Ahmad Ilham, L. Assaffat, L. Khikmah, S. Safuan, Suprapedi Suprapedi
There is a significant imbalanced class in the village development index (called IDM - Indeks Desa Membangun) dataset, marked by the number of self-supporting classes more than the disadvantaged class. The traditional classifiers are able to achieve high accuracy (ACC) by training all cases of the majority class but forsaking the minority class, so that possible for the classification results to be biased. In this study, a random under-sampling technique was employed based on k-means cluster (KMC) and a meta-learning approach to improving ACC of the village status classification model. Furthermore, the AdaBoost and Random Forest were used as meta technique and base learner, respectively. The proposed model has been evaluated using the area under the curve (AUC), and experimental results showed that it yielded excellent performance compared to the prior studies with the AUC, ACC, precision (PR), recall (RC), and g-mean (Gm) values of 95.50%, 95.52%, 95.5%, 95.5%, and 92.95%, respectively. Similarly, the result of the t-test also showed the proposed model yielded excellent performance compared to previous studies. It can be concluded that the AdaBoost algorithm improved misclassification and changed the distribution of data loss function in random forests. It indicates that the proposed model effectively deals with imbalanced classes in the village development status classification model.
在村庄发展指数(称为IDM - Indeks Desa Membangun)数据集中，有一个显著的不平衡阶层，其标志是自给自足阶层的数量多于弱势阶层。传统的分类器通过训练多数类的所有案例而放弃少数类的案例来达到较高的准确率(ACC)，从而使分类结果有可能出现偏差。本研究采用基于k-均值聚类(KMC)的随机欠采样技术和元学习方法来改进村庄状态分类模型的ACC。此外，AdaBoost和Random Forest分别作为元技术和基础学习器。采用曲线下面积(area under The curve, AUC)对该模型进行了评价，实验结果表明，该模型的AUC、ACC、precision (PR)、recall (RC)和g-mean (Gm)分别为95.50%、95.52%、95.5%、95.5%和92.95%。同样，t检验的结果也表明，与以往的研究相比，所提出的模型取得了优异的性能。可以看出，AdaBoost算法改善了误分类，改变了随机森林中数据丢失函数的分布。结果表明，本文提出的模型有效地处理了村镇发展状况分类模型中的阶层不平衡问题。
{"title":"k-Means Cluster-based Random Undersampling and Meta-Learning Approach for Village Development Status Classification","authors":"Ahmad Ilham, L. Assaffat, L. Khikmah, S. Safuan, Suprapedi Suprapedi","doi":"10.30630/joiv.7.2.989","DOIUrl":"https://doi.org/10.30630/joiv.7.2.989","url":null,"abstract":"There is a significant imbalanced class in the village development index (called IDM - Indeks Desa Membangun) dataset, marked by the number of self-supporting classes more than the disadvantaged class. The traditional classifiers are able to achieve high accuracy (ACC) by training all cases of the majority class but forsaking the minority class, so that possible for the classification results to be biased. In this study, a random under-sampling technique was employed based on k-means cluster (KMC) and a meta-learning approach to improving ACC of the village status classification model. Furthermore, the AdaBoost and Random Forest were used as meta technique and base learner, respectively. The proposed model has been evaluated using the area under the curve (AUC), and experimental results showed that it yielded excellent performance compared to the prior studies with the AUC, ACC, precision (PR), recall (RC), and g-mean (Gm) values of 95.50%, 95.52%, 95.5%, 95.5%, and 92.95%, respectively. Similarly, the result of the t-test also showed the proposed model yielded excellent performance compared to previous studies. It can be concluded that the AdaBoost algorithm improved misclassification and changed the distribution of data loss function in random forests. It indicates that the proposed model effectively deals with imbalanced classes in the village development status classification model. ","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90695539","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}
Andry Chowanda, Vincentius Dennis, Virya Dharmawan, Joseph Danielson Ramli
Games are considered one of the most popular entertainment forms worldwide. The interaction in the game environment makes the players addicted to playing the game. One technique to build an addicting game is utilizing the player's emotions using Meta Artificial Intelligence (AI). The player's emotions can be utilized by adjusting the game difficulty. Most of the game offers static and steady difficulty development throughout the game. This research proposes a Meta AI game design using the player's affective states. We argue that a dynamic difficulty development throughout the game will increase the player's game experiences. The player's facial expressions are utilized to extract the player's affective state information. To recognize the player's facial expressions, a Facial Expressions Recognition (FER) model was trained using VGG-16 architecture and The Indonesian Mixed Emotion Dataset (IMED) dataset in addition to a self-collected dataset. The emotions recognition model (from player's facial expressions) achieved the best validation accuracy of 99.98%. The model was implemented in the proposed Meta AI game design. The Meta AI game design proposed in this game was implemented in several game scenarios to be compared and evaluated. The proposed Meta AI game design was evaluated by 31 respondents using Game Experiences Questionnaire (GEQ). Overall, the results show that the game with Meta AI and Augmented Reality implemented significantly improved the Game Experiences Questionnaire (GEQ) score and the player's overall satisfaction compared to the other game scenarios.
