Pendekatan Conjoin Analysis untuk Mengukur Tingkat Preferensi Mahasiswa terhadap Layanan Perpustakaan UIN Sunan Kalijaga Yogyakarta

Salah satu bentuk komitmen UIN Yogyakarta terhadap kualitas pelayanan terhadap mahasiswa adalah dengan membangun fasilitas pendukung seperti poliklinik, perpustakaan, kantin, masjid dan Godam Kusuka (SIA). Semua sarana dan prasarana yang dibangun diharapkan dapat mendukung berlangsungnya atmosfir kegiatan akademis. Mahasiswa UIN Yogyakarta sebagai customer dari perguruan tinggi tentunya mempunyai harapan yang beragam terhadap pelayanan fasilitas pendukung. Analisis konjoin adalah salah satu analisis statistika multivariat yang dapat digunakan untuk mendapatkan kombinasi atau komposisi atribut-atribut suatu produk atau jasa baik yang baru maupun yang lama yang paling disukai konsumen sehingga dapat diketahui preferensi konsumen terhadap suatu produk atau jasa tersebut. Wawancara dan observasi terhadap mahasiswa dilakukan untuk menentukan atribut terhadap layanan perpustakaan. Faktor yang menjadi pertimbangan dalam pelayanan perpustakaan adalah Lama Peminjaman, Jumlah Literatur yang dipinjam, Support Pencarian Literatur dan Penelusuran Literatur. Berdasarkan hasil analisis konjoin diketahui bahwa responden (mahasiswa) menganggap bahwa lamanya peminjaman literatur merupakan faktor atau atribut terpenting (40,014%). Faktor penting ke dua adalah penelusuran literatur (24,469%), faktor penting ke tiga adalah support/bantuan penelusuran literatur (20,001%) dan faktor jumlah buku yang boleh dipinjam menempati urutan terakhir (keempat) dengan tingkat kepentingan 15,516%. .Kata kunci: analisis konjoin, preferensi, utility

An Empirical Comparison between Robust Estimation and Robust Optimization to Mean-Variance Portfolio

Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Recently, there are two approaches designed to reduce the effect of estimation error: robust statistics and robust optimization. Two different robust portfolios were examined by assessing the out-of-sample performance and the stability of optimal portfolio compositions. The performance of the proposed robust portfolios was compared to classical portfolios via expected return, risk, and Sharpe Ratio. The aim is to shed light on the debate concerning the importance of the estimation error and weights stability in the portfolio allocation problem, and the potential benefits coming from robust strategies in comparison to classical portfolios

Pembentukan Portofolio Optimal dengan Menggunakan Mean Absolute Deviation dan Conditional Mean Variance

Penelitian ini membahas tentang pembentukan portofolio optimal menggunakan model Mean Absolute Deviation (MAD) dan model Conditional Mean Variance (CMV). Pada model MAD risiko portofolio diukur menggunakan rata–rata deviasi standar sehingga portofolio optimal dapat diperoleh dengan menggunakan pemrograman linear. Sedangkan portofolio model CMV, rata–rata return diestimasi menggunakan model Autoregressive (AR) dan risiko (variansi) diestimasi menggunakan model GARCH. Selanjutnya kedua model portofolio diterapkan dalam membentuk portofolio optimal pada saham–saham yang terdaftar dalam Indeks Saham Syariah Indonesia (ISSI) periode 4 Juli 2016 sampai 4 Juli 2018. Kinerja kedua portofolio dianalisis menggunakan indeks Sortino. Hasilnya menunjukan bahwa kinerja portofolio model CMV lebih baik dibandingkan model portofolio MAD. [This study discusses the formation of optimal portfolios using the Mean Absolute Deviation (MAD) model and the Conditional Mean Variance (CMV) model. The MAD portfolio model measures portfolio risk by using average standard deviations so that optimal portfolios solved by using linear programming. Meanwhile the CMV portfolio model, the average return estimated by using the Autoregressive (AR) model and the risk (variance) estimated by using the GARCH model. Furthermore, both portfolio models applied in forming optimal portfolios for stocks listed in the Indonesian Syariah Stock Index (ISSI) for the period 4 July 2016 to 4 July 2018. The performance of both portfolios analyzed by using the Sortino index. The results show that the portfolio performance of the CMV model is better than MAD portfolio model.

COMPARISON OF ROBUST ESTIMATION ON MULTIPLE REGRESSION MODEL

This study aimed to compare the robustness of the OLS method with a robust regression model on data that had outliers. The methods used on the robust regression model were M-estimation, MM-estimation, and S-estimation. The step taken was to check the characteristics of the data against outliers. Furthermore, the data were modeled with and without outliers using the OLS method and the M-, MM-, and S-estimations. The results were very different between the data with and without the outlier models in the OLS method. It was reflected in the intercept and standard error variables generated from the models. Meanwhile, the regression model with the M-, MM-, and S-estimations was quite stable and able to withstand the presence of outliers. Based on the three estimations that were robust against the outliers, the MM-estimation was the best candidate because, in addition to having a stable intercept parameter estimation, it also had the smallest standard error, which was 61.9 in the resulting model

THE OPTIMAL PORTFOLIO WEIGHTS USING THE PROPORTIONAL TYPE ESTIMATORS

Error estimation in both the expected returns and the covariance matrix hamper the construction of optimal mean-variance portfolio model. In order to overcome this problem, we consider the class of proportional type estimators. The sensitivity of the proposed estimators to errors is measured by the expected loss function (the risk function). The simulation study is conducted when multivariate returns are normally distributed and serially independent. Furthermore, simulation study is complemented by an investigation of the ex post excess returns for empirical datasets, i.e., average, standard deviation, Sharpe ratio, and utility. It turns out that the unbiased proportional estimator and the maximum likelihood estimator are underperformed compared to “the dominant” estimator

