Judul: Kajian Ekstraksi Aturan dan Aturan Asosiasi untuk Interpretasi Model Hutan Acak
Abstrak: Random forest is one of the analytical methods for classification and regression derived from a set of decision trees. It is the most popular ensemble method with many advantages, such as applying to various prediction problems, producing competitive accuracy, adjusting parameters, and handling small sample sizes in high-dimensional feature spaces. However, the random forest is often criticized as a "black box" model because random forests are difficult to interpret. In contrast, interpretation is much needed to understand the relationship between the predictor and target variables. This study discusses the interpretation of a random forest using an association rule technique using rules extracted from each decision tree in the random forest model. The association rule technique is an "if-then" statement, a pattern mining technique that aims to obtain frequently occurring item combinations in large data sets and can also be used to find association rules among item combinations. In this study, the itemset refers to the rules (order of branches) formed by each decision tree in the random forest model. Based on the simulation results obtained, the application of rule extraction techniques and association rules in the random forest model can determine thresholds for each factor that affects the target variable. It can also determine interacting factors that have an association relationship with the target variable so that is obtained a more interpretable random forest model. The results obtained in the simulation are then applied to answer empirical questions regarding poverty. Poverty is a complex problem in Indonesia and has always been an important indicator in measuring the success of a region's development. According to the Central Bureau of Statistics of Indonesia (BPS) report in 2022, West Java Province is the province that occupies the second position with the most significant number of poor people in Indonesia, with a poverty percentage of 8.06%. Tasikmalaya City has the largest percentage of poverty in West Java for 14 consecutive years from 2008 to 2021. According to the results of the empirical study, the factors that most influence the status of poor households in West Java Province are households with at least five members and using firewood as the primary fuel for cooking. In Tasikmalaya City, the factors influencing the status of poor households in Tasikmalaya City, the most dominating variables in describing the characteristics are the number of household members of more than four people, the primary fuel for cooking using firewood, and not having their land.
Keyword: association rule, poverty, random forest, rule extraction

Judul: Performance Comparison of Random Forest and Double Random Forest for Poverty Status Classification at District/City Level
Abstrak: Prediksi berdasarkan sebuah model baik untuk dilakukan karena dapat menghasilkan prediksi yang akurat. Salah satu model yang sudah banyak digunakan dalam memprediksi adalah model klasifikasi Random Forest. Namun, ukuran minimum nodesize pada random forest mungkin kurang optimal untuk menghasilkan kinerja model yang terbaik. Double Random Forest mengatasi hal tersebut dengan membentuk pohon yang lebih besar. Penelitian ini ingin membandingkan kinerja model klasifikasi random forest dan double random forest dalam memprediksi status kemiskinan rumah tangga di Jawa Barat pada tahun 2019. Jumlah penduduk miskin yang bernilai sangat kecil dibandingkan jumlah seluruh penduduk di suatu wilayah menyebabkan adanya ketidakseimbangan pada data. Salah satu langkah penanganan yang dapat dilakukan adalah dengan menggunakan teknik SMOTE. Kinerja metode random forest dan double random forest tidak menunjukkan hasil yang berbeda dalam memprediksi status kemiskinan rumah tangga di Jawa Barat. Hal ini dikarenakan ukuran pohon yang dihasilkan random forest sudah dinilai cukup untuk memberikan kinerja model terbaik sehingga tidak diperlukan adanya perbaikan dengan double random forest. Berdasarkan model-model yang terbentuk, jumlah anggota rumah tangga (ART) merupakan peubah paling penting saat memprediksi rumah tangga miskin di Jawa Barat., Predictive modeling leads to more accurate prediction therefore good to be used. One of model that has been widely used in prediction is Random Forest classification model. However minimum nodesize in random forest is not optimal enough to produce the best model performance, so Double Random Forest overcome this issue by forming bigger trees. This study aims to compare the performance of random forest and double random forest classification models in predicting poverty status of households in West Java at 2019. The number of people living in poverty is very small compared to total population in certain area. This causes an imbalance class in the data. One way that can be done to overcome imbalance class is by using SMOTE technique. Performance of random forest and double random forest methods did not show different result in predicting poverty status of households in West Java. This is due to size of the trees produced by random forest that considered good enough to provide best model, so there is no room for improvement with double random forest. Based on the models, age of the householder showing as the most important variable in classifying household poverty status in West Java.
Keyword: classification, double random forest, poverty, random forest, SMOTE

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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