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Diskontinuitas saluran pada resonator silinder tipe-H sangat mempengaruhi perambatan gelombang akustik di dalamnya yang menimbulkan singularitas di ujung luas penampang resonator yang lebih kecil. Singularitas efek tepi ini memunculkan pola osilasi sehingga dapat mengurangi nilai koefisien transmisi akustik. Tujuan dari penelitian ini adalah menentukan desain optimal resonator dan frekuensi resonansinya dengan mempertimbangkan pengaruh diskontinuitas saluran. Metode yang digunakan dalam penelitian ini adalah simulasi lewat metode matriks transmisi (MMT) dan optimasi menggunakan algoritma genetika (AG). Adapun hasil yang diperoleh dalam penelitian ini adalah panjang buffer (l_buf )=48,8 mm, panjang resonator (l_res )=102,5 mm, jari-jari buffer (r_buf )=9,2 mm, dan jari-jari resonator (r_res )=2,9 mm, serta nilai frekuensi resonansi f=1610,6 Hz.

Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

Intensitas bunyi dari perambatan akustik pada resonator tipe-H dipengaruhi oleh konfigurasi saluran buffer. Bertambahnya panjang buffer tidak lebih berpengaruh terhadap intensitas bunyi dibandingkan dengan perubahan diameter saluran. Hal ini disebabkan adanya head loss, yaitu energi gelombang yang datang tidak semuanya ditransmisikan, tapi sebagian dipantulkan sehingga akan mengurangi intensitas bunyi yang ditransmisikan. Efek head loss ini terjadi di antaranya berupa gesekan pada sistem saluran dan tumbukan karena sambungan atau diskontinuitas saluran. Dalam penelitian ini dilakukan simulasi perambatan akustik dalam resonator tipe H menggunakan metode matriks transmisi dan persamaan Bernoulli. Dengan memvariasi panjang dan jari-jari buffer diperoleh nilai simulasi dari frekuensi resonansi resonator tipe H tersebut sebesar 1631 Hz.

Teh dapat dikenal kualitasnya melalui aroma yang dihasilkan. Penelitian klasifikasi teh menggunakan e-nose umumnya hanya mendeteksi kualitas aroma menggunakan general sensor gas. Namun, adanya redundansi fitur sensor dapat menyebabkan penurunan performa sistem e-nose. Oleh karena itu diperlukan sebuah sistem yang dapat menyeleksi fitur sehingga performa klasifikasi menjadi lebih optimal. Pada penelitian ini dibentuk sistem perangkat lunak yang mampu menyeleksi fitur untuk mengoptimalkan performa klasifikasi. Data input untuk sistem adalah respon sensor e-nose terhadap 3 kualitas teh hitam dengan jumlah sampel 300. Fitur yang diseleksi berupa sensor-sensor pada instrumen e-nose. Proses seleksi fitur dilakukan dengan pendekatan wrapper, algoritma ABC digunakan untuk seleksi fitur, kemudian hasil fitur yang terpilih dievalusi dengan klasifikasi menggunakan SVM. Hasil sistem ABC-SVM kemudian dibandingkan dengan sistem SVM tanpa seleksi fitur. Hasil penelitian menunjukkan bahwa dari 12 sensor e-nose, sensor yang paling mencirikan teh hitam kualitas 1-3 yaitu sensor TGS 2600, TGS 813, TGS 825, TGS 2602, TGS 2611, TGS 832, TGS 2612, TGS 2620 dan TGS 822. Sedangkan untuk sensor MQ-7, TGS 826 dan TGS 2610 merupakan sensor yang redundant pada sistem dikarenakan gas yang dideteksi oleh 3 sensor tersebut dapat diwakili oleh sensor lainnya. Dengan berkurangnya fitur menjadi 9, performa akurasi klasifikasi meningkat 16,7%.

The ability of electronic nose (e-nose) in classifying is determined by methods used in preprocessing, features extraction, and pattern recognition. Each method has advantages in choosing unique features that are hidden in sensor response. Comparison of the methods is used to obtain the best approach in preprocessing. The aroma of black teas (Broken Orange Pekoe, Broken Pokoe II, and Bohea) was measured 160 times. Sensor response is processed with three preprocessing models, and features are extracted using the maximum method. The best method is determined based on the classification of three black teas that are formed, and it was carried out after data clustering was successfully made with principal component analysis (PCA). As a result, three black teas can be clustered with 98.0% of total variant of data. In general, classification can be done with these methods. However, the best classification uses difference because signal amplitude high, difference amplitude between signals and noise are small.

