Hasil Pencarian  ::  Simpan CSV :: Kembali

"Masalah identifikasi hewan merupakan masalah yang penting untuk melakukan fungsi registrasi dan traceability demi keamanan dan ketahanan produk hewani. Penggunaan penanda buatan untuk hewan merupakan langkah paling mungkin untuk saat ini walaupun disisi lain telah dibuktikan bahwa memiliki banyak keterbatasan. Penggunaan Biometrik hewan memberikan solusi terhadap batasan-batasn tersebut. Investigasi telah dilakukan beberapa peneliti terhadap metode identifikasi berbasis biometrik hewan walaupun masih ada kekurangan-kekurangan.Biometrik hewan yang digunakan adalah citra pola moncong dan wajah. Biometrik pola moncong dan wajah telah didapatkan dari Sapi Bali dan Sapi Peranakan Ongole. Pemilihan biometrik ini didasarkan pada peluang penelitian dan pengambilan data yang terbilang murah. Pendekatan SIFT pada data pola moncong pada kertas menghasilkan performa yang secara signifikan lebih baik dibandingkan dua penelitian sebelumnya dan metode perbaikan pencocokan yang diusulkan telah dapat meningkatkan performa dari hasil identifikasi menggunakan original SIFT. Pendekatan SURF dengan pengukuran kecocokan yang diusulkan pada data foto pola monconng menghasilkan hasil yang stabil dan lebih baik dari pada pendekatan sebelumnya. Penggunaan metode penggabungan dua sumber ciri biometrik dengan memperhatikan nilai derajat kepercayaan dan nilai voting meningkatkan performa dari sistem biometrik bersumber tunggal. Berdasarkan hasil eksperimen yang telah didapatkan, penggunaan biometrik hewan sebagai alat identifikasi otomatis sangat menjanjikan walaupun masih banyak hal yang harus diteliti dan dikembangkan.

---

The cattle identification problem is an important issue in a cattle registration and traceability in order to fulfill the security and resilience cattle's products. The usage of artificial marker was a feasible mean for identification although it had a several limitations. The animal biometric gives the solution of this limitations. Several investigations related with issues of identification based on biometric have been done although still have problems.The biometrics used in this research are muzzle pattern and face photo. The muzzle and face biometric of cattle have been collected from Balinese Cow and Hybrid Ongole Cow. The choice of muzzle and face biometric is because of the research opportunities and a relatively cheap way in the data gathering. The SIFT approach in the muzzle pattern lifted on paper data has shown a significantly better performance than the two previous research and the proposed matching refinement has increased the performance of the identification result. The SURF approach using proposed similarity measurement in the muzzle pattern photo has provided a stable result and also a better result than the previous approach. The proposed biometric fusion using the degree of confidence and voting value has increased the performance of the unimodal biometric system. Based on the experiment results, the automatic identification based on animal biomatrics has a promising result although there are many things that have to be investigated and developed next."

"Latar belakang: Pencetakan pasien GTP dengan sendok cetak biometrik dapat memposisikan bibir dan pipi ke posisi sebelum ekstraksi, dibuat berdasarkan pedoman biometrik paling stabil yaitu buccolingual breadth (BLB). Namun, ukuran BLB masih berdasarkan ukuran ras Kaukasoid. Belum ditemukan penelitian mengenai ukuran BLB pada ras Deutero Melayid dan perbedaannya antara pria dan wanita. Tujuan: Mengetahui ukuran BLB dan menganalisis perbedaannya antara pria dan wanita ras Deutero Melayid. Metode: Cross sectional pada 68 mahasiswa FKG UI. Rahang atas subjek dicetak untuk mendapakan model studi. Setiap model studi dibuatkan garis referensi HIP plane, dipotong menjadi soliter die, kemudian diukur ketebalannya di regio gigi insisif pertama, kaninus, premolar pertama dan kedua, serta molar pertama dan kedua di kuadran kiri dan kanan. Hasil: Hasil uji Mann-whitney menunjukkan terdapat perbedaan bermakna ukuran BLB antara pria dan wanita tiap gigi-geligi (p < 0,05). Namun tidak berbeda bermakna ukuran BLB kudran kiri dan kanan (p > 0,05) dan pada regio gigi Premolar kedua kuadran kiri dan Premolar pertama kuadran kanan (p > 0,05). Kesimpulan: Ukuran BLB pada ras Deutero Melayid adalah regio gigi insisif pertama 6.44 mm, kaninus 7.69 mm, premolar pertama 10.6 mm, premolar kedua 10.97 mm, molar pertama 13.04 mm, dan molar kedua 12.14 mm. Ukuran BLB pada pria lebih lebar dibandingkan pada wanita.

---

Background: Impression of edentulous mouth with biometric tray can place the lip and cheeks in pre-extraction positions, made based on the most constant biometric guidance that is buccolingual breadth (BLB). It was conducted in Caucasian but there has not been found in Deutero Malay and its difference between male and female.

Objective: To determine the measurement of the BLB and to analyze its difference between male and female in Deutero Malay.

Methods: Cross sectional study was performed towards 68 students. Maxillary jaw was taken the impression to get study model. Study model was drawn​​ a HIP plane, and cut to be a solitary die in the central incisivum, canine, first and second premolar and molar on each regions. Measurements were performed by measuring the thickness in each of tooth.

Result: Mann-whitney test showed there was a significancy difference each teeth between male and female (p<0,5) but there was no significancy between left and right maxillary jaw (p>0,05) and also in left second and right first Premolar (p > 0,05).

Conclusion: BLB measurement in Deutero Malay are 6.44 mm in central incisivum, 7.69 mm in canine, 10.6 mm in first premolar, 10.97 mm in second premolar, 13.04 in first molar and 12.14 mm in second molar. Furthermore, measurement of BLB in male is widther than female."

"Isolasi daerah iris yang tepat dan kecepatan waktu proses yang cepat sangat dibutuhkan pada proses segmentasi dari suatu sistem. Terlebih lagi apabila dihadapkan dengan pangkalan data yang besar. Biometrik iris merupakan salah satu tipe biometrik dengan tingkat akurasi yang tinggi tetapi banyak pemakaian memori. Segmentasi merupakan proses paling awal dari suatu sistem biometrik iris yang akan sangat menentukan kinerja dari suatu sistem. Umumnya, algoritma segmentasi yang banyak digunakan adalah Daugman Integro Differential Operator (IDO). Algoritma ini mempunyai akurasi yang cukup baik tetapi mempunyai kekurangan penurunan akurasi pada masalah pencahayaan suatu citra dan waktu proses yang cukup lama karena banyak menggunakan iterasi. Dalam tesis ini, disimulasikan algoritma Region of Interest (ROI) untuk mempersingkat waktu proses tanpa mengurangi kinerja dari akurasi segmentasi. Algoritma yang disimulasikan melakukan tahapan blurring terhadap citra yang diolah kemudian melakukan pembagian daerah pencarian untuk menetapkan daerah kasar dari posisi iris dalam. Setelah melakukan optimasi posisi iris dalam maka akan dicari titik perpotongan untuk mencari jari-jari dan pusat dari iris dalam. Langkah terakhir adalah mencari jari-jari iris luar dengan acuan iris dalam dengan menggunakan operasi dasar statistik. Hasil simulasi menunjukkan ROI telah berhasil mempersingkat waktu proses segmentasi dan meningkatkan akurasi dibandingkan IDO dengan waktu proses rata-rata sebesar 0,343 detik, EER sebesar 4,12% dan akurasi segmentasi sebesar 1,826%.

---

The time process and accurate isolation of iris region are really needed in segmentation process of a system, in particular if the system are faced with huge databases. Iris Biometric is one of biometric types that has high accuracy eventhough it uses a lot of memory. Segmentation is the first process of this biometric system that will determine the performance of the system. Generally, the segmentation algorithm uses Daugman Integro Differential Operator (IDO). The algorithm has good accuracy but in some cases due to illumination problem it may create circular patches allowing to the algorithm detect a false region and processing in long period of time because too many iteration.

In this thesis, an algorithm based on Region of Interest is simulated in order to shorten the time process without sacrificing the accuracy. The simulated algorithm use blurring phase and splitting the search region to determine the rough position of inner iris region. Following the optimization of the inner iris position, the intersection point will be searched to determine the radius and the center of the inner iris. The last step is finding the outer iris boundary relative to the inner iris by using the statistical operation. The simulation result shows that ROI algorithm has succeeded to shorten the time process and increase the accuracy compared with IDO with 0.343 s for average time process, 4.12% for EER and 1.826% for segmentation accuracy."

"Perlakuan etis terhadap hewan sudah menjadi salah satu pertimbangan di dalam penerapan etika lingkungan. Kemunculan Animal Rights sebagai advokasi yang berangkat dari perlakuan etis bersifat serius, manambahkan perlindungan hukum sebagai jalan keluar. Skripsi ini bertujuan memaparkan penerapan yang dilakukan Animal Rights, serta konsekuensinya melalui analisis konsep tentang perlakuan etis yang telah mengalami pergeseran makna. Pengembalian kemanusiaan dan peran manusia sebagai moral agent menjadi titik tolak dari penentangan perlakuan etis yang berlebihan melalui Animal Rights.

---

Ethical treatment of animals has become one of the considerations in the application of environmental ethics. Emerging of Animal Rights as advocating that departs from the ethical treatment of a serious nature, adding legal protection as a way out. This thesis aims to describe the implementation carried Animal Rights, and its consequences through the analysis of the concept of ethical treatment that has undergone a shift in meaning. Returns humanity and the role of humans as moral agents became the starting point of opposition to the ethical treatment of excessive through Animal Rights."

"Skripsi ini berisi tentang pengidentifikasian biometrik melalui pola pembuluh darah telapak tangan dengan menggunakan metode Hidden Markov Model (HMM), dengan membandingkan keseluruhan sistem terhadap perubahan ukuran codebook dan jumlah iterasi. Metode HMM secara garis besar terdiri dari dua tahapan proses, yakni proses training database, dan proses identifikasi. Pada sistem pengidentifikasian ini, gambar pembuluh darah telapak tangan yang digunakan adalah gambar dari database CASIA-MS-PalmprintV1 yang dikumpulkan oleh Chinese Academy of Sciences Institute of Automation (CASIA). Gambar tersebut terlebih dahulu diolah dengan menentukan ROI. ROI yang sudah didapatkan kemudian diekstraksi dengan melakukan penambahan kontras, pengubahan gambar ke biner dan melakukan thinning terhadap garis-garis yang ada pada gambar sehingga pola pembuluh darah terlihat jelas.

---

This thesis contains a biometric identification through palm vein patterns using Hidden Markov Models (HMM), by comparing the overall system to changes in the size of the codebook and the number of iterations. HMM method mainly consists of two stages of the process, first one is database training process, and the identification process.

This identification system is using palm vein images from Casia-MS-PalmprintV1 database that collected by the Chinese Academy of Sciences Institute of Automation (Casia). First, images are processed by determining the ROI. ROI then extracted by adding contrast, convert to binary image and do the thinning of the lines in the image so that the pattern of vein clearly visible."

"[Dewasa ini, teknologi berkembang dengan sangat pesat, salah satu contoh teknologi yang sedang marak beberapa tahun belakangan ini adalah 3D face recognition. Teknologi ini menggabungkan data biometrik berupa wajah orang yang diambil dari beberapa sudut (horizontal dan vertikal) dan jaringan saraf tiruan. Untuk memperbaiki tingkat rekognisi yang rendah pada saat menggunakan data crisp, maka digunakanlah metode fuzzy. Percobaan akan dilakukan sebanyak tiga kali karena terdapat tiga cluster yang masing-masing cluster terdiri dari beberapa set orang. Pertama-tama, data akan diolah secara bertahap pada fase fuzzification dimulai dari parameter ekspresi, orang, dan sudut. Tahapan selanjutnya adalah membuat referensi pada fase fuzzy manifold untuk kemudian digunakan pada fase fuzzy nearest distance. Pada fase fuzzy nearest distance akan dicari jarak terpendek dari data testing dengan referensi yang sudah ada. Hasil keluaran dari sistem ini adalah kombinasi sudut horizontal dan vertikal dari tiap-tiap cluster yang nantinya akan dimasukkan kedalam Jaringan Saraf Tiruan (JST) dengan lapis tersembunyi berstruktur hemisfer untuk mendapatkan tingkat rekognisi. Secara keseluruhan rata-rata tingkat rekognisi setiap cluster sudah bisa mencapai 80%. Hal ini menunjukkan sistem sudah cukup optimal dalam mengenali pola wajah yang ada. ;The development of technology is growing rapidly, one of the examples of the technology that is emerging in recent years is 3D face recognition. This technology combines biometric data in form of faces which are taken from several angles (combination of horizontal and vertical angles) and artificial neural network. In order to improve the low recognition rate from crisp data, fuzzy method is used. The experiment will be performed three times because there are three cluster which are consist of several set of person. Firstly, the data will be processed step by step in fuzzification phase starting from the level of expression continued with the level of face and lastly is the level of person. With the use fuzzification, the crisp data can be converted into fuzzy. The next step is to make references in fuzzy manifold phase in order to be used in fuzzy nearest distance phase. In fuzzy nearest distance phase, the shortest distance between the testing data the references will be processed in artificial neural network with hemispheric structured hidden layer. Generally, the average of the all recognition rate can reach up to 80% which means that the system can recognize the face pattern quite good. ;The development of technology is growing rapidly, one of the examples of the technology that is emerging in recent years is 3D face recognition. This technology combines biometric data in form of faces which are taken from several angles (combination of horizontal and vertical angles) and artificial neural network. In order to improve the low recognition rate from crisp data, fuzzy method is used. The experiment will be performed three times because there are three cluster which are consist of several set of person. Firstly, the data will be processed step by step in fuzzification phase starting from the level of expression continued with the level of face and lastly is the level of person. With the use fuzzification, the crisp data can be converted into fuzzy. The next step is to make references in fuzzy manifold phase in order to be used in fuzzy nearest distance phase. In fuzzy nearest distance phase, the shortest distance between the testing data the references will be processed in artificial neural network with hemispheric structured hidden layer. Generally, the average of the all recognition rate can reach up to 80% which means that the system can recognize the face pattern quite good. , The development of technology is growing rapidly, one of the examples of the technology that is emerging in recent years is 3D face recognition. This technology combines biometric data in form of faces which are taken from several angles (combination of horizontal and vertical angles) and artificial neural network. In order to improve the low recognition rate from crisp data, fuzzy method is used. The experiment will be performed three times because there are three cluster which are consist of several set of person. Firstly, the data will be processed step by step in fuzzification phase starting from the level of expression continued with the level of face and lastly is the level of person. With the use fuzzification, the crisp data can be converted into fuzzy. The next step is to make references in fuzzy manifold phase in order to be used in fuzzy nearest distance phase. In fuzzy nearest distance phase, the shortest distance between the testing data the references will be processed in artificial neural network with hemispheric structured hidden layer. Generally, the average of the all recognition rate can reach up to 80% which means that the system can recognize the face pattern quite good. ]"