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<![CDATA[Pendekatan Conjoint Analysis untuk Mengukur Tingkat Preferensi Mahasiswa terhadap Layanan Sistem Informasi Akademik di UIN Yogyakarta ]]>
<![CDATA[Supandi, Epha Diana]]>
<![CDATA[ Jurnal Fourier Vol 1, No 1 (2012) ]]>
Publisher : <![CDATA[UIN Sunan Kalijaga Yogyakarta ]]>
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<![CDATA[Information Technology (IT) represent one of main indicator to support the academic atmosphere at the university. Therefore UIN Sunan Kalijaga (Suka) Yogyakarta has owned sistem information technology and it is called Academic Information System (SIA). UIN Suka shall has knowledge preference and perception of consumer to the service, which is like what required by consumer. By using Conjoint Analysis method would have been obtained combination from level-level factor (stimuly) took a fancy by consumer according to value of highest utility from every level factor. The objective of this research is to measure preference level of consumers (students) to the SIA services in UIN Suka used Conjoint Analysis method. The result shows that the most important factor in using SIA service is the benefit (importance value is 66,623%, the second important factor is accesibility of SIA ( importance value is 19,227%) and the last important is ability of staff (importance value is 14,15%). According to value of utility estimate, it shows that consumers like to use SIA for key in courses (utility estimate is 2,104), online service (utility estimate is 0,577) and SIA staff who are very friendly when they were servicing the students (utility estimate is 0,210).]]>
<![CDATA[Optimisasi Robust Melalui Second Order Cone Programming dengan Aplikasi pada Penentuan Portofolio Optimal ]]>
<![CDATA[Supandi, Epha Diana]]>;
<![CDATA[Rosadi, Dedi]]>;
<![CDATA[Abdurakhman, Abdurakhman]]>
<![CDATA[ Jurnal Matematika dan Sains Vol 19 No 3 (2014) ]]>
Publisher : <![CDATA[Institut Teknologi Bandung ]]>
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<![CDATA[ Pada makalah ini, kami meneliti mengenai optimisasi robust (robust optimization), metode ini berguna untuk menangani masalah optimisasi dimana data permasalahan tidak diketahui dengan pasti tetapi diasumsikan berada dalam suatu himpunan ketidakpastian (uncertainty set). Selanjutnya Second Order Cone Programming (SOCP) digunakan untuk menyelesaikan masalah optimisasi robust. SOCP adalah masalah pemrograman konveks dimana fungsi tujuannya berbentuk linear dengan kendala second order cone. Penerapan SOCP pada pembentukan masalah portofolio mean variance berhasil dilakukan. Berdasarkan studi kasus, portofolio robust melalui SOCP lebih unggul dibandingkan portofolio klasik ditinjau dari capital gain. Kata Kunci : Optimisasi robust, Second order cone programming, Portofolio mean-variance. Robust Optimization Through Second Order Cone Programming with Applications on the Establishment of Optimal Portfolio Abstract In this paper, we studied about robust optimization, this method is useful for dealing with optimization problems where data are not known certainly but assumed belong to uncertainty set. Furthermore, Second Order Cone Programming (SOCP) is used to solve the robust optimization problems. SOCP is a convex programming problem where the objective function in the form of linear with constraints in the form of second order cone. Application of SOCP in the formation of mean variance portfolio problem successfully conducted. Based on case studies, robust portfolios through SOCP are superior compared to classical portfolios in terms of capital gain. Keywords: Robust optimization, Second order cone programming, Mean variance portfolio. ]]>
<![CDATA[Pembentukan Portofolio Optimal dengan Menggunakan Mean Absolute Deviation dan Conditional Mean Variance ]]>
<![CDATA[Vanti, Eka Nur]]>;
<![CDATA[Supandi, Epha Diana]]>
