Identifikasi Website Phishing Menggunakan Algoritma Classification And Regression Trees (CART)

With the increase in internet users and the development of technology, the threats to its security are increasingly diverse. One of them is phishing which is the most important issue in cyberspace. Phishing is a threatening and trapping activity someone by luring the target to indirectly provide information to the trapper. The number of phishing crimes, this has the potential to cause several losses, one of which is namely about the loss of privacy of a person or company. This study aims to identify phishing websites. The Classification And Regression Trees (CART) algorithm is one of the classification algorithms, and the dataset in this research taken from the UCI Repository Learning obtained from the University of Huddersfield. The method used in this research is problem identification, data collection, pre-processing stage, use of the CART algorithm, validation and evaluation and withdrawal conclusion. Based on the test results obtained the value of accuracy of 95.28%. Thus the value of the accuracy obtained using the CART algorithm of 95.28% categorized very good classification.

SUPPORT VECTOR MACHINE FOR DETERMINING EULER ANGLES IN AN INERTIAL NAVI-GATION SYSTEM

The paper discusses the improvement of the accuracy of an inertial navigation system created on the basis of MEMS sensors using machine learning (ML) methods. As input data for the classifier, we used information obtained from a developed laboratory setup with MEMS sensors on a sealed platform with the ability to adjust its tilt angles. To assess the effectiveness of the models, test curves were constructed with different values of the parameters of these models for each core in the case of a linear, polynomial radial basis function. The inverse regularization parameter was used as a parameter. The proposed algo-rithm based on MO has demonstrated its ability to correctly classify in the presence of noise typical for MEMS sensors, where good classification results were obtained when choosing the optimal values of hy-perparameters.

Acoustic traits of bat-pollinated flowers compared to flowers of other pollination syndromes and their echo-based classification using convolutional neural networks

Bat-pollinated flowers have to attract their pollinators in absence of light and therefore some species developed specialized echoic floral parts. These parts are usually concave shaped and act like acoustic retroreflectors making the flowers acoustically conspicuous to the bats. Acoustic plant specializations only have been described for two bat-pollinated species in the Neotropics and one other bat-dependent plant in South East Asia. However, it remains unclear whether other bat-pollinated plant species also show acoustic adaptations. Moreover, acoustic traits have never been compared between bat-pollinated flowers and flowers belonging to other pollination syndromes. To investigate acoustic traits of bat-pollinated flowers we recorded a dataset of 32320 flower echoes, collected from 168 individual flowers belonging to 12 different species. 6 of these species were pollinated by bats and 6 species were pollinated by insects or hummingbirds. We analyzed the spectral target strength of the flowers and trained a convolutional neural network (CNN) on the spectrograms of the flower echoes. We found that bat-pollinated flowers have a significantly higher echo target strength, independent of their size, and differ in their morphology, specifically in the lower variance of their morphological features. We found that a good classification accuracy by our CNN (up to 84%) can be achieved with only one echo/spectrogram to classify the 12 different plant species, both bat-pollinated and otherwise, with bat-pollinated flowers being easier to classify. The higher classification performance of bat-pollinated flowers can be explained by the lower variance of their morphology.

A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.

Teaching Indonesian-English Combined Task to Improve Students’ Skills in Writing English Sentences

This research aimed to find out the effectiveness of Teaching Indonesian-English Combined Task to improve students’ skills in writing English sentences. It was undertaken in PDF Ulya As’adiyah Putri Sengkang in Academic Year 2016/2017. It applied pre-experimental method. Writing test was employed as the instrument of the research. The population of this research was 16 students. The technique of data collection involved giving pre-test to know the writing skills gained previously of the students and post-test after giving the treatment to know how the skills of the students in writing English sentences by using Indonesian-English combined task. The result of the calculation of the students’ score indicated that the mean score of the students’ pre-test was (55.25) which was classified as fair classification and the mean score of the students’ post-test was (73,93) as good classification. The value of paired sample test for post-test was greater than the t-table (10,239>2.131). In short, teaching Indonesian-English combined task is effective to improve students’ skills in writing English sentences.

