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: 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: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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