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Tohari Ahmad
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Computer Science & IT Dentistry Education Library & Information Science

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ANALISIS DAN IMPLEMENTASI SISTEM UNTUK MENGKLASIFIKASIKAN CITRA KOROSI MENGGUNAKAN ANALISIS TEKSTUR Tohari Ahmad; Rully Soelaiman; Esther Hanaja
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 3, No 1 Januari 2004
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.092 KB) | DOI: 10.12962/j24068535.v3i1.a126

Abstract

Korosi bisa terjadi pada berbagai material dengan tipe yang berbeda. Dua tipe utama dari korosi adalah lobang (pit formation) dan pecahan (cracking). Terdapat beberapa metode yang dapat digunakan untuk mengklasifikan kedua tipe tersebut, diantaranya adalah dengan menggunakan analisis tekstur. Dengan menggunakan metode di atas, suatu citra akan mengalami pemrosesan awal sebelum diklasifikasikan. Pemrosesan awal tersebut meliputi ekstraksi fiturmenggunakan dekomposisi wavelet, dan perhitungan energi. Dari proses tersebut didapatkan suatu nilai yang selanjutnya digunakan untuk proses pelatihan (training) terhadap system jaringan syaraf. Sampai dengan tingkat pelatihan tertentu, sistem akan mendapatkan suatu yang stabil. Nilai-nilai tersebut digunakan untuk mengklasifikasikan citra korosi yang ada. Uji coba dilakukan terhadap beberapa citra korosi yang mempunyai karakteristik berbeda, jumlah pelatihan yang berbeda, menggunakan beberapa variasi dari jaringan syaraf LVQ (learning vector quantization). Kaca kunci : analisis tekstur, citra korosi, wavelet
DATA REFINEMENT APPROACH FOR ANSWERING WHY-NOT PROBLEM OVER K-MOST PROMISING PRODUCT (K-MPP) QUERIES Vynska Amalia Permadi; Tohari Ahmad; Bagus Jati Santoso
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 16, No. 2, Juli 2018
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v16i2.a754

Abstract

K-Most Promising (K-MPP) product is a strategy for selecting a product that used in the process of determining the most demanded products by consumers. The basic computations used to perform K-MPP are two types of skyline queries: dynamic skyline and reverse skyline. K-MPP selection is done on the application layer, which is the last layer of the OSI model. One of the application layer functions is providing services according to the user's preferences.In the K-MPP implementation, there exists the situation in which the manufacturer may be less satisfied with the query results generated by the database search process (why-not question), so they want to know why the database gives query results that do not match their expectations. For example, manufacturers want to know why a particular data point (unexpected data) appears in the query result set, and why the expected product does not appear as a query result. The next problem is that traditional database systems will not be able to provide data analysis and solution to answer why-not questions preferred by users.To improve the usability of the database system, this study is aiming to answer why-not K-MPP and providing data refinement solutions by considering user feedback, so users can also find out why the result set does not meet their expectations. Moreover, it may help users to understand the result by performing analysis information and data refinement suggestion.
Co-Authors Bagus Jati Santoso Esther Hanaja Rully Soelaiman Vynska Amalia Permadi