Judul: Identifikasi Peubah-peubah Seleksi yang Memengaruhi Prestasi Mahasiswa Sarjana Angkatan 2023 IPB University Menggunakan Extra-Trees
Abstrak: Transisi Seleksi Nasional Berbasis Prestasi (SNBP) oleh Kementerian Pendidikan dan Kebudayaan memicu penyesuaian internal di IPB University. Indeks Prestasi Kumulatif (IPK) merupakan metrik untuk mengukur prestasi mahasiswa. IPK dapat dipengaruhi oleh faktor multidimensional seperti aspek akademik dan latar belakang mahasiswa yang pada umumnya jenis datanya campuran antara kategorik dan numerik. Peubah-peubah seleksi SNBP yang jenis datanya campuran diduga memengaruhi IPK mahasiswa. Data IPK mahasiswa pada umumnya merupakan data tidak seimbang. Supervised machine learning seperti Extra-Trees memiliki kemampuan untuk mengklasifikasikan berbagai jenis data, salah satunya data seleksi SNBP tersebut. Penelitian ini bertujuan untuk mengidentifikasi peubah-peubah yang memengaruhi IPK mahasiswa di IPB University berdasarkan peubah-peubah seleksi SNBP 2023 menggunakan Extra- Trees. Data dibagi menjadi empat kelas IPK kemudian dipartisi menjadi data uji dan data latih dengan empat skema berbeda. Setiap data latih diseimbangkan dengan ADASYN dan tidak diseimbangkan, sehingga menghasilkan delapan set data. Model Extra-Trees dibangun dengan data latih menggunakan GridSearchCV untuk menentukan kombinasi hyperparameter terbaik. Model Extra-Trees pada data uji secara konsisten memiliki kinerja yang baik dalam memprediksi IPK mahasiswa dengan akurasi tertinggi mencapai 68,62% pada model tanpa proses ADASYN dengan skema partisi 70% data latih dan 30% data uji. Interpretasi Shapley Additive exPlanation (SHAP) pada model terbaik menunjukkan bahwa peubah paling berpengaruh terhadap IPK adalah program studi pilihan pertama mahasiswa saat mendaftar SNBP. Selain itu, prestasi, asal daerah, dan nilai mahasiswa semasa SMA juga termasuk peubah-peubah yang berpengaruh terhadap IPK., Transition to the Achievement-Based National Selection by the Ministry of Education and Culture has prompted internal adjustments at IPB University. Grade Point Average (GPA), a key metric for assessing student achievement, can be influenced by multidimensional factors, including academic aspects and student background, which encompass both categorical and numerical data types. It is hypothesized that SNBP selection variables, characterized by mixed data types, impact student GPA. Notably, student GPA data tends to be unbalanced. Supervised machine learning algorithms, such as Extra-Trees, are proficient in classifying diverse data types, including SNBP selection data. This study aims to identify variables influencing student GPA at IPB University based on the 2023 SNBP selection variables using the Extra-Trees algorithm. The data were categorized into four GPA classes and partitioned into training and test sets using four distinct schemes. Each training dataset was balanced using ADASYN and left unbalanced, resulting in eight distinct datasets. The Extra-Trees models were constructed using GridSearchCV to optimize hyperparameter combinations. The Extra-Trees model consistently demonstrated robust performance in predicting student GPA on the test data, achieving the highest accuracy of 68.62% in the model without the ADASYN process under a partition scheme of 70% training data and 30% test data. Shapley Additive exPlanation (SHAP) analysis of the best model revealed that the most influential variable on GPA was the student's first-choice study program during SNBP registration. Additionally, academic achievements, regional origin, and high school grades were identified as significant variables influencing GPA.
Keyword: extra-trees classifier, indeks prestasi kumulatif, klasifikasi multikelas, shaley additive explanation

Judul: Application of Synthetic Minority Oversampling Technique (SMOTE) in Binary Logistics Regression Modeling on Student Achievement
Abstrak: Indikator Kinerja Utama Perguruan Tinggi Negeri (IKU-PTN) adalah salah satu ukuran yang digunakan untuk melihat ketercapaian kinerja perguruan tinggi dalam meningkatkan kualitas pendidikannya. Prestasi mahasiswa merupakan salah satu indikator yang diukur pada IKU-PTN, sehingga penting untuk diperhatikan. Peubah-peubah yang memengaruhi prestasi mahasiswa Institut Pertanian Bogor (IPB) program sarjana pada penelitian ini akan diidentifikasi menggunakan regresi logistik biner karena mahasiswa tingkat sarjana IPB terdiri dari mahasiswa yang berprestasi dan tidak berprestasi. Perbedaan proporsi mahasiswa yang berprestasi dan tidak berprestasi menyebabkan data yang tidak seimbang sehingga menyebabkan klasifikasi cenderung ke kelas dengan proporsi amatan terbesar. Oleh karena itu, peran Synthetic Minority Oversampling Technique (SMOTE) dalam menangani ketidakseimbangan data akan dilihat pada penilitian ini. Data yang digunakan yaitu data demografi mahasiswa Program sarjana yang diperoleh dari Direktorat Administrasi dan Pendidikan (Dit-Ap) dan Data prestasi mahasiswa yang diperoleh dari Direktorat Kemahasiswaan dan Pengembangan Karir (Ditmawa) IPB. Hasil penelitain yang diperoleh menunjukkan model klasifikasi regresi logistik biner dengan SMOTE menghasilkan model yang lebih baik dibandingkan model tanpa SMOTE. Hal ini dapat terlihat berdasarkan nilai sensitivitas, spesitifitas dan akurasi yang diperoleh secara berturut-turut sebesar 53%, 71%, dan 70%, dimana nilai sensitivitas dengan SMOTE mengalami kenaikan yang signifikan jika dibandingkan model tanpa SMOTE dan ketepatan klasifikasi untuk masing-masing kategori juga menjadi lebih seimbang. Terdapat lima peubah penjelas yang berpengaruh terhadap prestasi mahasiswa program sarjana IPB pada taraf nyata 5% yaitu Tahun Masuk IPB, IPK terkahir, Jenis Kelamin, Fakultas dan SKS kumulatif., Main Performance Indicators of State Universities (IKU-PTN) is one of the measurements used to see the achievement of higher education performance in improving the quality of its education. Student achievement is one of the indicators measured at IKU-PTN, so it is important to pay attention. Variables affecting undergraduate students’ achievement at the Bogor Agricultural University (IPB) program in this study will be identified using binary logistic regression because undergraduate students at IPB consist of students who achieve and do not achieve. The difference in the proportion of students who achieve and do not achieve results in unbalanced data, which causes the classification to tend to the class with the largest proportion of observations. Therefore, the role of the Synthetic Minority Oversampling Technique (SMOTE) in dealing with data damage will be seen in this research. The data used are demo data from undergraduate students obtained from the Directorate of Administration and Education (Dit-Ap) and student achievement data obtained from the Directorate of Student Affairs and Career Development (Ditmawa) IPB. The research results obtained show that the binary logistic regression classification model with SMOTE produces a better model than the model without SMOTE. This can be seen based on the sensitivity, specificity and accuracy obtained respectively by 53%, 71%, and 70%, where the sensitivity value with SMOTE has increased significantly when compared to the model without SMOTE and the classification accuracy for each category also become more balanced. There are five explanatory variables that influence the achievement of IPB undergraduate students at a significant level of 5%, namely Year of Entry of IPB, last GPA, Gender, Faculty, and cumulative credits.
Keyword: mahasiswa, perguruan tinggi, prestasi, regresi logistik biner, SMOTE

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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