{"title":"Player's Affective States as Meta AI Design on Augmented Reality Games","authors":"Andry Chowanda, Vincentius Dennis, Virya Dharmawan, Joseph Danielson Ramli","doi":"10.30630/joiv.7.2.1022","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1022","url":null,"abstract":"Games are considered one of the most popular entertainment forms worldwide. The interaction in the game environment makes the players addicted to playing the game. One technique to build an addicting game is utilizing the player's emotions using Meta Artificial Intelligence (AI). The player's emotions can be utilized by adjusting the game difficulty. Most of the game offers static and steady difficulty development throughout the game. This research proposes a Meta AI game design using the player's affective states. We argue that a dynamic difficulty development throughout the game will increase the player's game experiences. The player's facial expressions are utilized to extract the player's affective state information. To recognize the player's facial expressions, a Facial Expressions Recognition (FER) model was trained using VGG-16 architecture and The Indonesian Mixed Emotion Dataset (IMED) dataset in addition to a self-collected dataset. The emotions recognition model (from player's facial expressions) achieved the best validation accuracy of 99.98%. The model was implemented in the proposed Meta AI game design. The Meta AI game design proposed in this game was implemented in several game scenarios to be compared and evaluated. The proposed Meta AI game design was evaluated by 31 respondents using Game Experiences Questionnaire (GEQ). Overall, the results show that the game with Meta AI and Augmented Reality implemented significantly improved the Game Experiences Questionnaire (GEQ) score and the player's overall satisfaction compared to the other game scenarios.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81574258","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. Santosa, Yonathan P. Santosa, Garup Lambang Goro, -. Wahjoedi, Jamal Mahbub
Concrete mixture design for concrete slump test has many characteristics and mostly noisy. Such data will affect prediction of machine learning. This study aims to experiment on H2O Deep Learning framework and Bagging for noisy data and overfitting avoidance to create the Concrete Slump Model. The data consists of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, age, slump, and compressive strength. A primary data for concrete mixed design using the fine aggregate material from Merapi Volcano, the hills of Muntilan, and Kalioro. The coarse aggregate was obtained from Pamotan, Jepara, Semarang, Ungaran, and Mojosongo Boyolali Central Java. The cement was using Gresik and Holcim product and the water was from Tembalang, Semarang. The experiment model with one input layer with 7 neurons, one hidden layer with 20 neurons, and one output layer with 1 neuron using activation function TanH, with parameter L1=1.0E-5, L2=0.0, max weight=10.0, epsilon=1.0E-8, rho=0.99, and epoch=800 is able to achieve RMSE of 2.272. This result shows that after introducing Bagging, the error can be reduced up to 2.5 RMSE approximately (50% lower) compared to the model without Bagging. The manually tested mixture data was used to model evaluation. The result shows that the model was able to achieve RMSE 0.568. Following this study, this model can be used for further research such as creating slump design practicum equipment/ application software.