There are some manufacturers of wet noodles that use hazardous substances to extend shelf life. Detection of formalin content in the noodles is still difficult for ordinary people to do. Standard chemical analysis tends to quantitatively analyze the elements or compounds in the sample. Qualitative analysis using Kernel Principal component analysis (K-PCA) can be done by detecting the aroma of wet noodles using electronic nose. Pure wet noodles and wet noodle mixed with 1%, 5%, 10%, 15% and 20% formalin solutions used as samples. With each sample was weighed with a mass of 20 grams and placed in a closed sample room. Using e-nose system with 5 gas sensors (TGS 2610, TGS 2620, TGS 2611, TGS 2602, and TGS 2600) and K-PCA with linear, polynomial, RBF and sigmoid methods, the samples were clustered. The results proved that pure wet noodles and wet noodles mixed by formalin can be clustered using the K-PCA method with a total variance of 98.83%. So that this e-nose can be used as an alternative instrument to detect the purity of wet noodles from the influence of harmful substances such as formalin.

During this time, determining the shelf life of tofu still uses human smell and vision. Although this method is realistic, but it is subjective and cannot be used as standardization of measurement. Electronic nose as the alternative instrument, can be used to predict the shelf life of tofu based on the qualitative aroma pattern produced. The information stored in the response sensor is the result of the conversion of metal oxide semiconductor (MOS) gas sensor array. Then the information is extracted using the maximum method and predicted shelf life of Tofu with curve fitting. Six fresh tofu (20 grams) is placed in a plastic container at room temperature. Experiments are made after the tofu has finished production and ready to sell (first day) until it decays (day six). Sensor response is obtained through a sniffing process (collecting 120 s, purging 180 s) with 60 samples per day. The results showed that the coefficient of determination (R2) was 0.834 and the Root Mean Square Error (RMSE) was 8.414 can be obtained. Based on this analysis, the prediction of the tofu shelf life with electronic nose using the curve fitting method was successfully carried out.

An electromyogram is a recording of muscle activity. These signals have been used both for medical diagnosis and engineering such as finger motion detection in healthy people and rehabilitation patients. Many studies have been conducted to map the relationship between electromyogram and finger movements, one of which is the relationship between the number of channels used and the complexity of the system. The number of channels used is directly proportional to the complexity of a system. The more complex the system, the heavier the data processing is so that it requires greater resources. Therefore, this study focuses on the construction of a classification system for human finger movements using fewer channels. The number of channels used in this study is 4. Root Mean Square is applied in a sliding window as feature extraction. The classifier used is the artificial neural network. System validation is done with 10-fold cross-validation. The test results of the average accuracy value for the thumb, index finger, middle finger, ring finger, little finger, grip, and relaxation were 89%, 90%, 93%, 95%, 93%, 94%, and 91% respectively which can be said to be quite good considering the number of channels relatively few compared to previous studies.

When fish die, fish freshness start to reduce gradually until cannot be eaten anymore. Properness of fish meat can be identified by odor that come out from fish itself. An instrument called electronic nose that can detect pattern of fish odor has been designed and implemented in this research. To be able to detect scent of freshwater fish, electronic nose will drain the air from sample chamber to sensor chamber using fan. When taking sample aroma, fan will drain air that contain sample scent from sample chamber to sensor chamber, and air from the outside flowed into sensor chamber when odor off. Scent stimulus captured by sensor array in form of signal response will be extracted with integral method so that the digital fingerprint from samples can be obtained. This pattern then analyzed by PCA (Principal Component Analysis) to determine patterns of freshwater fish odor. Result from this study indicated that electronic nose system can detect scent of freshwater fish with percentage variance of two major components are 98.7% (pomfret), 98.8% (catfish), and 99.5% (tilapia). Sensors that give high response in each samples is TGS 2620, and TGS 2600. TGS 822 give high response when fish is rotting.

