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"Dalam disertasi ini, diusulkan suatu metoda pendekatan statistik untuk transformasi tekstur, berdasarkan penggunaan gabungan antara penguatan tingkat keabuan atau "greylevel" secara linier oleh kejadian-gandeng atau "co-occurence", dan filter spatial rata-rata dalam sebuah jendela bergerak atau "moving window" yang berukuran tertentu.Dua parameter yakni tingkat keserupaan atau " range of similarity" dan jarak spatial, dapat dipilih dalam mengverifikasikan model dari metoda ini untuk menghasilkan beberapa citra transformasi baru yang dipakai pada pengsegmentasian tekstur. Citra transformasi yang dihasilkan itu, tidak bergantung kepada orientasi pola masukan, dan unjuk-kerja separabilitas, telah dipakai sebagai kriteria gemilihan untuk klassifikasi per-titik.Untuk menguji-coba metoda ini, telah dipakai suatu "testchart" citra sederhana yang terdiri dari beberapa pola tekstur dan disusun dalam pelbagai orientasi dan hasilnya ternyata cukup baik untuk pcngklasifikasian dan mendeteksi batas antar kelas tekstur.Kemudian metoda tersebut diterapkan pula pada bebcrapa citra praktis antara lain citra-citra : Sonar, Pemotretan udara, Shuttle Imaging Radar (SIR-B), dan Satelit SPOT untuk menyelidiki parameter-parameter yang dominan dari citra yang bersangkutan.

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In this dissertation, it is proposed a method of statistical approach for texture transformation. The basic operational principle of this method is image transformation, based on the combined operations of greylevel contrast enhancement by the use of the number of co-occurence, and of calculating the mean by using a local filter operator within a certain moving window.The changes in texture can be detected by selecting an appropriate input parameters, i.e. the range of similarity and the spatial distance, those are related to the input textural patterns, and will also produce the output greylevel changes. The transformed images are not depend on the orientation of input images, and the separability performance was used as a criteria for image selection in the per-point classification.A simple typical testchart textural image was selected to test the capability of this method for the image classification and boundary detection. The experimental results indicated that it provided more than 95% of the classification accuracy, and the boundaries between each textural class can also be provided. This method have applied to some practical imageries, such as the Sonar image, the Aerial-photographic image, the SIR-B image, the SPOT-satellite image, for investigating the dominant parameters from the corresponding images."

"In detection of breast cancer using mammography, accurate diagnosis often depends on the visibility of small low contrast objects within the breast image. This is even more critical in the case of small tumor detection at early stage. Radiographic examination including mammography, have reduced the contrast and visibility of small objects. Due to the high X-ray penetration of the objects scattered radiation, and the limited capability of the film to develop maximum contrast over an extended range of exposure values. Density slicing method based on the interval setting of image histogram has been applied for the image enhancement and "quick classification". But for mammography it is also required to detect and identify the disease region clearly in order to decide a proper treatment. By using clustering method it was obtained some classes as training areas for the supervised classification and by selection of the operators of image enhancement the best boundaries of tissues had been detected. Finally by using the NGLDM (Neighboring Grey Level Dependence Matrix) method, the textural features of several diseases has also been extracted. From the experimental results that were obtained in this study, a simpler technique for accurate diagnostic has been yielded without increasing the dose of X-ray on patient or without any other psychological effects."

