Judul: Perbandingan Algoritme Feature Selection Information Gain dan Symmetrical Uncertainty pada Data Ketahanan Pangan
Abstrak: Regional grouping based on indicators of food security is very important to take the proper policy in terms of deciding the targets and providing recommendations for tackling food insecurity. One method that can be used to classify objects into classes is decision tree algorithm. In this research, two decision tree models are constructed. The first decision tree utilizes information gain feature selection algorithm, whereas the second decision tree uses symmetrical uncertainty feature selection algorithm. These methods are used to classify the indicator of food security data from all districts in the provinces of Indonesia, obtained from the United Nations World Food Programme and Food Security Council. Then, the accuracy of both methods are compared. The result showed that the first decision tree is better than the second decision tree. The average accuracy of the first decision tree is 52.02%, while the average accuracy of second decision tree is 49.84%.
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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: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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