The existence of cooking oil which is mixed with dangerous material is very difficult to be detected by human olfactory system yet can be detected by sophisticated equipment. However, the equipment is very expensive and require an expert to operate it. Static system of electronic nose based on unselected gas sensor array has been built for classifying pure cooking oil’s odour and mixed cooking oil’s odour. Sensor array consist of sensor MQ-9, TGS-2600, MQ-2, TGS-2620, and TGS-822 along with a heat exchanger system that can reduce the temperature of sample’s odour. The sample chamber and sensor chamber are made from stainless steel with cylinder shape. The system tested by boiling the sample consist of each pure cooking oil form both palm and coconut oil with its mixture such as used lubricant, candle, and diesel fuel up to 300 0 C to realese its odour. After the odour has been detected by electronic nose the next step is to classifying the data with PCA method. The result show that the response of sensor’s output is stable and the output of score plot from each pure cooking oil’s data from both palm and coconut oil can be well-classified from the mixed cooking oil.

Chili Sauce is food products made from tomatoes, chili, and additives. The quality classification of chili sauce is very important to help consumers choose the products by their qualities. In this research, Electronic Nose (E-nose) is used to classify few quality grade of chili sauce. E-nose has ability to analyze the sample based on its aroma. The data acquisition is sampled and Data processing is applied through few step including pre-signal processing using Baseline manipulation method, feature extraction using maximum value method and data analysis using Principal Component Analysis Method. The results showed that the electronic nose with 5 sensor array i.e. TGS 2620, TGS 813, TGS 822, TGS 2600 and TGS 2620 give the difference responses from different the quality based on its aroma. The similarity and dissimilarity of sensor responses due to the composition of the chili sauces explored by multivariate pattern recognition techniques based on principal component analysis (PCA). It can be concluded that by using chosen feature in sensor responses of various sauce samples, PCA can be successfully used to reveal the clusters existing in chili sauces according to their organic composition. PC1 give the accuracy of 92% and PC2 give 5.8%, so the PCA give accuracy value of 97.8%.

This research aims to classification of samples of green tea and black tea originated from different planting sites, Tambi and Pagilaran. Samples of green tea and black tea; quality I (BOP), quality II (BP), quality III (Bohea) were each collected from Tambi and Pagilaran to analyze the charasteristic of both sample from both sites. Measurements of tea samples were performed using a dynamic e-nose device based on a MOS gas sensor, with a maximum set point temperature of 40ºC, flushing 300 seconds, collecting 120 seconds, and purging 80 seconds for 10 cycles repeatedly. The resulting sensor response is then processed using the difference method for baseline manipulation. Characteristic of extraction process on the sensor response results is carried out in three methods; relative, fractional change, and integral. Matrix data of the feature extraction results was reduced using the PCA method by mapping the aroma patterns of each sample using 2-PCA components. The PCA reduction results in integral feature extraction showed the largest percentage of cumulative variance in classifying green tea sample data by 97% and black tea by 100%. The large percentage value of cumulative variance indicates PCA can differentiate samples of green tea and black tea from Tambi and Pagilaran well.

An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1, area under curve preprocessing, F2, and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production), and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry.

One of the problems in intelligent mobile robot fire fighting contest is how the robot can find and can extinguish a fire without alot of sensors. The fire is located in a white circle area at one of the corner in room with random combinations. The way to solve this problems is conventional method-called-wall tracing. The robot uses wall tracing method to navigate, when entering the room this method still in used by the robot until its find the fire spot then uses one additional sensor to detect the white circle area where the fire located at. After detecting the white circle area, the robot will extinguishes the fire. This paper proposed a new method as optimization of conventional method. The proposed method is implementation of current algorithm (proportional derivatives algorithm) not only to wall tracing but also to TPA81 sensor (fire tracing) -called-wall and fire tracing method. Furthermore, this method is combination method between wall tracing and fire tracing. The robot will approach directly to the fire without wall tracing and without detecting white circle area. Then, the robot extinguishes the fire quickly. The robot uses only TPA81 sensor that can be used to detects the fire spot and its position in the room. Test result shows that 50% of the fire position variation on Indonesian Intelligent Robot Contest can be optimized using this method.

