Judul: Prediction of Undergraduate Student’s Completion Status using  MissForest Imputation in Random Forest and XGBoost Models
Abstrak: All tertiary education institutions in Indonesia are required to report data about their education process to Higher Education Database (PDDikti), Ministry of Education, Culture, Research, and Technology. One of the reported data is about student completion status: whether graduate or drop out. The number of higher education graduates is calculated based on this completion status. However, many undergraduate students have reached the maximum length of study, but their completion status is unknown. This condition makes it difficult to calculate the actual number of graduates. Prediction of student completion status can be carried out with classification models. In this study, the unknown completion status of undergraduate students was predicted by ensemble trees, which were Random Forest and XGBoost. The data used in this study was incomplete, and the proportion of the missing data was 20.9% of the total data. Missing data may cause bias in the parameter estimates of analysis. This study used MissForest imputation to overcome the missing data. MissForest works based on the Random Forest algorithm. As a comparison, Mean/Mode and Median/Mode imputation was also carried out in this study. This study aims to evaluate the performance of MissForest imputation, Mean/Mode imputation, and Median/Mode imputation to overcome missing data in predicting the completion status of undergraduate students who have reached the maximum length of study. Best model selection was also conducted between Random Forest and XGBoost models. There were 5 (five) goodnesses of fit employed in this study, namely accuracy, sensitivity, specificity, G-Mean, and AUC. The classification model with MissForest was significantly superior compared to the other two imputations. Regardless of the imputation method used, Random Forest and XGBoost's performance was significantly different. On the data imputed by MissForest, the average of XGBoost’s performance was better than Random Forest’s Performance. The best model chosen was XGBoost with MissForest imputation, which had an average accuracy of 94.42%, sensitivity 90.21%, specificity 95.67%, G-Mean 92.90%, and AUC 97.77%. This model was used to predict the unknown completion status of undergraduate students who have reached the maximum length of study. The prediction results showed that 62.1% of 1,502 students were dropouts, and the remaining were graduates.
Keyword: Classification, Imputation, MissForest, Random Forest, XGBoost

Judul: Penerapan Metode Rotation Forest dalam Memprediksi Status Kelulusan Mahasiswa FMIPA IPB University
Abstrak: Salah satu indikator mutu pendidikan adalah lamanya waktu yang dibutuhkan untuk menyelesaikan studi. Data yang diperoleh dari Dit-AP IPB University menyebutkan masih banyak mahasiswa FMIPA yang belum berhasil menyelesaikan studinya tepat waktu. Hal ini menjadi evaluasi bagi IPB University khususnya mahasiswa FMIPA IPB agar lebih peduli terhadap kelulusan mahasiswanya. Penelitian ini dilakukan untuk memodelkan klasifikasi data mahasiswa FMIPA IPB University tahun 2018, dengan peubah respon yaitu lulus tepat waktu dan lulus tidak tepat waktu dengan profil dan latar belakang mahasiswa sebagai peubah penjelas. Metode klasifikasi yang digunakan adalah rotation forest. Tujuan dari penelitian ini adalah untuk melihat bagaimana tingkat akurasi dalam memprediksi serta mengetahui peubah penting yang mempengaruhi status kelulusan mahasiswa. Dari hasil tersebut dapat disimpulkan bahwa rotation forest mampu memprediksi status kelulusan mahasiswa FMIPA IPB University dengan kinerja yang cukup baik. Metode ini memperoleh nilai akurasi sebesar 69,89% sebelum Synthetic Minority Oversampling Technique (SMOTE) sedangkan setelah SMOTE memperoleh nilai akurasi sebesar 76,34%. Peubah terpenting dalam model sebelum penerapan SMOTE adalah Jalur Masuk (X4), Jenis Kelamin (X1), dan IPK (X2), sedangkan setelah penerapan SMOTE adalah Jalur Masuk (X4), IPK (X2), dan UKT (X6)., One indicator of the quality of education is the length of time required to complete studies. Data obtained from Dit-AP IPB University states that there are still many FMIPA students who have not succeeded in completing their studies on time. This is an evaluation for IPB University, especially FMIPA IPB students, to be more concerned about their students' graduation. This research was conducted to model the classification of FMIPA IPB University student data in 2018, with the response variables being on-time graduation and delayed graduation with student profiles and backgrounds as explanatory variables. The classification method used is rotation forest. This study aims to assess the accuracy of prediction and to identify the variable importance that affects students' graduation status. The results show that Rotation Forest can predict the graduation status of FMIPA IPB University students with fairly good performance. This method achieved an accuracy of 69.89% before applying the Synthetic Minority Oversampling Technique (SMOTE) and 76.34% after applying SMOTE. The variable importance in the model before the implementation of SMOTE are Driveway (X4), Gender (X1), and GPA (X2), while after SMOTE implementation are Driveway (X4), GPA (X2), and Tuition Fee (X6).
Keyword: graduation status, prediction, rotation forest, variable importance

Judul: Enteric Collibacillosis Pada Anak Babi Baru Lahir: Patogenesa dan Perubahan-Perubahan Patologisnya
Abstrak: Diare disebabkan oleh infeksi bakteri Escherichia coli pada anak-anak babi baru lahir yang peka terhadap en terotoksin produk bakteri tersebut. Dikenal sebagai colibacillosis. Patogenesa dari colibacillosis anak babi baru lahir adalah (1) infeksi oleh E. coli patogen, (2) proliferasi E. coli di lumen usus halus, (3) produksi dan pelepasan enterotoksin, dan (4) aktifitas toksin pada usus halus. Dalam proses proliferasi, adanya antigen pili K88 sangat penting karena memungkinkan bakteri perlekatan dengan epitel mucosa usus halus. mengadakan Setelah ko lonisasi dan proliferasi segera toksin dilepaskan. Akti- fitas toksin menginduksi sekresi yang berlebihan dari u- sus sehingga banyak cairan yang dikeluarkan ke lumen usus. Hal ini mengakibatkan banyak cairan dan elektrolit hilang sehingga timbul diare, dehidrasi dan asidosis yang sering menyebabkan kematian. Perubahan-perubahan patologis yang umum adalah dis tensi usus halus, dilatasi lambung dan infark pada kurva- tura mayor. Perubahan-perubahan histologis yang terjadi adalah enteritis hemorrhagica acuta, oedem dan kongesti pembuluh darah di lamina propria, akumulassi leukosit inti polimorf, sel monosit, dan inti sel mengalami piknosis Thrombus pada vena dan degenerasi serta desquamasi sel epitel usus….
Keyword: