Judul: Perbandingan regresi logistik biner dengan jaringan syaraf tiruan (studi pada klasifikasi status akreditasi Sekolah Menengah Pertama Provinsi DKI Jakarta)
Abstrak: Classification is one of statistical technique in classifying data compiled systematically. One popular example of classification in the field of education is the accreditation status of the school. Accreditation status of schools affected by the standard of Teachers and Education Personnel (TOD). There are many statistical methods that can be used to solve classification problems, including binary logistic regression and Artificial Neural Network (ANN). The result of the calculations in studies using binary logistic regression gives an Area Under Curve (AUC) of 0.852 for training data and 0.834 for testing data. Calculation with a back propagation ANN for training data and testing data give an AUC of 0.911 and 0.899. Thus, the back propagation ANN can predict better than the binary logistic regression. The variables that significantly affect the classification of the accreditation status of the school is the number of teachers (X1) and the qualifications of chief administrative personnel (X6).
Keyword: Bogor Agricultural University (IPB), binary logistic regression., neural network, classification, back propagation

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: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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