Judul: The Calibration Model for Predicting Non- Invasive Blood Glucose Levels by Using the Principal Component of Quantile Regression
Abstrak: Diabetes mellitus (DM), a chronic metabolic disorder, is caused by pancreas inability to produce enough insulin (a hormone that regulates blood glucose) or of which the body cannot use the insulin produced effectively. As a result, there is an increase in the concentration of glucose in the blood (hyperglycemia). If it is not immediately prevented or addressed, DM will induce complications leading to death. DM is one of the global health problems with the fastest-growing emergency in the 21st century, so it is necessary to take preventive actions. Including checking blood glucose levels regularly. Furthermore, blood glucose level test is usually conducted in invasive ways such as by using injection and a glucometer which may injure the body. This method is takes times and spends a big expense. Based on those phenomena, the IPB bio marking team was motivated to develop a noninvasive blood glucose monitoring device that is non-injurious for the body. The development of this blood test requires calibration analysis model. The previous research had shown that there were outlier data on the results of invasive blood glucose levels measurement. However, in the study the outlier data were omitted. The outlier data in this case cannot be simply omitted, because there was suspicion of which a respondent has a blood glucose level that is extremely higher than the normal blood glucose level. The quantile regression method was applied in this case, since the method is robust to outliers and does not need a normal distribution assumption. However, the calibration modeling requires a variable reduction in advance because in general, the calibration data is highly multi collinear in explanatory variables. The method that can be used to solve this problem is the principal component analysis. This study aimed to build a calibration model for predicting non-invasive blood glucose level by using the principal component of Quantile Regression method. The response variable (Y) was the result of invasive measurements in the form of blood glucose levels (mg/dL). The explanatory variable (X) was the result of non – invasive measurement in the form of a spectrum of residual light intensity toward the time domain. This study applied 2017 and 2019 data with different tool designs. In 2017, the design of the tool with the light captured by the sensor was the light transmitted/passed by the bloods. In 2019, the tool designed sensor which captured the light reflected by the bloods. Prior to modeling, data pre-processing was carried out by two approaches. First, the approach was to summarize the area under the residual intensity curve for each periode time (period area). Second, the approach was to summarize the area under the residual intensity curve for each the lamps were on (peak area). The model was constructed by various quantiles and several numbers of principal components which were selected based on various proportions of the cumulative variance. Based on our analysis, overall RMSEP value from summarizing period area data were steadier compared to summarizing of peak area. The sixth quantile had RMSEP value of 0,0184, which was the best in predicting blood glucose level tested
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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: Budidaya dan pelestarian buaya air tawar irian (Crocodylus novaeguineae)
Abstrak: Di Indonesia terdapat beberapa spesies buaya diantaranya adalah Crocodylus porosus, Crocodylus novaeguineae, Tomistoma schlegelii dan Crocodylus siamenesis. Crocodylus novaeguineae baru ditemukan tahun 1928 di lrian Jaya dan Papua New Guinea oleh Schmidt. Dengan adanya perdagangan kulit buaya maka masyarakat mulai memburu buaya-buaya alam untuk dijual tanpa memperhatikan kelangsungan hidup spesies tersebut. Tempat hidup buaya-buaya inipun semakin sempit karena diubah jadi perladangan, lalu lintas air dan daerah pemukiman sehingga terjadi penurunan jumlah buaya secara cepat.
Keyword: Crocodylus porosus, Crocodylus novaeguineae, Tomistoma schlegelii, Crocodylus siamenesis