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All Journal Jurnal Sains dan Informatika INOVTEK Polbeng - Seri Informatika Elsains : Jurnal Elektro Jurnal ULTIMATICS ScientiCO : Computer Science and Informatics Journal Jurnal Sistem Informasi dan Sistem Komputer Telcomatics Journal of Applied Computer Science and Technology (JACOST) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Borneo Informatika dan Teknik Komputer (JBIT) Engineering: Journal of Mechatronics and Education
Yunita Prastyaningsih
Politeknik Negeri Tanah Laut
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Other

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Journal : ScientiCO : Computer Science and Informatics Journal

 1 
IDENTIFIKASI JENIS BIJI KOPI MENGGUNAKAN EKSTRAKSI FITUR TEKSTUR BERBASIS CONTENT BASED IMAGE RETRIEVAL Prastyaningsih, Yunita; Noor, Agustian; Supriyanto, Arif
ScientiCO : Computer Science and Informatics Journal Vol 3, No 2 (2020): Scientico : November
Publisher : Fakultas Teknik, Universitas Tadulako

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Every type of coffee have a different shape, texture, calor based on roasting system used and the taste is quite significant. It makes coffee beans have different price. However, not all of coffee shop owners and coffee farmer can identify the type of coffee beans by only looking these coffee beans. So, it can lead something wrong to identify the type of coffee beans If the coffee shop owners don't have enough knowlegde about  coffee beans. Identifying the type of coffee with the naked eye is difficult to distinguish so that special expertise is needed, one of the methods can be used to identify the type of coffee is digital image processing such as CBIR (content based image retrieval) which aims to identify the characteristic or feature of the object, this method used to do feature identification process, one of them is the texture feature that are owned by severay types of coffee. Extraction of texture feature is used by using GLCM ( gray level co-occurrence matrix), the dataset used is an image of coffee beans taken from 3 types, namely, robusta coffee beans, Arabica coffee beans and liberica coffee beans. The amount dataset used is 165 images. The CBIR system is able to identify the types of coffee beans by precision value 55.20%.
ANALISIS EVOLUSI EKOSISTEM PERANGKAT LUNAK OPEN SOURCE : TINJAUAN PUSTAKA SISTEMATIS Angreni, Dwi Shinta; Prastyaningsih, Yunita
ScientiCO : Computer Science and Informatics Journal Vol 2, No 1 (2019): Scientico : April
Publisher : Fakultas Teknik, Universitas Tadulako

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The development of an Open Source Software (OSS) can influence the development of other Open Source Systems. The relationship between OSS is often called an ecosystem, there are several aspects to the OSS ecosystem that can affect ecosystem evolution in the software. This study reports a systematic literature review on the influence of several aspects of the OSS ecosystem on the evolution of OSS. The Sistematic Literature Review method based on Kitchenham was used to analyze 1099 articles published in leading journals and conferences. The Results showed that Social aspects have a significant impact on ecosystem evolution, where communication between communities in an OSS ecosystem influences aspects of contributions and dependencies that encourage an ecosystem to develop and evolve.
IMPLEMENTASI METODE RABIN KARP PADA APLIKASI PENGECEKAN KEMIRIPAN JUDUL TUGAS AKHIR (Studi Kasus: Jurusan Teknik Informatika Politala) Novia, Risda; Prastyaningsih, Yunita; Rhomadhona, Herfia
ScientiCO : Computer Science and Informatics Journal Vol 4, No 1 (2021): Scientico : April
Publisher : Fakultas Teknik, Universitas Tadulako

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Abstract

Checking the similarity of Final Project (TA) titles can be done by building a similarity detection application and implementing methods to determine the similarity of these TA titles. The method used is the Rabin Karp method, which is a word search method that searches for a pattern in the form of a substring using the hash. The data used in this study are student data and data on the 2016-2018 academic year of the Informatics Engineering Department at Tanah Laut State Polytechnic. The results of the similarity experiment can be divided into 3 (three) parts, namely not similar, somewhat similar, and very similar. It is said to be not similar if the percentage of the equation is 0% -20% as happened in k-gram 6 to k-gram 10. While it is somewhat similar if the percentage is between 21%-55% as seen in k-gram 3, k- gram 4, and k-gram 5. For very similar categories based on the percentage above 56% seen in k-gram 1 and k-gram 2. So it can be concluded that the more or greater the value of k-grams, the smaller the percentage results obtained, on the contrary, if the smaller the value of k-grams, the higher the percentage value.
Co-Authors ahmad habibi Arif Suprianto Arif Supriyanto Aristi, Nina Mia Ariyati Lestari, Ariyati Lestari Budiarti, Sefhia Febriana Dwi Shinta Angreni Fathurrahmani Fathurrahmani Fathurrahmani, Fathurrahmani Hayati, Milda Herfia Rhomadhona herpendi, herpendi Husnah, Rafikatul Khairul Anwar Hafizd, Khairul Anwar Khuzairin, Rusma M. Najamudin Ridha Noor, Agustian Norfifah Novia, Risda Nurbayti, Annisa Nurhamisah, Rahma Nurmalia, Isma Sabella, Billy Santoso Sayyidati, Rabini Veri Julianto Wan Yuliyanti Wan Yuliyanti Wardani, Rizal Wiwik Kusrini Yunita Yunita