Articles
<![CDATA[Pembentukan Portofolio Saham Menggunakan Klastering Time Series K-Medoid dengan Ukuran Jarak Dynamic Time Warping ]]>
<![CDATA[La Gubu]]>;
<![CDATA[Dedi Rosadi]]>;
<![CDATA[Abdurakhman Abdurakhman]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 13 No 2 (2021): Jurnal Aplikasi Statistika dan Komputasi Statistik ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v13i2.295
<![CDATA[Pada penelitian ini akan disajikan pembentukan portofolio saham dengan preprocessing data menggunakan klastering time series dengan ukuran jarak Dynamic Time Warping (DTW). Pertama-tama saham-saham dikelompokkan ke dalam beberapa klaster menggunakan klastering time series Partitioning Around Medoids (PAM) berdasarkan ukuran jarak DTW. Setelah proses klastering, saham dipilih untuk mewakili masing-masing klaster untuk membangun portofolio optimum. Saham yang dipilih dari masing-masing klaster merupakan saham yang memiliki Sharpe ratio tertinggi. Portofolio optimal ditentukan dengan menggunakan tiga model portofolio, yaitu: model portofolio MV klasik, model portofolio MV robust FMCD dan model portofolio robust S. Dengan menggunakan prosedur ini, dapat diperoleh portofolio optimum secara efisien bila ada banyak saham yang terlibat dalam proses pembentukan portofolio. Untuk mengukur kinerja portofolio yang terbentuk digunakan Sharpe ratio. Hasil kajian empiris menunjukkan bahwa kinerja portofolio yang dihasilkan dengan menggunakan klastering time series PAM dengan ukuran disimilaritas jarak DWT yang dikombinasikan dengan model portofolio MV klasik mengungguli kinerja portofolio yang dihasilkan kombinasi dengan model yang lain.]]>
<![CDATA[Prediksi Harga Emas Dunia di Masa Pandemi Covid-19 Menggunakan Model ARIMA ]]>
<![CDATA[Dara Puspita Anggraeni]]>;
<![CDATA[Dedi Rosadi]]>;
<![CDATA[Hermansah Hermansah]]>;
<![CDATA[Ahmad Ashril Rizal]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 1 (2020): Journal of Statistical Application and Computational Statistics ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v12i1.264
<![CDATA[Penelitian ini bertujuan memodelkan serta memprediksi harga emas dunia di masa pandemi COVID-19. Penelitian ini juga hanya memasukkan nilai masa lampau dari harga emas dunia tanpa adanya pengaruh faktor eksogen(independen) pada model. Model yang dipergunakan adalah model Autoregressive Integrated Moving Average (ARIMA). Adapun data yang dipergunakan pada permodelan sebanyak 240 data observasi dimana data merupakan data bulanan harga emas dunia bulan Agustus 2000 hingga Juli 2020. Model terbaik untuk harga emas dunia ini adalah ARIMA(0,1,1) dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 3,70%. Hasil prediksi harga emas dunia untuk bulan Agustus 2020 hingga Januari 2021 berturut-turut adalah sebesar 1930,046; 1945,651; 1961,381; 1977,240; 1993,227; 2009,343 US$/Troy Ons emas. Prediksi ini menunjukkan tren naik dengan rata-rata peningkatan selama periode tersebut (Agustus 2020-Januari 2021) sebesar15,8594 US$/Troy ons per bulannya.]]>
<![CDATA[Pengoptimuman Portofolio dengan Kendala Karakteristik Perusahaan Emiten ]]>
<![CDATA[Ezra Putranda Setiawan]]>;
<![CDATA[Dedi Rosadi]]>
<![CDATA[ Jurnal Teknik Industri Vol. 19 No. 2 (2017): Desember 2017 ]]>
Publisher : <![CDATA[Institute of Research and Community Outreach - Petra Christian University ]]>
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DOI: 10.9744/jti.19.2.93-102
<![CDATA[Diversifikasi saham merupakan salah satu cara yang dilakukan investor untuk memperkecil risiko investasi. Pada umumnya, diversifikasi mengacu pada hasil perhitungan matematis berdasarkan nilai pengembalian (return) masing-masing saham, misalnya pada model mean-variance. Dalam penelitian ini dibahas modifikasi model mean-variance yakni dengan menambahkan kriteria yang berkaitan dengan perusahaan emiten, misalnya bidang usaha, status perusahaan, dan sebagainya. Sebagai contoh, investor dapat menentukan bahwa portofolio yang dibentuk hanya melibatkan sejumlah saham dari sektor pertambangan, sejumlah saham dari sektor perdagangan, dan sebagainya. Penyelesaian masalah pengoptimuman portofolio ini ditentukan dengan metode algoritma genetika. Studi kasus dilakukan pada sejumlah saham yang diperdagangkan di Bursa Efek Indonesia.]]>
<![CDATA[FRAMEWORK PENGEMBANGAN CITY BRANDING KABUPATEN BANTUL MENGGUNAKAN PENDEKATAN SMART TOURISM ]]>
<![CDATA[Sri Redjeki]]>;
<![CDATA[Edi Faizal]]>;
<![CDATA[Edi Iskandar]]>;
<![CDATA[Dedi Rosadi]]>;
<![CDATA[Khabib Mustofa]]>
<![CDATA[ Jurnal TAM (Technology Acceptance Model) Vol 9, No 2 (2018): Jurnal TAM (Technology Acceptance Model) ]]>
Publisher : <![CDATA[LPPM STMIK Pringsewu ]]>
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<![CDATA[Perkembangan sektor pariwisata secara terarah dan berkesinambungan dapat dijadikan sebagai salah satu solusi meningkatkan pertumbuhan ekonomi suatu daerah. Dengan berkembangnya sektor pariwisata, dapat meningkatkan citra sebuah daerah yang sekaligus dapat meningkatkan pendapatan asli daerah. Pengelolaan pariwisata yang baik oleh sebuah kota dapat menjadi sebuah branding yang dapat meningkatkan kunjungan wisatawan. Pencapaian ini dapat terpenuhi dengan cepat melalui penggunaan teknologi informasi dalam pengelolaan wisata. Kabupaten Bantul dikenal sebagai salah satu Kabupaten di Yogyakarta karena obyek wisata yang memikat para wisatawan dan saat ini sedang mengembangkan konsep smart city.Penelitian ini bertujuan untuk mengembangkan model dalam implementasi salah satu komponen smart city yaitu smart branding dengan menggunakan pendekatan smart tourism di Kabupaten Bantul. Model ini dapat dikembangkan karena wilayah Kabupaten Bantul merupakan salah satu tujuan wisata utama di Yogyakarta dengan berbagai jenis wisata yang ada. Total obyek wisata di Kabupaten Bantul sebanyak 113 obyek wisata. Smart tourism yang dimodelkan pada penelitian ini adalah sistem wisata integratif yang meliputi sistem berbasis mobile, sistem pemetaan wisata, sistem desa wisata dan sistem pengolahan data. Sistem ini dapat digunakan oleh pelaku dunia wisata, pengunjung wisata dan pihak pengambil keputusan di Kabupaten Bantul Dengan model pendekatan smart tourism maka Kabupaten Bantul dapat melakukan percepatan pengembangan smart city melalui salah satu komponen yaitu pengembangan city branding.]]>
<![CDATA[CREDIT SPREADS PADA REDUCED-FORM MODEL ]]>
<![CDATA[Di Asih I Maruddani]]>;
<![CDATA[Dedi Rosadi]]>;
<![CDATA[Gunardi Gunardi]]>;
<![CDATA[Abdurakhman Abdurakhman]]>
<![CDATA[ MEDIA STATISTIKA Vol 4, No 1 (2011): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.4.1.57-63
<![CDATA[There are two primary types of models in the literature that attempt to describe default processes for debt obligations and other defaultable financial instruments, usually referred to as structural and reduced-form (or intensity) models. Structural models use the evolution of firms’ structural variables, such as asset and debt values, to determine the time of default. Reduced form models do not consider the relation between default and firm value in an explicit manner. Reduced form models assume that the modeler has the same information set as the market - incomplete knowledge of the firm’s condition. that leads to an inaccessible default time. The key distinction between structural and reduced form models is not whether the default time is predictable or inaccessible, but whether the information set is observed by the market or not. Consequently, for pricing and hedging, reduced form models are the preferred methodology. Credit spreads are used to measure credit premium, which compensates risk-averse investors for assuming credit risk. Therefore, the credit spreads should remain positive. The higher credit risk assumed by the investors, the higher credit premium got be payed by them. In this paper, we have to to determine the credit spreads of reduced-form model. Keywords: Reduced-Form Model, Hazard Rate, Credit Spreads ]]>
<![CDATA[FORECASTING COVID-19 IN INDONESIA WITH VARIOUS TIME SERIES MODELS ]]>
<![CDATA[Gumgum Darmawan]]>;
<![CDATA[Dedi Rosadi]]>;
<![CDATA[Budi Nurani Ruchjana]]>;
<![CDATA[Resa Septiani Pontoh]]>;
<![CDATA[Asrirawan Asrirawan]]>;
<![CDATA[Wirawan Setialaksana]]>
<![CDATA[ MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.15.1.83-93
<![CDATA[In this study, Covid-19 modeling in Indonesia is carried out using a time series model. The time series model used is the time series model for discrete data. These models consist of Feedforward Neural Network (FFNN), Error, Trend, and Seasonal (ETS), Singular Spectrum Analysis (SSA), Fuzzy Time Series (FTS), Generalized Autoregression Moving Average (GARMA), and Bayesian Time Series. Based on the results of forecast accuracy calculation using MAPE (Mean Absolute Percentage Error) as model evaluation for confirmed data, the most accurate case models is the bayesian model of 0.04%, while all recovered cases yield MAPE 0.05%, except for FTS = 0.06%. For data for death cases SSA and Bayesian Models, the best with MAPE is 0.07%.]]>
<![CDATA[Disaster Mitigation Efforts Using K-Medoids Algorithm and Bayesian Network ]]>
<![CDATA[Devni Prima Sari]]>;
<![CDATA[Media Rosha]]>;
<![CDATA[Dedi Rosadi]]>
<![CDATA[ EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 23 No. 03 (2022): Eksakta: Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464) ]]>
Publisher : <![CDATA[Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia ]]>
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DOI: 10.24036/eksakta/vol23-iss03/304
