Judul: Penerapan Propensity Score Matching untuk Analisis Faktor yang Memengaruhi Pre-Diabetes
Abstrak: Inappropriate comparisons between the control group and the treatment group can be caused by overlapping factors, usually call confounders. Propensity score methods allow researchers to reduce bias from measured confounding by summarizing the distributions of many measured confounders in a single score based on the probability of receiving treatment. Binary logistic regression will be use to estimate the probability score and identify risk factors that significant influence complications in fasting blood glucose levels. This study uses Nearest Neighbor Matching (NNM) in several combinations of caliper and different order to know which is the best to reduce bias. The research results indicate that gender becomes a confounding variable. The order of propensity scores appears to affect the outcome of the matching analysis, also caliper is effective in reducing bias. The condition with random order propensity scores with caliper is the best compared to other matching conditions, with 99,93 percent reduction bias. The significance of the average treatment effect for treated (ATT), all different condition order with caliper indicates that gender have a positive relationship and significantly affects fasting blood glucose levels. Also, based on the matching results with the best combination, it was found that the variables of age, academic position, structural position, education level, and lecturer performance do not have an influence on abnormal fasting blood sugar (FBS)., Perbandingan yang tidak tepat antara kelompok kontrol dan kelompok perlakuan dapat disebabkan oleh faktor yang saling tumpah tindih atau biasa disebut sebagai confounder. Metode Propensity Score Matching memungkinkan peneliti untuk mengurangi bias akibat dari confounder yang diukur dalam single score berdasarkan probabilitas bersyarat dari perlakuan. Regresi logistik biner akan digunakan untuk memperkirakan skor probabilitas dan mengidentifikasi faktor risiko yang secara signifikan memengaruhi kadar glukosa darah tidak normal. Penelitian ini menggunakan Nearest Neighbor Matching (NNM) dan kombinasi caliper serta urutan berbeda untuk mengetahui kombinasi yang terbaik dalam mereduksi bias. Hasil penelitian menunjukkan bahwa jenis kelamin merupakan peubah pembaur (confounder). Urutan pada propensity score memengaruhi hasil bias yang dapat tereduksi namun caliper yang paling efektif dalam mereduksi bias. Kombinasi dengan urutan propensity score acak dan dengan caliper mampu mereduksi bias sebesar 99,93 persen. Average Treatment and Treated (ATT) juga menunjukkan bahwa jenis kelamin berpengaruh signifikan terhadap gula darah puasa. Selain itu, berdasarkan data hasil matching dengan kombinasi terbaik dalam mereduksi bias, didapatkan bahwa peubah usia, jabatan akademik, jabatan struktural, pendidikan terakhir, dan beban kinerja dosen (BKD) tidak berpengaruh signifikan terhadap gula darah puasa.
Keyword: propensity score matching, regresi logistik biner, caliper, confounder, fasting blood sugar (FBS), structural position

Judul: Penerapan Partial Least Squares Regression untuk Menduga Kadar  Glukosa Darah Non-Invasif dengan Pendekatan Luas Dibagi Jumlah Titik
Abstrak: Diabetes mellitus adalah kelainan metabolisme pada tubuh manusia yang tidak dapat memproduksi atau menggunakan insulin dengan baik. Prevalensi diabetes mellitus di Indonesia diperkirakan akan terus meningkat. Data yang digunakan adalah data keluaran alat invasif sebagai peubah respon serta data keluaran alat non-invasif tahun 2019 terdiri dari 50 peubah bebas hasil pemilihan data puncak dan 25 peubah bebas hasil pemilihan data periode. Peubah bebas yang digunakan merupakan hasil peringkasan nilai di bawah grafik residu intensitas terhadap time domain serta jumlah titik yang ada. Alat non-invasif mengeluarkan data berupa spektrum residu intensitas dan time domain. Keluran alat non-invasif tersebut sangat beragam sehingga diperlukan metode peringkasan data untuk menyeragamkan jumlah peubah. Penelitian ini bertujuan melakukan pemilihan data berdasarkan puncak dan periode kemudian melakukan pendekatan beberapa peringkasan yakni peringkasan luas dan peringkasan ide baru yaitu luas dibagi jumlah titik. Partial Least Squares Regression digunakan untuk mengatasi masalah multikolinearitas pada data kalibrasi alat. Hasil penelitian ini diperoleh pemilihan data berdasarkan periode lebih tepat diterapkan pada data yang cenderung fluktuatif. Peringkasan luas dibagi jumlah titik memberikan pendugaan yang lebih akurat karena nilai rataan RMSEP lebih kecil pada sebagian besar skema pemodelan., Diabetes mellitus is a metabolic disorder in the human body that cannot produce or use insulin properly. The prevalence of diabetes mellitus in Indonesia is expected to continue to increase. The data used are invasive tool-output data as dependent variables and non-invasive tool-output data in 2019 consisting of 50 independent variables from peak data and 25 independent variables from period data. The independent variables used result from value residues below the intensity of the time domain and the number of points. Non-invasive tools produce spectrum data of intensity residue and time domains. The output of non-invasive tools is very diverse so data summation method is needed to homogenize the number of variables. This study aims to select data based on peaks and periods and then approach several summations of the graph area summation and the new idea of graph area summation divided by the number of points. Partial Least Squares regression addresses multicollinearity problems on calibration data. The research shows that the data selection based on periods is more appropriately applied to data that tends to fluctuate. The graph area summation divided by the number of points provides a more accurate estimate because of smaller RMSEP on most schemes modeling.
Keyword: blood glucose, calibration model, non-invasive, partial least squares regression

Judul: Posisi Filogenetik Kura-Kura Carettochelys Insculpta  (Reptillia: Testudinata)
Abstrak: Carettochelys insculpta is an endemic freshwater turtle of southern Papua and northern Australia. Phylogenetic position of this turtle is remain unresolved until now. Most of the research conducted show close relationships of C. insculpta and members of Trionychidae family. However, some of the research show distance between C. insculpta and members of Trionychidae family. This research is conducted to analyze phylogenetic relationship between C. insculpta and other members of Testudinata using 12S rRNA, 16S rRNA, and both gene as markers. DNA samples were obtained from three different organisms of C. insculpta. Two species used as outgroups were Alligator mississippiensis and Iguana iguana. Phylogenetic analysis was carried out using Neighbor-Joining method with Felsenstein 1981 (F81) modeltest. A polytomy tree of Testudinata was produced by using 12S rRNA gene. However, analysis using 16S rRNA and both genes as markers confirmed that C. insculpta was located between the Chelonidae and the Trionychidae family.
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