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All Journal Jurnal Informatika Jurnal Teknik Elektro Jurnal Teknik Industri Jurnal Titra Record and Library Journal Journal of Documentation and Information Science International Journal of Industrial Optimization (IJIO)

Siana Halim

Fakulty of Industrial Technology,Industrial Engineering Department, Petra Christian University, Jl. Siwalankerto 121-131, Surabaya 60236

Author-ID : 273217

Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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REGRESSION ANALYSIS OF PRODUCTIVITY USING MIXED EFFECT MODEL Halim, Siana; N Bisono, Indriati
Jurnal Teknik Industri Vol 9, No 2 (2007): DECEMBER 2007
Publisher : Institute of Research and Community Outreach - Petra Christian University

Production plants of a company are located in several areas that spread across Middle and East Java. As the production process employs mostly manpower, we suspected that each location has different characteristics affecting the productivity. Thus, the production data may have a spatial and hierarchical structure. For fitting a linear regression using the ordinary techniques, we are required to make some assumptions about the nature of the residuals i.e. independent, identically and normally distributed. However, these assumptions were rarely fulfilled especially for data that have a spatial and hierarchical structure. We worked out the problem using mixed effect model. This paper discusses the model construction of productivity and several characteristics in the production line by taking location as a random effect. The simple model with high utility that satisfies the necessary regression assumptions was built using a free statistic software R version 2.6.1.

PENENTUAN HARGA JUAL HUNIAN PADA APARTEMEN DI SURABAYA DENGAN MENGGUNAKAN METODE REGRESI SPASIAL Halim, Siana; Anastasia, Njo; Eval, Agnes; Tobing, Aida Fitriani
Jurnal Teknik Industri Vol 10, No 2 (2008): DECEMBER 2008
Publisher : Institute of Research and Community Outreach - Petra Christian University

In the modern society, apartments are one of the answers to the need of residential for the citizens due to limited area for landed housing and over crowded city for businesses. Therefore, it will be very interested to investigate the price of appartements, since they are not only influenced by, e.g., physical factors, location, external factor and distance to the central business district, but also they have dependency to the price of other appartements in the neighborhoods. This dependency is called the spatial dependency; to accommodate this dependency to model the price of apartemen by ordinary least square is not enough. Therefore, in this research we used the spatial regresion to model the price of appartements, particularly in Surabaya. Abstract in Bahasa Indonesia: Dalam masyarakat modern, apartemen merupakan salah satu jawaban akan kebutuhan tempat tinggal bagi warga kota, karena adanya keterbatasan lahan untuk perumahan dan kota yang padat dengan daerah bisnis. Oleh karena itu sangatlah menarik untuk menginvestigasi harga apartemen, karena mereka tidak hanya tergantung pada, misalnya, faktor fisik, lokasi, faktor eksternal dan jarak ke pusat bisnis, tetapi juga tergantung pada harga apartemen di daerah sekitarnya. Ketergantungan ini disebut dengan ketergantungan spasial. Untuk mengakomodasi ketergatungan ini memodelkan harga jual apartemen dengan menggunakan metode regresi linear biasa tidaklah cukup. Pada penelitian ini, akan digunakan model regresi spasial untuk memodelkan harga apartemen khususnya di Surabaya. Kata kunci: Ordinary Least Square (OLS), Spatial Regresion, Spatial Auto Regresion (SAR).

FUNGSI-FUNGSI KERNEL PADA METODE REGRESI NONPARAMETRIK DAN APLIKASINYA PADA PRIEST RIVER EXPERIMENTAL FORESTÂ’S DATA Halim, Siana; Bisono, Indriati
Jurnal Teknik Industri Vol 8, No 1 (2006): JUNE 2006
Publisher : Institute of Research and Community Outreach - Petra Christian University

In this paper we discuss some models for estimating the regression function, provided the data Y is given. The programming of these function are presented as well as the "tricks" in the R- programming, a statistical freeware. We applied these models to The Priest River Experimental Forest's data. Abstract in Bahasa Indonesia : Dalam paper ini akan diberikan beberapa model untuk mengestimasi fungsi regresi bila sebuah data Y diberikan. Pembuatan fungsi - fungsi estimasti tersebut juga diberikan dengan menggunakan freeware statistik R dan juga diberikan beberapa trik dalam pemrograman. Model-model ini diaplikasikan pada Priest River Experimental Forest's data. Kata kunci: regresi nonparametrik, smoothing, kernel, R.

APLIKASI MARKOV RANDOM FIELD PADA MASALAH INDUSTRI Halim, Siana
Jurnal Teknik Industri Vol 4, No 1 (2002): JUNE 2002
Publisher : Institute of Research and Community Outreach - Petra Christian University

Markov chain in the stochastic process is widely used in the industrial problems particularly in the problem of determining the market share of products. In this paper we are going to extend the one in the random field so called the Markov Random Field and applied also in the market share problem with restriction the market is considered as a discrete lattice and Pott's models are going to be used as the potential function. Metropolis sampler is going to be used to determine the stability condition. Abstract in Bahasa Indonesia : Rantai Markov dalam proses stokastik seringkali digunakan dalam penyelesaian masalah industri khususnya dalam masalah penentuan market share. Dalam artikel ini akan dibahas perluasan Rantai Markov tesebut ke dalam sebuah random field yang disebut sebagai Markov Random Field (MRF) yang juga akan diaplikasikan pada masalah market share dengan batasan daerah pemasarannya dianggap sebagai sebuah lattice diskrit dan fungsi potensial yang akan digunakan adalah Potts models. Akan digunakan Metropolis sampler untuk menentukan kondisi stabil. Kata kunci: proses stokastik, Markov Random Field, Gibbs Random Field, Potts model, Metropolis sampling.

PENDEKATAN MODEL MATEMATIS UNTUK MENENTUKAN PERSENTASE MARKUP HARGA JUAL PRODUK Yuliana, Oviliani Yenty; Wahyudi, Yohan; Halim, Siana
Jurnal Teknik Industri Vol 4, No 2 (2002): DECEMBER 2002
Publisher : Institute of Research and Community Outreach - Petra Christian University

The purpose of this research is to design Mathematical models that can determine the selling volume as an alternative to improve the markup percentage. Mathematical models was designed with double regression statistic. Selling volume is a function of markup, market condition, and substitute condition variables. The designed Mathematical model has fulfilled by the test of: error upon assumption, accurate model, validation model, and multi collinear problem. The Mathematical model has applied in application program with expectation that the application program can give: (1) alternative to decide percentage markup for user, (2) Illustration of gross profit estimation that will be achieve for selected percentage markup, (3) Illustration of estimation percentage of the units sold that will be achieve for selected percentage markup, and (4) Illustration of total net income before tax will get for specific period. Abstract in Bahasa Indonesia : Penelitian ini bertujuan untuk merancang model Matematis guna menetapkan volume penjualan, sebagai alternatif untuk menentukan persentase markup harga jual produk. Model Matematis dirancang menggunakan Statistik Regresi Berganda. Volume penjualan merupakan fungsi dari variabel markup, kondisi pasar, dan kondisi pengganti. Model Matematis yang dirancang sudah memenuhi uji: asumsi atas error, akurasi model, validasi model, dan masalah multikolinearitas. Rancangan model Matematis tersebut diterapkan dalam program aplikasi dengan harapan dapat memberi: (1) alternatif bagi pengguna mengenai berapa besar markup yang sebaiknya ditetapkan, (2) gambaran perkiraan laba kotor yang akan diperoleh setiap pemilihan markup, (3) gambaran perkiraan persentase unit yang terjual setiap pemilihan markup, dan (4) gambaran total laba kotor sebelum pajak yang dapat diperoleh pada periode yang bersangkutan. Kata kunci: model Matematis, aplikasi program, volume penjualan, markup, laba kotor.

Credit Scoring Modeling Halim, Siana; Humira, Yuliana Vina
Jurnal Teknik Industri Vol 16, No 1 (2014): JUNE 2014
Publisher : Institute of Research and Community Outreach - Petra Christian University

It is generally easier to predict defaults accurately if a large data set (including defaults) is available for estimating the prediction model. This puts not only small banks, which tend to have smaller data sets, at disadvantage. It can also pose a problem for large banks that began to collect their own historical data only recently, or banks that recently introduced a new rating system. We used a Bayesian methodology that enables banks with small data sets to improve their default probability. Another advantage of the Bayesian method is that it provides a natural way for dealing with structural differences between a bankâ€™s internal data and additional, external data. In practice, the true scoring function may differ across the data sets, the small internal data set may contain information that is missing in the larger external data set, or the variables in the two data sets are not exactly the same but related. Bayesian method can handle such kind of problem.

Defect Detection on Texture using Statistical Approach Halim, Siana
Jurnal Teknik Industri Vol 17, No 2 (2015): DECEMBER 2015
Publisher : Institute of Research and Community Outreach - Petra Christian University

In this paper we present several techniques for detecting simple defect on the texture. The simple defect means, that the defect can be detected via image histogram or via wavelet of the image histogram. Hill estimator is one of the techniques that we suggest to use to solve this problem, since it does not need estimate parameters for estimating the image density

PENGGUNAAN BOOTSTRAP DATA DEPENDEN UNTUK MEMBANGUN SELANG KEPERCAYAAN PADA PARAMETER MODEL PERAMALAN DATA STASIONER Halim, Siana; Mallian, Herman
Jurnal Teknik Industri Vol 8, No 1 (2006): JUNE 2006
Publisher : Institute of Research and Community Outreach - Petra Christian University

