Judul: Detection of Authenticity of Aceh Sigupai Rice Using Portable Near Infrared Spectrometer
Abstrak: Sigupai merupakan varietas beras aromatik yang menjadi ciri khas kabupaten Aceh Barat Daya. Banyaknya varietas beras dengan kemiripan karakter, memperbesar peluang pencampuran beras dalam pendistribusian. Tujuan dari penelitian ini adalah memprediksi keaslian beras sigupai dan persentase tingkat keaslian beras sigupai secara non-destruktif menggunakan portable near-infrared (NIR) spectrometer. Penelitian ini dianalisis dengan metode Partial Least Square-Driscriminant Analysis (PLS-DA) dan Partial Least Squares-Regression (PLS-R) pada dua perlakuan. Perlakuan pertama, beras yang digunakan adalah sigupai murni (tanpa campuran) dan sigupai campuran (sigupai dan inpari dengan persentase 1% sampai 30% atau pada tingkat keaslian 69,9% sampai 99%) masing-masing 43 sampel. Perlakuan kedua, beras yang digunakan adalah sigupai yang dicampur dengan inpari pada konsentrasi 0% sampai 30% atau pada tingkat keaslian 69,9% sampai 100% sebanyak 44 sampel. NeoSpectra dan SCiO dengan metode Partial Least Square-Driscriminant Analysis (PLS-DA) mampu memprediksi keaslian beras sigupai dengan hasil terbaik menggunakan data original (tanpa pre-treatment). Hasil terbaik dengan NeoSpectra menghasilkan nilai akurasi, sensitivitas, spesifisitas, dan false positive rate, yaitu masing-masing 89,29%; 92,86%; 85,71% dan 14,29%, sedangkan SCiO menghasilkan nilai akurasi, sensitivitas, spesifisitas, dan false positive rate, yaitu masing-masing 97,44%; 100%; 94,87% dan 5,13%. Analisis dengan metode Partial Least Squares-Regression (PLS-R) juga mampu menduga persentase tingkat keaslian beras sigupai dengan hasil terbaik menggunakan pre-treatment derivative SG1. NeoSpectra menghasilkan nilai koefisien korelasi kalibrasi, koefisien korelasi validasi, SEC, SEP, RPD, dan konsistensi, yaitu masing-masing 0,99; 0,96; 1,52%; 1,50%; 5,93, dan 100,69%. SCiO menghasilkan nilai koefisien korelasi kalibrasi, koefisien korelasi validasi, SEC, SEP, RPD, dan konsistensi, yaitu masing-masing 0,99; 0,98; 1,37%; 1,31%; 6,83 dan 104,78%. Hasil penelitian menunjukkan bahwa portable near-infrared spectrometer, baik NeoSpectra maupun SCiO berpotensi digunakan sebagai alat analisis keaslian beras sigupai di lapangan., Sigupai is one of the local rice varieties that characterizes the Southwest Aceh district. Wide rice varieties with similar characters increase the chances of mixing rice in distribution.The purpose of this study was to non-destructively predict the authenticity of sigupai rice and the percentage of authenticity in sigupai rice using a portable near-infrared (NIR) spectrometer. This study was analyzed using the Partial Least Square-Driscriminant Analysis (PLS-DA) and Partial Least Squares-Regression (PLS-R) methods for the two treatments. In the first treatment, the rice used was pure sigupai (without mixture) and mixed sigupai (sigupai and inpari with a percentage of 1% to 30% or at an authenticity level of 69,9% to 99%), each of 43 samples. In the second treatment, the rice used was sigupai mixed with inpari at a concentration of 0% to 30% or at an original level of 69,9% to 100% for 44 samples. NeoSpectra and SCiO with the Partial Least Square-Driscriminant Analysis (PLS-DA) method were able to predict the authenticity of sigupai rice with the best results using original data (without pre-treatment). The best results with NeoSpectra produced accuracy values, sensitivity, specificity and false positive rate values, namely 89,29%; 92,86%; 85,71% and 14,29%, while SCiO produced accuracy values, sensitivity, specificity, and false positive rate, namely 97,44%; 100%; 94,87% and 5,13%. Analysis using the Partial Least Squares-Regression (PLS-R) method was also able to estimate the percentage of authenticity in sigupai rice with the best results using pre-treatment derivatives SG1.
