Including Data Classification Research Articles

Nilai-Nilai Moral dalam novel Pulang karya Leila S. Chudori

This study aims to describe the moral values regarding human bonds with God, individual relationships with themselves, individual relationships with other individuals in a social sphere in a novel entitled Pulang. The type of this research is literary research using the content analysis method. The data source in this research is the novel Pulang by Leila S. Chudori. Data collection techniques in this study were carried out by reading, marking events and inventorying the data found. Data analysis techniques include data classification, data analysis, and conclusion drawing. The results of this study show that human relationships with God consist of tawakal, gratitude, and prayer. Furthermore, human relationships with themselves consist of firmness, regret, and optimism. Third, human relationships with other humans in the social sphere consisting of caring for others, being grateful, honest, and respecting others. This study concludes that Pulang's novel is rich in moral values that can be exemplified by readers. These moral values can help readers build harmonious relationships with God, themselves, and others.

Optimizing ant colony system algorithm with rule-based data classification for smart aquaculture

<span>Aquaculture is one of many industries where the use of artificial intelligence (AI) techniques has increased dramatically in recent years. Internet of things (IoT), AI, and big data are just a few of the technologies being used in smart aquaculture to increase productivity, efficiency, and system sustainability of aquaculture systems. Data classification, which involves finding patterns and relationships in huge datasets, is one of the most important tasks in smart aquaculture. The ant colony system (ACS) has been used to solve a number of optimization issues, including data classification. To provide a more practical and successful solution, this study proposes an improved ACS algorithm for rule-based data classification in smart aquaculture. The proposed algorithm combines the advantages of ACS and rule-based classification to optimize the number of rules and accuracy. The experimental results showed that the proposed algorithm outperformed the traditional AntMiner algorithm in terms of the number of rules and accuracy. The improved pheromone update technique could potentially increase data classification accuracy and convergence in smart aquaculture systems.</span>

Kreativitas Guru Dalam Meningkatkan Kualitas Pembelajaran

Teachers can support the quality of learning taught, mastery of the material, and mastery of the learning process. This study aims to determine teacher creativity in improving learning at SMPN 4 BONTONOMPO. This research took a qualitative approach. The descriptive nature of this research makes teachers' creativity very visible. Data collection in this study includes interviews and observations, documentation studies and data analysis including data classification, data reduction, data description, and conclusions. The results of this study show that there are four key components in the development of this qualitative research related to teacher creativity in improving the quality of learning at SMPN 4 BONTONOMPO, such as teacher creativity in understanding the learning material to be taught, creativity in managing the classroom environment, creativity in the use of various learning methods and teacher creativity in the use of learning media.

Internet of Medical Things with a Blockchain-Assisted Smart Healthcare System Using Metaheuristics with a Deep Learning Model

The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively.

Analisis Sistem Pengendalian Manajemen di LAZISNU Ranting Klampok di Desa Klampok Kecamatan Benjeng Kabupaten Gresik

The digital era has led us to participate in the flow of pucliv conversation that is not certain. A lot of content is produced, but we cannot confirm the truth of the content. In addition, the existence of a religious moderation framework must be implemented, especially on media social. AIS Nusantara as a community of sudent who concentrate in the digital space takes this role in implementing religious moderation in the digital spacae. This study uses netnographic reseacrh, namely planning reseaerch implementation, compiling problem formulation and study object from sosial media, data collection, data interpretation which includes data classification and coding. AIS Nusantara has a strategic role to easetensions and clear up various discourses and issues that are carried freely in the digital space with the existence of the AIS Nusantara community with many accounts that have milions of followers able to construct discourses on sosial media.

Peran Komunitas Arus Informasi Santri Nusantara Dalam Implementasi Moderasi Beragama Di Media Sosial

The digital era has led us to participate in the flow of pucliv conversation that is not certain. A lot of content is produced, but we cannot confirm the truth of the content. In addition, the existence of a religious moderation framework must be implemented, especially on media social. AIS Nusantara as a community of sudent who concentrate in the digital space takes this role in implementing religious moderation in the digital spacae. This study uses netnographic reseacrh, namely planning reseaerch implementation, compiling problem formulation and study object from sosial media, data collection, data interpretation which includes data classification and coding. AIS Nusantara has a strategic role to easetensions and clear up various discourses and issues that are carried freely in the digital space with the existence of the AIS Nusantara community with many accounts that have milions of followers able to construct discourses on sosial media.

