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: Perbandingan Metode CHAID dan Random Forest dalam Klasifikasi Status Kemiskinan Rumah Tangga di Jawa Tengah
Abstrak: Jawa Tengah menempati posisi kedua sebagai provinsi dengan jumlah penduduk miskin terbanyak di Indonesia pada Maret 2020 lalu. Upaya pengentasan kemiskinan sudah dilakukan, namun masih banyak yang belum tepat sasaran. Tujuan dari penelitian ini adalah melakukan pemodelan klasifikasi status kemiskinan rumah tangga di Jawa Tengah menggunakan metode CHAID dan random forest serta membandingkan kedua metode tersebut. Data yang digunakan dalam penelitian ini adalah data hasil Survei Sosial Ekonomi Nasional (SUSENAS) 2020 yang dilakukan oleh Badan Pusat Statistik (BPS) untuk wilayah Provinsi Jawa Tengah. Jumlah rumah tangga miskin jauh lebih sedikit dibandingkan dengan rumah tangga tidak miskin. Oleh karena itu, dilakukan Synthetic Minority Oversampling Technique (SMOTE) untuk menangani data tidak seimbang. Metode random forest menghasilkan performa klasifikasi yang lebih baik dibandingkan metode CHAID dengan nilai akurasi, sensitivitas, spesifisitas dan AUC berturut-turut 93,95%, 98,43%, 89,92%, dan 0,94. Peubah penting yang membangun model random forest adalah peubah luas lantai rumah, umur kepala rumah tangga, bahan bakar memasak, tempat pembuangan akhir tinja dan kepemilikan tempat buang air besar., Central Java was in the second position as the province with the highest number of poor people in Indonesia in March 2020. Poverty alleviation efforts have been carried out, but many are still not on target. The purpose of this study was to model the classification of household poverty status in Central Java using CHAID and random forest methods and compare the two methods. The data used in this study is data from the 2020 National Socioeconomic Survey (SUSENAS) conducted by the Central Bureau of Statistics (BPS) for Central Java. The number of poor households is much less than non-poor households. Therefore, SMOTE was performed to handle unbalanced data. The random forest method produced better classification performance than the CHAID method with accuracy, sensitivity, specificity, and AUC of 93,95%, 98,43%, 89,92%, and 0,94, respectively. The important variables that build the random forest model are the floor area of the house, the age of the head of the household, cooking fuel, the place for the final disposal of feces, and ownership of the place to defecate.
Keyword: CHAID, 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.
Keyword: