Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)

Threats or fraud for credit card owners and banks as service providers have been harmed by the actions of perpetrators of credit card thieves. All transaction data are stored in the bank's database, but are limited in information and cannot be used as a knowledge. Knowledge built with credit card transaction data can be used as an early warning by the bank. The outlier analysis method is used to build the knowledge with a local outlier factor algorithm that has high accuracy, recall, and precision results and can be used in multivariate data. Testing uses a matrix sample and confusion method with attributes date, categories, numbers, and countries. The test results using 1803 transaction data from five customers, indicating that the average value accuracy of LOF algorithms (96%), higher than the average accuracy values of the INFLO and AFV algorithms (84% and 77%)

Face Expression Classification in Children Using CNN

One of the turbulent emotions can be recognized from facial expressions. When compared with adults, children's facial expressions are more expressive for positive emotions and ambiguous for negative emotions so that they are much more difficult to recognize. Ambiguous in terms of negative emotions, for example, when children are angry, sometimes they show an expressionless face, making it difficult to know what emotions the child is experiencing. Therefore, it is proposed research using Convolutional Neural Network with ResNet-50 architecture. According to [1] CNN Resnet-50 is superior to other facial recognition methods, specifically in the classification of facial expressions. CNN ResNet-50 generates a model during the training process, and the model will be used during the testing process. The dataset used is Children's Spontaneous facial Expressions (LIRIS-CSE) data proposed by [2]. CNN ResNet-50 can identify children's expressions well, including expressions of anger, disgust, fear, happy, sad and surprise. The results showed a very significant increase in accuracy, namely in testing data testing reached 99.89%

Seleksi Fitur dengan Artificial Bee Colony untuk Optimasi Klasifikasi Data Teh menggunakan Support Vector Machine

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%

Suhu Pemanas Sampel Optimal Untuk Klasifikasi Teh Hitam Menggunakan Electronic Nose

 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

Karakterisasi Pola dan Konsentrasi Gas Polutan Berbasis E-Nose

Abstrak 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, HM

Rancang Bangun Sistem Deteksi Posisi Objek dalam Rumah dengan Metode Support Vector Machine Berdasar Kekuatan Sinyal Wi-Fi

 Indoor Positioning System (IPS) is an object tracking technology that utilizes networks such as Wireless Fidelity (Wi-Fi) to determine the location of an object. IPS is closely related to the implementation of the Internet of Things (IoT) to carry out an order in a smart home. However, the weakness of IPS is the attenuation of the signal received when the tag or target moves to a room that borders another room, causing errors in tracking. The IPS implementation will be carried out based on the 2.4 GHz Wi-Fi signal emitted from the ESP32.The research will use the trilateration method which requires three sink nodes to receive signal strength, then a machine learning algorithm, namely Support Vector Machine (SVM), to classify rooms in three different scenarios, namely when the target is stationary, moving between rooms, and is on the edge room adjacent to another room.The results of the test show that the three scenarios provide different levels of accuracy. The accuracy of the system on the target scenario while still in the room reaches 100%, on the target moving room scenario reaches 86.15%, and on the target scenario that is at the edge of the room adjacent to another room reaches 80%

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

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

Karakterisasi Pola Aroma Salak Pondoh dengan E-Nose Berbasis Sensor Metal Oksida

Abstrak Salak merupakan salah satu komiditi ekspor yang memiliki prospek di Indonesia. Untuk memenuhi standar mutu pasar luar negeri maka kualitas buah salak harus bermutu baik. Hasil panen yang baik sangat ditentukan oleh penanganan pasca panen buah salak. Salah satu teknik pasca panen adalah dengan menentukan umur petik salak. Namun sayangnya data pendukung untuk menentukan umur petik salak secara optimal belum banyak didapati. Salah satu cara untuk menyeleksi mutu buah salak sekaligus menentukan umur petik salak adalah dengan mengkarakterisasi aromanya. Untuk itu dibutuhkan suatu instrumen analitik yang secara efektif dapat melakukan penciuman layaknya hidung manusia. Dimana alat penciuman tersebut diharapkan secara khusus dapat menganalisis buah salak mulai dari keadaan matang hingga menjadi busuk berdasarkan pola aroma sampel salak yang didapat. Electronic nose merupakan suatu sistem pengindra bau elektronik (odor sistem) yang dapat digunakan sebagai rujukan standarisasi secara ilmiah berdasarkan aroma suatu objek. E-nose yang dibangun pada penelitian ini menggunakan 8 sensor gas TGS yang memiliki spesifikasi yang berbeda-beda berdasarkan gas yang dideteksinya. Data keluaran dari array sensor akan dikonversi menjadi sinyal digital pada mikrokontroler, kemudian hasilnya akan diolah pada computer. Penggunaan E-nose ini dapat memberikan gambaran mengenai karakteristik salak pondoh dari pertama petik hingga keadaan membusuk berdasarkan analisa pola yang terbentuk dari kedelapan sensor yang digunakan pada saat mendeteksi aroma buah salak pondoh.   Kata kunci— Electronic Nose, array sensor, sensor gas TGS, mikrokontroler, odor   Abstract  Salak is one of the commodities that have export prospects in Indonesia. To meet the quality standards of the overseas market, the quality should be good quality fruits. Good harvest is determined by post-harvest handling of fruits. One technique is to determine the post-harvest is life quotes barking. But unfortunately the supporting data to determine the optimal age picking bark has not been found. One way to select quality fruits as well as determining the date of quotation barking is to characterize the aroma.  That requires an analytical instrument that can effectively do smell like a human nose. Where the olfactory apparatus is specifically expected to analyze salak ranging from mature state to be rotten by the pattern of bark samples obtained aromas. Electronic nose is an electronic odor sensing system (odor system) that can be used as reference standards are scientifically based aroma of an object.  E-nose is built on a study using eight TGS gas sensors with different specifications based on the detected gas. Data output from the aray sensor is converted into a digital signal on the microcontroller, then the results will be processed on a computer. The use of E-nose can give an idea about the characteristics of the first quotation of salak to state decay, based on pattern analysis of the eight sensors are used when detect flavors of salak.     Keyword— Electronic nose, biomimetic sensor, array sensor, TGSgassensor

Otomasi Trigger dengan Penentuan Sudut dalam Foto Panorama Berbasis Arduino Uno

In 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