Judul: Penggunaan Gage Linearity and Bias Study Untuk Menduga Kadar Glukosa Darah pada Alat Non-Invasif.
Abstrak: Diabetes adalah penyakit metabolik kronis yang ditandai dengan peningkatan kadar glukosa darah. Kontrol metabolik mengupayakan agar kadar glukosa darah berada di batas normal tanpa harus kekurangan glukosa darah. Penelitian ini dilakukan untuk menyusun model melalui pendekatan gage linearity and bias study pada pendeteksian kadar glukosa darah berdasarkan spektrum time domain terhadap frekuensi hasil keluaran alat pengukur glukosa darah non-invasif. Alat pada penelitian ini menggunakan konsep spektroskopi. Hasil dari spektrum residu intensitas cahaya yang kemudian dimodelkan untuk menduga kadar glukosa darah. Metode yang digunakan untuk memodelkan dugaan kadar glukosa darah tersebut menggunakan gage linearity and bias study dengan pendugaan parameter menggunakan regresi ortogonal. Hasil yang diperoleh mengindikasikan bahwa gage linearity and bias study dapat digunakan sebagai model untuk menduga kadar glukosa dalam darah dengan cara non-invasif. Dalam penelitian ini, setelah ditemukan model yang sesuai, validasi dilakukan menggunakan uji hipotesis dua contoh berpasangan antara hasil dari model dengan hasil kadar glukosa darah invasive.
Keyword: gage linearity and bias study, non-invasif, spektroskopi

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: Thermal Characteristics and Dynamics of Tropical Cyclone over Western North Pacific Ocean: A Case Study of Typhoon Songda (Chedeng) 2011
Abstrak: The frequent occasion of Tropical Cyclone (TC) over Western North Pacific (WNP) Ocean lies from 10˚N to 26˚N and 121˚E to 170˚E. Sources of TC development come from warm ocean temperature where latent heat is released during condensation processes and subsequently concentrated in the boundary layer. Relative humidity at 850-700 hPa is responsible to produce the most intense storm. Meanwhile, weak vertical wind shear between 850-200 hPa is one of the dynamical factors that lead to the development of TC. Pressure level of 850-200 hPa is chosen to minimize the thermodynamic effect in the lower troposphere. Typhoon Songda (Chedeng in the Philippines) or TSC lasted from May 18th to 30th, 2011 and strengthened into powerful Super Typhoon stage between May 24th and 27th. Long curving track and the exhibition of all sequences of TC life cycle, TSC is then considered as an ideal case of TC. The objective of the study with case of TSC is to investigate the role of thermal characteristics and dynamics on TC intensity. The research is carried out by using Advanced Research of Weather Research and Forecasting model (WRF ARW model) version 3.3. Two domains are employed in the model. The first domain situate from 02˚N to 40˚N and 118˚E to 149˚E and cover spatial resolution () of 33.1 km. The second domain situate from 09˚N to 27˚N and 119˚E to 132˚E with of 11 km. Timestep of the model () is 120 seconds. The model is supported by the following physical parameterization, i.e., Yonsei University Scheme (YSU) in the boundary layer, Kain-Fritsch scheme (KF) in the cumulus, and WRF Single-Moment 3-class scheme in the microphysics. Initial meteorological condition prior to the formation of the cyclone is generated using WRF ARW standard-release model. Sensitivity of the model output is obtained from a selected single-parameter change scenarios, namely, 10 % to 20 % decrease and increase of wind shear initial data at 850-200 hPa, and by 10 % to 20 % decrease of relative humidity initial data at 850-700 hPa. The result showed that the negative correlations between wind shear and intensity are found in wind shear scenarios. Wind shears of 10 ms-1 which lead to strengthening of the wind intensity in the early period of Typhoon development are found in wind shear scenarios. The effect of wind shear changes on wind intensity is statistically significant in the 20 % wind shear scenarios. During mature stage, weak wind intensity occurred when wind shear increase as given in the WRF standard-release and the 20 % decreasing scenario. Different situation is found in the 20 % increasing scenario where wind shear tend to weak and lead to strong wind intensity at this stage. Meanwhile, the reduction of moisture supply at 850-700 hPa resulting from 20 % decreasing relative humidity play a major role in the weakening of wind intensity and reduces mechanical energy of the cycle process as much as 300 J kg-1.
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