOIUrl":"https://doi.org/10.30630/joiv.7.3.2048","url":null,"abstract":"With a population of 273.5 million in 2020, the people's economy is very important for Indonesia. The populist economic model has existed in Indonesia for a long time, locally known as “Koperasi”. However, Koperasi could not keep up with information and technology in the industrial 4.0 era. When other industrial models adopt information technology massively, Koperasi seems to struggle to shift from the conventional model. Slowly but surely, Koperasi become less popular, especially among Generation Z, who considers information technology part of their daily needs and lifestyle. The scope of Koperasi’sbusiness shrinks and becomes limited, along with their business capital. On the other hand, information technology provides opportunities for small and medium-sized entrepreneurs. The data shows that this sector contributes to 60.5% of the national GDP, absorbs 96.9% of the workforce, and provides 99.9% of total employment. Unfortunately, many Micro, Small, and Medium Enterprises (MSME) have limited capital and tend to prefer fintech services that offer easier accessibility than Koperasi. This paper aims to propose an E-Koperasi and the synergy of Koperasi and MSMEs model platform, specifically the digital marketplace platform. We design the platform requirements using the Software Requirement Specification with the User Acceptance Test. As a result, an application is developed as a model platform available for Koperasi and MSMEs. The platform is proposed to support the Government’s program to digitalize the Koperasi and the MSMEs and increase their competitiveness in Industrial 4.0.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106270","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}
It has become a necessity for people to communicate with each other to complete their needs. The exchange of information conveyed in communication often cannot be directly assessed, especially online news. They just get news and are unable to filter out inappropriate stuff. The media website conveys a great deal of information. Popular news websites are one source for keeping up with the newest news. It requires a significant amount of work to deliver news on prominent websites and to choose content that is not incorrect. To crawl the web and analyse enormous data, massive computer power is required, and solutions to lower the process's space and temporal complexity must be created.Data mining is seen to be a solution to the aforementioned difficulties since it extracts particular information based on defined attributes. This research investigated a model to determine the content of false news information in Indonesian popular news. Firstly, preprocessing process from dataset that collected from keaggle. Secondly, we try use classification methods to determined which the optimal method to classify fake news. Thirdly, we use another public dataset for testing method. Furthermore, five machine learning classifiers are compared: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC), and Random Forest (RF). These classifications are utilized independently before being compared based on receiver operating characteristic curves and accuracy. The experimental result shows that DTC has the lowest accuracy of 75.33% and SVM has the highest accuracy of 83.55%.
{"title":"Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment","authors":"Liliek Triyono, Rahmat Gernowo, Prayitno Prayitno, Mosiur Rahaman, Tri Raharjo Yudantoro","doi":"10.30630/joiv.7.3.1243","DOIUrl":"https://doi.org/10.30630/joiv.7.3.1243","url":null,"abstract":"It has become a necessity for people to communicate with each other to complete their needs. The exchange of information conveyed in communication often cannot be directly assessed, especially online news. They just get news and are unable to filter out inappropriate stuff. The media website conveys a great deal of information. Popular news websites are one source for keeping up with the newest news. It requires a significant amount of work to deliver news on prominent websites and to choose content that is not incorrect. To crawl the web and analyse enormous data, massive computer power is required, and solutions to lower the process's space and temporal complexity must be created.Data mining is seen to be a solution to the aforementioned difficulties since it extracts particular information based on defined attributes. This research investigated a model to determine the content of false news information in Indonesian popular news. Firstly, preprocessing process from dataset that collected from keaggle. Secondly, we try use classification methods to determined which the optimal method to classify fake news. Thirdly, we use another public dataset for testing method. Furthermore, five machine learning classifiers are compared: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC), and Random Forest (RF). These classifications are utilized independently before being compared based on receiver operating characteristic curves and accuracy. The experimental result shows that DTC has the lowest accuracy of 75.33% and SVM has the highest accuracy of 83.55%.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136107223","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}
Handwriting recognition is a study of Optical Character Recognition (OCR) which has a high level of complexity. In addition, everyone has a unique and inconsistent handwriting style in writing characters upright, affecting recognition success. However, proper pre-processing and classification algorithms affect the success of pattern recognition systems. This paper proposes a pre-processing method for handwriting image recognition using a convolutional neural network (CNN). This study uses public datasets for training and private datasets for testing. This pre-processing consists of three processes: image cleaning, skew correction, and segmentation. These three processes aim to clean the image from unnecessary ink streaks. In addition, to make angle corrections to characters in italics in their writing. The model testing process uses image test data of handwriting that are not straight. There are three images based on the inclination angle: less than 45 degrees, equal to 45 degrees, and more than 45 degrees. Picture cleaning removes unnecessary strokes (noise) from the image using a layer mask, whereas skew correction changes the handwriting to an upright posture based on the detected angle. The pre-processing model we propose worked optimally on handwriting with a skew angle of fewer than 45 degrees and 45 degrees. Our proposed model generally works well for handwriting with fewer than 45 degrees skew with an accuracy of 88,96%. Research with a similar scope can continue to improve optimization with a focus on algorithms related to analysis layout studies. Besides that, it can focus more on automation in the segmentation process of each character.
