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All Journal JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Informatika dan Rekayasa Elektronik
Abdussalam Amrullah
Universitas Singaperbangsa Karawang
Computer Science & IT

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Implementasi Algoritma SVM (Support Vector Machine) Dalam Klasifikasi Penyakit Paru-Paru Berdasarkan Fitur Pola Bentuk Teguh Muhammad Prasetyo; Abdussalam Amrullah; Syahman Syahrir; Betha Nurina Sari
(JurTI) Jurnal Teknologi Informasi Vol 6, No 1 (2022): JUNI 2022
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v6i1.2429

Abstract

There are many implementations of classification data mining algorithms in a case, and for this study the Support Vector Machine algorithm was used to classify lung images based on normal and pneumonia categories. The lung image used was obtained from the kaggle site. Lungs are organs of respiration (breathing) associated with the respiratory system and circulation (blood circulation) in the body of vertebrates that breathe air. In order for the identification of lung disease to be optimal, it will be more effective and efficient to create an application system for classifying lung diseases. This application system is built using the method Support Vector Machine (SVM). The method is Support Vector Machine used to classify diseases in the lungs and the variables used in this algorithm are taken from the extraction of shape features, including the Metric and Eccentricity values. This application system is built using the Matlab IDE. Matlab is a environment computing numerical and language programming fourth generation computer. The method used is data collection and system design. The result of this application system is a classification between normal lungs and diseased lungs, based on the results of calculations grayscale on x-ray images.
ANALISIS CLUSTER FAKTOR PENUNJANG PENDIDIKAN MENGGUNAKAN ALGORITMA K-MEANS (STUDI KASUS: KABUPATEN KARAWANG) Abdussalam Amrullah; Intam Purnamasari; Betha Nurina Sari; Garno; Apriade Voutama
Jurnal Informatika dan Rekayasa Elektronik Vol. 5 No. 2 (2022): JIRE Nopember 2022
Publisher : LPPM STMIK Lombok

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Abstract

Education is one of the means to create and improve the quality of better human resources. This is expected to improve human welfare. Based on data from the Ministry of Education and Culture, there are 4 sub-districts in Karawang Regency that do not have state high schools. This can result in difficulties for students who have financial deficiencies which can ultimately lead to dropping out of school. Then apart from that distance can also be an obstacle. The purpose of this study is to apply the clustering method for the distribution of educational supporting factors in Karawang District so that later this research can improve the quality of education evenly in Karawang District, not only concentrated in certain areas by paying attention to educational supporting factors. The algorithm used in this research is K-Means. The elbow method used in determining the optimal cluster supported by the silhouette method resulted in the best number of clusters being 2 clusters. The results of the clustering evaluation resulted in Davies-Bouldin Index (DBI) value of 0.408 and Silhouette Coefficient value of 0.747 (strong structure). Cluster 1 consists of 7 sub-districts and Cluster 2 consists of 23 sub-districts. Based on the results of clustering analysis, Cluster 1 has the average number of attributes of schools, teachers, classes, laboratories, libraries at all levels of education (SD, SMP, SMA, SMK, and SLB) with state status and the population shows higher results if compared with the average number of each attribute in Cluster 2. So it can be concluded that Cluster 1 is a high category and Cluster 2 is a low category.
Co-Authors Apriade Voutama Garno Intam Purnamasari Syahman Syahrir Teguh Muhammad Prasetyo