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All Journal Jurnal Script Prosiding SNAST
Luay Nabila El Suffa
Institut Sains & Teknologi AKPRIND Yogyakarta
Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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KOMBINASI METODE TOPSIS DAN SAW DALAM MENDUKUNG KEPUTUSAN SELEKSI KELAYAKAN PROPOSAL PENELITIAN DOSEN Renna Yanwastika Ariyana; Erna Kumalasari Nurnawati; Luay Nabila El Suffa
PROSIDING SNAST Prosiding SNAST 2018
Publisher : IST AKPRIND Yogyakarta

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The selection process of research proposals is one of the important aspects carried out by each research institution in the University. The proposal selection phase is carried out so that each proposal submitted by each lecturer has the priority results of research proposals that meet the standard research standards that have been set, and the proposals submitted are eligible to obtain research funding. The proposal selection phase is carried out so that each proposal submitted by each lecturer has the priority results of research proposals that meet the standard research standards that have been set, and the proposals submitted are eligible to obtain research funding. In this study used a combination of TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) and the SAW (Simple Additive Weighting) method in selecting research proposals submitted by lecturers. For the parameters used are the results of a review of the research proposal with the aim of looking at the suitability of the manual calculation with the method of the system being built. The results obtained by using 5 assessment criteria, it was found that the TOPSIS and SAW methods have a percentage of conformity between manual calculations with TOPSIS and SAW systems by 100%, this value was obtained from the test performed using 20 test samples by comparing manual calculations and system calculations who apply a combination of TOPSIS and SAW methods. The final stage of this study produces a prototype decision support system that can do the ranking in selecting the feasibility of the research proposal submitted by the lecturer by recommending proposals that are eligible to receive research funds based on the criteria and assessment weights set by each university.
DETEKSI TAHU AMAN KONSUMSI DENGAN CITRA DIGITAL OBJEK MENGGUNAKAN METODE K-NEAREST NEIGHBOR Isnanto Nugroho; Luay Nabila El Suffa; Erliana Dewi
PROSIDING SNAST Prosiding SNAST 2018
Publisher : IST AKPRIND Yogyakarta

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In Indonesia, tofu has two color variants, white tofu and yellow tofu. White tofu is the result of processed soybeans without coloring mixture while yellow tofu is the result of processed soybeans added with coloring, usually turmeric is used as a natural dye from yellow tofu. Nowadays a lot of cheating is done by tofu producers such as using textile dyes as a substitute for food coloring, where textile dyes have a bad impact on health if consumed continuously. Therefore, further research is needed regarding tofu coloring. This study aims to identify the safe consumption of tofu, namely tofu with natural dyes and tofu that is unsafe consumption, namely yellow tofu given synthetic dyes using textile dyes. Identification using tofu features such as contrast, energy, correlation, and homogenity to get the GLCM value, then the value is analyzed using the K-Nearest Neighbor (K-NN) method. The research begins with the stage of taking object images from know. The next step is doing image analysis. The final stage of this analysis is to draw conclusions to distinguish safe tofu consumption and know unsafe consumption. Previously conducted training of 42 tofu data to get knowledge base by using order feature feature extraction, then performed tests on 11 random data tofu images. The test results with the K-Nearest Neighbor method with k = 7 obtained an accuracy of 90.91%.
IDENTIFIKASI CITRA DAGING AYAM BERFORMALIN MENGGUNAKAN METODE GREY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN K-NEAREST NEIGHBOR (KNN) Luay Nabila El Suffa; Uning Lestari; Erma Susanti
Jurnal SCRIPT Vol. 9 No. 2 (2021): Vol 9 No. 2 Desember 2021
Publisher : Jurusan Informatika INSTITUT SAINS & TEKNOLOGI AKPRIND YOGYAKARTA

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Produksi daging ayam meningkat, hal ini menyebabkan persediaan berlebih pada pedagang. Akibatnya bila tidak dilakukan pengawetan maka pedagang akan merugi. Salah satu cara pengawetan adalah menggunakan formalin. Formalin digunakan untuk mengawetkan mayat akan tetapi apabila dicampurkan dengan daging ayam maka daging ayam tersebut akan menjadi lebih awet dari pembusukan, lebih berat dan warna lebih bagus. Kondisi ini sangat merugikan kalangan konsumen yang membeli daging ayam. Identifikasi daging dapat dilakukan secara manual dengan kasat mata maupun dengan menekan dagingnya untuk mengetahui tekstur daging. Cara ini memiliki banyak kelemahan bila para konsumen tidak jeli untuk mengidentifikasi daging ayam berformalin. Teknologi pengolahan citra digital memungkinkan untuk mengidentifikasi daging ayam berformalin secara otomatis dengan bantuan aplikasi pengolahan citra. Penelitian ini bertujuan untuk mengidentifikasi citra daging ayam segar broiler dan kampung dengan menggunakan metode K-Nearest Neighbor (KNN) sebagai classifier dan metode Grey Level Co-occurrence (GLCM) sebagai ekstraksi ciri. KNN adalah metode klasifikasi berdasarkan data yang jaraknya paling dekat dengan objek tersebut, sedangkan GLCM merupakan matriks yang mempresentasikan hubungan ketetanggaan antarpiksel dalam citra pada berbagai arah orientasi ɵ dan jarak spasial d. Pengujian menggunakan dataset 60 dan hasil pengujian didapatkan akurasi sebesar 85%.
Co-Authors Ariyana, Renna Yanwastika Erliana Dewi Erma Susanti Erna Kumalasari Nurnawati Isnanto Nugroho Uning Lestari