Prenatal Diagnosis of Aortic Coarctation: Prediction Algorithm according to Gestational Age

<b><i>Objectives:</i></b> The aim of the study was first to quantify the diagnostic accuracy of predictive anatomical factors of aortic coarctation (CoA) and second to design a postnatal CoA probability algorithm according to gestational age (GA) in prenatal period. <b><i>Methods:</i></b> Global and according to GA diagnostic performance of cardiac anatomical variables using the ROC curve were evaluated in a retrospective cohort of fetuses with suspicion of CoA (2004–2020). A serial testing strategy to predict postnatal CoA by fetal echocardiography was designed. <b><i>Results:</i></b> 114 fetuses were included. Isthmus-to-ductal (I/D) ratio provided the best discrimination between healthy fetuses and those with CoA (AUC 0.91, 95% CI: 0.86–0.96, I/D &#x3c; 0.74 sensitivity 96.3%, I/D &#x3c; 0.6, specificity 92.5%) with good classification capacity in both the second and third trimesters of gestation. Isthmus <i>z</i>-score and pulmonary/aortic valve ratio increased accuracy in fetuses &#x3e;28 and tricuspid/mitral valve ratio (TV/MV) in fetuses ≤28 weeks. Study of I/D plus TV/MV ratio in fetuses ≤28 and I/D ratio plus isthmus <i>z</i>-scores in fetuses &#x3e;28 weeks allowed to correctly classify 91.8% of fetuses as high or low probability of postnatal CoA. <b><i>Conclusions:</i></b> Diagnostic discrimination of anatomic predictive factors for CoA varies according to GA. Specific algorithms according to GA increase accuracy in CoA’s prenatal prediction.

Authentication of Transylvanian Spirits Based on Isotope and Elemental Signatures in Conjunction with Statistical Methods

The potential association between stable isotope ratios of light elements and mineral content, in conjunction with unsupervised and supervised statistical methods, for differentiation of spirits, with respect to some previously defined criteria, is reviewed in this work. Thus, based on linear discriminant analysis (LDA), it was possible to differentiate the geographical origin of distillates in a percentage of 96.2% for the initial validation, and the cross-validation step of the method returned 84.6% of correctly classified samples. An excellent separation was also obtained for the differentiation of spirits producers, 100% in initial classification, and 95.7% in cross-validation, respectively. For the varietal recognition, the best differentiation was achieved for apricot and pear distillates, a 100% discrimination being obtained in both classifications (initial and cross-validation). Good classification percentages were also obtained for plum and apple distillates, where models with 88.2% and 82.4% in initial and cross-validation, respectively, were achieved for plum differentiation. A similar value in the cross-validation procedure was reached for the apple spirits. The lowest classification percent was obtained for quince distillates (76.5% in initial classification followed by 70.4% in cross-validation). Our results have high practical importance, especially for trademark recognition, taking into account that fruit distillates are high-value commodities; therefore, the temptation of “fraud”, i.e., by passing regular distillates as branded ones, could occur.

Object-based image analysis (OBIA) for mapping mangrove using Unmanned Aerial Vehicle (UAV) on Tidung Kecil Island, Kepulauan Seribu, DKI Jakarta Province

Tidung Kecil Island is a conservation and mangrove cultivation area. Therefore, the potential of mangrove ecosystems on Tidung Kecil Island will have a direct role in coastal ecosystems. Accurate mangrove mapping is necessary for the effective planning and management of ecosystems and resources because mangroves function as protectors of ecological systems. The utilization of remote sensing technology that is near real-time can be used as an alternative in providing spatial data effectively. Mapping earth’s surface objects method is growing especially after the development of design, research, and production of flexible Unmanned Aerial Vehicle (UAV) platforms. The use of object-based classification methods is currently an alternative in classifying an object of the Earth’s surface using both satellite and aerial photo imagery data (orthophoto) that has a high accuracy value. This research aim is to map object based mangrove ecosystems using UAV technology on Tidung Kecil Island, Kepulauan Seribu, DKI Jakarta. The K-NN algorithm result was a good classification with 81.081% overall accuracy (OA) at the optimum value of the MRS segmentation scale 300;0,1;0.7 and divided into two classes which are mangrove and non-mangrove for 0.381 ha and 20.912 ha respectively.

Audio classification for music information retrieval of Hindustani vocal music

An important task in music information retrieval of Indian art music is the recognition of the larger musicological frameworks, called ragas, on which the performances are based. Ragas are characterized by prominent musical notes, motifs, general sequences of notes used and embellishments improvised by the performers. In this work we propose a convolutional neural network-based model to work on the mel-spectrograms for classication of steady note regions and note transition regions in vocal melodies which can be used for finding prominent musical notes. It is demonstrated that, good classification accuracy is obtained using the proposed model.