The Bootstrap is a lively research area. A lot Of ideas are around and have let to quiet different proposals. In this paper we sketch briefly some Bootstrap methods for independent and dependent data. Finally we give an Bootstrap example for constructing confidence interval in the forecasting for stationer data. Abstract in Bahasa Indonesia : Bootstrap merupakan area penelitian yang terus berkembang. Ada banyak ide dan proposal-proposal yang berbeda telah diberikan oleh para peneliti. Namun demikian, dalam makalah ini hanya akan diulas secara singkat beberapa metode Bootstrap untuk data independen maupun data dependen. Akhirnya akan diberikan sebuah contoh kasus penggunaan Bootstrap untuk membangun selang kepercayaan pada peramalan data stasioner. Kata kunci: Bootstrap, resampling, peramalan

MODEL MATEMATIK UNTUK MENENTUKAN NILAI TUKAR MATA UANG RUPIAH TERHADAP DOLLAR AMERIKA Halim, Siana; Adelia, Shirley; Rahardjo, Jani
Jurnal Teknik Industri Vol 1, No 1 (1999): JUNE 1999
Publisher : Institute of Research and Community Outreach - Petra Christian University

The main objective of this paper is to estimate parameters in the heteroskedasticity models, particularly in Auto Regressive Conditional Heteroskedasticity - ARCH(1) and Generalized Autoregressive Conditional Heteroskedasticity- GARCH(1,1). These models will be used to fit, to forecast and to update the volatility of Rupiah Vs US.Dollar rate. In order to get the estimation of fitting and updating parameters of ARCH(1) and GARCH(1,1), here will be used iterative method which is derived from the standard maximum likelihood estimation and the initial values are taken from the result of Yule Walker Estimation. The updating parameters will be estimated by using the approach of ARIMA(p,d,q) updating parameters models. The heteroskedasticity models will give a good fitting even a good forecast in near stasioner condition, however this models can not detect the jump that can be happend due to the changes of political situation that happend in Indonesia. Abstract in Bahasa Indonesia : Tujuan utama dari penelitian ini adalah untuk menentukan nilai estimasi pada parameter-parameter yang terdapat pada model-model heteroskedastik, khususnya dalam Auto Regressive Conditional Heteroskedasticity - ARCH(1) dan Generalized Autoregressive Conditional Heteroskedasticity- GARCH(1,1). Model-model ini akan digunakan untuk menentukan, meramalkan dan memperbaharui nilai parameter dari nilai tukar mata uang Rupiah terhadap Dollar Amerika. Nilai estimasi pada model ARCH(1) dan GARCH(1,1) diperoleh dengan metode iteratif yang diturunkan dari estimasi maksimum likelihood baku dan nilai awalnya didapat dari pendekatan Yule Walker. Penentuan nilai parameter yang diperbaharui akan diestimasi dengan menggunakan pendekatan model ARIMA(p,d,q). Model-model heteroskedastik memberikan nilai pendekatan nilai tukar yang baik bahkan memberikan nilai peramalan yang baik pula, namun demikian model ini belum dapat mendeteksi terjadinya loncatan yang terjadi yang diakibatkan oleh perubahan situasi politik di Indonesia. Kata kunci: ARCH, GARCH, YWE, MLE, Heteroskedasticity

PERAMALAN MULTIVARIATE UNTUK MENENTUKAN HARGA EMAS GLOBAL Christian, David; Halim, Siana
Jurnal Teknik Industri Vol 18, No 2 (2016): DECEMBER 2016
Publisher : Institute of Research and Community Outreach - Petra Christian University

Gold is one of the most enticing commodities and a very popular way of investing. Gold?s price is allegedly influenced by another factors such as US Dollar, oil?s price, inflation rate, and stock exchange so that its model is not only affected by its value. The aim of this research is to determine the best forecasting model and influencing factors to gold?s price. This research is modeling gold using multivariate analysis and reviews the univariate modeling as a benchmark and comparison to the multivariate one. Univariate time series is modeled using the ARIMA model which indicates that the fluctuation of the gold prices are following the white noise. Gold?s multivariate modeling is built using the Vector Error Correction Model with oil?s price, US Dollar and Dow Jones indices, and inflation rate as its predictors. Research?s result shows that the VECM model has been able to model the gold?s price well and all factors investigated are influencing gold?s price. US Dollar and oil?s price are negatively correlated with gold?s price as the inflation rate is positively correlated. Dow Jones Index is positively correlated with gold?s price only at its first two periods.

Co-Authors Adrian Michael Wibisono Agnes Eval Aida Fitriani Tobing Amelia Agnes Sunjono, Amelia Agnes Arif Chandra Cicely Elviani David Christian, David David Soebiantoro Demmy Kasih, Demmy Dian Wulandari Felecia - Felecia . Felecia Felecia Felicia Tedja Widjaja, Felicia Tedja Genesius Chandra Hendrik Sumawi Herman Mallian Indriati Bisono Indriati N Bisono Inggrid -, Inggrid Inggrid Inggrid Jani Rahardjo Johan Widjaja Njo Anastasia Olivia Sigit, Olivia Oviliani Yenty Yuliana Pauline Astari Singgih Putri, Kristina Sanjaya Shirley Adelia Stefanie Hartanto Tan Didik Wardoyo Tanusi Tanti Octavia Valencia Suseno veronica - Yohan Wahyudi Yuliana Vina Humira

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