Judul: Comparison of Classification Models on Unbalanced Data Output of Non-invasive Blood Glucose Level Tools.
Abstrak: Diabetes mellitus is one of the most dangerous non-communicable diseases. In 2021, Indonesia is in fifth place with the number of diabetes aged 20-79 years amounting to 19.5 million people. In 2045, the number of diabetes is estimated to increase 28.6 million people. IPB’s Non-Invasive Biomarking team developed a non-invasive blood glucose measuring tool with faster results and without harming the body. This tool uses the principle of infrared spectroscopy. The residual intensity data of each peak in observations and repeats can be read differently so that there are many different observations. Therefore, it is necessary to carry out handling, one of which is area summarization. Classification can be used to analyze the relationship between the class of blood glucose levels from invasive measuring instruments and the residual intensity spectrum from non-invasive measuring instruments. The problem that arises in classification is unbalanced data so it needs to be handled, one of which is Synthetic Minority Oversampling Technique (SMOTE). Comparison of the performance of Random Forest, Support Vector Machines, and eXtreme Gradient Boosting with SMOTE is used to determine the best classification results. This study aims to compare and evaluate the performance of Random Forest, Support Vector Machines, and eXtreme Gradient Boosting with SMOTE in improving the performance of multiclass classification. The classification results show that SMOTE can improve the classification performance. The performance evaluation of the classification method shows that eXtreme Gradient Boosting has the highest accuracy value compared to Random Forest and Support Vector Machines., Diabetes melitus merupakan salah satu penyakit paling berbahaya tidak menular. Pada tahun 2021, Indonesia menempati urutan kelima dengan angka penderita diabetes pada usia 20-79 tahun sebesar 19,5 juta orang. Pada 2045, jumlah penderita diperkirakan mengalami peningkatan menjadi 28,6 juta orang. Tim Non-Invasif Biomarking IPB mengembangan alat pemantauan kadar glukosa darah non-invasif dengan hasil lebih cepat dan tanpa melukai tubuh. Alat ini menggunakan prinsip spektroskopi inframerah. Data residu intensitas setiap puncak pada amatan dan ulangan dapat terbaca berbeda sehingga memiliki banyak amatan tidak sama. Maka dari itu, perlu dilakukan penanganan, salah satunya peringkasan luas. Pemodelan klasifikasi dapat digunakan untuk menganalisis hubungan antara kelas kadar glukosa darah hasil alat ukur invasif dengan spektrum residu intensitas hasil alat ukur non-invasif. Permasalahan yang sering timbul pada pemodelan klasifikasi yaitu data tidak seimbang sehingga perlu dilakukan penanganan, salah satunya Synthetic Minority Oversampling Technique (SMOTE). Perbandingan kinerja Random Forest, Support Vector Machines, dan eXtreme Gradient Boosting dengan penanganan SMOTE digunakan untuk menentukan hasil klasifikasi terbaik. Penelitian ini bertujuan untuk membandingkan dan mengevaluasi kinerja Random Forest, Support Vector Machines, dan eXtreme Gradient Boosting dengan penanganan data Synthetic Minority Oversampling Technique (SMOTE) dalam meningkatkan kinerja klasifikasi multikelas. Dari hasil klasifikasi menunjukkan bahwa SMOTE dapat meningkatkan kinerja klasifikasi. Evaluasi kinerja dari metode klasifikasi tersebut menunjukkan bahwa eXtreme Gradient Boosting memiliki nilai akurasi paling tinggi dibandingkan dengan Random Forest dan Support Vector Machines
Keyword: blood glucose level, classification method, non-invasive tool, synthetic minority oversampling technique, unbalanced data, alat ukur non-invasif, data tak seimbang, metode klasifikasi, kadar glukosa darah, synthetic minority oversampling technique

Judul: Perbandingan Metode Peringkasan Data Hasil Keluaran Alat Pengukur Glukosa Darah Non-Invasif Menggunakan Model Klasifikasi
Abstrak: Diabetes melitus merupakan penyakit menahun (kronis) yang dapat diderita seumur hidup. Pada tahun 2021, Indonesia menempati urutan kelima dari 144 negara yang diteliti dengan penderita diabetes terbanyak dengan angka penderita diabetes pada usia 20—79 tahun sebesar 19,5 juta orang. Tim non-invasif biomarking IPB sedang mengembangkan alat pengukur glukosa darah dengan metode non-invasif. Prinsip kerja alat ini menggunakan konsep spektroskopi inframerah yang menghasilkan output berupa data spektrum residu intensitas. Keluaran nilai residu intensitas dapat terbaca dengan jumlah berbeda setiap amatan. Penyeragaman jumlah nilai residu yang menjadi peubah prediktor ini dapat dilakukan menggunakan beberapa pendekatan peringkasan data, antara lain rataan, simpangan baku, rataan dibagi simpangan baku, dan luas puncak. Hubungan antara kelas kadar glukosa darah hasil pengukuran alat invasif dengan spektrum residu intensitas cahaya hasil pengukuran alat non-invasif dapat dianalisis menggunakan metode klasifikasi. Tujuan penggunaan metode klasifikasi adalah untuk melihat seberapa tepat alat pengukur glukosa darah non-invasif mengelompokkan nilai residu intensitas responden berdasarkan kelas kadar glukosa darah. Perbandingan kinerja decision tree, random forest, dan double random forest digunakan untuk menentukan hasil terbaik. Penelitian ini bertujuan untuk mengidentifikasi peringkasan data terbaik menggunakan model klasifikasi pada data kadar glukosa darah hasil keluaran alat invasif dan non-invasif. Evaluasi hasil klasifikasi menunjukkan metode peringkasan luas puncak merupakan metode peringkasan terbaik dibandingkan dengan peringkasan rataan, simpangan baku, dan rataan dibagi simpangan baku. Peringkasan luas puncak memiliki nilai akurasi paling tinggi yaitu sebesar 65,22% pada model klasifikasi decision tree dan random forest. Pada pemodelan double random forest, peringkasan luas puncak memiliki akurasi yang lebih kecil dibandingkan dengan peringkasan rataan dan rataan dibagi simpangan baku. Pemodelan double random forest tidak mutlak menghasilkan akurasi yang lebih baik dari random forest., Diabetes mellitus is a chronic disease that can last a lifetime. In 2021, Indonesia is in fifth place from 144 countries studied with the most diabetes sufferers aged 20-79 years, amounting to 19.5 million people. IPB's non-invasive biomarking team is developing a blood glucose measuring device with a non-invasive method. The working principle of this tool uses the concept of infrared spectroscopy which produces output in the form of residual intensity spectrum data. The output residual intensity value can be read different amount for each observation. Standardize the number of residual values that become independent variables can be done using several data summary approaches, such as mean, standard deviation, mean divided by standard deviation, and graph area. The relationship between classes of blood glucose levels measured by invasive devices and the residual intensity measured by non-invasive devices can be analyzed using a classification method. The aim of using the classification method is to see how accurate the non-invasive blood glucose measuring device classifies respondent’s residual intensity values based on blood glucose level classes. Comparison of the performance of decision trees, random forests, and double random forests is used to determine the best results. This study aims to identify the best data summary using some classification models on blood glucose level data resulting from invasive and non-invasive devices. Evaluation of the classification results shows that the graph area summarization method is the best summarization method compared to the summarization of the mean, standard
Keyword: blood glucose, decision tree, random forest, double random forest, non-invasive device

Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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