Judul: Penerapan Synthetic Minority Oversampling Technique (SMOTE) terhadap Data Tidak Seimbang pada Pembuatan Model Komposisi Jamu
Abstrak: As the times many people use herbal remedies (jamu) to address health issues. Herbal medicines are made from plants with a specific composition to produce certain properties, so a model is needed to be made in order to find the right formula to make herbal medicine with certain properties. In this study, the response being investigated is a potent herbal medicine in treating mood and behavior disorder. In this analysis, the model is developed using logistic regression. The accuracy of the model can be seen from the Area Under Curve (AUC). Imbalanced data on the response variable can cause the value of AUC become low. One of the ways to solve it is using Synthetic Minority Oversampling Technique (SMOTE). From this analysis, Nagelkerke R2 values generated by the model with SMOTE 3.2% lower than model without SMOTE. Nonetheless, the model with SMOTE is more accurate than model without SMOTE because has higher AUC value. The resulting AUC is equal to 0.976 for the model with SMOTE and 0.908 for model without SMOTE. The results show that SMOTE can increase the accuracy of the model for imbalanced data.
Keyword: imbalance data, logistic regression, SMOTE

Judul: Implementasi Hybrid Sampling Technique untuk Prediksi Interaksi Senyawa Aktif dan Protein pada Data yang Tidak Seimbang
Abstrak: Sistem prediksi formula jamu (Indonesia Jamu Herbs-Ijah) dikembangkan untuk memprediksi khasiat jamu berdasarkan hubungan interaksi senyawa aktif dan protein penyakit. Data hubungan interaksi senyawa aktif dan protein merupakan jenis data tidak seimbang karena banyak data senyawa aktif yang belum diketahui interaksinya dengan protein target, sehingga menyebabkan hasil prediksi yang kurang optimal. Pada penelitian ini, hybrid sampling technique dengan mengombinasikan complementary fuzzy support vector machine (CMTFSVM) dan sytnthetic minority oversampling technique (SMOTE) digunakan untuk menangani data interaksi senyawa aktif dan protein yang tidak seimbang pada data Ijah. Pengujian dilakukan menggunakan geometric mean (Gmean), area under curve (AUC), dan akurasi. Hasil penelitian menunjukkan bahwa hybrid sampling technique pada senyawa aktif dan protein pada Ijah berhasil meningkatkan kelas data minoritas mecapai tiga kali lipat dari data sampel yang digunakan. Model prediksi yang dihasilkan memiliki akurasi, Gmean, dan AUC secara berurutan sebesar 0.8346, 0.6812, dan 0.5319.
Keyword: CMTFSVM, data tidak seimbang, hybrid sampling technique

Judul: Posisi Filogenetik Kura-Kura Carettochelys Insculpta  (Reptillia: Testudinata)
Abstrak: Carettochelys insculpta is an endemic freshwater turtle of southern Papua and northern Australia. Phylogenetic position of this turtle is remain unresolved until now. Most of the research conducted show close relationships of C. insculpta and members of Trionychidae family. However, some of the research show distance between C. insculpta and members of Trionychidae family. This research is conducted to analyze phylogenetic relationship between C. insculpta and other members of Testudinata using 12S rRNA, 16S rRNA, and both gene as markers. DNA samples were obtained from three different organisms of C. insculpta. Two species used as outgroups were Alligator mississippiensis and Iguana iguana. Phylogenetic analysis was carried out using Neighbor-Joining method with Felsenstein 1981 (F81) modeltest. A polytomy tree of Testudinata was produced by using 12S rRNA gene. However, analysis using 16S rRNA and both genes as markers confirmed that C. insculpta was located between the Chelonidae and the Trionychidae family.
Keyword: