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All Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer International Journal of Educational Review, Law And Social Sciences (IJERLAS) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Metakognisi : Jurnal Kajian Pendidikan BEGIBUNG: Jurnal Penelitian Multidisiplin Abdi Ilmiah Untuk Masyarakat
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DISEASE CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) WITH JAVA STANDARD EDITION (JSE) Eka Utaminingsih; Rifki; Zanuar Rizkiansyah; Arista Ardilla; Fitriani
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 8 (2025): JULY
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v4i8.1064

Abstract

This research focuses on disease clustering, which is a crucial aspect of effective diagnosis and treatment. With the increasing complexity of health data generated from various sources, such as electronic health records and laboratory results, efficient methods are needed to cluster and analyze this data. The use of machine learning algorithms, particularly Support Vector Machine (SVM), offers a promising solution to address this issue. SVM is known for its ability to handle multidimensional data and identify patterns that are not immediately visible. The challenges faced in disease clustering include difficulties in managing large and complex data, as well as the inability of traditional methods to provide accurate and rapid results. Additionally, many healthcare professionals lack access to adequate analytical tools, hindering appropriate clinical decision-making. Therefore, it is essential to develop solutions that can effectively assist in disease clustering. The proposed solution in this study is the development of a Java Standard Edition (JSE) based application that implements the SVM algorithm for disease clustering. This application is designed to provide an intuitive user interface, allowing users to upload data, run the SVM algorithm, and easily obtain clustering results. This research uses clinical data from various diseases, including heart disease, diabetes, hypertension, cancer, asthma, and stroke. Evaluation results show that SVM can cluster diseases with an accuracy of up to 92%. Thus, this study concludes that the application of SVM in a JSE-based application is an effective solution for enhancing disease clustering and supporting better clinical decision-making.
ANALYSIS OF EFFECTIVE SENTENCES IN THE NEWS TEXT OF CLASS XI STUDENTS OF SMK NEGERI 2 LHOKSEUMAWE Nurmalawati; Widia Tamara; Rifki
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 1 No. 2 (2022): JANUARY
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v1i2.698

Abstract

This study aims to find out the effective sentences in news texts and understand the form or form of effective sentences in the news texts of students in grade XI of SMK Negeri 2 Lhokseumawe. This study focuses on the use of effective sentences in students' news texts which are reviewed from the aspects of parallelism, firmness, frugality, precision, and cohesion. The data in this study is effective sentence errors in the news text of grade XI students of SMK Negeri 2 Lhokseumawe. The source of this research data is the news text of grade XI students of SMK Negeri 2 Lhokseumawe. Based on the results of the research that has been carried out, the use of effective sentences in students' news texts has not led to the effectiveness of sentences, because the requirements to achieve effective sentences have not been met. The analysis of twenty student news texts found many errors, such as three errors in equivalence and unity, three errors in parallelism (pararelialism), four errors in sentence emphasis, and ten errors in frugality.
Co-Authors Arista Ardilla Azhari Fitriani Hamka Handayani, Suci Dwi Helmiyadi Kusmana, Nandang Muh. Saiful Iqbal Muhammad Yany Nadira Nur Hidaya Nurmalawati Pebrianti Rani Alfia Sari Sri Rahayu Ningrum Teguh Ardianto, Teguh Utaminingsih, Eka Widia Tamara Zanuar Rizkiansyah Zulfuadi Basrah Zulkarnain