"Analisis Tekstur Menggunakan Metode Difraksi Neutron pada Baja Austenite Non Standar yang Diproses dengan Permesinan, Penganilan, dan Pengerolan. Baja austenitik merupakan salah satu jenis baja tahan karat yang banyak digunakan dalam industri. Banyak studi telah dilakukan pada baja tahan karat austenitik dengan menggunakan berbagai jenis peralatan dan metode untuk menentukan sifat fisika. Dalam penelitian ini telah dibuat dari mineral yang diekstraksi dari tambang di Indonesia dan dikarakterisasi dengan metode difraksi neutron. Bahan terdiri dari butiran ferro-scrap, nikel, ferro-krom, ferro-mangan, dan ferro-silikon dan ditambahkan sedikit titanium. Karakterisasi bahan dilakukan dalam tiga proses, yaitu: proses permesinan, proses anil, dan proses pengerolan. Hasil eksperimen yang diperoleh dari proses pemesinan secara umum menghasilkan tekstur dalam arah 〈100〉. Dari proses permesinan ke proses anil indeks tekstur turun dari 3,0164 ke 2,434. Kekuatan tekstur dalam proses pemesinan (sampel BA2N) adalah 8,13 mrd kemudian turun menjadi 6,99 dalam proses anil (A2DO sampel). Dalam proses anil terlihat tiga komponen tekstur yaitu, tekstur jenis kubus-pada-tepi {110}〈001〉, tekstur jenis-kubus {001}〈100〉, dan tekstur jenis-kuningan {110}〈112〉. Tekstur yang sangat kuat terutama mempunyai arah orientasi {100}〈001〉, sedangkan orientasi {011}〈100〉, lebih lemah dibandingkan dengan {100}〈001〉, dan tekstur dengan orientasi {110}〈112〉 merupakan orientasi yang lemah. Dalam proses anil terjadi pelepasan tegangan yang ditunjukkan oleh pole yang lebih acak dibandingkan dengan pelepasan tegangan pada proses pemesinan. Dalam proses pengerolan tampak tekstur jenis kuningan {110}〈112〉 menyebar dengan mengarah pada tekstur jenis goss {110}〈001〉, dan komponen tekstur jenis kuningan nyata diperkuat dibandingkan dengan keadaan tak terdeformasi (sebelum pengerolan). Selain itu, adanya komponen tambahan {110} diamati di pusat pole figure (110). Kerapatan pole dari tiga komponen meningkat dengan meningkatnya tingkat pengurangan ketebalan. Dengan meningkatkan derajat pengerolan sebesar 81-87%, nilai fungsi distribusi orientasi meningkat dengan faktor sekitar tiga kali.

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Austenitic steel is one type of stainless steel which is widely used in the industry. Many studies on austenitic stainless steel have been performed to determine the physical properties using various types of equipment and methods. In this study, the neutron diffraction method is used to characterize the materials which have been made from minerals extracted from the mines in Indonesia. The materials consist of a granular ferro-scrap, nickel, ferro-chrome, ferro-manganese, and ferro-silicon added with a little titanium. Characterization of the materials was carried out in three processes, namely: machining, annealing, and rolling. Experimental results obtained from the machining process generally produces a texture in the 〈100〉 direction. From the machining to annealing process, the texture index decreases from 3.0164 to 2.434. Texture strength in the machining process (BA2N sample) is 8.13 mrd and it then decreases to 6.99 in the annealing process (A2DO sample). In the annealing process the three-component texture appears, cube-on-edge type texture {110}〈001〉, cube-type texture {001}〈100〉, and brass-type {110}〈112〉. The texture is very strong leading to the direction of orientation {100}〈001〉, while the {011}〈100〉 is weaker than that of the {001}, and texture with orientation {110}〈112〉 is weak. In the annealing process stress release occurred, and this was shown by more randomly pole compared to stress release by the machining process. In the rolling process a brass-type texture{110}〈112〉 with a spread towards the goss-type texture {110}〈001〉 appeared, and the brass component is markedly reinforced compared to the undeformed state (before rolling). Moreover, the presence of an additional {110} component was observed at the center of the (110) pole figure. The pole density of three components increases with the increasing degree of thickness reduction. By increasing degrees of rolling from 81% to 87%, the value of orientation distribution function increases by a factor about three times."

"For many years, observations of gingival color has been a popular area of dental research. However these methods are hard to analyze for any other than the different base conditions and colors. Thus we introduced an alternative method using image analysis to measure gingival color. For the research we performed a dental examination on 30 female students.The system is set up by aligning the camera area and facial area. The subject's chin is placed in a fixed chin cup mounted 30 cm from the camera lens. Each image is acquired such that comparisons may be made with the original bite holder as well as a standard color scale. After converted to computer we used a curves dialog box for color adjustment. The curves dialog box allows adjustment of the entire tonal range of an image.The results of the analysis of the free gingiva compared to the attached gingiva are that attached gingiva was more vivid red and yellow compared to the free gingival. In conclusion, the system described herein of digital capture and comparison of color images, analysis and separation in three channels of free and attached gingival surface images and matching with colorimetric scales may be useful for demonstrating the diversity of gingival color as well as analyses of gingival health."

