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Abstrak :
[ABSTRAK
Di beberapa negara, perkembangan bioteknologi telah meluncurkan produk pangan yang dikenal dengan makanan kesehatan. Dimana mikroalga saat ini dapat dijual dalam bentuk kapsul atau di dalam makanan seperti aneka minuman dan pasta yang telah menunjukkan khasiat pengobatan dalam perlakuan kondisi seperti hiperkolesterolemia dan aterosklerosis. Penelitian ini bertujuan untuk mengetahui kadar protein pada mikroalga Botryococcus braunii dengan variasi spektrum cahaya tampak dengan metode Biuret. Hasil kadar protein untuk variasi spektrum cahaya tampak adalah dengan cahaya langsung 17,60 % , pemberian cahaya merah 13,48 % dan pemberian cahaya biru 11,82 %. Asam amino tertinggi yang dimiliki B.braunii baik sampel A, sampel B dan sampel C adalah Leusin/Leucine untuk asam amino esensial dan Alanin/Alanine untuk asam amino non esensial. Pada penelitian ini juga dapat didapatkan metode nilai kapasitansi lebih relevan dibandingkan metode absorbansi untuk melihat pertumbuhan mikroalga B. braunii. ABSTRACT
In some countries, the development of biotechnology has launched a food product known as health food. Now microalgae can be sold in capsule or in foods such as drinks and pasta that has shown efficacy in the treatment of treatment of conditions such as hypercholesterolemia and atherosclerosis. This study aims to determine levels of protein in microalgae Botryococcus braunii with variations in the visible light spectrum with Biuret method. Results for the protein content of the visible light spectrum variation is 17.60% for direct light, 13.48% for giving the red light and 11.82% for blue light giving. The highest amino acid B.braunii owned both the sample A, sample B and sample C is Leucine for amino acids essential and Alanine for non-essential amino acids. In this study, can also be obtained capacitance value method is more relevant than the absorbance method to see the growth of microalgae B. braunii.;In some countries, the development of biotechnology has launched a food product known as health food. Now microalgae can be sold in capsule or in foods such as drinks and pasta that has shown efficacy in the treatment of treatment of conditions such as hypercholesterolemia and atherosclerosis. This study aims to determine levels of protein in microalgae Botryococcus braunii with variations in the visible light spectrum with Biuret method. Results for the protein content of the visible light spectrum variation is 17.60% for direct light, 13.48% for giving the red light and 11.82% for blue light giving. The highest amino acid B.braunii owned both the sample A, sample B and sample C is Leucine for amino acids essential and Alanine for non-essential amino acids. In this study, can also be obtained capacitance value method is more relevant than the absorbance method to see the growth of microalgae B. braunii., In some countries, the development of biotechnology has launched a food product known as health food. Now microalgae can be sold in capsule or in foods such as drinks and pasta that has shown efficacy in the treatment of treatment of conditions such as hypercholesterolemia and atherosclerosis. This study aims to determine levels of protein in microalgae Botryococcus braunii with variations in the visible light spectrum with Biuret method. Results for the protein content of the visible light spectrum variation is 17.60% for direct light, 13.48% for giving the red light and 11.82% for blue light giving. The highest amino acid B.braunii owned both the sample A, sample B and sample C is Leucine for amino acids essential and Alanine for non-essential amino acids. In this study, can also be obtained capacitance value method is more relevant than the absorbance method to see the growth of microalgae B. braunii.]