Salah satu gadget yang sering digunakan adalah telepon pintar berbasis Android. Android bersifat Open Source sehingga memungkinkan pengguna dan pengembang dalam mengoperasikan maupun membuat aplikasi berbasis Android. Ada berbagai macam permasalahan yang membutuhkan citra sebagai masukan atau input sistem dikarenakan keterbatasan manusia dalam hal kecepatan memproses suatu fungsi matematis maupun algoritma pendukung didalamnya, selain itu juga masalah waktu dan lain sebagainya. Salah satu sistem yang membutuhkan citra sebagai masukannya adalah penentuan nilai resistor berdasarkan gelang warna. Untuk melakukan seleksi warna digunakan metode segmentasi warna pemodelan warna HSV. Dengan menggunakan model warna HSV dapat menjadi model warna yg dapat digunakan sebuah sistem untuk menentukan nilai warna resistor, karena komponen nilai hue adalah representasi dari nilai warna yang sebenarnya. Hal ini didukung dengan saturation yang berfungsi sebagai tingkat kejenuhan suatu warna dan nilai value sebagai nilai kecerahan warna. Uji coba sistem dilakukan dengan pengujian pengaruh intensitas cahaya dan jarak pendeteksian antara kamera dan resistor.Hasil dari penelitian ini berupa sebuah implementasi pengolahan citra digital sebagai pengukur nilai resistor. Hasil terbaik dicapai dengan kondisi ruangan pada intensitas cahaya lampu antara 400 lux hingga 1200 lux dengan jarak pendeteksian antar kamera dan resistor yaitu maksimal 20 cm. Kata kunci— pengolahan citra digital, Android, resistor, HSV, intensitas cahaya, java AbstractOne of the gadget that is often used is Android smart phones. Android is an OpenSource, it could help user and developer to operate and develop Android Application. There are several problems that need image as an input system, it is caused by the humas’s ability in doing some mathematic functions or supported algorythm. To make the selection color used HSV color space. By using HSV color space allows a system to determine the color value resistor, because the hue value of the component is a representation of the actual color value. This is supported by the saturation level that serves as a color saturation and value as a brightness of color.The results of this research is an implementation of digital image processing as a measure of the value of the resistor. The system is tested by the influence of light intensity and the distance between the camera and resistor. The best results were achieved with the conditions of the room in light intensity between 400 lux to 1200 lux the detection distance between the camera and resistor is 20 cm of maximum value. Keywords—digital image processing, Android, resitor, HSV, light intensity, java

E-nose is an instrument used to detect odor. E-nose developed with Bidirectional Associative memory (BAM) algorithm has advantages in processing incomplete input data and noise. The purpose of the study was to implement the BAM algorithm to detect pure beef among samples of beef, pork, and mixed meat from aroma with e-nose. Data processing of the sample reading results begins by performing the baseline manipulation process , then do difference and integral feature extraction for the data. The characteristic extraction data will be converted into bipolar matrix patterns ( 1 and -1 ) so that the threshold data is needed to be able to determine the feature extraction data to be bipolar. Data that have become bipolar matrices will be used as test and reference data in the program with cross validation testing to obtain the percentage of truth of meat detection using BAM based e-nose. Detection of meat with BAM using integral feature extraction with bipolar the first way yields a 14,8% success percentage and the second way bipolar yields a 15,7% success rate. The extraction of characteristic difference with bipolar the first way yields a success percentage of 17,3% and the second way bipolar yields a success rate of 16,4%.

Electronic tongue sensors consisting of 16 sensor array made of TOMA and OA lipids that have been used to classify samples of pure cannabis, cannabis mixed with tea and cannabis mixed with tobacco does not involve the feature selection technique so that a lot of duplicated data is generated from data sampling. Feature selection is performed using PCA. Data analysis resulted in loading values shows the contribution of each sensor, and the similarity in sensor performance in characterizing samples, then analyzed using the correlation test so that the sensors that produce redundant information are known. Validation is performed using the SVM method and the classification performance is compared to the original sensor.The sensor optimization produces a subset of features with 6 sensors (Sensor 7, Sensor 10, Sensor 12, Sensors 13, Sensor 14 and Sensor 15) in the cannabis-tea sample test and a feature subset with 3 sensors (Sensor 3, Sensor 7 and Sensor 14) in the cannabis-tobacco sample test. Sensor optimization that has been done produced classification accuracy by 100% and shorten the running time by a difference of 0.578 microseconds in the test of cannabis-tea samples and a difference of 1.696 microseconds in the test of cannabis-tobacco samples.

