Devi, Putri Aisyiyah Rakhma
Universitas Pesantren Tinggi Darul Ulum (Unipdu) Jombang

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Apakah kombinasi power lbp dan fourier descriptor dapat digunakan untuk klasifikasi citra kerang? Devi, Putri Aisyiyah Rakhma; Suciati, Nanik; Khotimah, Wijayanti Nurul
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 6, No 2 (2016): Juli-Desember (dalam proses: 2/8)
Publisher : Universitas Pesantren Tinggi Darul Ulum (Unipdu) Jombang

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Abstract

ABSTRAKPermasalahan pengklasifikasian secara manual biasanya terletak pada hasil akurasi dan waktu klasifikasi. Pengklasifikasi citra kerang pada umumnya dilakukan berdasarkan pada karakteristik bentuk dan tekstur cangkang kerang. Pengembangan perangkat lunak untuk pengklasifikasian secara otomatis diharapkan dapat meningkatkan hasil akurasi dan memperbaiki waktu klasifikasi. Pada penelitian ini bertujuan untuk mengkombinasikan fitur tekstur berbasis metode Power LBP dan fitur bentuk berbasis metode fourier descriptor yang digunakan untuk klasifikasi citra kerang.Citra input yang digunakan, sebelumnya telah melalui praproses dan  segmentasi untuk memisahkan objek dengan background. Citra objek yang sudah terpisah ditransformasi menjadi citra biner dan citra grayscale untuk proses ekstraksi fitur. Hasil dari kedua fitur yang sudah diperoleh akan dilakukan kombinasi dengan mempertimbangkan bobot masing-masing fitur yang kemudian dilakukan normalisasi. Dengan mengkombinasikan fitur tekstur dan fitur bentuk diharapkan memperoleh fitur yang signifikan yang dapat meningkatkan akurasi sebuah klasifikasi.Uji coba dilakukan pada 3 jenis dataset kerang yakni kerang darah, kerang pasir dan kerang bulu dengan menggunakan SVM cross validation dengan k=2 . Hasil uji coba menunjukkan bahwa ada keterkaitan antara mengkombinasikan fitur tekstur dan fitur bentuk pada permasalahan klasifikasi citra kerang dapat diperbaiki dengan hasil akurasi klasifikasi yang diperoleh sebesar 99,39% dengan fitur tekstur lebih dominan daripada fitur yang lainnya. Kata Kunci: citra kerang, ekstraksi fitur, fourier descriptor, klasifikasi, power LBP. ABSTRACTShells image classification are generally conducted based on the characteristics of the shape and texture of the shells. The problems of classification usually occur results of accuracy and timing classification. The software development for classification is expected to increase the yield of accuracy result and optimize the time of classification. In this study, we combine extracting texture features based Power LBP method and extracting shape features based Fourier Descriptor method for shells image classification.   The used input images had been conducted preprocessing  and segmentation to separate object and background using Otsu methods. The objects images that had been separated are transformed into a binary image and grayscale image for feature extraction process. Texture features are extracted using Power LBP (PLBP) method and grayscale image as input. Shape features are extracted using Fourier Descriptor (FD) method and binary image as input. The results of these two features will be combined by considering the weight of each feature and then normalized. Combines texture features and shape features, we expect to obtain significant features that can improve the accuracy of classification.Tests was performed on three types of shells dataset that is blood clams, mussels and scallops feather sand by using SVM cross validation with k = 2 fold. The results show that there is a link between features combine texture and shape features on the image classification problems that can be solved with the results obtained classification accuracy of 99.39% with a texture feature more dominant than the other features. Keywords: classification, feature extraction, Fourier Descriptor , Power LBP, Shellfish image.
Return on Invesment (ROI) of Software Product: A Systematic Literature Review Bintana, Rizqa Raaiqa; Devi, Putri Aisyiyah Rakhma; Yuhana, Umi Laili
ULTIMA InfoSys Vol 6 No 1 (2015): UltimaInfoSys :Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

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Abstract

The quality of the software can be measured by its return on investment. Factors which may affect the return on investment (ROI) is the tangible factors (such as the cost) dan intangible factors (such as the impact of software to the users or stakeholder). The factor of the software itself are assessed through reviewing, testing, process audit, and performance of software. This paper discusses the consideration of return on investment (ROI) assessment criteria derived from the software and its users. These criteria indicate that the approach may support a rational consideration of all relevant criteria when evaluating software, and shows examples of actual return on investment models. Conducted an analysis of the assessment criteria that affect the return on investment if these criteria have a disproportionate effort that resulted in a return on investment of a software decreased. Index Terms - Assessment criteria, Quality assurance, Return on Investment, Software product