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All Journal MATRIK: JURNAL MANAJEMEN, STRATEGI BISNIS, DAN KEWIRAUSAHAAN IJoICT (International Journal on Information and Communication Technology) MAJALAH ILMIAH GLOBE Risâlah, Jurnal Pendidikan dan Studi Islam International Journal of Supply Chain Management Jurnal Sosial Humaniora JURNAL VISIONIDA Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Jurnal Pengabdian Mandiri Innovative: Journal Of Social Science Research Karimah Tauhid SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Edukasia Islamika: Jurnal Pendidikan Islam Jurnal Cahaya Edukasi JIMEK
Sri Harini
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Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Public Health Social Sciences Transportation Other

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Journal : IJoICT (International Journal on Information and Communication Technology)

 1 
Java Island Health Profile Clustering using K-Means Data Mining Muhammad Andryan Wahyu Saputra; Sri Harini
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.606

Abstract

Health is the best gift in life, because with health humans can carry out daily activities. Administratively, Java Island consists of 85 administrative regions and 34 cities. Therefore, it is very important to understand the health level of each area. The main objective of this research is to divide each region (district and city) into several groups and use the K-means method to determine health status based on 8 data parameters into certain groups. Algorithm in groups, will place the data based on the similarity of characteristics between groups. The results showed that there were 4 clusters of health profiles in Java, with 1 high health quality cluster in Central Jakarta, 55 regencies/municipalities with low health quality, 52 regencies/cities with low health quality. and the quality of health is quite low there are 13 districts/cities, it can be concluded that the health indicators in Java
Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification Sri Suryani Prasetiyowati; Sri Harini; Juniardi Nur Fadila; Hilda Fahlena
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.965

Abstract

Non-linear SVM functions to modify the kernel in the SVM. Each kernel function in linear and non-linear SVMs has several parameters that are used in the classification process. SVM is a method that has advantages in classification, but there are still obstacles in selecting optimal parameters. This research investigates the effect of parameter variations on SVM classification performance on the COVID-19 dataset, using linear, RBF, Sigmoid and polynomial kernels. The analysis shows that the polynomial kernel is superior with the highest performance compared to other kernels. The highest accuracy of 77.57% was achieved with a combination of C values ??of 0.75 and Gamma of 0.75, and an F1-Score value of 76.67% indicating an optimal balance between precision and recall. The performance stability produced by the polynomial kernel provides advantages in classifying the COVID-19 dataset, with more controlled fluctuations compared to other kernels. The interaction between the C and Gamma parameters shows that a Gamma value of 0.75 consistently provides good results, while adjusting the C parameter shows more controlled performance variations. This confirms that appropriate Gamma parameter settings are key in improving the accuracy and consistency of SVM model predictions in this case.
Co-Authors Ade Budi Setiawan Ade Kurniawan Ahmad Lubab Airahmawati Airahmawati Anita Septiana Annisa Lutfiah Zahra Apendi Arsyad Army Purwanti Awa Awa Dede Dirgahayu Erni Yuningsih Fachrul Kurniawan Hamidah Hamidah Hapzi Ali Hilda Fahlena IM Ashari ISMARTAYA Jalu Tejo Nugroho Juniardi Nur Fadila Made Parsa Mas Rifqiyah M. Alfisyah Moch. Ichsan Nugraha Much Zuyyinal Haqqul Barir Muchlis R. Luddin Muhammad Abdul Ghoffar Muhammad Andryan Wahyu Saputra Muhammad Yasin Fatchul Barry Palahudin Palahudin Rahman, Muhammad Fuady Ranidya Ramitha Sayidah Sulma Siti Maftuhah Sitti Hadijah Sri Suryani Prasetiyowati Sudarijati Tia Rahmawati Tini Kartini Yenni Vetrita Yuni Yuni Arifatur Rohmah