Judul: Linear Support Vector Machine-Recursive  Feature Elimination with Cross Validation to Determine Characteristics of Food  Insecurity in West Java
Abstrak: Seleksi peubah dapat digunakan untuk mengetahui informasi mengenai penentuan tingkat kepentingan peubah yang menyebabkan suatu kejadian, salah satunya kerawanan pangan. Pengentasan kerawanan pangan merupakan tujuan kedua Sustainable Development Goals (SDGs), yaitu mengakhiri kelaparan yang diharapkan tercapai pada tahun 2030. Penelitian ini bertujuan mengukur kekuatan asosiasi peubah dengan kerawanan pangan serta mengidentifikasi peubah-peubah yang mencirikan kerawanan pangan di Jawa Barat menggunakan algoritma Support Vector Machine-Recursive Feature Elimination with Cross Validation (SVM-RFECV). Berdasarkan hasil penelitian, peubah yang memiliki asosiasi kuat dengan kerawanan pangan adalah tingkat pendidikan kepala rumah tangga (KRT) dan jumlah anggota rumah tangga (ART) yang memiliki tabungan. Umur KRT, status bekerja KRT, listrik, air minum layak, kepemilikan Jaminan Kesehatan Daerah (Jamkesda), akses Kredit Usaha Rakyat (KUR), dan jumlah ART memiliki asosiasi yang sangat lemah dengan kerawanan pangan. Peubah-peubah yang mencirikan kerawanan pangan di Jawa Barat adalah jumlah ART yang memiliki tabungan, KRT dengan pendidikan tertinggi SMP, jumlah buta huruf, jumlah ART, jumlah perokok, kepemilikan aset tanah, status bangunan dinas, penerima transfer, penerima Bantuan Pangan Non-Tunai (BPNT), kepemilikan BPJS Kesehatan, dan umur KRT. Banyaknya ART yang memiliki tabungan menempati urutan teratas peubah penciri kerawanan pangan di Jawa Barat., Feature selection can be used to find out information about determining the level of importance of variables that cause an event, one of which is food insecurity. Alleviation of food insecurity is the second goal of the Sustainable Development Goals (SDGs), which aims to end world hunger by 2030. This study aims to measure the strength of the association of variables with food insecurity and identify the variables that characterize food insecurity in West Java using Support Vector Machine-Recursive Feature Elimination with Cross Validation (SVM-RFECV) algorithm. Based on the study’s results, the variables strongly associated with food insecurity are the education level of the head of the household and the number of household members who have savings. The age of the head of the household, the working status of the head of the household, electricity, decent drinking water, grantee of health insurance local program (Jamkesda), access to KUR, and the number of household members have very weak associations with food insecurity. The variables that characterize food insecurity in West Java are the number of household members who have savings, the head of the household with the highest education in junior high school, the number of illiterates, the number of household members, and the number of smokers. In addition, other characterized variables were ownership of land assets, ownership status of the building is an official house, transfer recipient, grantee of non cash social assistance (BPNT), grantee of health insurance national program (BPJS Kesehatan), and age of head of household. The number of household members who have savings ranks at the top of the variables that characterize food insecurity in West Java.
Keyword: association, feature selection, food insecurity, SVM-RFECV

Judul: Variable Importance Analysis Based on The Extra-Trees Model for Identification of The Characterizing Variables of Food-Insecure Households in West Java
Abstrak: Sekitar 2,3 miliar orang di dunia mengalami kerawanan pangan. Kerawanan pangan dapat memiliki konsekuensi serius bagi kesehatan dan kesejahteraan. Pengentasan kerawanan pangan menjadi salah satu isu krusial dalam mewujudkan tujuan kedua pembangunan berkelanjutan, yaitu tanpa kelaparan. Penelitian ini bertujuan untuk mengidentifikasi peubah penting yang mencirikan kerawanan pangan rumah tangga di Jawa Barat. Penelitian akan dibagi menjadi dua subpopulasi yaitu rumah tangga perdesan dan perkotaan. Data yang digunakan dalam penelitian ini adalah data Survei Sosial Ekonomi Nasional tahun 2021. Algoritma yang digunakan untuk melakukan pemodelan klasifikasi kejadian rawan pangan adalah Extremely Randomized Trees (Extra-Trees) dengan penanganan data tidak seimbang menggunakan oversampling ADASYN dan pembobotan kelas peubah respon. Peubah penting yang mencirikan kerawanan pangan diidentifikasi menggunakan SHapley Additive exPlanations (SHAP). Model dengan penanganan menggunakan pembobotan kelas menghasilkan kinerja yang lebih baik dibanding tanpa penanganan dan dengan penanganan ketidakseimbangan data menggunakan ADASYN. Hasil evaluasi menunjukan balanced accuracy model perdesaan sebesar 60,76% dan model perkotaan sebesar 64,08%. Jenis lantai, kepemilikan aset tanah, Bantuan Pangan Non-Tunai (BPNT), bantuan Pemda, jenis dinding, banyaknya penabung, pendidikan KRT, Penerima Bantuan Iuran (PBI) BPJS, bahan bakar masak, dan jenis kelamin KRT merupakan peubah penting yang mencirikan kejadian rumah tangga rawan pangan perdesaan. Banyaknya penabung, PBI BPJS, kepemilikan aset tanah, jenis lantai, pendidikan KRT, BPNT, bantuan Pemda, Program Keluarga Harapan (PKH), sanitasi layak, dan jumlah perokok merupakan peubah penting yang mencirikan kejadian rumah tangga rawan pangan perkotaan., Approximately 2,3 billion people in the world have experienced food insecurity. Food insecurity can have serious consequences for health and well-being. Alleviation of food insecurity is one of the crucial issues in realizing the second goal of the Sustainable Development Goals, zero hunger. This study aims to identify important variables that characterize household food insecurity in West Java Province. This study will be divided into two subpopulations, rural and urban households. The data used in this study is the 2021 National Social and Economic Survey. The algorithm used to classify the incidence of household food insecurity is Extremely Randomized Trees (Extra-Trees) with imbalanced data handling using ADASYN and class weight of response variables. Important variables that characterize food insecurity are identified using SHapley Additive exPlanations (SHAP). Models that handle by using class weights produce better performance than without handling imbalanced data and with handling by using ADASYN. The evaluation results show that the balanced accuracy of the rural model is 60,76%, and the urban model is 64,08%. Floor type, land assets ownership, Non-Cash Food Assistance (BPNT), local government assistance, wall type, number of savers, education of household head, Premium Assistance Beneficiaries (PBI) BPJS, cooking fuel, and gender of household head are important variables that characterize the incidence of food insecurity in rural households. The number of savers, PBI BPJS, land assets ownership, floor type, education of household head, BPNT, local government assistance, Keluarga Harapan Program (PKH), decent sanitation, and number of smokers are important variables that characterize food insecurity in urban households.
Keyword: Extra-Trees, food insecurity, imbalanced data, SHAP, West Java

Judul: Hydrothermal Synthesis of Biphasic Calcium Posphate
Abstrak: Biphasio calsium phosphate (BCP), which was a mixture of hydroxypatite (HA) and tricalcium phosphate (TCP), was synthesized by hydrothermal method followed by sintering process. Hydroxyapatite was produced by using natural calcium source CaO from hen's eggshell and (NH4)2HPO4 as phosphate souce, other hydroxyapatite sample was produce by using synthetic calcium source Ca(OH) and H3PO4 as phosphate source.
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