<![CDATA[Disaster mitigation is a series of efforts to reduce disaster risk. One of the disaster mitigation efforts is the supervision of the implementation of spatial planning. Knowing the level of damage to buildings in a region in the event of a disaster can supervise the implementation of spatial planning. To predict the level of damage to buildings in an area, we can use the Bayesian network (BN). There are several types of BN based on the variable type; discrete, continuous, and hybrid BN. A discrete BN is a model in which all the variables involved are discrete. Therefore, if there is a continuous variable, it is necessary to discretize the variable. In this paper, modifications are made to the algorithm commonly used in the clustering process to be used in the discretization process. The algorithm used is the K-Medoids algorithm, where this algorithm uses existing data as a representative of the cluster center. Then, the BN model and the K-Medoids algorithm were used to determine the level of damage to buildings due to the earthquake that occurred in West Sumatra in 2009. From 25,000 house damage data used in this study, we obtain an accuracy rate is 95.17%.]]>
<![CDATA[CAN ZAKAT AND PURIFICATION BE EMPLOYED IN PORTFOLIO MODELLING? ]]>
<![CDATA[Retno Subekti]]>;
<![CDATA[Abdurakhman Abdurakhman]]>;
<![CDATA[Dedi Rosadi]]>
<![CDATA[ Journal of Islamic Monetary Economics and Finance Vol 8 (2022): Special Issue: Islamic Social Finance ]]>
Publisher : <![CDATA[Bank Indonesia ]]>
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DOI: 10.21098/jimf.v8i0.1418
<![CDATA[The Capital Asset Pricing Model (CAPM), which has interest rates in its specification, can be deemed non-Shariah compliant. Therefore, the sukuk rate has been proposed to replace these rates in CAPM. This study analyses portfolio modelling by involving two essential elements in Islamic principles, namely zakat and purification. The concept of purification has been applied in the Shariah stock selection process in Indonesia, while at the same time, zakat has been widely socialised in stock investment. The study highlights two models that consider the concept of zakat reduction and the purification factors for portfolios in the Indonesian stock market. According to the robustness tests conducted, the proposed Shariah-compliant asset pricing model developed in this study is valid. Zakat reduction and purification factor integration in mathematical models can be applied in portfolio modelling.]]>
<![CDATA[MEASUREMENT OF SUPPORT VECTOR REGRESSION PERFORMANCE WITH CLUSTER ANALYSIS FOR STOCK PRICE MODELING ]]>
<![CDATA[Izza Dinikal Arsy]]>;
<![CDATA[Dedi Rosadi]]>
<![CDATA[ MEDIA STATISTIKA Vol 15, No 2 (2022): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.15.2.163-174
<![CDATA[Risk-averse investors will seek out stock investments with the minimum risk. One step that can be taken is to develop a model of stock prices and predict their fluctuations in the coming months. Significant studies on the modeling of stock movements have used the ARCH/GARCH method, but this method requires some assumptions. This paper will discuss the performance of stock modeling using Support Vector Regression. The performance is measured using the root mean square error value in two stock clusters based on its volatility value, e.g., stocks with large volatility and stocks with small volatility. This case study makes use of daily closing price data from 10 LQ-45 index shares from October 12, 2018 to October 11, 2019. In conclusion, SVR's performance on stocks with high volatility produces RMSE, which is considerably higher than SVR's performance on stocks with low volatility.]]>
<![CDATA[Improving the term weighting log entropy of latent dirichlet allocation ]]>
<![CDATA[Muhammad Muhajir]]>;
<![CDATA[Dedi Rosadi]]>;
<![CDATA[Danardono Danardono]]>
<![CDATA[ Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijeecs.v34.i1.pp455-462
<![CDATA[The process of analyzing textual data involves the utilization of topic modeling techniques to uncover latent subjects within documents. The presence of numerous short texts in the Indonesian language poses additional challenges in the field of topic modeling. This study presents a substantial enhancement to the term weighting log entropy (TWLE) approach within the latent dirichlet allocation (LDA) framework, specifically tailored for topic modeling of Indonesian short texts. This work places significant emphasis on the utilization of LDA for word weighting. The research endeavor aimed to enhance the coherence and interpretability of an Indonesian topic model through the integration of local and global weights. Local Weight focuses on the distinct characteristics of each document, whereas global weight examines the broader perspective of the entire corpus of documents. The objective was to enhance the effectiveness of LDA themes by this amalgamation. The TWLE model of LDA was found to be more informative and effective than the TF-IDF LDA when compared with short Indonesian text. This work improves topic modeling in brief Indonesian compositions. Transfer learning for NLP and Indonesian language adaptation helps improve subject analysis knowledge and precision, this could boost NLP and topic modeling in Indonesian.]]>