Abstrak :
Latar belakang: Berdasarkan data National Institute of Health Amerika Serikat tahun 2015, kanker usus halus merupakan salah satu kanker langka dengan dengan insidensi yang diperikirakan meningkat lebih dari 100% selama 4 dekade terakhir di berbagai negara. Teknik diagnosis penyakit ini membutuhkan berbagai pendekatan karena sering terlambat didiagnosis. Standar emas diagnosis kanker usus halus saat ini adalah penilaian histopatologi oleh ahli. Kekurangan metode ini adalah sulit dideskripsikan secara objektif dan belum terdigitalisasi. Sejumlah penelitian menunjukkan bahwa metode spektrofotometri reflektans cahaya tampak dapat digunakan dalam diagnosis sejumlah jenis kanker, seperti kanker kulit dan lesi oral. Metode tersebut lebih terkuantifikasi, dapat didigitalisasi, sangat terjangkau dan mudah digunakan. Namun, penggunaan spektrofotometri cahaya tampak belum digunakan untuk lesi kanker usus halus. Tujuan: Studi ini merupakan studi pendahuluan untuk mengetahui kemampuan spektrofotometer reflektans cahaya tampak sederhana dalam mengklasifikasi derajat lesi kanker usus halus pada mencit berdasarkan pengukuran intensitas cahaya. Metode: Penelitian ini merupakan penelitian analitik potong lintang menggunakan sampel bahan biologis tersimpan blok parafin usus halus mencit Mus musculus. Sampel dikelompokkan berdasarkan derajat lesi menjadi normal, prekanker, dan kanker berdasarkan penilaian ahli patologi anatomi. Seluruh sampel diukur intensitas cahaya reflektansinya pada 132 panjang gelombang cahaya tampak. Hasil pengukuran dianalisis menggunakan perangkat lunak SPSS 24.0 untuk uji komparatif dan Orange Data Mining untuk pengelompokan derajat lesi berdasarkan data yang diperoleh dengan machine learning. Hasil dan Pembahasan: Hasil uji komparatif menunjukkan sebanyak 105 dari 132 panjang gelombang cahaya tampak memiliki perbedaan intensitas reflektans bermakna (p<0,05) antar kelompok sampel. Pengelompokan derajat lesi berdasarkan data intensitas cahaya oleh machine learning dilakukan terbaik dengan model k-nearest neighbors yang memiliki akurasi sebesar 83,3%, AUC sebesar 90,8%, nilai F1 sebesar 0,836, presisi sebesar 0,856, dan recall 0,833. Analisis Tree menunjukkan panjang gelombang 450,3 nm terbaik dalam membedakan sampel. Simpulan: Metode spektrofotometer reflektans cahaya tampak sederhana mampu membedakan jaringan normal, prekanker, dan kanker usus halus pada mencit berdasarkan perbedaan intensitas cahaya. ......Background: According to the United States National Institue of Health in 2015, small intestine cancer is one of the rare cancer with estimated to increase the incidence by more than 100% in the last 4 decades in many countries. The diagnosis of this disease needs various approaches because it is usually late to diagnose. The current gold standard for diagnosing small intestine cancer is histopathology evaluation by the expert. The disadvantages of this method are hard to describe objectively and have not been digitalized. Some studies showed that visible light reflectance spectrophotometry method can be used in cancer diagnoses, such as skin cancer and the oral lesion. This method is quantified, able to be digitalized, affordable, and easy to use. However, the use of visible light spectrophotometry has not been used for small intestine cancer lesions. Objective: This is a pilot study that aims to evaluate the potency of simple visible light reflectance spectrophotometry to classify mice’s small intestine cancer lesion degree based on intensity measurement. Method: This analytical cross-sectional study was done using paraffin block preserve Mus musculus mice small intestine tissue. The samples were grouped according to the lesion degree that had been evaluated by a pathology expert. The reflectance intensity of all samples were measured in 132 different visible light wavelengths. The results were analyzed by using SPSS 24.0 for comparative test and Orange Data Mining’s machine learning for lesion degree classification based on obtained data. Results and Discussion: Comparative test results show that 105 of 132 visible light wavelengths have a significant difference (p<0,05) between groups. The best machine learning to classify lesion degree based on light intensity was performed by k-nearest neighbor, with accuracy 83,3%, AUC 90,8%, F1 score 0,836, precision 0,856, and recall 0,833. Tree analysis showed that 450,3 nm is the best wavelength to differentiate the sample. Conclusion: Simple visible light reflectance spectrophotometer is able to differentiate normal, precancer, and cancer on mice small intestine tissue based on the light intensity difference.

