Judul: Perbandingan Metode Logistic Regression, Metode Decision Tree, dan Metode Logistic Model Tree untuk Pengklasifikasian Data (Studi Kasus Kaggle Maternal Health Risk)
Abstrak: Metode Logistic Model Tree (LMT) merupakan metode klasifikasi yang menggabungkan metode Decision Tree dan metode Logistic Regression. Metode Decision Tree unggul dalam memecah proses pengambilan keputusan yang kompleks menjadi proses yang lebih sederhana namun keragaman model yang dihasilkan tinggi. Sebaliknya, metode Logistic Regression adalah metode klasifikasi yang menghasilkan model dengan keragaman rendah tetapi bias tinggi. Metode LMT dikembangkan dengan tujuan mencapai keuntungan dari kedua metode tersebut. Implementasi ketiga metode dalam penelitian ini dilakukan terhadap data Kaggle Maternal Health Risk. Hasil yang diperoleh menunjukkan bahwa metode Decision Tree dan LMT menunjukkan performa yang sangat baik (AUC - Area Under Curve = 0,92), sementara metode Logistic Regression “dapat diterima” (AUC = 0,73) saja. Nilai akurasi yang dihasilkan metode LMT dan metode Decision Tree relatif sama baiknya, masing-masing bernilai 81,7% dan 81,3%. Namun demikian, metode LMT unggul dibandingkan dengan metode Decision Tree dalam hal jumlah simpul, yaitu dari 287 simpul yang dihasilkan metode Decision Tree menjadi hanya 99 simpul pada metode LMT., The Logistic Model Tree Method (LMT) is a classification method that combines decision tree learning and logistic regression. Decision Tree is superior in the ability to break down complex decision-making processes into simpler but the variety of result models is high. Meanwhile, Logistic Regression is a classification method that produces models with low variance but high bias. LMT method has the purpose to gain the advantages of both methods. The implementation of the three methods in this study is employed to analyze on the Kaggle Maternal Health Risk data. The obtained result shows that the Decision Tree and LMT methods perform very well (AUC - Area Under Curve = 0,92), while the logistic regression method is at "acceptable" level (AUC = 0,73). The accuracy values produced by the LMT method and the Decision Tree method are relatively good, at 81.7% and 81.3%. However, the LMT method is superior to the Decision Tree method in terms of the number of nodes, from 287 nodes created by the Decision Tree method to only 99 nodes in the LMT method.
Keyword: decision tree, logistic regression, logistic model tree, data classification

Judul: Perbandingan Performa Metode Pohon Model Logistik dan Random Forest pada Pengklasifikasian Data
Abstrak: Multikolinearitas dan missing data merupakan dua masalah yang sering ditemukan pada data besar. Keberadaan missing data dapat mengurangi akurasi hasil prediksi. Logistic model tree (LMT) dapat menangani multikolinearitas dengan baik karena multikolinearitas bukan suatu masalah dalam metode pohon keputusan. Random forest terbukti dapat menurunkan keragaman dalam kasus prediksi. Penelitian ini bertujuan untuk mengkaji perbandingan metode LMT dan random forest pada data yang mengandung multikolinearitas dan missing data menggunakan data simulasi dan data riil. Tolak ukur evaluasi model adalah nilai ketepatan klasifikasi dan nilai AUC. Hasil yang diperoleh menunjukkan random forest memiliki performa yang lebih baik dalam memodelkan data dengan tingkat multikolinearitas sedang, banyak amatan dan persentase missing data yang besar. Sedangkan untuk data dengan tingkat multikolinearitas tinggi, LMT dengan penghapusan missing data terbukti menghasilkan performa yang lebih baik. Analisis selanjutnya dilakukan pada data dengan dimensi data yang berbeda. Hasil yang diperoleh menunjukkan bahwa random forest menghasilkan performa yang lebih baik. Kemudian untuk data “breast cancer” yang memiliki missing data hanya 0,3% penghapusan missing data menghasilkan performa yang lebih baik., Multicollinearity and missing data are two common problems in big data. Missing data could decrease the prediction accuracy. Logistic model tree (LMT) is used to handle multicollinearity because multicollinearity does not affect the decision tree. Random forest can be used to decrease variance in prediction case. This study aimed to study the comparison of two methods, LMT and random forest, in multicollinearity and missing data in various cases using simulation study and real data as dataset. Evaluation model is based on classification accuracy and AUC measurement. The result stated that random forest had better performance if the multicollinearity level is moderate. LMT with omitted missing data is proven to have better performance for big data and when a high percentage of missing data occurred, and the multicollinearity level is severe. The next step is analysed real data with different sample size. The result stated that random forest have better performance. Omitted missing data have better performance in classification “breast cancer” data which consist 0,3 % missing data.
Keyword: Dimension of data, LMT, missing data, multicollinearity, random forest

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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