<![CDATA[ Jurnal Fourier Vol 9 No 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta ]]>
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DOI: 10.14421/fourier.2020.91.25-34
<![CDATA[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.]]]>
<![CDATA[STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS ON THE RELATIONSHIP BETWEEN RENUMERATION AND MOTIVATION ON EMPLOYEE PERFORMANCE AT UIN SUNAN KALIJAGA YOGYAKARTA ]]>
<![CDATA[Supandi, Epha Diana]]>
<![CDATA[ MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.13.2.136-148
<![CDATA[Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze the structural relationships between observed variables and latent constructs. SEM has several methods one of which is Generalized Structured Component Analysis (GSCA). An empirical application concerning the relationship between renumeration and work motivation on employee performance is presented to illustrate the usefulness of the GSCA method. Data were collected by a questionnaire distributed to lecturers and staffs at UIN Sunan Kalijaga Yogyakarta. The result showed that the remuneration variable had a significant and positive impact on work motivation. Also, the work motivation variable had a significant and positive effect on employee performance.]]>
<![CDATA[Pendekatan Conjoint Analysis untuk Mengukur Tingkat Preferensi Mahasiswa terhadap Layanan Sistem Informasi Akademik di UIN Yogyakarta ]]>
<![CDATA[Epha Diana Supandi]]>
<![CDATA[ Jurnal Fourier Vol. 1 No. 1 (2012) ]]>
Publisher : <![CDATA[Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta ]]>
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DOI: 10.14421/fourier.2012.11.1-9
<![CDATA[Information Technology (IT) represent one of main indicator to support the academic atmosphere at the university. Therefore UIN Sunan Kalijaga (Suka) Yogyakarta has owned sistem information technology and it is called Academic Information System (SIA). UIN Suka shall has knowledge preference and perception of consumer to the service, which is like what required by consumer. By using Conjoint Analysis method would have been obtained combination from level-level factor (stimuly) took a fancy by consumer according to value of highest utility from every level factor. The objective of this research is to measure preference level of consumers (students) to the SIA services in UIN Suka used Conjoint Analysis method. The result shows that the most important factor in using SIA service is the benefit (importance value is 66,623%, the second important factor is accesibility of SIA ( importance value is 19,227%) and the last important is ability of staff (importance value is 14,15%). According to value of utility estimate, it shows that consumers like to use SIA for key in courses (utility estimate is 2,104), online service (utility estimate is 0,577) and SIA staff who are very friendly when they were servicing the students (utility estimate is 0,210).]]>
<![CDATA[Pengklasteran Kabupaten/Kota di Jawa Tengah berdasarkan Tenaga Kesehatan dengan Menggunakan Metode Ward dan K-Means ]]>
<![CDATA[Sri Puji Lestari]]>;
<![CDATA[Epha Diana Supandi]]>;
<![CDATA[Pipit Pratiwi Rahayu]]>
<![CDATA[ Jurnal Fourier Vol. 7 No. 2 (2018) ]]>
Publisher : <![CDATA[Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta ]]>
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DOI: 10.14421/fourier.2018.72.103-109
