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"Tesis ini akan membahas karakterisasi absorbansi darah pada rentang 190 sampai dengan 1100 nm per 10 nm pada pasien demam dengue dengan menggunakan spektrofotometer UV-Vis. Data numerik yang diperoleh kemudian dilakukan pengenalan pola karakteristiknya menggunakan kecerdasan buatan. Hasil yang diperoleh menggambarkan karakteristik yang berbeda antara rentang 190 s/d 380 dan 610 s/d 1100 nm dengan 400 s/d 600 nm. Data numerik absorbansi 400 s/d 600 nm diproses dengan metoda self organizing maps menunjukan kestabilan hasil walaupun tingkat pengenalannya masih rendah.

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This thesis is describing characterization of blood absorbance in range of 190 through 1100 nm per 10 nm of dengue fever patient using UV-Vis spectrophotometer. Collected numerical data is processed by pattern recognition using artificial intelligence. Result shown that characteristics between 190-380 nm and 610?1100 nm differ from 400 nm?600 nm. 400 - 600 nm absorbance numerical data processed using self organizing maps showing output of recognition stability, even degree of recognition was still low."

"Pada kesempatan ini akan dievaluasi metode partisi fuzzy dalam menyelesaikan suatu permasalahan nonlinier dalam hal ini permasalahan klasifikasi. Metode ini mempartisi input space ke dalam bin-bin dan membuat aturan fuzzy dari tiap bin. Keseluruhan aturan fuzzy yang dihasilkan membentuk suatu sistem berbasis aturan fuzzy (sistem fuzzy) yang memodelkan sistem nonlinier dari permasalahan yang diberikan. Metode partisi fuzzy diperkenalkan dan digunakan untuk prediksi unjuk kerja pada masalah kredit industri finansial oleh Yinghua Lin. Pada makalah ini metode partisi fuzzy digunakan untuk menyelesaikan masalah klasifikasi dengan data input dan data output 'Wine Recognition Data'. Metode ini memberikan hasil yang cukup baik terutama ketika dilakukan penambahan kemungkinan lokasi pemartisian, dari maksimum pengenalan 91,67% (tiga lokasi pemartisian) menjadi maksimum 94,44% (lima lokasi pemartisan). Dalam percobaan ini juga diterapkan preprocessing PCA yang mentransformasikan data input ke dalam ruang eigen. Peningkatan yang diperoleh cukup tinggi hingga mencapai tingkat pengenalan 97,22%. Kata kuci: paritisi fuzzy, sistem fuzzy, klasifikasi, PCA."

"Penelitian ini dilakukan untuk menguji kelayakan penerapan teknik pencitraan hiperspektral di wilayah variabel 400-1000 nm untuk mengetahui kandungan formalin. Sistem pencitraan hiperspektral terdiri dari kamera hiperspektral, slider, motor slider, dua sumber lampu halogen dan komputer yang digunakan untuk proses akuisisi data dan pengolahan data. Citra hiperspektral merupakan sebuah hypercube yang berisi informasi spasial dan spektral. ROI digunakan untuk memilih area sampel yang homogen. Data ROI diekstraksi dengan merata-ratakan data spasialnya, sehingga hanya diperoleh data spektral. Metode principle component analysis PCA digunakan untuk mereduksi dimensi data data spektral dan memilih fitur yang akan digunakan sebagai masukan dalam klasifikasi. Linear discriminant analysis LDA digunakan sebagai model untuk mengklasifikasikan kelas yang berbeda, yaitu formalin dan nonformalin. Model PCA regresi digunakan untuk menguji akurasi nilai prediksi terhadap nilai pengujian laboratorium. Dari hasil percobaan pada pengamatan hari pertama, kedua dan ketiga menunjukkan keefektifan model LDA dalam memisahkan sampel tahu berformalin dan tahu tanpa formalin dengan akurasi diatas 86,81 , 93,06 , dan 100 . Serta dari hasi regresi linier pada pengamatan hari pertama, kedua dan ketiga diperoleh koefisien korelasi R2 sebesar 0,98, 0,99 dan 0,99 serta nilai RMSE sebesar 1,83, 1,40 dan 1,27. Hasil ini menunjukkan bahwa pencitraan hiperspektral merupakan pendekatan yang menjanjikan untuk memprediksi kandungan formalin yang dengan cepat dan akurat.

