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: Hybrid solar cells of ZnO nanoparticles/chlorophyll was modified metal ions (Zn2+ and Cu2+ )
Abstrak: Zinc Oxide (ZnO) is a n-type semiconductor with a wide the energy band (Band Gap) is 3.37 eV at room temperature and 3.34 eV at low temperature with a large exiton binding energy is 60 meV. ZnO has been synthesized by hydrothermal method, resulting in a decrease in particle size over the duration of the hydrothermal changes. Crystallographic structure of ZnO were characterized using XRD, has shown the characteristic pattern of a hexagonal wurtzite structure, the crystallite size increases to changes in particle size. Changes in particle size has also led to a change in the absorption peak of ZnO as has been demonstrated posted under test using the optical properties of UV-Vis spectrophotometer. Changes of this uptake has also led to changes in the energy band gap, which increases its band gap energy of the particle size. In electronic applications, especially in solar cells. Chlorophyll is considered less effective, because of their very nature are easily degraded as a result of several reactions which are acid, lighting and temperature. The results of the stability test using a halogen lamp 34 W/m2 for 5 hours, that the substitution of chlorophyll with metal ions Zn2+ and Cu2+ has been shown to form more stable than natural chlorophyll. The characterization of the optical properties, the combination of ZnO / chlorophyll in the form of hybrid films has led to a shift in the absorption region of ZnO becomes more widely from the UV to Visible. ZnO/chlorophyll has absorption range of 350-515 nm, ZnO / Zn-feofitin 332-401 nm and 332-632 nm for Cu-feofitin. The combination of ZnO/chlorophyll and CuSCN as a solid electrolite in hybrid solar cell, a modified chlorophyll with metal ions Zn2+ and Cu2+ has been shown to form a more stable I-V curve. It is characterized by its fill factor value greater than natural chlorophyll. Nevertheless, solar cells with natural chlorophyll has been able to produce higher efficiency values of chlorophyll were substituted.
Keyword: Hydrothermal, ZnO/Chlorophyll modified, Hybrid Solar Cell