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All Journal Building of Informatics, Technology and Science PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Adhiva, Jeni
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Pengembangan Sistem Informasi Konversi Kelapa di Kabupaten Indragiri Hilir-Riau Adhiva, Jeni; Mustakim, Mustakim; Suryani, Penti
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): December 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (470.078 KB) | DOI: 10.47065/bits.v3i3.1039

Abstract

Coconut plants are very much found throughout Indonesia, especially located in Riau province, making coconut plantations an economic base, the largest coconut plantation area to be precise in Indragiri Hilir Regency which has a coconut plantation area of 501,576 hectares. The contribution of coconut plantations in Indragiri Hilir makes Indonesia as a whole as the center of the largest coconut plantations in the world. The majority of Indragiri Hilir people work as farmers. The lack of knowledge and understanding of farmers regarding the management and utilization of coconut production is one of the reasons for the lack of coconut processing business in Indragiri Hilir Regency. It is necessary to have information related to the processing of coconut derivative products. This of course can be an illustration to farmers about the benefits that farmers get when selling coconuts that have been processed into products. Based on the results of ICT Literacy processing, 83% was obtained, this shows that post-harvest information is very important about the processing and utilization of coconut plants. Therefore we need an information system that can provide information about the use or management of coconut plants and the conversion of coconut derivatives in Indragiri Hilir district.
Application of the Supervised Learning Algorithm for Classification of Pregnancy Risk Levels Dwinnie, Zairy Cindy; Khairani, Luthfia; Putri, Margareta Amalia Miranti; Adhiva, Jeni; Tsamarah, Muhammad Inas Farras
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.806

Abstract

MMR is the number of women who die due to disorders during pregnancy or their treatment (excluding accidents, suicides, or incidental cases) during pregnancy, childbirth, and during the puerperium or 42 days after giving birth. This research aims to classify pregnancy risk datasets, namely to compare the performance of the NBC, K-NN, and SVM methods on the pregnancy risk status dataset and to find out the accuracy comparison of the algorithm results above. From the results of the analysis, it was found that of the three algorithms it resulted in a classification of pregnancy risk levels with the highest value occurring at a high level. To determine the accuracy of the data, a comparison was made between the three algorithms. Based on the confusion matrix namely Accuracy, Precision, and Recall. The results of the comparison can be concluded that the KNN algorithm provides the highest accuracy of 77.55%, NBC of 69.39%, and the lowest accuracy by SVM of 67.35%. These results state that the KNN algorithm classifies pregnancy risk level data better than the other two algorithms
Co-Authors Dwinnie, Zairy Cindy Khairani, Luthfia Mustakim Mustakim Penti Suryani Putri, Margareta Amalia Miranti Tsamarah, Muhammad Inas Farras