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This study was carried out to examine the feasibility of applying hiperspektral imaging technique in the spectral region 400 1000 nm for classification formaldehyde tofu. The system hardware of hiperspektral imaging consists of hiperspektral camera with spectral region 400 1000 nm, workbench, motor slider, two halogen lamp source and personal computer used for the data acquisition process and data processing. Hyperspectral image is a hypercube that contains of spatial and spectral information. ROI is used to select a homogeneous sample area. ROI data is extracted by averaging its spatial data, so that only spectral data are obtained. The principle component analysis PCA method is used to reduce dimensions of the data and select the features to be used as input in the classification. The linear discriminant analysis LDA is used as a model to classify to distinct classes, that is formaldehyde tofu and without formaldehyde tofu. PCA Regression model is used to test the accuracy prediction values against the value of laboratory testing. Result from the experiment on the first, second and third day observations showed the effectiveness of the LDA model in separating the informal sample of formalin and tofu without formalin with an accuracy above 86.81 , 93.06 , and 100 . As well as from the results of linear regression on first, second and third observations obtained correlation coefficient R2 of 0.98, 0.99 and 0.99 and RMSE of 1.83, 1.40 and 1.27. These results suggest that hyperspectral imaging is a promising approach to predicting rapidly and accurately formaldehyde content."

"Latar belakang: Nyeri pascabedah ortopedi ekstremitas bawah masih menjadi masalah yang berkaitan dengan risiko pascabedah dan lama perawatan di rumah sakit. PCA intravena morfin dan oxycodone masih belum dikaji lebih jauh sebagai analgesia pascabedah ortopedi ekstremitas bawah.Metode: Penelitian ini merupakan uji klinik acak tersamar ganda untuk menilai efektivitas PCA intravena morfin dengan oxycodone untuk analgesia pascabedah ortopedi ekstremitas bawah. Subjek penelitian berjumlah 50 orang yang didapatkan dengan consecutive sampling selama Januari-April 2019. Pasien dibagi menjadi 2 kelompok, dirandomisasi menjadi kelompok morfin dan kelompok oxycodone. Efektivitas dinilai dengan banyaknya konsumsi opioid dalam 24 jam pascabedah dan efek samping antara 2 kelompok. Penilaian derajat nyeri diam dan bergerak pada jam ke-0, 6, 12, dan 24 dengan menggunakan Visual Analogue Score (VAS) dan kepuasan pasien pada penggunaan PCA juga dinilai untuk komponen penilaian tambahan. Hasil dianalisis dengan SPSS.Hasil: Seluruh subjek penelitian menyelesaikan penelitian dan tidak didapatkan perbedaan karakteristik yang signifikan antara 2 kelompok. Banyaknya konsumsi opioid dalam 24 jam pertama pascabedah antara 2 kelompok (p 0,574) dan kejadian efek samping antara 2 kelompok tidak berbeda. Derajat nyeri istirahat dan bergerak juga tidak didapatkan hasil yang berbeda bermakna (p 0,109 ; 0,163). Kepuasan pasien pada penggunaan PCA juga tidak berbeda bermakna, namun secara umum pasien puas dengan penggunaan PCA, dan kepuasan pasien pada PCA oxycodone (76%) lebih banyak dibanding PCA morfin (52%)Simpulan: PCA intravena oxycodone tidak lebih efektif dibandingkan PCA intravena morfin untuk analgesia pascabedah ortopedi ekstremitas bawah pada penelitian ini. Pasien yang setuju dengan penggunaan PCA sebanyak 30 subjek, tidak ada perbedaan signifikan antara 2 kelompok.

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Background: Postoperative pain after lower extremity orthopedic surgery may increase morbidity after surgery and prolong the length of hospitalization. The study investigating effectiveness intravenous PCA morphine and oxycodone has not been extensively studied for managing pain after lower extremity orthopedic surgery.