<![CDATA[Analisis klaster merupakan suatu metode yang digunakan untuk mengelompokkan objek (kasus) ke dalam klaster (kelompok) yang relatif sama. Tujuan penelitian ini untuk mengklasterkan Kabupaten/Kota di Provinsi Jawa Tengah berdasarkan tenaga kesehatan tahun 2015 seperti tenaga medis, tenaga keperawatan, tenaga kebidanan, tenaga kefarmasian dan tenaga kesehatan lainnya dengan menggunakan metode Ward dan K-Means. Hasil penelitian menunjukkan ada tiga klaster terbentuk dimana metode Ward menghasilkan nilai rasio simpangan baku sebesar 0,3019% lebih besar jika dibandingkan dengan nilai rasio simpangan baku pada metode K-Means yaitu 0,2974%. Pada kasus ini, metode K-Means merupakan metode yang lebih baik dibandingkan metode Ward. [Cluster analysis is a method used to group objects (cases) into clusters (groups) that are relatively the same. The purpose of this study is to classify districts/cities in Central Java Province based on health worker in 2015 such as medical personnel, nursing staff, midwifery staff, pharmacy personnel and health workers using the Ward and K-Means methods. The results show that there are three clusters formed where the Ward method produce a standard deviation ratio of 0.3019% greater than the standard deviation ratio in the K-Means method, which is 0.2974%. In this case, the K-Means method is a better method than the Ward method.]]]>
<![CDATA[Pembentukan Portofolio Optimal dengan Menggunakan Mean Absolute Deviation dan Conditional Mean Variance ]]>
<![CDATA[Eka Nur Vanti]]>;
<![CDATA[Epha Diana Supandi]]>
<![CDATA[ Jurnal Fourier Vol. 9 No. 1 (2020) ]]>
Publisher : <![CDATA[Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta ]]>
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DOI: 10.14421/fourier.2020.91.25-34
<![CDATA[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.]]]>
<![CDATA[Predicting Interval of Product Reliability With Bootsrap Percentile Method ]]>
<![CDATA[Akhmad Fauzy]]>;
<![CDATA[Epha Diana Supandi]]>
<![CDATA[ Jurnal ILMU DASAR Vol 11 No 2 (2010) ]]>
Publisher : <![CDATA[Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember ]]>
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<![CDATA[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.]]>
<![CDATA[Membangun Interval Kepercayaan Proporsi Dengan Menggunakan Metode Jackknife Terhapus-1 ]]>
<![CDATA[Wahyu Suryadi Suryadi]]>;
<![CDATA[Epha Diana Supandi]]>
<![CDATA[ STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019) ]]>
Publisher : <![CDATA[Program Studi Statistika Unisba ]]>
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DOI: 10.29313/jstat.v19i1.4721
<![CDATA[Metode Jackknife merupakan teknik resampling nonparametric. Prinsip dari metode Jackknife yaitu membangkitkan data dari sampel asli untuk mendapatkan sampel tiruan. Tujuan penelitian ini adalah mengestimasi prosentase suara pada pemilihan umum Kepala Daerah Kabupaten Bantul tahun 2015 dengan menggunakan sampel asli dan metode Jackknife sampel terhapus-1. Keakuratan kedua metode dihitung dengan menggunakan standar error. Hasil penelitian menunjukan bahwa selang kepercayaan menggunakan metode Jackknife sampel terhapus-1 memiliki tingkat keakuratan yang lebih baik dibandingan dengan menggunakan sampel asli, hal ini ditunjukan dengan nilai standar error yang lebih kecil.]]>
<![CDATA[Penerapan Estimasi Fast-MCD dan SOCP dalam Pembentukkan Portofolio Robust Mean Variance ]]>
<![CDATA[Epha Diana Supandi]]>;
<![CDATA[Dedi Rosadi]]>;
<![CDATA[Abdurakhman Abdurakhman]]>
<![CDATA[ STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 14, No 1 (2014) ]]>
Publisher : <![CDATA[Program Studi Statistika Unisba ]]>
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DOI: 10.29313/jstat.v14i1.1086
<![CDATA[Portofolio model Mean Variance (MV) menitikberatkan pada penggunaan vektor rata-rata danmatriks kovarian dalam pembentukkan portofolio optimal. pembentukkan portofolio menggunakanmodel MV menjadi optimal, karena Σ????dan ????̂ adalah Maximum Likelihood Estimator bagi Σ dan μ. Padakenyataanya data keuangan sering menyimpang dari kenormalan, sehingga pembentukkan portofoliorobust menjadi sangat penting. Pada penelitian ini akan membandingkan portofolio mean variancemelalui pendekatan Fast-MCD dan SOCP (second order cone programming). Hasil studi kasus padasaham yang terdaftar di Jakarta Islamics Index menunjukkan portofolio dengan pendekatanoptimisasi robust (SOCP) lebih unggul dibandingkan portofolio model MV maupun Fast MCD.]]>