Predicting Interval of Product Reliability With Bootsrap Percentile Method

In this article, two methods are proposed to give the interval estimation for reliability function from a product. Reliability function is a probability of an individual (product) surviving till time t. Some resecearchers usually use traditional method to construct interval estimation. This interval needs an assumption that sample is exponentially distributed. This research applied another method, namely Bootstrap percentile. Bootstrap method is more potential in constructing interval estimation for reliability function from a product

Fourier Series Nonparametric Regression Modeling in the Case of Rainfall in West Java Province

The Fourier series is a trigonometric polynomial that has flexibility, so it adapts effectively to the local nature of the data. This Fourier series estimator is generally used when the data used is investigated for unknown patterns and there is a tendency for seasonal patterns. This study aims to determine the results of the best Fourier series nonparametric regression model and the level of accuracy of the Fourier series nonparametric regression model on rainfall data by month in West Java Province in 2015-2019. This research is about a nonparametric regression model of Fourier series which is estimated using Ordinary Least Square method. Nonparametric regression using the Fourier series approach was applied to Rainfall data in West Java Province in 2015-2019. The independent variables used were the average air humidity, air pressure, wind speed, and air temperature. The model used to model the amount of rainfall in West Java Province is a nonparametric Fourier series. The nonparametric regression model is the best Fourier series with K =13 values obtained Generalized Cross Validation, Mean Square Error, and R2 respectively at 549.92; 462.09; and 97.30%. The results showed that the variables of air humidity and air pressure had a significant effect on rainfall

The Formation of Portfolio with Fuzzy Approach and Multi-objective Method

Forming a portfolio in the investment process is a crucial component. Itis because investors want maximum profi t while expecting a minimum levelof risk. The portfolio composition is inseparable from the weighting of eachobserved stock. In fact, mathematically, there are still problems when tryingto fulfi ll the preferences that investors want. The research objective was theformation of a portfolio using a fuzzy approach and a multi-objective method.This model simultaneously maximized the return and risk of the preparedportfolio. The result was the formation of a portfolio with two categories,namely risk-seeking and risk-averse, equipped with a λ value of each method,the weight of each stock, the expected return, and risk. Parameter λ wasthe value obtained from selecting the risk level determined by the investor.Parameter λ was used to assess the level of risk and the expected return onthe portfolio preparation. The last section compared the weights, expectedreturn, and risk values of the two methods. As a result, investors in therisk seeker category have the potential to get higher expected returns whenusing the multi-objective method. In contrast, the fuzzy approach producesthe possibility of a higher expected return for investors in the risk-aversecategory

Aplikasi Multidimensional Scalling (Studi Kasus : Analisis Segmentasi dan Peta Posisi UIN Sunan Kalijaga terhadap Perguruan Tinggi di Yogyakarta)

Semakin ketatnya persaingan di antara perguruan tinggi menuntut harus mampu menghadapi dan memenangkannya dengan menerapkan strategi-strategi pemasaran. Segmentasi dan pemetaan posisi adalah salah satu tujuan di dalam strategi pemasaran modern yang dapat membantu perusahaan sama halnya dengan universitas untuk dapat membantu mengidentifikasikan kesempatan pemasaran untuk masa depan yang lebih baik. Tujuan utama penelitian ini adalah pertama untuk mengetahui segmentasi pasar Universitas Islam Negeri Yogyakarta, kedua untuk mengetahui peta posisi persaingan Universitas Islam Negeri Sunan Kalijaga terhadap perguruan tinggi lain di Yogyakarta dan ketiga untuk mengetahui hubungan antara karakteristik demografi dan perilaku dengan berbagai kelompok/segmen perguruan tinggi yang terbentuk. Penelitian ini menggunakan tujuh universitas sebagai objek penelitian yaitu : UIN Sunan Kalijaga Yogyakarta (UIN Sunan Kalijaga) : Universitas Gajah Mada (UGM), Universitas Negeri Yogyakarta (UNY), Universitas Islam Indonesia (UII), Universitas Muhamadiyah Yogyakarta (UMY), Universitas Ahmad Dahlan (UAD) dan Universitas Pembangunan Negeri (UPN) ”Veteran” Yogyakarta. Teknik pengumpulan data adalah dengan mendistribusikan kuesioner kepada responden dengan menggunakan teknik pensampelan acak proposional. Atribut penelitian yang digunakan adalah : biaya, lokasi, pelayanan akademik, proses pembelajaran, suasana religi, sarana prasarana, reputasi, promosi dan kualitas lulusan. Metode analisis menggunakan teknik klaster, multidimensional scalling dan tabulasi silang. Hasil analisis klaster memperlihatkan bahwa bahwa UIN Sunan Kalijaga mempunyai kekuatan pada variabel biaya dan suasana religius dibandingkan dengan universitas lain. berada satu segmen/kelompok dengan UNY dan UPN. Berdasarkan analisis klaster UIN Sunan Kalijaga berada dalam satu kelompok/segmen dengan UNY dan UPN, segmen ini dinamakan segmen ekonomis. Sedangkan berdasarkan metode multidimensional scalling memperlihatkan bahwa pesaing terdekat UIN Sunan Kalijaga adalah UNY. Kata Kunci : Segmentasi, Pemetaan posisi, Analisis Klaster, Segmentasi, Pemetaan posisi, Analisis Klaster