混凝土坍落度试验的混凝土配合比设计特点较多，噪声较大。这些数据会影响机器学习的预测。本研究旨在实验H2O深度学习框架和Bagging对噪声数据和过度拟合的避免，以创建混凝土坍落度模型。数据包括水泥、高炉矿渣、粉煤灰、水、高效减水剂、粗骨料、细骨料、年龄、坍落度和抗压强度。混凝土混合设计的主要数据，使用来自默拉皮火山、穆蒂兰山和卡利奥罗的细骨料材料。粗骨料采自帕莫坦、杰帕拉、三宝垄、云加兰和Mojosongo Boyolali中爪哇。水泥使用Gresik和Holcim产品，水来自三宝垄的Tembalang。实验模型采用激活函数TanH，输入层7个神经元，隐藏层20个神经元，输出层1个神经元，参数L1=1.0E-5, L2=0.0, max weight=10.0, epsilon=1.0E-8, rho=0.99, epoch=800，可以实现RMSE为2.272。该结果表明，引入Bagging后，与没有Bagging的模型相比，误差可以减少到2.5 RMSE左右(降低50%)。人工测试的混合数据用于模型评估。结果表明，该模型能够达到RMSE 0.568。通过本研究，该模型可用于进一步的研究，如创建坍落度设计实习设备/应用软件。
{"title":"Computational of Concrete Slump Model Based on H2O Deep Learning framework and Bagging to reduce Effects of Noise and Overfitting","authors":"S. Santosa, Yonathan P. Santosa, Garup Lambang Goro, -. Wahjoedi, Jamal Mahbub","doi":"10.30630/joiv.7.2.1201","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1201","url":null,"abstract":"Concrete mixture design for concrete slump test has many characteristics and mostly noisy. Such data will affect prediction of machine learning. This study aims to experiment on H2O Deep Learning framework and Bagging for noisy data and overfitting avoidance to create the Concrete Slump Model. The data consists of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, age, slump, and compressive strength. A primary data for concrete mixed design using the fine aggregate material from Merapi Volcano, the hills of Muntilan, and Kalioro. The coarse aggregate was obtained from Pamotan, Jepara, Semarang, Ungaran, and Mojosongo Boyolali Central Java. The cement was using Gresik and Holcim product and the water was from Tembalang, Semarang. The experiment model with one input layer with 7 neurons, one hidden layer with 20 neurons, and one output layer with 1 neuron using activation function TanH, with parameter L1=1.0E-5, L2=0.0, max weight=10.0, epsilon=1.0E-8, rho=0.99, and epoch=800 is able to achieve RMSE of 2.272. This result shows that after introducing Bagging, the error can be reduced up to 2.5 RMSE approximately (50% lower) compared to the model without Bagging. The manually tested mixture data was used to model evaluation. The result shows that the model was able to achieve RMSE 0.568. Following this study, this model can be used for further research such as creating slump design practicum equipment/ application software.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82684687","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}
Nur Farahin Mohd Joharia, Muhammad Amirul Naim Muhammad Ridzuan L, Norshahidatul Hasana Ishak, Hazrati Zaini
Vaccination is one preventive measure to prevent oneself from getting any diseases. Vaccinations work by training our immune system to produce antibodies and weaken the targeted disease. Unfortunately, the number of unvaccinated children has increased because some parents reject or doubt the effectiveness of vaccines. This skepticism could result in a resurgence of vaccine-preventable diseases. Additionally, some contribute to vaccine refusal due to other reasons like vaccine misinformation, religious convictions, and insufficient knowledge. The game aims to develop knowledge and awareness to enhance vaccine behavior and acceptance among individuals. eGameFlow model were used as the methodology for game development. This model was chosen as it focuses on the educational game environment, which addresses learning components in the game. There are eight criteria to be considered to evaluate enjoyment using this model. To measure the users’ enjoyment, a set of questionnaires adopted from the eGameFlow model has been used for the evaluation. It is created explicitly to measure learners’ enjoyment of e-Learning games. This game gathers positive feedback from 30 respondents and shows promising results to achieve the objective. All eGameFlow criteria were positive towards enjoyment, with knowledge improvement being the highest contributor. The overall average of the evaluation was at an agreeable level, with a score of 81%, considered as achieving the goal. For future enhancement in increasing player’s enjoyment and game effectiveness, the game can be created in 3D environment to provide deep immersion and autonomy to the player.