The optimization of heating temperature of black tea samples for the measurement of aroma with electronic nose (e-nose) has been successfully performed. Sample heating is done because black tea has a low aroma intensity and easily lost. However, the selection of such temperature should be selective because it can result in damage to the aroma of the sample. Therefore, temperature optimization needs to be done so that the resulting sensor response comes from the transformation of the undamaged aroma.The method used to obtain the optimum heating temperature by analyzing the sensor response of the aroma transformation that is captured by e-nose. Consistency and pattern changes formed from the sensor response are used as a comparison of optimal heating temperature selection. The measured sample varied in temperature (30 - 60 °C) so that the resulting sensor response was observed. Change in patterns indicate the aroma has been burning. After optimal temperature is obtained then black tea (50 gr) Broken Orange Pokoe, Broken Pokoe II and Bohea with a total sample of 300 bags were measured with e-nose. For further analysis, the result of classification by method of Principal Component Analysis (PCA) as proof of sample heating temperature optimization successfully done.The experimental results show optimal sample heating for black tea 3 quality 40 - 45 °C. After then with the third PCA the sample can be classified up to 92.5% of the total data variant. This indicates the aroma of tea is relatively constant and there is no pattern change.

An electronic nose (e-nose) with twelve MOS-based gas sensors have been used to classify the quality of the Indonesia black tea. The black tea samples were obtained from local tea shops in Yogyakarta, including first quality (Q1) or BOP (Broken Orange Pokoe), second quality (Q2) or BPII (Broken Pokoe II) and third quality (Q3) or Bohea. For each dry tea sample was tested 31 times, at temperatures (40.0 ± 1.0) °C without any treatment and it analyzed by unsupervised principal component analysis (PCA). The experimental results showed that all three samples almost have the same aroma when observed from the sensor response. The difference from the aroma samples lie on the sensor response of the signal intensity and it can be classified clearly by the PCA with first and second principal component accounted for 80.3% and 15.3% of the variance, respectively. Besides that, the analysis of PCA indicates that the sample Q2 is closed to Q1 and Q3. It suggests that a small constituent element of black tea in Q1 and Q3 also contained in Q2. The overall results e-nose with the non-selective gas sensor can be used to classify the quality of Indonesia black tea properly.

During this time to clasify quality of cacao based on color and aroma involving human taster. But this cacao tester still has weaknesses such as subjective. Besides that, the standard chemical analytical methods requires a high cost and need expertise to analyzing it. Basically aroma of cacao is determined by volatile compounds such aldehid and alcohol. Electronic nose based on unselected gas sensor array has the ability to analyze samples with complex compositions that can be known characteristics and qualitative analysis of the samples. Stimulus aroma is transformed by electronic nose into fingerprint data then it is used by feature extraction process using the differential method. The results of feature extraction is used to process the neuro fuzzy training to obtain optimal parameters. The parameters have been optimized is then tested on cacao. Based on test results, neuro fuzzy can clasify samples with 95,21% accuracy rate so that the clasification of cacao quality with electronic nose using neuro fuzzy has been successfully carried out.

An electronic nose (e-nose) equipped with a stable temperature controller has been successfully developed. Sensor response is very influenced by the temperature of the sample. The performance of the e-nose was then evaluated to differentiate the quality levels of black tea (Q1, Q2, and Q3). The black tea samples were purchased from Tambi Tea Industry in Central Java, Indonesia. Here, the quality is based on the information from the factory. For each quality level, the measurement was repeated 31 times. Therefore, the total of measurements is 93, and only six sensors, i.e. MQ-7, TGS 813, TGS 2602, TGS 826, TGS 2620 and TGS 825 have the highest influence on the patterns. By using linear discriminant analysis (LDA), the e-nose is able to classify the quality levels of all sample almost 100%. The Q1 and Q2 are 100% predicted, while for the 31 data from Q3, total 29 data can be predicted correctly.

