<p><em>This research is based on previous research’s result that there was a image changing of Surakarta City from Batik’s City toward City of traditional Food. There are four objectives of this research, those are; (1) identify Surakarta traditional food’s as functional foods; (2) Mapping the growth of Surakarta’s traditional foods at least five periode; (3) mapping the potential of Surakarta’s traditional foods as a culture heritage’s foods, functional food dan popular foods. (4) mapping the economic potential of Surakarta’s tradisional foods. </em><em></em></p><p><em>The objective of this research will be achieved toward analysis primary and secondary datas. Primary datas are obtained by survey method with 200 respondents and analyzed by using BiPlot, Pearson Correlation and Descriptive method. Sencondary datas are obtained from BPS, and Disperindag, and analyzed by using linier regression dan Pearson Correlation.</em></p><p><em>This researh results that the eight of Surakarta’s traditional foods are culture inheritage’s foods, functional foods, favorite foods and have economic potential to rise t Surakarta’s economy growth. Therefore if the Surakarta’s region government should employ three stages to develope these traditional foods as their food city branding. There are clustering analysis, SAP analysis and developing trust (social capital) toward their food city branding.</em></p>

<em><span lang="EN-US">This study aims to map the position of public mutual fund products and investment managers based on the results of perceptual mapping. The samples used in this study were 5 public mutual funds and 9 investment managers in each type of public mutual fund based on the investment portfolio selected using the purposive sampling method. The method of analysis used descriptive statistical analysis and multidimensional scaling. The results of the data processing of the mutual fund product attributes are processed in Microsoft Excel which further passed through the statistical test stage of multidimensional scaling ALSCAL (Alternative Least Square Scaling) using the SPSS application. The result of the multidimensional scaling test show that the perceptual mapping is divided into 4 quadrants based on the determinant attributes of public mutual fund products. It found that 6 mutual fund products divided by Risk and Costs determinant attributes, then 6 other mutual fund products found divided by  NAB/UP and return, then 4 other mutual fund products found are not divided by determinant attributes and the rest 4 other mutual fund products divided by other determinant attribute in this research.</span></em>

Pengembangan pengetahuan terapan bidang teknologi yang memajukan industri di masyarakat merupakan rencana induk penelitian Perguruan Tinggi yang salah satu bidang fokusnya adalah pengembangan teknologi informasi. Bidang Teknologi informasi dibutuhkan hampir disemua bidang industri, diantaranya adalah industri pangan dengan salah satu komoditinya yaitu beras. Bahwa Beras merupakan salah satu komoditi unggulan pertanian di Indonesia yang memiliki berbagai level kualitas dan jenis. Banyaknya jenis dan level kualitas beras memerlukan database dan identifikasi yang tepat serta konsisten dalam mengklasifikasikannya. Penentuan level kualitas dan jenis beras dapat dilakukan dengan menggunakan data visual yaitu citra beras. Setiap jenis beras memiliki ciri fisik yang relatif berbeda, begitu juga setiap level kualitas beras memliki ciri fisik yang berbeda. Melalui ciri fisik pada citra yaitu bentuk dan warna akan ditemukan level kualitas dan jenis beras.Ciri atau fitur fisik beras yang tersimpan pada citra sangat dipengaruhi oleh kondisi pada saat pengambilan citra beras seperti pencahayaan, posisi pengambilan dan jarak kamera dengan obyek yang selanjutnya akan berpengaruh terhadap hasil identifikasi. Hal ini memerlukan metode pengolahan citra untuk meningkatkan akurasi deteksi agar identifikasi kualitas dan jenis beras memiliki tingkat validasi yang tinggi.Pada penelitian ini dikembangkan model identifikasi level kualitas dan jenis beras melalui pengenalan citra beras  menggunakan fitur bentuk dan warna. Pengolahan citra meliputi preprocessing, ekstraksi dan klustering.  Hasil penelitian menunjukkan bahwa dengan menggunakan kombinasi fitur bentuk dan warna identifikasi kualitas dan jenis beras memiliki  akurasi mencapai lebih dari 80%.