"ABSTRACTUmumnya untuk membedakan antara lidah perokok dan lidah bukan perokok adalah dengan melihat secara visual yang dilakukan oleh praktisi medis dan masih bersifat invasif. Dalam penelitian ini, sistem pengenalan lidah perokok dibangun dengan menggunakan teknik pencitraan hiperspektral dengan rentang spektrum panjang gelombang VNIR Visible Near Infrared berbasis kombinasi ciri spektral dan ciri tekstur. Tujuan penelitian ini adalah membangun sistem pengenalan lidah perokok berbasis kombinasi ciri spektral dan ciri tekstur untuk meningkatkan nilai akurasi pada sistem pengenalan lidah perokok yang berbasis ciri spektral saja. Ciri spektral yang digunakan adalah nilai reflektansi yang didapat langsung dari ROI Region of Interest citra lidah, sedangkan untuk ciri tekstur yang digunakan adalah nilai energi, homogenitas, korelasi, dan kontras yang didapat pada metode ekstraksi ciri GLCM Gray level Co-occurence Matrix. Kedua ciri tersebut dikombinasikan sebagai input yang digunakan pada tahapan seleksi ciri dengan metode PLS Partial Least Square, yang kemudian akan diklasifikasikan menggunakan metode SVM Support Vector Machine. Hasil klasifikasi SVM kemudian dilakukan validasi dengan menggunakan metode k-cross validation. Nilai Akurasi yang didapat dari hasil klasifikasi SVM dengan kombinasi ciri spektral dan ciri tekstur di 4 bagian lidah, lebih baik dibandingkan dengan nilai akurasi yang didapat dari hasil klasifikasi SVM dengan ciri spektral saja, dengan kenaikan akurasi sebesar 1,19 untuk lidah bagian anterior, 3,35 untuk lidah bagian posterior, 7,95 untuk lidah bagian lateral A, dan 1,02 untuk lidah bagian lateral B.

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ABSTRACTGenerally, to differentiate between smoker 39s tongue and non smoker 39s tongue is by doing an eye examination, which is invasive and performed by medical practitioners. In this research, smoker 39s tongue recognition system is built by using hyperspectral imaging technique with range of VNIR wavelength spectra, which is based on a combination of spectral features and texture features. The aim of this study is to built smoker 39s tongue recognition system based on a combination of spectral features and texture features to increase the value accuracy of smoker 39s tongue recognition system based on its spectral features only. The spectral features used are the reflectance value obtained from ROI Region of Interest from tongue images, while the texture characteristics used are the energy value, homogenity, correlation, and contrast obtained from extraction method of GLCM Gray Level Co occurence Matrix features. Both features are combined as an input used in the feature selection stage by using PLS Partial Least Square method, which then will be classified by using SVM Support Vector Machine method. After that, the SVM classification result will be validated by using k cross validation method. The value accuracy which is obtained from SVM classification result, by combining the spectral features and the texture characteristics in four regions of tongue, is better than the value accuracy from SVM classification result with spectral features only, with an accuracy increase of 1.19 for anterior region of tongue, 3.35 for posterior region of tongue, 7.95 for lateral A region of tongue, and 1,02 for lateral B region of tongue."