Keyword: Authenticity, NeoSpectra, SCiO, Portable NIR, Sigupai rice

Judul: Analysis of NIRS Spectrum Characteristics and Identification of Origin of Robusta Coffee Beans Using PLS-DA Method
Abstrak: Varietas kopi yang banyak dibudidayakan di Indonesia salah satunya adalah kopi robusta. Beragamnya indikasi geografis di Indonesia membuat varietas kopi robusta juga beragam, sehingga memperbesar peluang pemalsuan produk dalam pendistribusian. Penelitian ini bertujuan untuk menganalisis karakteristik spektrum biji kopi robusta dari Sumatera Barat, Jawa Barat, dan Jambi secara non destruktif menggunakan Near Infrared Spectroscopy (NIRS) serta membangun model Partial Least Square Discriminant Analysis (PLS-DA) untuk mengidentifikasi keaslian dari biji kopi robusta berdasarkan asal daerahnya. Penelitian ini menggunakan biji kopi robusta yang masih dalam bentuk green bean yang diambil dari 3 daerah, yaitu Sumatera Barat, Jawa Barat, dan Jambi. Total sampel yang digunakan berjumlah 180 sampel, sebanyak 120 sampel dipakai untuk kalibrasi dan 60 sampel untuk evaluasi. Spektra reflektansi biji kopi diukur menggunakan NIRS pada panjang gelombang 1000-2500 nm. Setelah pengukuran reflektansi, sampel biji kopi dilakukan analisis komposisi kimia menggunakan metode proksimat. Spektra reflektansi diolah dengan berbagai pretreatment (Savitzky-Golay First Derivative (SG1D), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), dan pretreatment kombinasi). Model PLS-DA yang dibangun menggunakan pretreatment Savitzky-Golay First Derivative baik tunggal maupun kombinasi (SG1D+SNV, SG1D+MSC, SG1D+SNV+MSC) mampu memprediksi biji kopi sesuai asal daerahnya dengan sangat baik, dengan nilai akurasi, sensitivitas, spesifisitas, dan presisi, masing-masing mencapai 99%; 98%; 99% dan 98%. Hasil ini menunjukkan bahwa biji kopi robusta yang berbeda asal daerahnya dapat diklasifikasikan atau diidentifikasi keasliannya secara non destruktif menggunakan NIRS., One of the coffee varieties that is widely cultivated in Indonesia is robusta coffee. The variety of geographical indications in Indonesia means that robusta coffee varieties are also diverse, thereby increasing the opportunity for product counterfeiting in distribution. This research aims to analyze the spectral characteristics of robusta coffee beans from West Sumatra, West Java, and Jambi non-destructively using Near Infrared Spectroscopy (NIRS) and build a Partial Least Square Discriminant Analysis (PLS-DA) model to identify the authenticity of robusta coffee beans based on area of origin. This research uses robusta coffee beans which are still in green bean form taken from 3 regions, namely West Sumatra, West Java, and Jambi. The total samples used were 180 samples, 120 samples were used for calibration, and 60 samples for evaluation. The reflectance spectrum of coffee beans was measured using NIRS at a wavelength of 1000-2500 nm. After measuring the reflectance, the coffee bean samples were subjected to chemical composition analysis using the proximate method. Reflectance spectra are processed with various pretreatments (Savitzky-Golay First Derivative (SG1D), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and pretreatments combination). The PLS-DA model built using Savitzky-Golay First Derivative pretreatment, both single and combined (SG1D+SNV, SG1D+MSC, SG1D+SNV+MSC), is capable of predicting coffee beans according to their origin very well, with accuracy, sensitivity, specificity, and precision values reaching 99%, 98%, 99%, and 98%, respectively. These results show that robusta coffee beans from different regions of origin can be classified or identified for their authenticity non-destructively using NIRS.
Keyword: authenticity, classification, coffee beans, NIRS, PLS-DA, robusta

Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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