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All Journal Sistemasi: Jurnal Sistem Informasi KOMPUTIKA - Jurnal Sistem Komputer
Alfirdausy, Roudlotul Jannah
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Optimasi K-Nearest Neighbor Dengan Particle Swarm Optimization Untuk Klasifikasi Idiopathic Thrombocytopenic Purpura Alfirdausy, Roudlotul Jannah; Aliyyah, Izzatul; Fanani, Aris
Komputika : Jurnal Sistem Komputer Vol. 13 No. 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.10436

Abstract

ABSTRACT – Immune Thrombocytopenic Purpura (ITP) is a hematological disease caused by autoimmune damage to platelets, causing a person to bruise easily or bleed excessively. ITP disease must be detected early because it can cause chronic or long-term disorders, so this study aims to classify ITP disease in order to avoid misdiagnosis of patients and can be treated and treated immediately. This classification uses the PSO-KNN combination method. The results obtained from the classification using the PSO-KNN combination method are an accuracy value of 91.8% with an increase of 4.9% from the KNN standard, a sensitivity value of 91.2% with an increase of 11.8% from the KNN standard, and a specificity value of 92.6% with a decrease of 3.7% from the KNN standard. % The training and testing time of PSO-KNN is also faster than standard KNN so that PSO is able to optimize and improve the classification results of KNN.
Analysis of Regency/City Human Development Index Data in East Java Through Grouping Using Hierarchical Agglomerative Clustering Method Alfirdausy, Roudlotul Jannah; Ulinnuha, Nurissaidah; Hafiyusholeh, Moh.
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2959

Abstract

The evaluation of human development is typically done using the Human Development Index (HDI), which measures the level of development in terms of various essential aspects of quality of life. In the case of East Java, the HDI is categorized as high. However. the distribution of HDI among the Regencies/Cities in East Java is still uneven. Therefore, it becomes necessary to cluster the districts/cities based on their HDI and the achievement of each indicator contributing to the HDI. Clustering is a data analysis technique used to group similar data together. Hierarchical agglomerative clustering is one of the methods used for this purpose. The aim of this study is to provide a reference for the government to understand the distribution of characteristic groupings among the districts/cities based on their HDI profiles in East Java. The analysis of East Java's HDI data for 2021 revealed that the best method and cluster was obtained using Average Linkage, with a Cophenetic coefficient value of 0.8105891, resulting in two clusters. The cluster with the highest Silhouette coefficient value of 0.6196077 comprised 34 districts/cities, classified as the low cluster, while the high cluster consisted of four cities/regencies.
Co-Authors Aliyyah, Izzatul Aris Fanani Moh. Hafiyusholeh Nurissaidah Ulinnuha Wika Dianita Utami