Political wisdom of the Kajang "Ammatoa" indigenous community in South Sulawesi

Democracy, which is implemented in Indonesia through general elections (Elections), places the community as a whole as the holder of people's sovereignty. This is guaranteed in the 1945 Constitution of the Republic of Indonesia (UUD). However, the implementation of people's sovereignty is still a problem, because there is still a low understanding of the people's sovereignty that they have, especially in elections. One component of society that also has political sovereignty is indigenous communities, especially in South Sulawesi, namely the Kajang "Ammatoa" indigenous community. Therefore, this study examines the meaning of political sovereignty in the Kajang indigenous community "Ammatoa" and the local wisdom owned and implemented in the 2019 elections, including the 2020 Regional Head Election (Pilkada) in Bulukumba Regency. The method used is descriptive qualitative, and the processed data is primary and secondary. This research begins with the identification and formulation of problems, data collection, data processing, and analysis, including data classification and reduction, drawing conclusions, and verification. The results of this study; (1) Although the Kajang indigenous community is closed, the application of democratic values has been realized for a long time and has become its main local wisdom in choosing leaders called "ammatoa" (traditional chairmen). That is, "ammatoa" is democratically elected and not because of the principle of blood or hereditary ties (traditional leadership); (2) The meaning of democracy and political wisdom, also expressed in elections and regional elections, "ammatoa" as a leader gives freedom to its citizens to choose according to their beliefs without any intervention. So, even though there are "ammatoa" children who became Caleg in the 2019 elections, they were not elected; (3) responsibility as a citizen is also evident in the high level of participation of the Kajang indigenous community in elections and regional elections, even though initially there were cultural and administrative obstacles. This proves that the Kajang indigenous community has practiced the meaning of popular sovereignty for a long time and has become a form of political wisdom. As a suggestion, it is necessary to make a follow-up study that captures the meaning of people's sovereignty in other indigenous communities as a reference in formulating inclusive election policies in Indonesia.

Investigating the geometric structure of neural activation spaces with convex hull approximations

Neural networks have achieved great success in many tasks, including data classification and pattern recognition. However, how neural networks work and what representations they learn are still not fully understood. For any data sample fed into a neural network, we wondered how its corresponding vectors expanded by activated neurons change throughout the layers and why the final output vector could be classified or clustered. To formally answer these questions, we define the data sample outputs of each layer as activation vectors and the space expanded by them as the activation space. Then, we investigate the geometric structure of the high-dimensional activation spaces of neural networks by studying the geometric characters of the massive activation vectors through approximated convex hulls. We find that the different layers of neural networks have different roles, where the former and latter layers can disperse and gather data points, respectively. Moreover, we also propose a novel classification method based on the geometric structures of activation spaces, called nearest convex hull (NCH) classification, for the activation vectors in each layer of a neural network. The empirical results show that the geometric structure can indeed be utilized for classification and often outperforms original neural networks. Finally, we demonstrate that the relationship among the convex hulls of different classes could be a good metric to help us optimize neural networks in terms of over-fitting detection and network structure simplification.

PROBLEMATIKA AKSELERASI PENANGANAN KEMISKINAN NELAYAN DI MASA PANDEMI COVID-19 DI KOTA MAKASSAR

Sustainable development goals, or SDGs, are still difficult to realize, especially the goal of ending poverty in all its forms. The Covid-19 pandemic, which has been around for more than a year, has actually exacerbated the condition of poverty in the community. In the city of Makassar, the poverty rate increased dramatically from 4.1 percent to 7.2 percent during 2020 (Makassar Ministry of Social Affairs data). Ironically, the poor who live on islands such as the people of Kodingareng, Barrang Lompo, Barrang Caddi, Lumu-Lumu, Laikang and others, are now getting poorer because of the Covid-19 outbreak. Therefore, this paper seeks to describe the problems of accelerating the handling of fishermen's poverty during the COVID- 19 pandemic in the city of Makassar in accelerating the SDGs. The method used is descriptive qualitative, and the data processed are secondary data and primary data. This research begins with identification and problem formulation, data collection, data processing and analysis including data classification and reduction, conclusion drawing and verification. The results of this study conclude that, the poorer fishermen on the islands, due to the decreasing number of fish catches, decreasing fish prices, higher operating costs at sea, swelling third party loan funds, social restrictions during the pandemic and the impact of sea sand mining in Makassar. . In addition, the social structure of island fishing communities is still trapped by structural poverty conditions. As a suggestion, it is necessary to accelerate intervention in implementing the implementation of village SDGs so that poor fishermen in the islands of Makassar city can get out of poverty.

Research Progress and Trend of the Machine Learning based on Fusion

Machine learning is widely used in the data processing including data classification, data regression, data mining and so on, and based on a single type of machine learning technology, it is often difficult to meet the requirements of data processing; in recent years, the machine learning based on fusion has become an important approach to improve data processing effect, and at the same time, corresponding summary study is relatively limited. In this study, we summarize and compare different types of fusion machine learning such as ensemble learning, federated learning and transfer learning from the perspectives of classification, principle and characteristics, and try to explore the research development trend, in order to provide effective reference for subsequent related research and application; furthermore, as an application of fusion machine learning，we also conduct a study on the modeling optimization for car service complaint text classification.