Abstrak :
Dalam tugas akhir ini telah dirancang dan dibuat suatu sistem untuk mengukur besarnya intensitas cahaya tampak (visible light). Sistem tersebut berbasis pada mikrokontroler sebagai pengolah data. Selanjutnya hasil pengukuran ditampilkan pada sebuah layar LCD. Untuk dapat mengetahui informasi mengenai intensitas cahaya, maka dibutuhkan suatu sistem perangkat keras pengukuran yang dilengkapi dengan perangkat lunak. Perangkat keras yang digunakan yaitu rangkaian sensor cahaya LDR (Light Dependent Resistor) untuk mendeteksi intensitas cahaya, kemudian mengkonversikannya menjadi tegangan. Rangkaian ADC (Analog to Digital Converter) untuk mengubah tegangan analog yang berasal dari rangkaian sensor cahaya, untuk menjadi data pengukuran digital. Sistem mikrokontroler untuk mengolah dan mengkalibrasi data hasil pengukuran tersebut untuk ditampilkan di layar LCD (Liquid Crystal Display). Karena keterbatasan tidak tersedianya monokromator, maka tidak dapat dilaksanakan pengukuran panjang gelombang sinar yang diamati. Selanjutnya, untuk mempermudah pengukuran intensitas cahaya, dikelompokan dalam beberapa warna yaitu cahaya putih, merah, kuning, hijau, dan biru. Untuk mendekati nilai yang sebenarnya telah dilakukan kalibrasi untuk masing-masing warna sesuai dengan spektrum sensitivitas LDR.

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An instrument prototype for visible light intensity measurement has been designed and fabricated for the purpose of final project to obtain Sarjana Teknik degree of Electrical Engineering, Universitas Indonesia. The instrument is mainly supported by microcontroller AT89S52 system as the measurement data processing center. Further more, the result of the measurement processing is displayed on LCD screen. To obtain the light intensity measurement data, it is required an instrument system which consists of microcontroller system, light dependent resistor (LDR) circuit to detect light intensity and convert it to analog voltage, and analog to digital converter (ADC) to convert the analog voltage from LDR circuit to be digital measured data for microcontroller. Furthermore, the microcontroller will process and calibrate the measurement data and diplays the data to the ouput screen. Due to limited facilities, for example unavailability of monochromator, the wavelength measurement cannot be conducted. Moreover, to simplify the light intensity measurement for specific color light, the light is grouped into several groups of color such as white, red, yellow, green and blue. To obtain a better accuracy, it has been done intensity callibatrion for every group of color according to LDR sensitivity spectrum and the callibration data is used in microcontroller system to determine accurate measurement data.

Abstrak :
Backpropagation (BP) memiliki performa yang baik dalam mengklasifikasi citracitra wajah bertingkat iluminasi seragam. Namun untuk citra wajah yang bertingkat iluminasi beragam seperti pada aplikasi kamera pengintai maka BP akan kesulitan dalam mempelajari dan mengenalinya. Skripsi ini menggunakan metode Probabilistic Neural Network Teroptimasi (OPNN) sebagai Sistem Pengenal Wajah untuk spektrum gabungan infra merah dan cahaya tampak dengan intensitas yang berubah-ubah. Skripsi ini juga menggunakan metode Normalisasi dan Kompensasi Iluminasi untuk mengurangi dampak variasi iluminasi pada citra. Hasil penelitian menunjukkan bahwa performa OPNN untuk mengenali wajah akan meningkat jika Data Train yang digunakan berisi citra dengan tingkat iluminasi yang beragam, dimana Tingkat Rekognisi rata-rata OPNN 18.36% lebih tinggi dari BP. ......Backpropagation (BP) has a good performance in classifying face images with uniform illumination level. But Backpropagation have difficulty in learning and recognizing face images with varied ilumination level such in surveillance camera. This thesis uses Optimized Probabilistic Neural Network (OPNN) method as Face Recognition System for the joint spectrum of infrared and visible light with varying intensity. This thesis also uses uses Illumination Normalization and Compensation method to reduce the impact of illumination variance on the image. The research shows that OPNN performance to recognize face will increase if Train Data used contains images with varying levels of illumination, which recognition rate of OPNN is 18.36% higher than BP.