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Kab. jember,
Jawa timur
INDONESIA
INFORMAL: Informatics Journal
Published by Universitas Jember
ISSN : 2503250X     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 12 Documents
 1   2 
Search results for , issue "Vol 6 No 3 (2021): Informatics Journal (INFORMAL)" : 12 Documents clear
Optimasi K-Klasterisasi Ketahanan Pangan Kabupaten Jember Menggunakan Metode Elbow Aski Widdatul Fuadah; Fajrin Nurman Arifin; Oktalia Juwita
INFORMAL: Informatics Journal Vol 6 No 3 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i3.28363

Abstract

Clustering is a process of grouping data based on similarities or similarities with other members in a group. Food security is the condition of a country to provide food for individuals, which does not conflict with beliefs, religion and culture and leads a healthy, active and productive life. Food instability and food insecurity can be caused by many factors, one of which is natural disasters. In 2020, Jember Regency experienced 121 natural disasters. Determination of the optimal K value is done to get the right number of group divisions from the clustering process, in this case using the elbow method. The data used in the clustering process are sub-districts in Jember Regency using transient attributes or natural disaster events. Based on the results of sub-district data grouping from the number of clusters k=1 to k=10, the optimal k value was found at the value of k = 4 with the SSE (Sum of Square Error) value = 24,809.
Klasifikasi Resiko Kehamilan Menggunakan Ensemble Learning berbasis Classification Tree Muhamad Arief Hidayat
INFORMAL: Informatics Journal Vol 6 No 3 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i3.28396

Abstract

In health science there is a technique to determine the level of risk of pregnancy, namely the Poedji Rochyati score technique. In this evaluation technique, the level of pregnancy risk is calculated from the values ​​of 22 parameters obtained from pregnant women. Under certain conditions, some parameter values ​​are unknown. This causes the level of risk of pregnancy can not be calculated. For that we need a way to predict pregnancy risk status in cases of incomplete attribute values. There are several studies that try to overcome this problem. The research "classification of pregnancy risk using cost sensitive learning" [3] applies cost sensitive learning to the process of classifying the level of pregnancy risk. In this study, the best classification accuracy achieved was 73% and the best value was 77.9%. To increase the accuracy and recall of predicting pregnancy risk status, in this study several improvements were proposed. 1) Using ensemble learning based on classification tree 2) using the SVMattributeEvaluator evaluator to optimize the feature subset selection stage. In the trials conducted using the classification tree-based ensemble learning method and the SVMattributeEvaluator at the feature subset selection stage, the best value for accuracy was up to 76% and the best value for recall was up to 89.5%

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