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TEKNOLOGI: Jurnal Ilmiah Sistem Informasi
Published by Universitas Pesantren Tinggi Darul ulum
ISSN : 20878893     EISSN : 25273671     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi published by the Department of Information Systems Unipdu Jombang. TEKNOLOGI published twice a year, in January and July, TEKNOLOGI includes research in the field of Information Technology Design and Development of Information Systems; Business intelligence; Functions and Organization Management Information Systems; and others. Editors invite lecturers researchers, reviewers, practitioners, industry, and observers to contribute to this journal. The language used in the form of Indonesian and English. TEKNOLOGI is the national scientific journals are open to seeking innovation, creativity and novelty. Either in the form of letters, research notes, Articles, supplemental Articles Articles or reviews in the field of information systems and information technology. TEKNOLOGI aims to achieve state-of-the-art in the theory and application of this field. TEKNOLOGI provide a platform for scientists and academics across Indonesia to promote, share, and discuss new issues and the development of information systems and information technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
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Search results for , issue "Vol 6, No 2 (2016): July" : 12 Documents clear
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): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v6i2.768

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.
Kakas bantu perhitungan nilai kopling menggunakan conceptual coupling metrics Aulia, Laras Husna; Pradana, Fajar; Priyambadha, Bayu
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 6, No 2 (2016): July
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v6i2.598

Abstract

ABSTRAKKualitas perangkat lunak dapat diukur dengan nilai kopling pada fase perancangan. Tingkat kopling pada perancangan mengindikasikan seberapa besar hubungan antar komponen dalam sebuah sistem. Untuk menentukan nilai kopling dapat menggunakan empat parameter metrik kopling konseptual antara lain Conceptual Similarity be-tween Methods (CSM), Conceptual Similarity between a Method and a Class (CSMC), Conceptual Similarity between two Classes (CSBC) dan Conceptual Coupling of Class (CoCC). Perhitungan nilai kopling dapat dilakukan dengan cara mengambil method-method pada klas kemudian dihitung nilai kecocokan antar dokumen kode sumber dengan menggunakan Latent Semantic Indexing (LSI). Metode LSI digunakan untuk menghitung kecocokan antar dokumen kode sumber berdasarkan kesamaan kata maupun kesamaan makna kata, kemudian nilai tersebut digunakan untuk menghitung nilai metrik kopling konseptual. Pengujian akurasi pada kakas bantu ini dilakukan dengan membandingkan dengan kakas bantu lain sebagai pembanding. Pengujian dikelompokan ke dalam 3 (tiga) nilai toleransi yang berbeda yaitu 0,05; 0,1 dan 0,20. Penggunaan nilai toleransi yang berbeda disebabkan karena terdapat selisih antara kakas bantu yang dikembangan dengan kakas  bantu pembanding. Hasil pengujian akurasi dengan nilai toleransi sebesar 0,05; didapatkan nilai akurasi sistem sebesar 24,83%. Jika nilai toleransi sebesar 0,1 maka nilai akurasi sistem sebesar 43,41%. Sedangkan, jika nilai toleransi sebesar 0,20 maka nilai akurasi yang diperoleh diatas 50% yaitu 69,78%.Kata kunci:   kopling, kualitas, Latent Semantic Indexing (LSI), metrik kopling konseptual parsing method, perangkat lunak. ABSTRACT   Coupling is one of the parameter of quality of software. Coupling is a paramater for measuring how much the relationship between components of a system. To determine the value of coupling can use four of the parameters of conceptual coupling metrics among other Conceptual Similarity between Methods (CSM), Conceptual Similarity be-tween a Method and a Class (CSMC), Conceptual Similarity between two Classes (CSBC) and Conceptual Coupling of Class (CoCC). Before counting the value of the coupling, parsing method carried out using spoon library and com-putation macth between source code documents to be done by using a method of information retrieval that is Latent Semantic Indexing (LSI). The use of LSI method because this research requires the calculation of a match between the document's source code based on common words or similarity of meaning of the word. After that, the LSI value is used to calculate the Conceptual Coupling Metric Testing accuracy of these tools is done by comparing with other systems as a comparison.. Testing is classified into 3(three) different tolerance values are 0.05, 0.1 and 0.20. The use tolerance values differ, because there is a difference between systems developed and the comparator system.  If tolerance value is 0.05 then the accuracy value is 24,83%. If tolerance value is 0.1 then the accuracy value is 43,41%. Meanwhile, If tolerance value is 0.2 then the accuracy value is up to 50%, it is 69,78%.Keywords: Conceptual coupling metrics, coupling, Latent Semantic Indexing, LSI, parsing method.

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