Methods: This study is a double-blind randomized study clinical trial to evaluate effectiveness intravenous PCA morphine and oxycodone for post-operative analgesia after lower extremity orthopedic surgery. Total of 50 subjects were enrolled with consecutive sampling within January-April 2019. Subjects were randomly allocated into 2 groups, received intravenous PCA morphine or intravenous PCA oxycodone. Post-operative opioid consumption in 24 hours and side effects were considered the primary efficacy variable. Pain scores were measured using Visual Analogue Score (VAS) at time 0, 6, 12, and 24 after surgery. Patient satisfaction in both groups was also evaluated. Data was analyzed statistically using SPSS.

Results: All the subjects done this study. There were no differences in the characteristics of both groups. Opioid consumption between two groups no significantly different (p 0,574) and incidence of side effects between two groups were similar. Pain scores during rest and move also no significant differences (p 0,109 ; 0,163). Patient satisfaction no significant difference, but almost patient satisfied with using PCA, while group oxycodone (76%) higher than group morphine (52%).

Conclusion: Intravenous PCA oxycodone had no more effective than intravenous PCA morphine for post-operative analgesia after lower extremity orthopedic surgery in this study. Patient satisfaction was higher in group oxycodone than in group morphine."

"Penelitian sebelumnya menunjukkan Multiwavelength UV-Vis spektrofotometer pada darah terinfeksi virus membuat perubahan optik dari darah dan mempunyai karakter pola-pola tertentu serta dapat dikenali dengan jaringan sataf tiruan Backpropagation dan algoritma PCA. Penyakit infeksi endemik di Indonesia salah satunya disebabkan oleh virus dengue. Penelitian ini, dilakukan pengukuran absorbansi optik darah Demam Dengue (DD), non DD dan orang sehat dengan spektrofotometer UV-Vis 190-1100 nm. Rentang 400-600 nm dengan 21 data input memperlihatkan pola-pola sangat berbeda dl."baDdingkan 190-400 dan 400-1100 nm. Kemudian spektmm absorbansi darah dianalisa menggunakan BP dengan hidden layer 20 nilai k:eberhasilan mengenali pola DD, non DD dan orang sebat mencapii 27%, l'edangkan PCA + BP 20 dan 10 dimensi dengan hidden layer 25 nilai keberhasilannya mencapai 60"10.

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Previous studies showed that multiwavelength uv-vis spectrophotometer in blood virus infection can make changes in optical properties and bas character with certain patterns. These patterns are recognized by artificial neural network Backpropagation and algorithm PCA. One of endemic iofectious disease in Indonesia is caused by dengue viral iofection. In this studies, measurement of the optic absorbance blood from DF, non DF and healthy person by spectrophotometer UV-Vis in 190-1100 mn. Range 400-600 nm with 21 datas input show patterns very differ between DF, non DF and health person compared 190-400 and 400-llOOnm. Then blood absorbance spectrum pattem analyzed using BP with layer hidden 20 efficacy value recognize pattern DF, non DF and healthy people reach 27%, while PCA + BP with 20 and 10 dimension having layer hidden 25 efficacy value reaching 60".4."

"[ABSTRAKFPGA merupakan piranti yang bersifat dapat dikonfigurasi-ulang (reconfigurable). Dengan mengambil keuntungan dari paralel hardware, eksekusi FPGA dapat lebih cepat dari pemrosesan DSP(Digital Signal Processor). Disain dan Implementasi Pengenalan wajah menggunakan FPGA, untuk mengidentifikasi citra wajah yang diberikan dengan menggunakan Fitur utama dari wajah. Dalam tesis ini Algoritma Artificial Neural Network metode Back Propagation disajikan, untuk mendeteksi pandangan frontal wajah. Extraksi Penciri citra wajah di lakukan dengan (PCA) dan identifikasi menggunakan Back Propagation. Citra wajah diambil dari 100 At&T Database menghasilkan 90 % acceptance ratio.