{"title":"Adopting the eGameFlow Model in an Educational Game to Increase Knowledge about Vaccination","authors":"Nur Farahin Mohd Joharia, Muhammad Amirul Naim Muhammad Ridzuan L, Norshahidatul Hasana Ishak, Hazrati Zaini","doi":"10.30630/joiv.7.2.1809","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1809","url":null,"abstract":"Vaccination is one preventive measure to prevent oneself from getting any diseases. Vaccinations work by training our immune system to produce antibodies and weaken the targeted disease. Unfortunately, the number of unvaccinated children has increased because some parents reject or doubt the effectiveness of vaccines. This skepticism could result in a resurgence of vaccine-preventable diseases. Additionally, some contribute to vaccine refusal due to other reasons like vaccine misinformation, religious convictions, and insufficient knowledge. The game aims to develop knowledge and awareness to enhance vaccine behavior and acceptance among individuals. eGameFlow model were used as the methodology for game development. This model was chosen as it focuses on the educational game environment, which addresses learning components in the game. There are eight criteria to be considered to evaluate enjoyment using this model. To measure the users’ enjoyment, a set of questionnaires adopted from the eGameFlow model has been used for the evaluation. It is created explicitly to measure learners’ enjoyment of e-Learning games. This game gathers positive feedback from 30 respondents and shows promising results to achieve the objective. All eGameFlow criteria were positive towards enjoyment, with knowledge improvement being the highest contributor. The overall average of the evaluation was at an agreeable level, with a score of 81%, considered as achieving the goal. For future enhancement in increasing player’s enjoyment and game effectiveness, the game can be created in 3D environment to provide deep immersion and autonomy to the player.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78900307","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}
L. Zahrotun, Utaminingsih Linarti, Banu Harli Trimulya Suandi As, Herri Kurnia, L. Y. Sabila
One sign of how successfully the educational process is carried out on campus in a university is the timely graduation of students. This study compares the Analytic Hierarchy Clustering (AHC) approach with the K-Medoids method, a data mining technique for categorizing student data based on school origin, region of origin, average math score, TOEFL, GPA, and length study. This study was carried out at University X, which contains a variety of architectural styles. The R department, the S department, the T department, and the U department make up one of them. K-Medoids and AHC techniques Utilize the number of clusters 2, 3, and 4 and the silhouette coefficient approach. The evaluation's findings indicate a value. Although there is a linear silhouette between the AHC and K-Medoids methods, the AHC approach (departments R: 0.88, S: 0.87, T: 0.88, and U: 0.88) has a more excellent Silhouette value than K-Medoids (department R: 0.35, department S: 0.65 number of cluster 2, department T: 0.67 number of cluster 2 and program Study U: 0,52). The results of the second approach, which includes the K-Medoids and AHC procedures, are determined by the data distribution to be clustered rather than by the quantity of data or clusters. Based on this methodology, University X can refer to the grouping outcomes for the four departments with two achievements to receive results on schedule.
{"title":"Comparison of K-Medoids Method and Analytical Hierarchy Clustering on Students' Data Grouping","authors":"L. Zahrotun, Utaminingsih Linarti, Banu Harli Trimulya Suandi As, Herri Kurnia, L. Y. Sabila","doi":"10.30630/joiv.7.2.1204","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1204","url":null,"abstract":"One sign of how successfully the educational process is carried out on campus in a university is the timely graduation of students. This study compares the Analytic Hierarchy Clustering (AHC) approach with the K-Medoids method, a data mining technique for categorizing student data based on school origin, region of origin, average math score, TOEFL, GPA, and length study. This study was carried out at University X, which contains a variety of architectural styles. The R department, the S department, the T department, and the U department make up one of them. K-Medoids and AHC techniques Utilize the number of clusters 2, 3, and 4 and the silhouette coefficient approach. The evaluation's findings indicate a value. Although there is a linear silhouette between the AHC and K-Medoids methods, the AHC approach (departments R: 0.88, S: 0.87, T: 0.88, and U: 0.88) has a more excellent Silhouette value than K-Medoids (department R: 0.35, department S: 0.65 number of cluster 2, department T: 0.67 number of cluster 2 and program Study U: 0,52). The results of the second approach, which includes the K-Medoids and AHC procedures, are determined by the data distribution to be clustered rather than by the quantity of data or clusters. Based on this methodology, University X can refer to the grouping outcomes for the four departments with two achievements to receive results on schedule.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80507128","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}
