Judul: Multilevel Regression Analysis on Longitudinal Data of the Grade Point Average (GPA) of IPB Masters Program Students
Abstrak: Salah satu indikator untuk mengukur kualitas lulusan perguruan tinggi adalah keberhasilan studi mahasiswa. Capaian Indeks Prestasi Kumulatif (IPK) dan lama studi adalah indikator penting dalam mengevaluasi keberhasilan studi mahasiswa. Penelitian-penelitian sebelumnya menggunakan lama studi mahasiswa sebagai indikator keberhasilan mahasiswa program magister, sehingga struktur data yang dihasilkan berbentuk cross-sectional. Jika ditinjau berdasarkan capaian IPK, struktur data yang dihasilkan berbentuk longitudinal karena merupakan pengamatan berulang. Tujuan dari penelitan ini adalah untuk memodelkan hubungan antara capaian IPK mahasiswa sebagai data longitudinal, mengidentifikasi faktor-faktor yang memengaruhi capaian IPK mahasiswa, dan menduga komponen ragam capaian IPK program magister Sekolah Pascasarjana IPB. Data yang digunakan adalah data mahasiswa program magister kelas regular angkatan 2019. Metode yang digunakan adalah regresi multilevel dengan IPK sebagai nilai amatan berulang level kesatu tersarang pada level kedua (individu mahasiswa) tersarang pada level ketiga (fakultas). Capaian IPK mahasiswa program magister IPB dimodelkan sebagai data longitudinal dengan metode regresi 3-level. Diketahui faktor yang memengaruhi capaian IPK adalah semester mahasiswa, usia, status perkawinan, sumber biaya pendidikan, jumlah mahasiswa tiap fakultas, interaksi usia dengan sumber biaya pendidikan, serta antara semester dengan status perkawinan. Komponen keragaman menunjukkan adanya keragaman perbedaan IPK antar fakultas, keragaman perbedaan IPK antar mahasiswa dalam fakultas, serta keragaman IPK antar semester dalam mahasiswa dalam fakultas., The academic accomplishment of college graduates is one factor used to gauge their caliber. Academic performance indicators for students in the master's program at IPB Postgraduate School include cumulative grade point average (GPA) and length of study. Previous studies have used the length of study as an academic performance indicator for master's program students, so the resulting data structure is cross-sectional. In contrast, if we examine it in light of the GPA performance, the resulting data structure is longitudinal because it involves repeated measurements. Therefore, this research aims to model student GPA performance as longitudinal data, identify the factors that influence student GPA performance, and estimate the variance components of GPA performance in the master's program at IPB Postgraduate School. The technique utilized is multilevel regression, with the first level's repeated observation value (GPA) nested at the second level (individual students), which is nested at the third level (faculty). The GPA performance in the master's program at IPB Postgraduate School is modeled as longitudinal data using the 3-level regression method. The results show that the factors influencing GPA achievement are student semesters, age, marital status, sources of tuition fees, number of students in each faculty, age interaction with sources of tuition fees, and semester interaction with marital status. The variance component shows the variance of GPA differences between faculties, the variance of GPA differences between students within the faculty, and the variance of GPA over the semester among students within the faculty.
Keyword: IPK, Longitudinal, Magister, Regresi multilevel

Judul: Evaluasi Capaian Akademik Mahasiswa  Sekolah Vokasi IPB Menggunakan Regresi Logistik Biner Multilevel
Abstrak: Sekolah Vokasi IPB didirikan pada tahun 1979. Program diploma ini memiliki visi menjadi Sekolah Vokasi yang terdepan dan berkelas Internasional. Tercapainya tujuan tersebut dapat dilihat dari indeks prestasi kumulatif setiap mahasiswa. Oleh karena itu, penelitian ini mencatat data nilai IPK setiap mahasiswa setiap tahunnya. Data ini disebut data longitudinal dan cara melakukan pemodelannya adalah menggunakan model regresi linier multilevel. Eksplorasi data peubah respons pada penelitian ini menunjukkan tidak berdistribusi normal sehingga salah satu solusinya adalah dengan membuat model regresi logistik biner multilevel. Oleh karena itu, penelitian ini bertujuan membangun model regresi logistik biner multilevel pada data siswa Sekolah Vokasi IPB dan menduga komponen ragam IPK mahasiswa Sekolah Vokasi IPB. Hasil model yang terbentuk menunjukkan bahwa faktor-faktor yang memengaruhi IPK pada level satu adalah semester dan SKS per semester, kemudian pada level dua adalah jenis kelamin, jalur masuk, dan status beasiswa. Model terbaik mencakup ragam intersep acak pada tingkat mahasiswa dan tingkat program studi. Selain itu, model terbaik juga menunjukkan ragam kemiringan acak semester pada tingkat mahasiswa serta semester dan jalur masuk pada tingkat program studi., IPB Vocational School was established in 1979. This diploma program aims to become a leading and international-class Vocational School. The achievement of this goal can be seen in each student's cumulative grade point average. Therefore, this study records the GPA value data for each student every year. This data is called longitudinal data, and the way to do the modeling is using the multilevel linear regression model. The data exploration of the response variable in this study shows that it is not normally distributed, so one of the solutions is to make a multilevel binary logistic regression model. Hence, this research aims to build multilevel binary logistic regression model on IPB Vocational School student data and estimate the variance components of IPB Vocational School students' GPA. The results of the model formed show that the factors that influence GPA on level one are semester and credits per semester, and then on level two, they are gender, entry route, and scholarship status. The best model included the variance of random intercept on the student level and study program level. It also shows the variance of random slopes of semesters at the student level as well as semester and entry route at the study program level.
Keyword: binary logistic regression, GPA, longitudinal data, multilevel regression, study achievement

Judul: Elektrodeposisi Film Tipis Semikonduktor Cu2ZnSnS4 (CZTS) pada Substrat Kaca ITO
Abstrak: Semiconductor Cu2ZnSnS4 (CZTS) thin film that can be used as absorber layers on solar cell module was made via electrodeposition method. CZTS thin film was electrodeposited on the surface of a silicate glass coated with a thin layer of indium tin oxide (ITO). Deposition process was done using potentiostatic mode at room temperature with a potential of 1.05 V for 30, 45, and 60 minutes, followed with annealing the film at 2 different temperatures, 500 ºC using furnace and 180 ºC using hot plate, both treatments for 45 minutes in N2/H2S atmosphere. The X-ray difractograms were analyzed using Match 2 program and showed that the CZTS thin films had kesterit crystalline structure, with a low percentage of CZTS phase. The thin film also contained secondary phases such as Cu2S, ZnS, and SnS. The obtained CZTS thin films had a thickness between 1.039 and 1.676 μm. Absorption study showed that the band gap energy of CZTS thin films which was annealed using furnace ranged from 1.50 to 1.52 eV, whereas that annealed using the hot plate was not observed. The band gap energy value showed that CZTS thin films can be used as sunlight absorber layers on solar cell. The formation of CZTS crystal film on this investigation was effectively performed using furnace annealing process at a temperature of 500 ºC with deposition time of 45 minutes.
Keyword: Thin film, Electrodeposition, Cu2ZnSnS4