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ABSTRACTFPGA is a device that can be re-configured (reconfigurable). By taking advantage of parallel hardware, FPGA execution can be faster than processing DSP (Digital Signal Processor). Design and Implementation of face recognition using FPGA, to identify a given face image using the main features of the face. In this thesis Algorithm Artificial Neural Network Back Propagation method is presented, for detecting frontal view faces. Identifier face image extraction is done by (PCA) and identification using Back Propagation. 100 face images taken from At & T database generates 90% acceptance ratio.;FPGA is a device that can be re-configured (reconfigurable). By taking advantage of parallel hardware, FPGA execution can be faster than processing DSP (Digital Signal Processor). Design and Implementation of face recognition using FPGA, to identify a given face image using the main features of the face. In this thesis Algorithm Artificial Neural Network Back Propagation method is presented, for detecting frontal view faces. Identifier face image extraction is done by (PCA) and identification using Back Propagation. 100 face images taken from At & T database generates 90% acceptance ratio.;FPGA is a device that can be re-configured (reconfigurable). By taking advantage of parallel hardware, FPGA execution can be faster than processing DSP (Digital Signal Processor). Design and Implementation of face recognition using FPGA, to identify a given face image using the main features of the face. In this thesis Algorithm Artificial Neural Network Back Propagation method is presented, for detecting frontal view faces. Identifier face image extraction is done by (PCA) and identification using Back Propagation. 100 face images taken from At & T database generates 90% acceptance ratio., FPGA is a device that can be re-configured (reconfigurable). By taking advantage of parallel hardware, FPGA execution can be faster than processing DSP (Digital Signal Processor). Design and Implementation of face recognition using FPGA, to identify a given face image using the main features of the face. In this thesis Algorithm Artificial Neural Network Back Propagation method is presented, for detecting frontal view faces. Identifier face image extraction is done by (PCA) and identification using Back Propagation. 100 face images taken from At & T database generates 90% acceptance ratio.]"