Hamidulloh Ibda, Muhammad Fadloli Al Hakim, Khamim Saifuddin, Ziaul Khaq, Ahmad Sunoko
Several studies have explored esports games, and few have examined esports games in elementary schools with a systematic literature review. This research explores articles on the concept, features, training, implementation, and impact of esports games in elementary schools. The SLR and PRISMA methods were applied in this research with the stages of identification, screening, eligibility, and inclusion assisted by the Publish or Perish 7, VOS viewer, and NVIVO 12 Plus applications. There were 521 Scopus-indexed articles found. Furthermore, the articles were filtered according to the theme into 50 pieces. The findings of relevant topics are esports, esports games, the concept of esports games, elementary school, etc. The 50 articles were analyzed according to the specified topics through the NVIVO 12 Plus application, and the results were described. The findings of this study state that esports games are digital innovations in online video competitions, such as tournaments developing in education. The features of esports games in elementary school are manual sports integrated with digital augmentation, multiplayer and competitive, digitalization of physical sports, new digital-based features, and educational games, such as LoL (MOBA), Battle Royal, FIFA EA Sports, Mobile Legend, WISE game, and others. Training esports games through socialization, education, workshops, GDLC, curriculum development, and multimedia esports games. Implementation of esports games through competition, entertainment, game-based multimedia, SE, and TGfU, has positive and negative impacts. This research has limitations in that it only collects information from current literature, reviews esports at the elementary school level, and is not a field study. Future research needs to examine esports games according to the times in elementary school.
有几项研究探讨了电子竞技游戏，但很少有人对小学电子竞技游戏进行系统的文献回顾。本研究对小学电子竞技游戏的概念、特点、训练、实施和影响进行了探讨。本研究采用SLR和PRISMA方法，在Publish or Perish 7、VOS viewer和NVIVO 12 Plus应用程序的辅助下进行鉴定、筛选、合格和纳入阶段。共找到521篇scopus索引的文章。此外，根据主题将文章筛选为50篇。相关课题的调查结果有电子竞技、电子竞技游戏、电子竞技游戏的概念、小学等。通过NVIVO 12 Plus应用程序按照指定的主题对50篇文章进行分析，并对结果进行描述。这项研究的结果表明，电子竞技游戏是在线视频比赛的数字创新，例如教育领域的比赛。小学电子竞技游戏的特点是:人工体育与数字增强相结合、多人竞技、体育运动数字化、新型数字化、教育类游戏，如LoL (MOBA)、Battle Royal、FIFA EA sports、Mobile Legend、WISE game等。通过社会化、教育、工作坊、GDLC、课程开发、多媒体电子竞技游戏等方式培训电子竞技游戏。通过竞技、娱乐、基于游戏的多媒体、SE和TGfU来实施电子竞技游戏，既有积极的影响，也有消极的影响。这项研究的局限性在于，它只收集了现有文献中的信息，只回顾了小学阶段的电子竞技，并且不是实地研究。未来的研究需要根据小学时代对电子竞技游戏进行考察。
{"title":"Esports Games in Elementary School: A Systematic Literature Review","authors":"Hamidulloh Ibda, Muhammad Fadloli Al Hakim, Khamim Saifuddin, Ziaul Khaq, Ahmad Sunoko","doi":"10.30630/joiv.7.2.1031","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1031","url":null,"abstract":"Several studies have explored esports games, and few have examined esports games in elementary schools with a systematic literature review. This research explores articles on the concept, features, training, implementation, and impact of esports games in elementary schools. The SLR and PRISMA methods were applied in this research with the stages of identification, screening, eligibility, and inclusion assisted by the Publish or Perish 7, VOS viewer, and NVIVO 12 Plus applications. There were 521 Scopus-indexed articles found. Furthermore, the articles were filtered according to the theme into 50 pieces. The findings of relevant topics are esports, esports games, the concept of esports games, elementary school, etc. The 50 articles were analyzed according to the specified topics through the NVIVO 12 Plus application, and the results were described. The findings of this study state that esports games are digital innovations in online video competitions, such as tournaments developing in education. The features of esports games in elementary school are manual sports integrated with digital augmentation, multiplayer and competitive, digitalization of physical sports, new digital-based features, and educational games, such as LoL (MOBA), Battle Royal, FIFA EA Sports, Mobile Legend, WISE game, and others. Training esports games through socialization, education, workshops, GDLC, curriculum development, and multimedia esports games. Implementation of esports games through competition, entertainment, game-based multimedia, SE, and TGfU, has positive and negative impacts. This research has limitations in that it only collects information from current literature, reviews esports at the elementary school level, and is not a field study. Future research needs to examine esports games according to the times in elementary school.