Penelitian ini dilatar belakangi karena minimnya pengembangan dari sensor rasa yang ada selama ini dan bertujuan untuk mengimpelentasikan delapan buah sensor rasa berbasis komputer dengan menggunakan membran Decyl Alcohol (DA), Oleic Acid (OA), Dioctyl Phosphate (DOP), Trioctylmethyl ammonium chloride (TOMA), Dodevylamine (DDC), DA:OA 5:5, DA:DOP 5:5, dan DDC:TOMA 5:5 dilengkapi dengan semi auto sampler dan mampu menampilkan hasil pengukuran dan menyimpan data dari delapan sensor sekaligus. Sistem diimplementasikan pada beberapa merek kopi instan, serta dapat mempola karakter beberapa merek kopi dengan perbandingan pendeteksian secara fisis. Pengujian karakterisasi membran dilakukan setiap hari dengan menggunakan sampel beberapa merek kopi instan yang ada di pasaran yang kemudian dideteksi pola karateristiknya. Alat yang digunakan sebagai ADC adalah PhidgetInterFaceKit 8/8/8 yang merupakan elektrometer pada penelitian ini. Dan digunakan program yang menggunakan Microsoft Visual Basic 2010 sebagai antarmuka sehingga dapat berinteraksi dengan alat. Serta digunakan toolbox dari program Matlab R2009a untuk pemanfaatan program madaline neural network. Hasil penelitian menunjukkan pola yang dikarakterisasi menggunakan sistem ini dapat diidentifikasi jenisnya menggunakan madaline neural network. Data hasil dari sistem ini dapat disimpan dalam bentuk excel. Kata kunci— madaline neural network, membran, sensor rasa, antarmuka, kopi Abstract This research is motivated by the lack of the nowadays taste sensor development and this study aims ti implement eight computer-based taste sensor with Decyl Alcohol (DA), Oleic Acid (OA), Dioctyl Phosphate (DOP), Trioctylmethyl ammonium chloride (TOMA), Dodecylamine(DDC), DA:OA 5:5, DA:DOP 5:5, and DDC:TOMA 5:5 membranes with semi auto sampler and it could show the measuring result and store the data from eight sensors as one. System implemented on few instant coffees, and patterned characterization on the coffees with physical detection comparation. The membrane character testing was did everyday with some instant coffee samples and then the pattern characterization be done. Tool that used as ADC was PhidgetInterFaceKit 8/8/8 that was an electrometer for this research. And uses program based on Microsoft Visual Basic 2010 as the interface so it can be interacted with the tool. And used the toolbox of Matlab R2009a program for madaline neural network utilization. The results showed a pattern characterized using this system can be identified using the madaline neural network. Data results from this system can be stored in the form of excel. Keywords— madaline neural network, membrane, taste sensor, interface, coffee

Keterbatasan indera penciuman manusia dalam mendeteksi keberadaan gas-gas polutan dapat membahayakan kesehatan. Telah dibuat sebuah alat deteksi dan monitoring polusi udara berbasis array sensor gas. Tingkat keakuratan hasil pengukuran ditingkatkan menggunakan beberapa sensor dengan kemampuan mendeteksi beberapa gas polutan yang sama. Perangkat dibuat menggunakan sebuah array sensor gas yang terdiri delapan sensor, perangkat board Arduino berbasis mikrokontroler ATMega 2560, dan layar LCD sebagai penampil. Program yang diimplementasikan dalam mikrokontroler ditulis menggunakan aplikasi Arduino IDE 1.0 dan peralatan dikalibrasi menggunakan udara bebas bersih. Dalam pengujiannya, peralatan digunakan untuk mengukur berbagai sampel gas polutan dan gas polutan di lima jalan raya di Yogyakarta yang padat dengan lalu-lintas kendaraan bermotor. Beberapa sensor yang digunakan untuk mengukur gas hidrogen dapat menunjukkan kedekatan hasil pengukuran sebesar 0,09 ppm, gas butana sebesar 0,33 ppm, gas CO sebesar 0,4 ppm, dan gas H2S serta etanol sebesar 0,03 ppm. Sensor TGS 6812 hanya peka terhadap gas berkonsentrasi tinggi sedangkan sensor TGS 2602 mempunyai sensitivitas yang buruk terhadap gas hidrogen. Pengujian penggunakan sampel gas polutan menghasilkan hasil yang baik ketika konsentrasi gas tidak melebihi range kemampuan deteksi sensor. Pengukuran konsentrasi gas polutan di jalan raya menunjukkan konsentrasi rata-rata gas butana sebesar 10,99 ppm sudah melewati batas aman. Kata kunci—polusi udara, array sensor gas, jalan raya, kedekatan hasil pengukuran Abstract Limitation of human smelling sense in detecting the presence of air pollutans can cause health issue. By using gas sensor array, an air pollution detector and monitoring device has been made. The accuracy of the measurement is improved by using some gas sensors to detect one type of pollutant gas. The device is build using an array consist of nine gas sensors, an Arduino Mega 2560 board, and a LCD display. The program is written and compiled using Arduino IDE 1.0. The device is calibrated in free clean air, tested using several pollutant gas samples, and used to measure pollutants concentration in five major streets in Yogyakarta which have heavy traffic. Measurement result closeness from sensors for hydrogen, butane, CO, H2S, and ethanol is as close as 0.09 ppm, 0.33 ppm, 0.4 ppm, and 0.03 ppm respectively. TGS 6812 gas sensor is suitable for detecting high concentration gases. TGS 2602 has a bad respond in detecting hydrogen gas. When tested using pollutant gas samples, the device was able to give good results as long as the concentration measured is within the sensors detection range. The average concentration of butane gas in five major streets is above the safety limit. Keywords— air pollution, gas sensor array, street, measurement result closeness