"[Dendrobium crumenatum merupakan jenis anggrek yang memiliki variasi morfologi akibat adanya persebaran yang luas. Studi literatur menunjukkan bahwa penelitian terhadap variasi morfologi bunga Dendrobium crumenatum belum dilakukan, salah satunya akibat penjelasan deskripsi yang tidak sama dari tiap-tiap pulau. Oleh karena itu, penelitian yang dilakukan bertujuan untuk membuat deskripsi yang sama dan lebih lengkap, kemudian menganalisis karakter-karakter yang signifikan berbeda dan memberikan gambaran mengenai pola pengelompokan berdasarkan pola biogeografi. Penelitian yang dilakukan menggunakan 78 spesimen vegetatif dan 51 spesimen bunga. Sebanyak 33 karakter dari 37 spesimen bunga dianalisis menggunakan Principal Component Analysis (PCA). Hasil analisis menunjukkan bahwa ukuran perhiasan bunga, tepi lobus tengah, bentuk sepal dorsal dan sepal lateral, kalus, dan perbandingan panjang lobus tengah dan lobus samping labellum merupakan karakter-karakter yang signifikan berbeda. Hasil analisis juga menunjukkan tiga kelompok yang terpisah, yaitu kelompok 1 (Sumatera, Jawa, Kalimantan, dan Sulawesi) sebagai Dendrobium crumenatum, kelompok 2 (Nusa Tenggara) sebagai Dendrobium sp., dan kelompok 3 (Sulawesi Utara dan Maluku) sebagai Dendrobium papilioniferum. Hasil penelitian ini dapat menjadi bahan referensi untuk mengubah distribusi Dendrobium crumenatum, menjadikan Dendrobium papilioniferum menjadi jenis yang terpisah, dan menjadi data awal publikasi jenis baru Dendrobium dari Nusa Tenggara.;Dendrobium crumenatum is an orchid species that have morphological variation due to the broad distribution. The literature study shows that the study of morphological variation about Dendrobium crumenatum has not been done, one of them as a result of the description which are not the same from each island. Therefore, the aims of this research are to make the same and complete description, then analyze the significantly different characters and give a description of grouping based on biogeographic patterns. The conducted research using 78 specimens vegetative and 51 specimens of flowers. A total 33 morphological characters from 37 flower spesimens were analyzed using Principal Component Analysis (PCA). The analysis shows that the size of the flower parts, the edge of the middle lobe, dorsal sepals and lateral sepals form, callus, and the length ratio between middle lobe and the side lobe labellum are significantly different characters. The analysis also shows three separate groups, namely the group 1 (Sumatra, Java, Borneo, and Celebes) as Dendrobium crumenatum, group 2 (Lesser Sunda) as Dendrobium sp., and group 3 (North Sulawesi and Moluccas) as Dendrobium papilioniferum. Results of this study can be a reference material to restrict the distribution of Dendrobium crumenatum, to make Dendrobium papilioniferum a separate species, and be an early data into new species publication about Dendrobium sp. of Lesser Sunda.;Dendrobium crumenatum is an orchid species that have morphological variation due to the broad distribution. The literature study shows that the study of morphological variation about Dendrobium crumenatum has not been done, one of them as a result of the description which are not the same from each island. Therefore, the aims of this research are to make the same and complete description, then analyze the significantly different characters and give a description of grouping based on biogeographic patterns. The conducted research using 78 specimens vegetative and 51 specimens of flowers. A total 33 morphological characters from 37 flower spesimens were analyzed using Principal Component Analysis (PCA). The analysis shows that the size of the flower parts, the edge of the middle lobe, dorsal sepals and lateral sepals form, callus, and the length ratio between middle lobe and the side lobe labellum are significantly different characters. The analysis also shows three separate groups, namely the group 1 (Sumatra, Java, Borneo, and Celebes) as Dendrobium crumenatum, group 2 (Lesser Sunda) as Dendrobium sp., and group 3 (North Sulawesi and Moluccas) as Dendrobium papilioniferum. Results of this study can be a reference material to restrict the distribution of Dendrobium crumenatum, to make Dendrobium papilioniferum a separate species, and be an early data into new species publication about Dendrobium sp. of Lesser Sunda., Dendrobium crumenatum is an orchid species that have morphological variation due to the broad distribution. The literature study shows that the study of morphological variation about Dendrobium crumenatum has not been done, one of them as a result of the description which are not the same from each island. Therefore, the aims of this research are to make the same and complete description, then analyze the significantly different characters and give a description of grouping based on biogeographic patterns. The conducted research using 78 specimens vegetative and 51 specimens of flowers. A total 33 morphological characters from 37 flower spesimens were analyzed using Principal Component Analysis (PCA). The analysis shows that the size of the flower parts, the edge of the middle lobe, dorsal sepals and lateral sepals form, callus, and the length ratio between middle lobe and the side lobe labellum are significantly different characters. The analysis also shows three separate groups, namely the group 1 (Sumatra, Java, Borneo, and Celebes) as Dendrobium crumenatum, group 2 (Lesser Sunda) as Dendrobium sp., and group 3 (North Sulawesi and Moluccas) as Dendrobium papilioniferum. Results of this study can be a reference material to restrict the distribution of Dendrobium crumenatum, to make Dendrobium papilioniferum a separate species, and be an early data into new species publication about Dendrobium sp. of Lesser Sunda.]"

"This study was aimed to determine maturity and ripeness of sawo (Achras sapota L.) based on near infrared (NIR) spectroscopy using artificial neural network. The NIR system was developed and applied to 120 sawo samples at the wavelength range from 1400 - 1995 nm, the data was recorded in 5 nm interval. The samples were separated into three group, i.e. mature, ripe, and over ripe based on their harvest time. The principal component analysis (PCA) was used to reduce dimension of NIR reflectance data that has been smoothed with moving average method. The 5, 10, 15 principal component was fed into the neural network model as input and the level of maturity and ripeness as output. The result recommended the use of 10 and 15 principal component as input on various nodes in hidden layer that would provided the highest accurateness of 100% in classifying the sawo based on its maturity and ripeness"