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"The increasing prices of land and housing properties makes developing housing projects more expensive for developer. To overcome this problem, selection of contractor which has high efficiency, Optimizing cost to meet concepts and specifications of housing project with well planned time, is essential. There are two process of contractor selection which are pre-qualification and tendering process as follow up from pre-qualification process. In this paper, writer use multivariate discriminant analysis method for pre-qualification and analytical hierarchy process method for tendering process. Multivariate discriminant analysis method use statistical analysis to classify cases _from the independent variable into a group or dependent variable. Analytical hierarchy process method is a comprehensive method which is provides the ability to unite quantitative and qualitative factors in decision making for individual or group. As the results of this research, from the pre-qualification process a model or a discriminant function is generated which can classify qualified contractor so that this contractor can move on to the next process (tendering) based on determining criterions. From the tendering process, every criterions and sub criterions are given a weight which will affect in contractor selection."

"Directional multiscale systems, particularly shearlets, are now having the same dramatic impact on the encoding of multidimensional signals. Since its introduction about five years ago, the theory of shearlets has rapidly developed and gained wide recognition as the superior way of achieving a truly unified treatment in both a continuous and a digital setting. By now, it has reached maturity as a research field, with rich mathematics, efficient numerical methods, and various important applications."

"The subject of multivariate splines has become a rapidly growing field of mathematical research. The author presents the subject from an elementary point of view that parallels the theory and development of univariate spline analysis. To compensate for the missing proofs and details, an extensive bibliography has been included. There is a presentation of open problems with an emphasis on the theory and applications to computer-aided design, data analysis, and surface fitting. Applied mathematicians and engineers working in the areas of curve fitting, finite element methods, computer-aided geometric design, signal processing, mathematical modelling, computer-aided design, computer-aided manufacturing, and circuits and systems will find this monograph essential to their research."

"ABSTRAKIkan gindara (Lepidocybium flavobrunneum) adalah penghuni perairan mesopelagik yang merupakan hasil tangkapan sampingan dari tuna long line. Masuknya logam berat ke lingkungan perairan tersebut dapat memicu akumulasi logam berat pada organ tubuh ikan. Penelitian ini bertujuan untuk 1) menganalisis dan mengevaluasi kandungan logam berat merkuri (Hg) dan seng (Zn) pada ikan gindara, 2) membandingkan kadar logam berat merkuri (Hg) dan seng (Zn) pada ikan gindara berukuran 4 kg, 8 kg dan 12 kg. Sampel yang digunakan berupa organ (insang, hati) dan daging yang berasal dari ketiga kelompok ukuran tersebut. Pengujian logam berat dilakukan dengan alat Atomic Absorption Spectrometer. Analisa data menggunakan Multivariate Analysis. Hasil penelitian menunjukkan bahwa kandungan Hg untuk ikan kelompuk ukuran 4 kg pada insang, hati dan daging adalan yaitu insang : 0.34 ppm; 1.36 ppm; 1.07 ppm. Pada kelompok ikan ukuran 8 kg, kandungan Hg di insang, hati dan daging adalah 0.28 ppm; 1.49 ppm; 0.68 ppm. Pada kelompok ikan ukuran 12 kg, kandungan Hg pada insang, hati dan daging adalah 0.36 ppm; 3.17 ppm; 1.46 ppm. Kandungan Zn untuk ikan kelompuk ukuran 4 kg yaitu insang : 46.09 ppm, hati : 110.99 ppm daging : 8.96 ppm. Pada kelompok ikan ukuran 8 kg, kandungan Zn pada insang, hati dan daging adalah 49.59 ppm; 130.62 ppm; 9.49 ppm. Pada kelompok ikan ukuran 12 kg kandungan Zn pada insang, hati dan daging adalah 44.89 ppm; 149.14 ppm; 9.25 ppm. Pada penelitian ini, ukuran berpengaruh nyata terhadap kandungan Hg dan Zn dimana nilai P = 0.003

