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All Journal Techno.Com: Jurnal Teknologi Informasi Sistemasi: Jurnal Sistem Informasi
Roudlotul Jannah Alfirdausy
UIN Sunan Ampel
Computer Science & IT Electrical & Electronics Engineering Engineering

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Implementasi Algoritma K-Nearest Neighbor untuk Klasifikasi Diagnosis Penyakit Alzheimer Roudlotul Jannah Alfirdausy; Saiful Bahri
Techno.Com Vol 22, No 3 (2023): Agustus 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i3.8393

Abstract

Menurut  World Alzheimer Report 2021, Alzheimer's Disease International (ADI) memperkirakan bahwa 75% penderita demensia tidak terdiagnosis secara global, dengan angka tersebut diyakini meningkat hingga 90% di beberapa negara berpenghasilan rendah dan menengah. Saat ini ada dua jenis demensia yaitu vaskular dan non-vaskular. Demensia vaskular, juga dikenal sebagai Alzheimer, adalah Perubahan yang terjadi pada tubuh yang disebabkan oleh penyumbatan pembuluh darah di otak dapat menyebabkan melemahnya ingatan manusia. K-Nearest Neighbor (KNN) adalah suatu teknik yang digunakan untuk mengelompokkan objek berdasarkan kesamaannya dengan objek terdekat. Penelitian ini bertujuan untuk menangani masalah efektivitas dan akurasi dalam mendeteksi penyakit Alzheimer dengan mengimplementasikan algoritma KNN untuk diagnosis penyakit Alzheimer. Berdasarkan analisis hasil klasifikasi dengan pembagian data testing dan training 75:25 menggunakan metode KNN dengan K=3 dan K=5 menghasilkan nilai akurasi sebesar 99%  , sensitivitas  sebesar 100%, serta  spesifitas sebesar 96%.
Analysis of Regency/City Human Development Index Data in East Java Through Grouping Using Hierarchical Agglomerative Clustering Method Roudlotul Jannah Alfirdausy; Nurissaidah Ulinnuha; Moh. Hafiyusholeh
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 Moh. Hafiyusholeh Nurissaidah Ulinnuha Saiful Bahri