Penerapan Metode Artikulasi pada Mata Pelajaran Fikih Kelas VIII MTs Al-Akbar Kota Sorong

This research is motivated by the low learning outcomes of students of class VIII MTs Al-Akbar Sorong City on Jurisprudence subjects caused by Jurisprudence teachers in presenting subject matter which is sometimes monotonous. Teachers are more likely to use the lecture method in learning so as to make students bored. Therefore the researcher chose one of the Articulation learning methods to improve student learning outcomes. This method uses a chain message delivery system, which is from the teacher to students and is passed from one student to another student. This study aims to improve student learning outcomes in Jurisprudence subjects using Articating learning methods for students of class VIII MTs Al-Akbar Sorong City. This type of research is classroom action research (CAR). The subject was students in class VIII MTs Al-Akbar Sorong City in the odd semester of 2016/2017 academic year totaling 38 people. This research was conducted in 2 cycles, namely the first cycle and the second cycle carried out as many as 4 meetings. Data retrieval is done by using test results of learning and observation. The collected data is analyzed quantitatively and qualitatively. Quantitative data is calculated using the SPSS 16.0 formula. The results obtained after the action are given, namely: (1) the activeness of students during the learning process in class has increased, (2) in the first cycle the average score of student learning outcomes tests on Jurisprudence subjects between the first and second meetings in the first cycle is 62, 89% and 74.47% and in the second cycle, the average test score of student learning outcomes in fiqh subjects has increased ie, 80.79% and 94.34%. From the results of this study, in general, it can be concluded that an increase in student learning outcomes in the subjects of Jurisprudence VIII MTs Al-Akbar Sorong after applying the Articulation method

Potensi Tempurung Kelapa Sebagai Pembangkit Listrik Dengan Menggunakan Termoelektrik Generator

Around 35% of the total utilization of coconuts at this time is still not fully utilized. Thermoelectric is a technology that converts heat energy directly into electrical energy or converts electrical energy into heating and cooling energy. Data retrieval using two multimeters and an electric thermometer, data collection was carried out for 2 minutes. From the test results, this tool can produce an average voltage of 10.05 Volt for 200gram coconut shells, an average current of 0.99 Ampere and an average power of 13.84 Watts and can fully charge the battery up to 3 hours 33 minutes, while for 300 grams produces an average voltage of 10.59 Volts for 300gram coconut shells, an average current of 0.995 Ampere and an average power of 13.56 Watts and the battery can be fully charged in about 3 hours 36 minutes, while a coconut shell weighing 400 grams can produces an average voltage of 10.94 Volts, an average current of 1 Ampere and an average power of 13.70 Watts and the battery can be fully charged in about 3 hours 30 minutes. The more coconut shells used for combustion, the hotter the temperature and the faster the voltage and current are obtained, but with a note that the maximum temperature limit of the thermoelectric is T not more than 200o C. Keywords: Coconut Shell, Thermoelectric, Electrical Energy.

SSM: A Semantic Metasearch Platform for Scientific Data retrieval

Scientific research in all fields has advanced in complexity and in the amount of data generated. The heterogeneity of data repositories, data meaning and their metadata standards makes this problem even more significant. In spite of several proposals to find and retrieve research data from public repositories, there is still need for more comprehensive retrieval solutions. In this article, we specify and develop a mechanism to search for scientific data that takes advantage of metadata records and semantic methods. We present the conception of our architecture and how we have implemented it in a use case in the agriculture domain.

A Semantic Preprocessing Framework for Breaking News Detection to Support Future Drone Journalism Services

Nowadays, news coverage implies the existence of video footage and sound, from which arises the need for fast reflexes by media organizations. Social media and mobile journalists assist in fulfilling this requirement, but quick on-site presence is not always feasible. In the past few years, Unmanned Aerial Vehicles (UAVs), and specifically drones, have evolved to accessible recreational and business tools. Drones could help journalists and news organizations capture and share breaking news stories. Media corporations and individual professionals are waiting for the appropriate flight regulation and data handling framework to enable their usage to become widespread. Drone journalism services upgrade the usage of drones in day-to-day news reporting operations, offering multiple benefits. This paper proposes a system for operating an individual drone or a set of drones, aiming to mediate real-time breaking news coverage. Apart from the definition of the system requirements and the architecture design of the whole system, the current work focuses on data retrieval and the semantics preprocessing framework that will be the basis of the final implementation. The ultimate goal of this project is to implement a whole system that will utilize data retrieved from news media organizations, social media, and mobile journalists to provide alerts, geolocation inference, and flight planning.

Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

Aiming at the problems of low image data retrieval accuracy and slow retrieval speed in the existing image database retrieval algorithms, this paper designs a clothing image database retrieval algorithm based on wavelet transform. Firstly, it represents the color consistency vector of clothing image, reflects the composition and distribution of image color through color histogram, quantifies the visual features of clothing image, aggregates them into a fixed size representation vector, and uses the Fair Value (FV) model to complete the collection of clothing image data. Then, the size of the clothing image is adjusted by using the size transformation technology, and the clothing pattern is divided into four moments with the same size. On this basis, the clothing image is discretized with the help of Hu invariant moment to complete the preprocessing of clothing image data. Finally, the generating function of wavelet transform is determined, and a cluster of functions is obtained through translation and expansion. The wavelet filter is decomposed into basic modules, and then, the wavelet transform is studied step by step. The clothing image data are regarded as a signal, split, predicted, and updated and input into the wavelet model, and the retrieval research of clothing image database is completed. The experimental results show that the design of the retrieval algorithm is reasonable, the retrieval data accuracy is high, and the retrieval speed is fast.