EKSISTENSI LAFAL-LAFAL AL-MUARRAB DALAM PEMBELAJARAN AL-QUR’AN

This journal discusses the existence of al-muarrab pronunciations in learning the Qur'an from the historical, and linguistic aspects. In learning the Qur'an there is material about the vocabulary/mufrodat of the Qur'an in which there are foreign vocabularies or al-muarrab pronunciations. This vocabulary does not reduce the miracle of the Qur'an in terms of language, in fact the presence of absorption elements in it shows that the Qur'an accommodates other languages. The main factor in the existence of foreign languages in the Qur'an is because the Arabs interacted with foreign nations in pre-Islamic times through prolonged trade routes in historical records. This led to the borrowing of foreign languages which were then used by the Arabs at that time. Of course, through a long historical process naturally through a stage called arabization or language neutralization by adjusting Arabic spoken and what the Arabs wanted. This research is a qualitative research with the nature of library research so that after collecting data, researchers carry out data processing activities, which include data classification, verification, merging, and sorting data with the aim of preparing materials for data analysis activities using content analysis.

Research on Key Technologies of Industrial Internet Data Security

At present, industrial Internet data is facing a severe security situation in storage, transmission, and use. This paper proposes data security management and security protection strategies by studying the data security risks and status quo of industrial Internet platforms. Among them, the security management strategy includes data classification and classification, data security assessment and data security threat emergency management. The security protection strategy deploys industrial data security protection from the technical aspects of collection, storage, transmission, processing, sharing, and deletion, providing a reference for further strengthening the industrial Internet data security.

Soft Matter Informatics: Current Progress and Challenges

AbstractThe number of applications of informatics or data‐driven discovery is growing in many fields, including materials science. The large amount of data that is readily available, combined with high‐level statistical algorithms, is proving to be extremely useful in developing complex predictive models with little to no human supervision or bias. However, in the field of soft matter, which includes complex materials such as polymers, liquids, emulsions, colloids, and gels, there is a slower adoption of informatics strategies than in adjacent fields. Here, the current state of soft matter informatics is discussed. Challenges specific to soft materials, including data classification, various degrees of organization at multiple length scales, and process‐dependent properties require unique approaches by researchers in order to develop robust informatics approaches in soft matter. The current ability to extract and analyze the information from the PoLyInfo database is demonstrated by the fitting of the Flory–Fox equation for glass transition temperature for several polymers. This Progress Report serves to introduce and excite the scientific community about the remarkable potential of informatics for exploring the properties of soft materials.

OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

SummaryCloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.

Study of Time and Meteorological Characteristics of Wind Speed Correlation in Flat Terrains Based on Operation Data

The accurate calculation and characteristic analysis of wind speed correlation (WSC) is the basis of wind farm equivalent modeling, wind power prediction and other advanced applications. It is well known that the accurate calculation of WSC depends on the quality of the raw data, and the WSC of wind turbines is related to spatial, time and meteorological conditions. However, the researches on the statistical analysis of time/meteorological WSC characteristics and the original data quality improvement for WSC calculation are rarely carried out. This paper reviews and redefines the concept and connotation of spatial, time and meteorological WSC. On this basis, a general process is proposed for WSC calculation including data classification, extraction and cleaning. Then the WSC characteristics between wind turbines are analyzed from time and meteorological dimensions based on the actual operation data. In addition, the influence of time WSC and meteorological WSC on wind turbine equivalent modeling and wind power prediction was discussed. The results of case study shows that the proposed general WSC calculation process is feasible and effective; the WSC for different time scales, wind speed ranges and wind directions varies greatly; the spatial WSC cannot characterize the time variability and directionality of the WSC. And the time and meteorological WSC characteristics are of great engineering value to improve the wind turbine equivalent modeling and wind power prediction accuracy, the influence of time scale and meteorological conditions should be considered in the applications of WSC.

The Standard of Management and Application of Cultural Heritage Documentation

Using digital technology for cultural heritage documentation is a global trend in the 21 st century. Many important techniques are currently under development, including 3D digital imaging, reverse engineering, GIS (Geographic Information Systems) etc. However, no system for overall management or data integration is yet available. Therefore, we urgently need such a system to efficiently manage and interpret data for the preservation of cultural heritages. This paper presents a digitizing process developed in Taiwan by the authors. To govern and manage cultural property, three phases of property conservation, registration, restoration and management, has been set up along a timeline. In accordance with the laws of cultural property, a structural system has been built for project management, including data classification and data interpretation with self-documenting characteristics. Through repository information and metadata, a system catalogue (also called data dictionary) (Figure 1) was created. The primary objective of the study is to create an integrated technology for an efficient management of databases. Several benefits could be obtained from this structural standard: (1) cultural heritage management documentation can be centralized to minimize the possibility of data re-entry resulting inconsistency, and also to facilitate simultaneous updating of data; (2) since multiple data can be simultaneously retrieved and saved in real time, the incidence of errors can be reduced; (3) this system could be easily tailored to meet the administrative requirements for the standardization of documentation exchanged between cultural properties institutions and various county and city governments.