Judul: Comparison of Binary Logistic Regression with  and without SMOTE on the Timeliness of IPB Undergraduate Students
Abstrak: IPB memiliki tujuan menghasilkan lulusan unggul. Hal ini dapat dicapai dengan menghasilkan mahasiswa lulus tepat waktu. Mahasiswa program sarjana dianggap lulus tepat waktu jika menyelesaikan studi dalam waktu maksimal empat tahun. Oleh karena itu, penting dilakukan penelitian untuk mengetahui pengaruh faktor-faktor terhadap ketepatan waktu kelulusan mahasiswa. Salah satu analisis yang digunakan untuk melakukan analisis terhadap respon yang biner (lulus tepat waktu/tidak) adalah analisis regresi logistik biner. Data yang digunakan adalah data wisudawan program sarjana IPB bulan Agustus dan September 2023. Proporsi lulusan tidak tepat waktu sebesar 30% menunjukkan data relatif tidak seimbang, sehingga diterapkan metode SMOTE. Penelitian ini bertujuan untuk membandingkan performa model dengan dan tanpa SMOTE serta mengetahui besar pengaruh faktor-faktor terhadap ketepatan waktu kelulusan mahasiswa. Hasil yang diperoleh menunjukkan model regresi logistik biner tanpa SMOTE lebih baik dibandingkan model dengan SMOTE berdasarkan nilai sensitivitas model tanpa SMOTE yang lebih besar dibandingkan model dengan SMOTE yaitu sebesar 90,7%. Hal ini disebabkan ketidakseimbangan data didominasi oleh mahasiswa lulus tepat waktu, sehingga model tanpa SMOTE mampu menggambarkan mahasiswa lulus tepat waktu dengan baik. Model menunjukkan bahwa perbedaan fakultas, IPK, predikat kelulusan, beasiswa, jenis kelamin, jalur masuk, SKS, UKT, dan keikutsertaan organisasi memiliki pengaruh yang berbeda terhadap kecenderungan mahasiwa lulus tepat waktu., IPB aims to produce outstanding graduates by ensuring students graduate on time. Undergraduate students are considered to graduate on time if they complete their program within four years. Therefore, it is important to research factors influencing the punctuality of student graduation. Binary logistic regression analysis is used to analyze the binary response. This research used IPB undergraduate program graduates dataset in August and September 2023. The percentage of graduates who did not graduate on time is 30%, implying relatively unbalanced class in the dataset, thus the SMOTE method was applied. This research aims to compare the performance of models with and without SMOTE and to determine the factors influencing the punctuality of student graduation. The results showed that the binary logistic regression model without SMOTE was better than the model with SMOTE. Based on the sensitivity, which was higher at 90,7% compared to the model with SMOTE. Due to the imbalance in data dominated by students graduating on time, the model without SMOTE accurately represents students graduating on time. The model showed that differences in faculty, GPA, graduation predicate, scholarship, gender, admission path, cumulative credit, tuition fee, and organizational participation have different effects on the timeliness of student graduation.
Keyword: binary logistic regression analysis, unbalanced data, data tidak seimbang, ketepatan waktu kelulusan, regresi logistik biner, SMOTE, graduate on-time

Judul: ordinal logistic regression, SMOTE, student graduation
Abstrak: Jumlah mahasiswa Statistika IPB yang waktu kelulusannya melebihi 8 semester pada angkatan 2014 mengalami penurunan pada angkatan 2015, namun hingga April 2020 masih terdapat mahasiswa angkatan 2014 dan 2015 yang belum lulus. Oleh karena itu, perlu dilakukan penelitian untuk mengetahui faktor yang memengaruhi perbedaan waktu kelulusan tersebut menggunakan regresi logistik ordinal. Terdapat ketidakseimbangan data pada ketiga kategori peubah respon sehingga diterapkan Synthetic Minority Oversampling Technique (SMOTE) untuk mengatasi ketidakseimbangan tersebut. Penelitian ini juga dilakukan untuk membuat model regresi logistik ordinal dengan SMOTE dan tanpa SMOTE. Hasil penelitian menunjukkan bahwa regresi logistik ordinal dengan SMOTE merupakan model yang lebih baik dibandingkan tanpa SMOTE karena memiliki nilai R2 Nagelkerke, ketepatan klasifikasi, dan nilai MAUC yang lebih besar. Nilai R2 Nagelkerke yang dihasilkan sebesar 42.39%, ketepataan klasifikasi secara keseluruhan sebesar 59.70%, dan nilai MAUC sebesar 0.7554. Peubah-peubah penjelas yang berpengaruh terhadap waktu kelulusan mahasiswa Departemen Statistika IPB angkatan 2013 sampai 2015 yakni jenis kelamin, beasiswa, minor, riwayat penyakit berat, permasalahan pribadi, dan IPK semester 6., Sum of Statistics IPB students whose graduation time exceeds 8 semesters in the 2014 class has decreased in the 2015 class, however until April 2020 there were still students of 2014 and 2015 class who had not graduated. Therefore, it is necessary to conduct research to determine the factors that influence the difference in graduation time using ordinal logistic regression. There is an imbalance of data in the three categories of response variables so that the Synthetic Minority Oversampling Technique (SMOTE) is applied to overcome this imbalance. This research was also conducted to create an ordinal logistic regression model with SMOTE and without SMOTE. The results showed that ordinal logistic regression with SMOTE is a better model than without SMOTE because it has a greater value of R2 Nagelkerke, classification accuracy, and value of MAUC. The value of R2 Nagelkerke is 42.39%, the overall classification accuracy is 59.70%, and the value of MAUC is 0.7554. The explanatory variables that affect the graduation time of the 2013-2015 Statistics IPB students are gender, scholarship, minor, history of serious illness, personal problems, and 6th semester GPA.
Keyword: ordinal logistic regression, SMOTE, student graduation

Judul: Codiaeum Malesia
Abstrak: The Malesian genus Codiaeum has been revised based on the morphological character using available herbarium collections in Herbarium Bogor iense and loan specimens from Rijksherbarium Leiden. The study is based on the observation of 350 specimens. Eleven species have been recognized, namely Codiaeum variegatum, C. luzonicum, C. palawanense, C. trichocalyx, C. megalanthum, C. cilliatum, C. affine, C. tenerifolium dan C. hirsutum, C. macgregorii, C. ludovicianum. number of species is found in the Philippines The highest (8 species), Two species (C. tenerifolium and C. ludovicianum) endemic in Lousiade Archipelago of the New Guinea and C. affine endemic in Banguey Island. C. variegatum is widespread by nature or in cultivation almost in all of Malesian region. C. cillia tum is a species in the earliest level among the other species according to 20 selected characters.
Keyword: Codiaeum, Malesia