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82097203","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}
Muhammad Daffa Atthariq, Rizky Fauzi Ari Hidayat, Medina Kaulan Sadida, L. Syafa'ah, F. D. S. Sumadi
The implementation growth of the Internet of Things (IoT) may increase the complexity of the data transmission process between smart devices. The route generation process between available nodes on the network will burden the intermediary node. One of the possible solutions for resolving the problem is the integration of Software Defined Networks and IoT (SD-IoT) to provide network automation and management. The separation of networking control and data forwarding functions may provide a multipath delivery path between each node in the IoT environment. In addition, the controller can directly extract the resource usage of the intermediary devices, which can be utilized as the routing metric variable in order to maintain the resource utilization on the intermediary devices. Instead of using traditional routing, this paper aims to develop multipath routing based on Deep First Search (DFS) and Dijkstra algorithms for acquiring an efficient path using OpenFlow-based routing metrics. The traffic monitoring module delivered the metrics extraction process, which obtained the variables using Port and Aggregate Flow Statistic features. The metrics calculation aimed to provide the multipath, which was constructed based on switches resource usage. Each selected path was chosen based on the smallest cost and probability provided by the group table feature in OpenFlow. The results showed that the Dijkstra algorithm could create the multipath more swiftly than DFS with a time difference of 0.6 s. The Quality of Service (QoS) results also indicated that the proposed routing metric variables could maintain the transmission process efficiently.
{"title":"Multipath Routing Implementation in SD-IoT Network Using OpenFlow-based Routing Metrics","authors":"Muhammad Daffa Atthariq, Rizky Fauzi Ari Hidayat, Medina Kaulan Sadida, L. Syafa'ah, F. D. S. Sumadi","doi":"10.30630/joiv.7.2.1691","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1691","url":null,"abstract":"The implementation growth of the Internet of Things (IoT) may increase the complexity of the data transmission process between smart devices. The route generation process between available nodes on the network will burden the intermediary node. One of the possible solutions for resolving the problem is the integration of Software Defined Networks and IoT (SD-IoT) to provide network automation and management. The separation of networking control and data forwarding functions may provide a multipath delivery path between each node in the IoT environment. In addition, the controller can directly extract the resource usage of the intermediary devices, which can be utilized as the routing metric variable in order to maintain the resource utilization on the intermediary devices. Instead of using traditional routing, this paper aims to develop multipath routing based on Deep First Search (DFS) and Dijkstra algorithms for acquiring an efficient path using OpenFlow-based routing metrics. The traffic monitoring module delivered the metrics extraction process, which obtained the variables using Port and Aggregate Flow Statistic features. The metrics calculation aimed to provide the multipath, which was constructed based on switches resource usage. Each selected path was chosen based on the smallest cost and probability provided by the group table feature in OpenFlow. The results showed that the Dijkstra algorithm could create the multipath more swiftly than DFS with a time difference of 0.6 s. The Quality of Service (QoS) results also indicated that the proposed routing metric variables could maintain the transmission process efficiently.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78026111","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}
Kusnawi Kusnawi, M. Rahardi, Van Daarten Pandiangan
Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.