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ABSTRACTEscolar (Lepidocybium flavobrunneum) is mesopelagic fish and a common bycatch in tuna long line fisheries. Entry of heavy metals into the aquatic environment can lead to accummulation of heavy metals in fish. This study was aimed to 1) analyze and evaluate the heavy metal content of mercury (Hg) and zinc (Zn) in escolar, 2) compare the levels of mercury (Hg) and zinc (Zn) measured on three groups of escolar weight : 4 kg, 8 kg and 12 kg. The samples used were escolar organs (gills, livers) and meat from all three groups measured. Analysis of heavy metals was done using Atomic Absorption Spectrometer (AAS). Data were analysed using Multivariate Analysis. The results showed that Hg content for fish group size 4 kg in gills, livers and meat were: 0.34 ppm; 1.36 ppm; 1.07 ppm, respectively. In groups of fish size 8 kg, Hg content in gills, livers and meat were 0.28 ppm; 1.49 ppm; 0.68 ppm, respectively, while for groups of fish size 12 kg were 0.36 ppm; 3.17 ppm; 1.46 ppm in gills, livers and meat, respectively. Zn content for fish group size 4 kg in gills 46.09 ppm; livers 110.99 ppm and meat: 8.96 ppm. Zn content in groups of fish size 8 kg were 49.59 ppm; 130.62 ppm; 9.49 ppm in gills, liver and meat, respectively, while for groups of fish size 12 kg were 44.89 ppm; 149.14 ppm; 9.25 ppm in gills, liver and meat, respectively. In this research, size significantly affect the content of Hg and Zn where the value P = 0.003"

"Daerah penelitian terletak pada Pulau Seram merupakan salah satu wilayah yang memiliki potensi prospek komoditas emas dikarenakan ditemukannya indikasi mineralisasi emas. Hal ini juga dikorelasikan berdasarkan penelitian sebelumnya bahwa daerah penelitian merupakan endapan emas orogenik. Subjek utama penelitian ini adalah conto sedimen sungai aktif. Penelitian ini dilakukan menggunakan analisis statistik dan geologi. Analisis statistik dilakukan untuk mengetahui sebaran anomali dan juga asosiasi unsur yang terdapat pada daerah penelitian. Sedangkan untuk analisis geologi dilakukan dengan mengintegrasi conto sedimen sungai aktif dan conto konsentrat dulang yang berasal dari PSDMBP dengan melakukan analisis petrografi, morfografi dan morfometri  Hasil penelitian menunjukkan persebaran anomali yaitu Cu, Pb, Zn, Fe, Mn, Li, Co, Na, Bi, Sr dan Ba sebesar 44.255ppm, log 1.900ppm, 148.605ppm, 4.734ppm, log 3.189ppm, 27.302ppm, 41.374%, 7609.713ppm, 53.807ppm, log 1.480ppm, log 1.592ppm, 184.198ppm, log 1.924ppm dan 263.139ppm. Berdasarkan analisis multivariat, didapatkan 4 kelompok asosiasi unsur yaitu : Cu-Zn-Rb-Ba-(Fe), Cu-Co-Fe, Na-Sr dan Fe-Mn. Unsur yang digunakan sebagai pathfinder untuk deposit emas adalah unsur Cu, Pb, Zn, Fe, Mn, Na, Bi, Co, W, Rb, Sr dan Ba sehingga terdapat 4 daerah prospek pada daerah penelitian. Persebaran anomali pada daerah penelitian diinterpretasikan terjadi akibat faktor geologi berupa transportasi, erosi, serta pelapukan dari litologi dan mineral bijih yang terdapat pada daerah penelitian.

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The research area, Seram Island, is one of the areas that has potential prospects for the commodity gold due to the indications of gold mineralization were found. This also correlated with based on previous research, the research area is an orogenic gold deposit. The main subject of this research is sediment stream samples. This research was conducted using statistic and geological analysis. Statistic analysis was carried out to determine the distribution of anomalies and also the elemental associations found in the study area. Meanwhile, geological analysis was carried out by integrating active river sediment samples and pan concentrate samples originating from PSDMBP by conducting petrographic, morphographic and morphometric analysis. The results showed an anomalous distribution of Cu, Pb, Zn, Fe, Mn, Li, Co, Na, Bi , Sr and Ba of 44.255ppm, log 1.900ppm, 148.605ppm, 4.734ppm, log 3.189ppm, 27.302ppm, 41.374%, 7609.713ppm, 53.807ppm, log 1.480ppm, log 1.592ppm, 184.198ppm, log 1.924ppm and 263.139ppm. Based on multivariate analysis, 4 groups of elemental associations were obtained, namely: Cu-Zn-Rb-Ba-(Fe), Cu-Co-Fe, Na-Sr and Fe-Mn. The elements used as pathfinder for gold deposit are the elements Cu, Pb, Zn, Fe, Mn, Na, Bi, Co, W, Rb, Sr and Ba ​​so that there are 4 prospect areas in the study area. The distribution of anomalies in the study area is interpreted to occur due to geological factors in the form of transportation, erosion, and weathering of lithology and ore minerals found in the study area."

