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: The Implementation of Filter Feature Selection Method to Identify the Best Subset of Variables in Modeling of Food Insecurity in  West Java
Abstrak: Seleksi peubah berguna untuk memilih subset peubah yang revelan terhadap peubah respon, dan seleksi peubah dapat diterapkan untuk mengetahui peringkat peubah yang berhubungan erat dengan suatu kejadian, salah satunya rawan pangan. Penelitian ini bertujuan menyaring peubah yang berhubungan erat dengan kejadian rawan pangan di Jawa Barat berdasarkan algoritme filter univariat information gain, gain ratio, symmetrical uncertainty, gini gain, dan chi-square serta mengidentifikasi subset peubah terbaik berdasarkan model klasifikasi yang menghasilkan ukuran kinerja paling baik menggunakan algoritme klasifikasi Xtreme Gradient Boosting (XGBoost). Hasil penelitian menunjukkan status kepemilikan kulkas, komputer, emas minimal 10 gram, dan televisi layar datar minimal 30 inch merupakan peubah yang konsisten terpilih pada semua algoritme filter univariat tersebut. Pemodelan klasifikasi XGBoost dengan subset peubah terpilih gain ratio menghasilkan nilai sensitivitas dan spesifisitas yang seimbang sebesar 61,9% dan 61,4%. Hasil penyaringan peubah gain ratio mengindikasikan bahwa rumah tangga yang tidak memiliki modal ekonomi berupa emas minimal 10 gram, komputer, kulkas, televisi layar datar minimal 30 inch dan rumah tangga penerima Bantuan Pangan Nontunai (BPNT) cenderung rawan pangan., Feature selection is useful for selecting a subset of variables relevant to the response variable, and it can be applied to determine the rank of variables closely related to an incident, one of which is food insecurity. This study aims to select variables closely related to food insecurity in West Java based on information gain, gain ration, symmetrical uncertainty, gini gain, and chi-square univariate filter algorithms and identify the best subset of variables based on the classification model that produces the best performance measures using the Xtreme Gradient Boosting (XGBoost) algorithm. The study result shows that the ownership status of a refrigerator, a computer, a gold of at least 10 grams, and a flat-screen television of at least 30 inches are the consistent variables selected on all the univariate filter algorithms. XGBoost classification modeling with the subset of selected variables gain ratio has balanced sensitivity and specificity values of 61,9% and 61,4%. The gain ratio variables filtering result indicates that households that do not have economic capital in the form of gold at least 10 grams, computer, refrigerator, flat-screen television at least 30 inches, and households receiving Non-Cash Food Aid (BPNT) tend to be food insecure.
Keyword: classification, feature selection, food insecurity, univariate method, xgboost

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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