Sistem Penentuan Tingkat Kesejahteraan Anak Menggunakan Algortima C 4.5

Implementasi algoritma C 4.5 dalam menentukan tingkat kesejahteraan anak merupakan sebuah upaya preventif terhadap masa depan bangsa yang tergantung pada masa depan anak-anak . Penelitian ini bertujuan untuk memperoleh model sistem yang memiliki nilai akurasi tinggi lebih dari 80%. Sehingga diharapkan pengambilan keputusan peentuan tingkat kesejahteraan anak memiliki kepastian kebesaran yang memadai. Sistem yang diharapkan berupa basis data relasional yang menggambarkan aktivitas penentuan tingkat kesejahteraan anak. Data yang dikumpulkan dalam penelitian ini berupa data primer yang diperoleh dari hasil wawancara langsung dengan responden menggunakan kuesioner. Data responden dalam penelitian ini adalah sampel data sebanyak 212 data, dimana pengambilan data dilakukan secara acak (random) dari populasi 14291 KK. Dari 212 data tersebut 176 data digunakan sebagai data training dan 36 data lainnya. Dilakukan penghitungan entropy dan gain untuk memperoleh pohon keputusan. Diperoleh model sistem yang memiliki tingkat akurasi tinggi yaitu sebesar 95,65%. Dipeloeh basis data relasional untuk merealisasikan sistem penentuan kepusan penentuankesejahteraan anak

SISTEM PENENTUAN TINGKAT KESEJAHTERAAN ANAK MENGGUNAKAN ALGORTIMA C 4.5

Implementasi algoritma C 4.5 dalam menentukan tingkat kesejahteraan anak merupakan sebuah upaya preventif terhadap masa depan bangsa yang tergantung pada masa depan anak-anak . Penelitian ini bertujuan untuk memperoleh model sistem yang memiliki nilai akurasi tinggi lebih dari 80%. Sehingga diharapkan pengambilan keputusan peentuan tingkat kesejahteraan anak memiliki kepastian kebesaran yang memadai. Sistem yang diharapkan berupa basis data relasional yang menggambarkan aktivitas penentuan tingkat kesejahteraan anak. Data yang dikumpulkan dalam penelitian ini berupa data primer yang diperoleh dari hasil wawancara langsung dengan responden menggunakan kuesioner. Data responden dalam penelitian ini adalah sampel data sebanyak 212 data, dimana pengambilan data dilakukan secara acak (random) dari populasi 14291 KK. Dari 212 data tersebut 176 data digunakan sebagai data training dan 36 data lainnya. Dilakukan penghitungan entropy dan gain untuk memperoleh pohon keputusan. Diperoleh model sistem yang memiliki tingkat akurasi tinggi yaitu sebesar 95,65%. Dipeloeh basis data relasional untuk merealisasikan sistem penentuan kepusan penentuankesejahteraan anak

SISTEM PENENTUAN TINGKAT KESEJAHTERAAN ANAK MENGGUNAKAN ALGORTIMA C 4.5

Implementasi algoritma C 4.5 dalam menentukan tingkat kesejahteraan anak merupakan sebuah upaya preventif terhadap masa depan bangsa yang tergantung pada masa depan anak-anak . Penelitian ini bertujuan untuk memperoleh model sistem yang memiliki nilai akurasi tinggi lebih dari 80%. Sehingga diharapkan pengambilan keputusan peentuan tingkat kesejahteraan anak memiliki kepastian kebesaran yang memadai. Sistem yang diharapkan berupa basis data relasional yang menggambarkan aktivitas penentuan tingkat kesejahteraan anak. Data yang dikumpulkan dalam penelitian ini berupa data primer yang diperoleh dari hasil wawancara langsung dengan responden menggunakan kuesioner. Data responden dalam penelitian ini adalah sampel data sebanyak 212 data, dimana pengambilan data dilakukan secara acak (random) dari populasi 14291 KK. Dari 212 data tersebut 176 data digunakan sebagai data training dan 36 data lainnya. Dilakukan penghitungan entropy dan gain untuk memperoleh pohon keputusan. Diperoleh model sistem yang memiliki tingkat akurasi tinggi yaitu sebesar 95,65%. Dipeloeh basis data relasional untuk merealisasikan sistem penentuan kepusan penentuankesejahteraan anak