"Daging merupakan sumber protein hewani yang kandungan gizinya lengkap. Hal ini karena daging mengandung asam amino yang lengkap dengan perbandingan jumlah yang baik. Salah satu sumber daging yang dapat digunakan untuk memenuhi gizi masyarakat adalah itik lokal. Selama ini itik lokal hanya dimanfaatkan untuk produksi telur dan setelah afkir dagingnya kurang diminati karena alot, bertekstur kasar, dan mempunyai aroma amis. Pada penelitian ini akan dikaji karakteristik fisik Texture Profile Analysis dan organoleptik daging itik petelur afkir berbasis teknik restrukturisasi dengan penambahan enzim transglutaminase. Daging itik afkir yang memiliki kualitas rendah dapat diolah dengan cara restrukturisasi dengan penambahan enzim jenis Microbial transglutaminase MTG sebagai agen pengikat silang. Variasi kondisi pengolahan tersebut meliputi durasi inkubasi 1 dan 2 hari dan komposisi enzim 0,0 ; 0,3 ; 0,6 ; dan 1. Hasil penelitian menunjukkan penambahan konsentrasi dapat meningkatkan secara signifikan parameter profil tekstur hardness, springiness, cohesiveness dan parameter organoleptik tekstur, rasa, aroma, warna secara berurutan. Sedangkan reaksi enzimatik dibuktikan dengan uji FTIR yang menunjukkan adanya peningkatan intensitas gelombang C=O dan peningkatan nilai keasaman pH. Secara umum kadar optimum enzim MTG yang digunakan untuk restrukturisasi daging sebesar 0,6 dengan durasi inkubasi 2 hari dilihat dari semua uji yang dilakukan.

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Meat is a source of animal protein with complete nutritional content, because meat contains a good comparison numbers of amino acids. One of many sources of meats which can be used to meet the nutritional needs in the community is the local duck. All this time, local duck is only used for its eggs since after reaching maturity the meat is less desirable because of the tough, harsh texture, and its strong fishy odor. This research studied physical characteristics Texture Profile Analysis and organoleptic from meat of local laying ducks based on restructuring techniques with the addition of transglutaminase enzymes. Low quality meat from rejected ducks can be processed by restructuring with the addition of Microbial transglutaminase MTG enzyme as a crosslinking agent. Variations of treatment conditions include incubation duration 1 and 2 days and enzyme composition 0.0, 0.3, 0.6, and 1. The results showed that the addition of concentration can significantly improve the texture profile parameters hardness, springiness, cohesiveness and organoleptic parameters texture, taste, aroma, color sequentially. While the enzymatic reaction is evidenced by FTIR test which shows an increase in wave intensity C O and increase the acidity value pH . Generally, the optimum level of MTG enzyme used for meat restructuring is 0.6 with 2 day incubation duration seen from all test conducted. "

"In this paper, a method of location analysis for smart house is proposed. The proposed method uses projective transformation to process the input from visual sensor for determining coordinate of resident and also the entire device inside the smart house. With a good calculated coordinate, each device function in the smart house can be optimized for the good of the resident. From the experiment results, the proposed method successfully maps all coordinates of any device in the smart house up to 81% accuracy.Pada publikasi ini diajukan sebuah metode analisis lokasi yang digunakan pada rumah cerdas. Metode yang diajukan menggunakan transformasi proyektif terhadap masukan dari sensor visual untuk menentukan koordinat penghuni dan setiap benda yang ada pada rumah cerdas. Dengan penentuan koordinat yang baik, fungsi setiap benda dalam rumah cerdas dapat dioptimalkan untuk kebaikan penghuni. Dari uji coba yang dilakukan, metode ini berhasil memetakan koordinat benda-benda pada rumah cerdas dengan akurasi kebenaran 81%."

"ABSTRAKMicro-milling adalah pemesinan milling dalam skala mikro, dimana terdapat beberapa kesulitan dalam konversi dari skala makro ke skala mikro. Produk berskala mikro belakangan menjadi kebutuhan di bidang kesehatan, energi, manufaktur, bahkan pertahanan Pada umumnya untuk pemesinan pada micro-milling digunakan mata pahat dengan diameter kurang dari 600 μm sampai dengan 100 μm.Penelitian yang dilakukan adalah pembuatan tekstur permukaan (micro-texture) produk mikro berbasis dari citra 2D. Proses dilakukan dengan melakukan rekayasa terhadap suatu citra dimana nilai intensitas warna menjadi nilai level ketinggian. Pada perancangan tool path, dilakukan proses gouging avoidance untuk menhindari terjadinya over cut pada saat pemesinan. Nilai intensitas tersebut yang dijadikan kumpulan CL-Point yang selanjutnya akan dikonversi menjadi NC-File untuk dilakukan pemesinan.Pemesinan menggunakan benda kerja berbahan material aluminium A1100 dengan ukuran 3 x 3 x 3 mm. Dengan tingkat kekerasan 28 HRC, ini menjadi pertimbangan dalam penentuan kedalaman pemakanan (Depth of Cut) dan kecepatan pemakanan (Feed rate). Sebagai masukan data yang menjadi CL-File digunakan citra berukuran resolusi 150 x 127 piksel dan 300 x 254 piksel. Dengan metode rekayasa citra telah dapat dihasilkan 2 micro-texture part berbeda dengan menggunakan metode ini.