Terdapat banyak kandungan gas yang membentuk bau tertentu pada udara bebas. Dengan indera penciuman yakni hidung, manusia dapat merasakan suatu bau sesuai dengan intensitas bau tersebut. Namun hidung manusia belum dapat mengidentifikasi secara pasti bau yang tercium. Selain itu, untuk mengenali suatu udara berbahaya atau tidak, hidung manusia pun perlu menghirupnya terlebih dahulu. Hal ini bisa sangat fatal akibatnya bagi tubuh manusia jika udara terkontaminasi gas berbahaya. Oleh karena itu, diperlukan sebuah alat bantu penciuman yang bekerja seperti hidung manusia dengan mengenali gas yang terdapat pada udara bebas. Alat tersebut dinamakan Electronic nose. Electronic nose terdiri dari perangkat sensing dan perangkat pada ground segment dengan komunikasi nirkabel. Sistem ini terbentuk dari array sensor gas TGS yang berjumlah enam dengan spesifikasi selektif gas yang berbeda-beda. Pembacaan sensor kemudian dikonversi menjadi sinyal digital menggunakan board Arduino Mega 2560 lalu dikirim dengan modul RF APC 220 ke perangkat PC. Pada perangkat PC terdapat HMI yang digunakan untuk memantau secara online pengambilan data. Hasil pengambilan data disimpan dalam bentuk tabel pada Microsoft Excel kemudian diolah menjadi sebuah pola dari array sensor. Alat ini digunakan untuk mengenali gas NO2, SO2, H2S, CO, hidrogen, propana, dan isobutana yang terdapat pada udara bebas dengan cara membandingkan nilai konsentrasi antara sensor gas satu dengan lainnya yang dapat mendeteksi gas sama.. Pengujian dilakukan untuk mendeteksi gas yang terkandung pada bebagai kondisi udara seperti asap pembakaran sampah, asap rokok, kebocoran LPG, dan polusi lalu lintas. Hasil yang didapatkan pada penelitian ini adalah array sensor mengenali gas serta besar konsentrasinya. Kata kunci — Electronic Nose, array sensor, sensor gas TGS, Arduino Mega 2560, RF APC 220, HMI Abstract There are many gases that make up the content of a particular odor in the air. By nose, a man can perceive an odor accorsing to it’s intensity. However, a human nose cannot identify the smell exactly. Additionally, to recognize a hazardous air or not, a human nose also needs to smell it first. This may cause a fatal consequences to the human body if the air is contaminated with harmful gases. Therefore, we need an olfactory tool that works like a human nose to recognize gas contained in the air. Its called Electronic Nose. Electronic Nose is consist of a sensing and ground segment with wireless communication. The system is composed of eight gas sensors that makes an array. Each of them has a different selective specification of gases. The output of the sensor reading are then converted into a digital signal using an Arduino Mega 2560 board and sent to the APC RF module 220 to the PC. In the PC devices there is HMI which used to monitor data retrieval by online. The captured data is stored in tables in Microsoft Excel and then processed into a pattern of the array sensor. This tool is used to recognize NO2, SO2, H2S, CO, hydrogen, propane, dan isobutane in the air by compare concentration result of a gas sensor with other gas sensor that can one kind of gas. The purpose of the test is to recognize various of gasses in certain condition like the smoke of burning garbage, cigarette smoke, LPG leakage, and traffic pollution. The results obtained in this study is an array sensor recognize kind of gasses and how much concentration of gasses at each test condition. Keyword— Electronic Nose, array sensor, TGS gas sensor, Arduino Mega 2560, RF APC 220, HMI