"Most of the observable phenomena in the empirical sciences are of a multivariate nature. In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices. In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior. The underlying data structure of these and many other quantitative studies of applied sciences is multivariate. Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics. All chapters have exercises that highlight applications in different fields. The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features. A new Chapter on Regression Models has been added. All numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets."

"Runtun waktu adalah salah satu data yang paling umum dan banyak dijumpai di kehidupan sehari-hari. Runtun waktu dianalisis dengan dua tujuan utama, yaitu untuk memodelkan mekanisme stokastik dari runtun waktu tersebut dan untuk melakukan peramalan. Untuk keperluan dua tujuan tersebut, banyak model runtun waktu yang telah dikembangkan, salah satunya adalah model autoregressive moving average (ARMA). Model ARMA adalah model runtun waktu univariat yang cukup populer dan umum digunakan saat ini. Seiring berjalannya waktu, mulai dikembangkan model runtun waktu multivariat, yang dapat memodelkan runtun waktu dengan dua atau lebih variabel. Meng- gunakan model runtun waktu multivariat untuk memodelkan dua atau lebih variabel tentu lebih efektif dibandingkan memodelkannya satu per satu menggunakan model univariat. Selain itu, model runtun waktu multivariat juga dapat menjelaskan hubungan dinamis antarvariabel yang saling terkait. Dalam skripsi ini, akan dijelaskan versi multivariat dari model ARMA, yaitu model vector autoregressive moving average (VARMA), mulai dari karakteristiknya, spesifikasi model, penaksiran parameter, hingga melakukan pera- malan. Penaksiran parameter akan dilakukan dengan menggunakan metode conditional maximum likelihood. Model VARMA ini kemudian akan digunakan untuk melakukan peramalan dua variabel yang cukup berpengaruh dalam ekonomi makro, yaitu data harian indeks harga saham gabungan (IHSG) dan kurs mata uang rupiah terhadap dolar Amerika Serikat. Data tersebut juga akan dimodelkan menggunakan model ARMA(p,q) dan VAR(p). Model yang digunakan adalah model ARIMA(0,1,0) untuk data IHSG, model ARIMA(0,1,2) untuk data kurs rupiah, model VARI(3,1) dan model VARIMA(1,1,1). Menggunakan indikator mean absolute percentage error (MAPE), didapatkan hasil bahwa model VARI(3,1) memberikan hasil peramalan yang paling akurat.

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Time series is one of the most common data and is often found in everyday life. The purpose of time series analysis is generally twofold: to understand or model the stochastic mechanism that gives rise to an observed series and to predict or forecast the future values of a series based on the history of that series and, possibly, other related series or factors. For the purposes of these two objectives, many time series models have been developed, the most popular one is autoregressive moving average (ARMA) model. The ARMA model is a univariate time series model that is quite popular and commonly used today. Over time, multivariate time series models have been developed, which can model time series with two or more variables. Using a multivariate time series model to model two or more variables is certainly more effective than modeling them one by one using a univariate model. In addition, the multivariate time series model can also explain the dynamic relationship between interrelated variables. In this undergraduate thesis, we will explain the multivariate version of the ARMA model, the vector autoregressive moving average (VARMA) model, starting from its characteristics, model specifications, param- eter estimation, to forecasting. Parameter estimation will be done using the conditional maximum likelihood method. Then, this VARMA model will be used to forecast two maroeconomics indicators: daily data of Indonesia Composite Index and the USD-IDR exchange rate. The data will also be modeled using the ARMA(p,q) and VAR(p) models. In chapter 4, the models used are ARIMA(0,1,0) model for Indonesia Composite Index data, ARIMA(0,1,2) model for USD-IDR exchange rate data, VARI(3,1) model and VARIMA(1,1,1) model. Using the mean absolute percentage error (MAPE) indicator, the results show that VARI(3,1) model provides the most accurate forecasting results."