{"title":"Skew Correction and Image Cleaning Handwriting Recognition Using a Convolutional Neural Network","authors":"Shofwatul Uyun, Seto Rahardyan, Muhammad Anshari","doi":"10.30630/joiv.7.3.1712","DOIUrl":"https://doi.org/10.30630/joiv.7.3.1712","url":null,"abstract":"Handwriting recognition is a study of Optical Character Recognition (OCR) which has a high level of complexity. In addition, everyone has a unique and inconsistent handwriting style in writing characters upright, affecting recognition success. However, proper pre-processing and classification algorithms affect the success of pattern recognition systems. This paper proposes a pre-processing method for handwriting image recognition using a convolutional neural network (CNN). This study uses public datasets for training and private datasets for testing. This pre-processing consists of three processes: image cleaning, skew correction, and segmentation. These three processes aim to clean the image from unnecessary ink streaks. In addition, to make angle corrections to characters in italics in their writing. The model testing process uses image test data of handwriting that are not straight. There are three images based on the inclination angle: less than 45 degrees, equal to 45 degrees, and more than 45 degrees. Picture cleaning removes unnecessary strokes (noise) from the image using a layer mask, whereas skew correction changes the handwriting to an upright posture based on the detected angle. The pre-processing model we propose worked optimally on handwriting with a skew angle of fewer than 45 degrees and 45 degrees. Our proposed model generally works well for handwriting with fewer than 45 degrees skew with an accuracy of 88,96%. Research with a similar scope can continue to improve optimization with a focus on algorithms related to analysis layout studies. Besides that, it can focus more on automation in the segmentation process of each character.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106126","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}
Social Engineering Procedures and phishing are some of the standard procedures and problems today, mainly through sophisticated media such as email, the official means of communication companies use. Phishing emails are usually associated with Social Designing. They can be sent via joins and connections in this email, but they are not secure. Proliferation can be hacked into private/confidential data or total control over the computer/Email without the client's knowledge. The method used in this research is a cycle that will run continuously in a life cycle, starting from problem identification, then generating ideas and evaluating the Implementation of solutions. At each stage, a thorough checking process is needed to obtain results. Follow what you want. Achieved. The results of this study provide recommendations and some suggestions that companies can make; this aims to be one of the doors that provides restrictions for access from parties who are not entitled to access the application. Some thought has shown that this attack is growing and affecting the population. The evaluation stages in this study consist of 5 phases. Each phase is a step used to prevent both the system and the behavior in the company. Awareness is critical at the start considering this is the basis for the organization to determine who will take care of the personnel's knowledge related to information security. It thinks about using survey writing strategies and recommendations that can be made in anticipation of an attack, such as setting up representation or attention as early and often as possible.
{"title":"Security Awareness Strategy for Phishing Email Scams: A Case Study One of a Company in Singapore","authors":"Widia Febriyani, Dhiya Fathia, Adityas Widjajarto, Muharman Lubis","doi":"10.30630/joiv.7.3.2081","DOIUrl":"https://doi.org/10.30630/joiv.7.3.2081","url":null,"abstract":"Social Engineering Procedures and phishing are some of the standard procedures and problems today, mainly through sophisticated media such as email, the official means of communication companies use. Phishing emails are usually associated with Social Designing. They can be sent via joins and connections in this email, but they are not secure. Proliferation can be hacked into private/confidential data or total control over the computer/Email without the client's knowledge. The method used in this research is a cycle that will run continuously in a life cycle, starting from problem identification, then generating ideas and evaluating the Implementation of solutions. At each stage, a thorough checking process is needed to obtain results. Follow what you want. Achieved. The results of this study provide recommendations and some suggestions that companies can make; this aims to be one of the doors that provides restrictions for access from parties who are not entitled to access the application. Some thought has shown that this attack is growing and affecting the population. The evaluation stages in this study consist of 5 phases. Each phase is a step used to prevent both the system and the behavior in the company. Awareness is critical at the start considering this is the basis for the organization to determine who will take care of the personnel's knowledge related to information security. It thinks about using survey writing strategies and recommendations that can be made in anticipation of an attack, such as setting up representation or attention as early and often as possible.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136106267","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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