It has been made a device that could help driver from accidents when parking a car. The driver knows the range of the vehicle and any object behind. Moreover, the informing device should help to break the machine down automatically. This device used ultrasound as range finder, microcontroller AT89S52 as processes calculation and displays result in LCD screen. This calculation is also used for managing buzzer and relay as the actuator. In this experiment, device has been simulated by toy car which run reversely to object behind. On any ranges, buzzer sound would sign actively, and it continued to break the electric current of the vehicle if it was still running. The result showed that the device could perform appropriately and could calculate 3 cm of minimum range and 330 cm of maximum range to object. The sound of buzzer was active in caution and critical range, that is 200 cm to 3 cm, and the machine of vehicle broke down in a critical range under 20 cm.

Dalam pemotretan panorama diperlukan alat bantuyang dapat menentukan besar pergeseran arah kamera secara konsisten agar diperoleh foto-foto yang pas tanpa ada bagian yang hilang karena tidak terfoto. Dengan didapatnya foto yang saling tersambung maka dapat dilakukan penggabungan foto untuk menjadi sebuah foto panorama. Penggabungan foto-foto hasil pemotretan dapat dilakukan dengan cara image stiching (menjahit), yaitu menyambung foto-foto hasil pemotretan dari satu titik tetap.Alat yang dirancang merupakan alat yang otomatis menentukan besar sudut perputaran head kamera dengan input jumlah pergeseran dan akan melakukan pemotretan (triggering) bila sudut sudah sesuai dengan input. Pengujian sistem meliputi pengujian variasi jumlah pergeseran menggunakan satu nilai focal length dengan memvariasikan nilai pan dan tilt. Pengujian ketelitian konsistensi pergeseran dan arah kamera. Pengujian besar perpotongan setiap pergeseran dengan menggabungkan 2 foto yang sejajar kemudian dicari seberapa besar irisan yang dihasilkan.Berdasarkan pengujian menggunakan kamera DSLR diperoleh hasil alat dapat bekerja dengan baik secara otomatis berdasarkan nilai input pan dan tilt serta dapat melakukan triggering. Pergeseran efisien diperoleh pada nilai pan sebanyak 9 kali dan tilt 4 kali menggunakan lensa dengan focal length 18 mm. Hasil analisa tingkat kesalahan pergeseran bernilai kurang dari 3% yang menjadikan alat ini stabil dan konsisten dalam pergerakannya. Kata kunci—foto panorama, Arduino Uno, otomasi, fotografi AbstractIn landscape photography, some supporting devices are needed to assign how much the camera’s shifting consistently is. It is needed to get good photos without any blank spot photos. The linked photos could be rendered to be a landscape photo using a photography software. It is could be done by image stiching, a way to stich photos taken from one constant taking point. The device is an automatic device which could assign how much the rotation degree of the camera’s head with given input, how much the shift is. It will take a shot if the rotation degree is appropriate with the given input. The system testing by variying the shift value of camera using one focal length value. The accuration test of the consistency of camera’s shift and camera’s direction. The error value could be determined by joining two parallel photo.According to the testing, tool can work automatically and also trigering can do well. Efficient shift in the number of photos and good results using the pan 9 times and tilt 4 times. The results of the error rates shift analysis have value less than 3% and that makes the tool quite stable and consistent in its movements. Keywords— panoramic photo, arduino uno, automation ,photography.