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: Data Hilang Dalam Analisis Pohon Regresi (Studi Kasus: Tingkat Keberhasilan Mahasiswa Pascasarjana IPB)
Abstrak: The objective of this research are to build regression tree which is contains of missing value to estimate efficacy level of Bogor Agricultural University’s post graduate students and to study about factors that influence efficacy level of Bogor Agricultural University’s post graduate students for each major. Missing value in Classification and Regression Trees (CART) could be handled by nonparametric approach based on iteration of regression tree classification. Each major contains of missing value in their predictor variable. Therefore, surrogates variable is been used to impute this missing value. This variable has high association with primer variable that contains of missing value that will be use as surrogate variable. Efficacy level that researcher want to see is Bogor Agricultural University’s post graduate students academic abilities. This point can be count by post graduate GPA and period of post graduate study. Therefore, factors which are influence efficacy level of Bogor Agricultural University’s post graduate students will be done by regression tree approaching. This research shows that missing value in large amount of value causes increasing of error level in regression tree classification and decreasing of prediction accuracy. This shows that missing value still become a serious problem in CART. The variable that has high association with primer variable that contains missing value will be use as surrogate variable. Regression tree that built from missing value with surrogates will give the best result if we compare it with regression tree that built without surrogates. As a result, post graduate degree in Bogor Agricultural University need to consider age, gender, accreditation, bachelor degree’s Grade Prediction Academic (GPA), profession, and duration from bachelor to post graduate degree variables, because this factors influence students efficacy level in each 16 majors for a period of study. While to produce higher post graduate’s GPA, they need to concern about duration period from bachelor to post graduate degree, earlier college status, acceptance status, age, and accreditation variables. Bogor Agricultural University’s post graduate need to consider all variables that mentioned to produce quality students in future.
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Judul: Infeksin virus rota pada anak babi
Abstrak: Virus Rota sudah lama diduga sebagai salah satu penyebab diare pada anak babi yang baru dilahirkan. Hal ini diperkuat dengan berhasilnya Mebus dan kawan-kawan mengidentifikasi virus Rota pada kejadian diare yang ter jadi pada anak sapi yang baru dilahirkan. Infeksi virus Rota merupakan hal yang serius, kejadian yang mewabah pernah dilaporkan di Amerika Serikat, Inggris, Kanada dan Australia. Sedangkan kejadian di Indonesia yang dihubungkan dengan infeksi virus Rota belum dilaporkan. Virus Rota termasuk keluarga Reo viridea. Sesuai dengan namanya, virus Rota menyerupai roda (Rota = Wheel), mengandung asam nukleat (nucleid acid) yang berupa asam nukleat ribo (ribo nucleid acid, RNA) dan berserabut ganda (double stranded), tidak mempunyai selubung (envelop atau peplos). Hewan rentan adalah hewan yang berumur sangat muda. Selain menyerang babi, dapat pula menyerang sapi, domba, rusa, anjing, kucing, tikus, kelinci, unggas dan manusia. ...
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