Linear discriminant analysis and principal component analysis to predict coronary artery disease

Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction

An agent-based decision support system for ecological-medical situation analysis

This paper presents an architecture of an agent-based decision support system (ADSS) for ecological-medical situation assessment. The system receives statistical information in form of direct and indirect pollution indicator values. The ultimate goal of the modeled multi-agent system (MAS) is to evaluate the impact of the exposure to pollutants in population health. The proposed ADSS interacts with humans in real-time ?what-if? scenarios, providing the user with evidence for optimal decision making. A detailed description of all the agents and their BDI (beliefs, desires, intentions) cards is presented

A study of children’s musical preference: A data mining approach

Musical preference has long been a research interest in the field of music education, and studies consistently confirm the importance of musical preference in one’s musical learning experiences. However, only a limited number of studies have been focussed on the field of early childhood education (e.g., Hargreaves, North, & Tarrant, 2006; Roulston, 2006). Further, among these limited early childhood studies, few of them discuss children’s musical preference in both the East and the West. There is very limited literature (e.g., Faulkner et al., 2010; Szymanska, 2012) which explores the data by using a data mining approach. This study aims to bridge the research gaps by examining children’s musical preference in Hong Kong and in South Australia by applying a data mining technique – Self Organising Maps (SOM), which is a clustering method that groups similar data objects together. The application of SOM is new in the field of early childhood education and also in the study of children’s musical preference. This paper specifically aims to expand a previous study (Yim & Ebbeck, 2009) by conducting deeper investigations into the existing datasets, for the purpose of uncovering insights that have not been identified through data mining approach

Applying Particle Swarm Optimization-Base Decision Tree Classifier for Mental Illnesses

Background: Data mining techniques such as clustering and classification are used to explore patient's data and extract a predictive model. Medical data set are often classified by a large number of irrelevant disease measurements(features). Feature selection is one of the most common tasks which reduces the computational cost by removing insignificant features. Method: This paper presents a graph-based Louvain algorithm for mental illness dataset clustering and a particle swarm optimization combined with a decision tree as the classifier to select the small number of an informative feature from the thousands of features were collected from health centers consist of 1060 people in two groups of 550 patients and 510 healthy. Result: The results show that "aggression" Finding the greatest impact on the diagnosis of mental disorders has been observed in the number of 65. After that, the features such as "prisoner in the family" and "hard labor" with 63 observations had a greater impact on the disease also the third ranking "illiterate" and "elation and euphoria" had 61 and 58 observations. Conclusions: The classification accuracy shows that the proposed method is capable of producing good results with fewer features than the original datasets. Keywords: Mental illness, Graph clustering, Particle swarm optimization, ID3 DOI: 10.7176/JIEA/9-7-03 Publication date: December 31st 2019

Application of data mining techniques to predict students' mental health status to improve educational performance