目前，在工业4.0时代，信息和通信技术非常发达，然而，在这个时代，复杂的活动也在增加，其中之一就是银行业。随着网上金融的便捷和高效，人们希望转向使用数字银行。Neobank是由新商业银行(BCN)开发的网上储蓄和存款应用程序，公众可以通过互联网使用。其中一项在线服务是手机银行，安卓和iOS版本的客户都可以使用。用户可以通过Google Play商店对Neobank的表现和服务进行评价，以改进和评估Neobank的表现。Google Play Store对Neobank应用的评价也在不断增加。因此，有必要对Neobank的评价进行情绪分析，进行评价分析。这些数据共收集了3159条来自Google Play Store上Neobank应用的用户评论。本研究旨在对Neobank用户评论数据进行分类，包括正面或负面情绪。本研究采用的方法是一种使用支持向量机算法的实验方法。使用支持向量机算法得到的准确率结果为82.33%，属于90%训练数据和10%测试数据的场景。精密度为82%，召回率为81%。未来的研究可以增加各种来源的数据集，使数据集更多，从而提高模型分类的准确性。
{"title":"Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia","authors":"Kusnawi Kusnawi, M. Rahardi, Van Daarten Pandiangan","doi":"10.30630/joiv.7.2.1652","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1652","url":null,"abstract":"Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"135 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73959239","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}
Maritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved.
{"title":"An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling","authors":"Truong Cong My, Le Dang Khanh, Pham Minh Thao","doi":"10.30630/joiv.7.2.1817","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1817","url":null,"abstract":"Maritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83333460","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}
A. Rachman, H. Mubarok, Euis Nur Fitriani Dewi, Rama Edwinda Putra
Human Activity Recognition (HAR) is an interesting research topic, especially in identifying human movement actions focusing on video-based security surveillance. Symptom of an illness from a movement. The use of HAR in this research is the key to better understanding the various semantics contained in the video to find out the pattern of a human movement, especially in sports movements. In this study, a combination of the CNN and LSTM method algorithms was applied by using several variations of the model parameter values on the dropout layer and batch size to convert the pattern in the video into image form to produce a HAR model. Data processing at the convolution layer is used to extract spatial features in the frame. The extraction results are fed to the LSTM layer on each network for modeling the temporal sequence of human movement. In this way, the network on the model will learn spatiotemporal features directly in end-to-end data training tests to produce a robust model. The test data used are 10 sports activities obtained from related research from the University of Central Florida (UCF). The results showed that the performance was quite good, although there were still errors in the classification of sports activities because they had similarities in the movements of the activities carried out. The classification results show a loss value of 0.4 and an accuracy of 0.94. In further research, what needs to be corrected is the loss value which is still high so that several times the test results show an error in the classification of sports activities that have similarities in the movements of the activities.
{"title":"Implementation of Convolutional Neural Network and Long Short-Term Memory Algorithms in Human Activity Recognition Based on Visual Processing Video","authors":"A. Rachman, H. Mubarok, Euis Nur Fitriani Dewi, Rama Edwinda Putra","doi":"10.30630/joiv.7.2.1504","DOIUrl":"https://doi.org/10.30630/joiv.7.2.1504","url":null,"abstract":"Human Activity Recognition (HAR) is an interesting research topic, especially in identifying human movement actions focusing on video-based security surveillance. Symptom of an illness from a movement. The use of HAR in this research is the key to better understanding the various semantics contained in the video to find out the pattern of a human movement, especially in sports movements. In this study, a combination of the CNN and LSTM method algorithms was applied by using several variations of the model parameter values on the dropout layer and batch size to convert the pattern in the video into image form to produce a HAR model. Data processing at the convolution layer is used to extract spatial features in the frame. The extraction results are fed to the LSTM layer on each network for modeling the temporal sequence of human movement. In this way, the network on the model will learn spatiotemporal features directly in end-to-end data training tests to produce a robust model. The test data used are 10 sports activities obtained from related research from the University of Central Florida (UCF). The results showed that the performance was quite good, although there were still errors in the classification of sports activities because they had similarities in the movements of the activities carried out. The classification results show a loss value of 0.4 and an accuracy of 0.94. In further research, what needs to be corrected is the loss value which is still high so that several times the test results show an error in the classification of sports activities that have similarities in the movements of the activities.","PeriodicalId":32468,"journal":{"name":"JOIV International Journal on Informatics Visualization","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73849397","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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