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: Prediksi Efikasi Jamu Berdasarkan Metabolit pada Tanaman Obat Menggunakan Deep Belief Network
Abstrak: Indonesia memiliki sekitar 30.000 dari total 40.000 tanaman tropis yang ada di dunia, dan 1000 spesies diantaranya dijadikan bahan baku pembuatan jamu. Jamu adalah obat tradisional Indonesia yang dipercaya secara turun temurun dan biasanya dibuat dari campuran berbagai tumbuhan. Dengan banyaknya bahan yang bisa dibuat menjadi formula Jamu, maka perlu dibuat landasan saintifik untuk memberikan pengetahuan ilmiah mengenai Jamu. Tumbuhan sebagai bahan penyusun Jamu memiliki kandungan senyawa atau metabolit yang berbeda-beda. Penelitian ini melakukan klasifikasi khasiat Jamu berdasarkan kandungan metabolit. dengan tahapan penelitian meliputi pra-proses data, tuning parameter, pembuatan model, dan analisa hasil. SMOTE digunakan pada tahap pra-proses sebagai teknik untuk mengatasi imbalance data. Pada penelitian ini, dilakukan perbandingan hasil klasifikasi terhadap data yang tidak seimbang dengan data yang seimbang. Hasil yang didapat penelitian ini adalah model Deep Belief Network yang dibuat bekerja lebih baik pada data yang seimbang, dengan akurasi 75.25%. Sedangkan untuk data yang tidak seimbang, akurasi yang didapatkan adalah 62.68%, Indonesia has about 30,000 of the total 40,000 tropical plants in the world, and 1000 species of which are used as raw materials for herbal medicine. Jamu is a traditional Indonesian medicine that is passed down from generation to generation and is usually made from a mixture of various plants. With so many ingredients that can be made into Jamu formulas, it is necessary to create a scientific basis to provide scientific knowledge about Jamu. Plants as ingredients for herbal medicine contain different compounds or metabolites. This study classified the efficacy of herbal medicine based on its metabolite content, with the research stages covering data pre-processing, parameters tuning, models building, and results analyzing. SMOTE is used in the pre-processing stage as a technique to overcome imbalance datasets. In this study, a comparison of the classification results was carried out on unbalanced data with balanced data. The result of this research is that the Deep Belief Network model is made to work better on balanced data, with an accuracy of 75.25%. As for the unbalanced data, the accuracy obtained is 62.68%.
Keyword: Deep Belief Network, Klasifikasi, Jamu, Metabolit, Obat Herbal, Classification, Deep Belief Network, Herbal Medicine, Jamu, Metabolite

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.
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