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abstractNowadays, micro products become more demanding in several aspects such as health, energy, manufacturing, even military. One of ways to produce micro products is by using micro-milling process. Micro-milling is machining in micro scale. In general, micro-milling uses cutting tool with diameter less than 600 μm. In some micro products, texturing of the part surface maybe needed. This research conducted the manufacture of micro-texture of micro part based on 2D image. The color intensity values of 2D image were converted or mapped into the contour of the texture of micro product in Cartesian domain. Then, toolpaths are generated based on the contour values with gouging avoidance. The CL-point of generated toolpaths were then post processed into NC-point also known as NC-file. The workpieces used for this micro-texturing are using Aluminium A1100 material size of 3 x 3 x 3 mm dimension. The hardness of this material is 28 HRC, which is for determine the dept of cut and feed rate. The toolpaths generated on different size of image resolution 150 x 127 pixels and 300 x 254 pixels. Two different workpieces were successfully produced using the above developed method."

"Watermarking merupakan teknik untuk menyisipkan informasi yang disebut watermark kedalam suatu data digital lainnya, tetapi tidak diketahui kehadirannya oleh indera manusia. Sehingga data tersebut dapat didistribusikan tanpa adanya kecurigaan terdapat tanda rahasia didalamnya. Teknik watermarking dibagi menjadi dua, yaitu teknik watermarking yang bekerja pada domain spasial (domain waktu) dan teknik watermarking yang bekerja pada domain transformasi (domain frekuensi). Watermarking yang bekerja dalam domain spasial langsung merubah nilai piksel pada citra atau gambar aslinya. Watermarking pada domain transformasi diperoleh dengan melakukan transformasi image menjadi domain frekuensi. Watermarking dalam domain transformasi seperti Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) atau Discrete Laguerre Transform (DLT) memiliki lebih banyak keuntungan dan kinerja yang lebih baik daripada teknik yang bekerja dalam domain spasial. Pada makalah ini, dilakukan analisis mengenai DLT berdasarkan ukuran matriks dan membandingkannya dengan DCT.

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Watermarking is a technique to insert information called watermark into any other digital data, which is the presence is unknown by human senses. So the data can be distributed without any suspicions there are a secret message in it. There are two techniques in watermarking, spatial domain and frequency or transformation domain. Spatial domain watermarking technique works by changing the original image or picture pixels value into the new pixels value. Frequency domain watermarking technique works by transforming the original image into frequency domain. Frequency domain watermarking techniques such as Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) or Discrete Laguerre Transform (DLT) have a better performance and advantages than spatial domain watermarking technique. This paper analysed Discrete Laguerre Transform (DLT) by its matrix size and compared it with DCT."

"World Health Organisation (WHO) has predicted 300 million peoples will suffer of diabetic in 2025. Long-term diabetics can lead to diabetic retinopathy that can cause blindness in developing countries. One of the abnormalities of diabetic retinopathy is exudate. Exudates are classified into two categories, i.e. hard and soft exudates. This paper proposes feature extraction based on texture for distinguishing hard, soft and non-exudates. The green channel of the original images is enhanced by CLAHE and followed by median filtering and thresholding in red channel to detect and remove the optic disc. The enhanced image is segmented based on clustering to obtain the region of interest of exudates. Feature extraction based on texture is conducted by using GLCM and lacunarity. Results show that classification based on NaïveBayes algorithm achieves accuracy, specificity and sensitivity of 92.13%, 96% and 87.18%, respectively."