Background and Objective: Student mental health data has been recorded in the information systems of the universities across the country for several years, and due to its high volume, conventional statistical and psychoanalytic methods to predict patterns and factors affecting students' mental health are not effective. This is where data mining technology comes in handy and helps to predict and identify those at high risk based on the recorded data set of students 'physical and especially mental health status, and to make appropriate and timely decisions to improve students' condition. One of the main objectives of every managers of educational centers is making improvements in students’ educational performance. Besides the educational factors, physical and mental health is considerable which has a significant effect on students’ behavior. Therefore, some rules and patterns are required to make the best decisions, based on the prediction of students’ mental health state. This paper proposes a data mining approach for analyzing and extracting patterns in terms of new students’ mental health, which means whether they need to visit a psychologist. Our effort was on extracting hidden rules in new students’ mental health examination by employing classification approach. Methods:Techniques used in this study are decision tree, rule based classifier, neural network, logistic regression and support vector machine. Moreover, a parameter tuning process is done for all the techniques mentioned and the results presents the list of symptoms of individuals who need detailed examination. Findings:The results of the research represent that one can predict the status of students’ mental helath based on propsed model. One of the outcomes of decision tree is that if a person severely feels disappointed or seems to be obsessive by others, or feels that life is worthless, definitely a consultaion is needed. Conclusion: Considering that most of the existing research in the field of health data mining have focused on physical health, it is suggested that for future studies, all levels of health, i.e dimensions of students' health, including physical, social and spiritual health, as well as a combination of these dimensions be considered. In addition, a review of the various approaches and techniques appropriate to the psychological data set should be conducted with the aim of creating an appropriate classification for the existing techniques in this field. It is also suggested that the present data set or similar data sets (student health monitoring information) be examined with other classification techniques and the results be compared with the results of the present study. In general, it is suggested that data mining technology be used to extract hidden patterns in the mental health data set of school students at different levels of education, office workers and organizations. Finally, it is recommended that future research in this field first implement the clustering approach on the psychological data set and then use the classification and forecasting approaches.   ===================================================================================== COPYRIGHTS  ©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.  ====================================================================================

EARLY INTERVENTION EXPERIENCES OF PARENTS OF CHILDREN WITH DEVELOPMENTAL DISABILITIES

Rana intervencija kao oblik podrške djetetu i obitelji u prvim godinama djetetova života ima za cilj unapređenje razvoja djeteta. Uz usmjerenost na dijete rana intervencija ima i funkciju podrške članovima obitelji. Cilj rada je istražiti iskustva roditelja djece s teškoćama u razvoju s procesom rane intervencije. Riječ je o kvalitativnom istraživanju u kojem su sudjelovali roditelji djece s teškoćama u razvoju (N=13). Rezultati istraživanja upućuju na roditeljima važna područja za uspješnost rane intervencije koja uključuju: interdisciplinarni pristup, kompetentnost i motiviranost stručnjaka te suradni odnos s obitelji. Roditelji kao teškoće u sustavu vide: izostanak pravovremene podrške i informiranja, neprofesionalnost i organizacijske teškoće. Socijalni rad u ranoj intervenciji roditelji opisuju kroz iskustava obilježena roditeljskim nezadovoljstvom dobivenim informacijama, odnosom sa stručnjacima i organizacijom rada. Ipak, roditelji vide važnost uključenosti socijalnog rada u sustav rane intervencije te ulogu socijalnog radnika opisuju kroz ulogu koordinatora sustava rane podrške, informatora i savjetovatelja. Dobivene spoznaje daju smjernice za unapređenje postojećeg sustava rane intervencije te za aktualizaciju struke socijalnog rada koja aktivnim sudjelovanjem može doprinijeti unapređenju rane intervencije za dijete i obitelj.Early intervention as a mode of support to children and families in early years of a child’s life is aimed at enhancing the child’s development. In addition to being focused on the child, early intervention also functions as a support to family members. The aim of this paper is to explore experiences of parents of children with developmental disabilities with the early intervention process. The conducted qualitative research included parents of children with developmental disabilities (N=13). Research results indicate which areas parents see as important for a successful early intervention and they include an interdisciplinary approach, competence and motivation of professionals and cooperation with families. Parents have identified the following disadvantages within the system: the lack of timely support and information, unprofessionalism and organisational difficulties. Parents describe social work in early intervention through experiences marked with parents’ dissatisfaction with obtained information, i.e. relationship with professionals and the organisation of work. However, parents recognise the importance of the involvement of social work in the early intervention system and they describe the role of social workers as coordinators of the early support, providers of information and counsellors. Obtained findings may serve as guidelines for improving the existing early intervention system and for actualisation of the social work profession, the active participation of which can contribute to the improvement of early intervention for children and families