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: 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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