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All Journal Jurnal Penelitian Karet Jambura Journal of Biomathematics (JJBM)
Sarwinda, Devvi
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics

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DISTRIBUSI SPASIAL KESEHATAN TANAMAN KARET MENGGUNAKAN SENTINEL-1 Ayu, Farida; Riesnandar, Ariq Anggaraksa; Manessa, Masita Dwi Mandini; Supriatna, Supriatna; LESTARI, Retno; Bustamam, Alhadi; Sarwinda, Devvi; Stevanuse, Charlos Togi; Efriana, Anisya Feby
Jurnal Penelitian Karet JPK : Volume 42, Nomor 1, Tahun 2024
Publisher : Pusat Penelitian Karet - PT. Riset Perkebunan Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/ppk.jpk.v42i1.881

Abstract

Tanaman karet (Hevea brasiliensis) merupakan komoditas penting yang menjadi sumber pendapatan petani di Indonesia. Namun, dalam beberapa tahun terakhir perkebunan karet di Indonesia mengalami penurunan mutu dan produksi yang disebabkan oleh penyakit gugur daun Pestalotiopsis sp. Teknologi remote sensing dapat menjadi solusi dalam pemantauan kesehatan tanaman. Kendala tutupan awan dalam pemantauan perkebunan karet menggunakan citra optik menghambat keberlangsungan. Citra Sentinel-1 dilengkapi data Synthetic Aperture Radar (SAR) yang mampu untuk menembus awan. Sehingga, penelitian ini bertujuan untuk menganalisis distribusi spasial kesehatan tanaman dengan menggunakan multi indeks vegetasi RVI dan NDRVI pada citra Sentinel-1. Hasil penelitian menunjukan bahwa multi indeks vegetasi tidak memiliki hubungan yang signifikan dengan kelas kesehatan tanaman. Faktor noise, panjang gelombang, dan hamburan balik mengindikasikan rendahnya hubungan antar variabel.
Implementation of K-Prototypes with Feature Selection in Clustering Cervical Cancer Patients based on Risk Factors Hati, Wanda Puspita; Sarwinda, Devvi; Handari, Bevina Desjwiandra
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 3: September 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i3.30552

Abstract

Cancer is a leading cause of death worldwide, resulting in nearly 10 million deaths or almost one-sixth of all deaths in 2020. Effective primary prevention measures can prevent at least 40% of cancer cases. Cancer mortality rates are higher in developing countries than in developed countries, reflecting disparities in addressing risk factors, detection success, and available treatments. Women in developing countries most frequently suffer from cervical cancer. It is crucial for communities, especially women, to have knowledge about the risk factors for cervical cancer. One potential solution to this issue is the role of machine learning in analyzing cervical cancer patient data. This study uses the K-Prototypes clustering algorithm, which can cluster mixed data, both numerical and categorical. Cervical cancer risk factor data were used in this research. Feature selection was performed to improve the performance of the K-Prototypes algorithm, using feature selection methods Variance Threshold and Correlation Coefficient. The best performance of the K-Prototypes algorithm was obtained using the Correlation Coefficient, as reviewed based on a Silhouette Coefficient of 0.6, a Davies-Bouldin Index of 0.6, and a Calinski-Harabasz Index of 1.080. Interpretation of the clusters formed revealed major differences in the characteristics of risk factors between two clusters, namely age, menopause, and health conditions such as leukorrhea, bleeding, lower abdominal pain, and loss of appetite. Meanwhile, factors related to previous history, reproductive health, and nutritional issues did not show significant differences. The K-Prototypes algorithm is expected to be a solution in identifying groups based on cervical cancer risk factors to assist medical professionals in decision-making and subsequent actions, as well as to provide knowledge to the public.
Co-Authors Ayu, Farida Bevina D Handari Bustamam, Alhadi Efriana, Anisya Feby Hati, Wanda Puspita MASITA DWI MANDINI MANESSA, MASITA DWI MANDINI Retno Lestari Riesnandar, Ariq Anggaraksa Stevanuse, Charlos Togi Supriatna Supriatna