Semiotik Roland Barthes dalam Film Animasi Entong Sebagai Implikasi Penerapan Bahan Ajar Sastra di Sekolah Dasar

The problem in this research is the lack of alternative literature teaching materials used by teachers in the learning process. This study aims to analyze the semiotics of Roland Barthes in the animated film Entong which will be used as an alternative material for teaching literature in elementary schools. The research method applied in this research is content analysis method. Data collection techniques were carried out by observation and documentation. The results of the observation show that there are semiotic codes of Roland Barthes in the episodes of the animated film Entong. This happens because, data retrieval is taken from the YouTube application. The semiotic codes contained in the animated film Entong are hermeneutic codes that function to see the problems of a narrative and create a solution or an answer, semiotic codes function to a connecting relation code which is the connotation of a person, place, object whose sign is a character, code symbolic functions as symbols, preauretic codes function to see the basic narrative actions in various sequences that may be indicated, and cultural codes function to see the cultural side of a story. Based on Roland Barthes' semiotics contained in the animated film Entong, it can be used as an alternative to teaching literature in elementary schools so that students can apply it in their daily lives.

Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks

Accurate and prompt traffic data are necessary for the successful management of major events. Computer vision techniques, such as convolutional neural network (CNN) applied on video monitoring data, can provide a cost-efficient and timely alternative to traditional data collection and analysis methods. This paper presents a framework designed to take videos as input and output traffic volume counts and intersection turning patterns. This framework comprises a CNN model and an object tracking algorithm to detect and track vehicles in the camera’s pixel view first. Homographic projection then maps vehicle spatial-temporal information (including unique ID, location, and timestamp) onto an orthogonal real-scale map, from which the traffic counts and turns are computed. Several video data are manually labeled and compared with the framework output. The following results show a robust traffic volume count accuracy up to 96.91%. Moreover, this work investigates the performance influencing factors including lighting condition (over a 24-h-period), pixel size, and camera angle. Based on the analysis, it is suggested to place cameras such that detection pixel size is above 2343 and the view angle is below 22°, for more accurate counts. Next, previous and current traffic reports after Texas A&M home football games are compared with the framework output. Results suggest that the proposed framework is able to reproduce traffic volume change trends for different traffic directions. Lastly, this work also contributes a new intersection turning pattern, i.e., counts for each ingress-egress edge pair, with its optimization technique which result in an accuracy between 43% and 72%.

Understanding Cardiopulmonary Resuscitation Determines the Readiness of Doing Basic Life Support

Introduction: Cardiopulmonary Resuscitation (CPR) is an emergency lifesaving procedure performed when the heart stops beating. Basic Life Support (BLS) is the initial action to save life-saving conditions. BLS is one of the most important components in CPR. BLS greatly determines the fate of the next life-threatening victim. This study aimed to know the relationship of understanding CPR to readiness to do BLS for students of Faculty of Medicine, Universitas Airlangga, Surabaya.Methods: This was non-experimental study using the design of analytic and descriptive statistics. The sample of this study was the students of Faculty of Medicine, class of 2015, Universitas Airlangga, Surabaya and was taken by probability sampling method with a simple random sampling technique. Data retrieval was performed by giving a questionnaire to 100 respondents. This study was conducted in February 2019. The results of this study were then analyzed by SPSS using the Spearman test.Results: The results of this study showed that the most level of understanding CPR was in the good category with 56 people (56%), while the readiness to do BLS was mostly in the moderate category with 55 people (55%). Based on the results of statistical tests using the Spearman test, there was a relationship between the level of understanding CPR and the readiness to do BLS for students of Faculty of Medicine, Universitas Airlangga, Surabaya.Conclusion: There was relationship between the level of understanding CPR and the readiness to do BLS for students of Faculty Medicine, Universitas Airlangga, Surabaya.

Mapping the Literature on Financial Behavior: a Bibliometric Analysis Using the VOSviewer Program

Objective of the article is to study the current state of researches of financial behaviour. The article is conceptual and based on the use of the methodology of the bibliometric analysis. The analysis is based on the data retrieval functionalities of Scopus and Web of Science platforms. Is used the toolkit of the VOSviewer program, network visualization of keywords in scientific publications. Findings: The number of publications that directly study the nature and features of the financial behavior formation of various economic agents is insignificant, but is constantly growing. An important role in this process is played by digitalization processes of financial services, which have an important impact on the models transformation of both financial behavior of economic agents, and changes in the model of the financial services provision to customers by financial institutions.

Semantic-guided autoencoder adversarial hashing for large-scale cross-modal retrieval

With the vigorous development of mobile Internet technology and the popularization of smart devices, while the amount of multimedia data has exploded, its forms have become more and more diversified. People’s demand for information is no longer satisfied with single-modal data retrieval, and cross-modal retrieval has become a research hotspot in recent years. Due to the strong feature learning ability of deep learning, cross-modal deep hashing has been extensively studied. However, the similarity of different modalities is difficult to measure directly because of the different distribution and representation of cross-modal. Therefore, it is urgent to eliminate the modal gap and improve retrieval accuracy. Some previous research work has introduced GANs in cross-modal hashing to reduce semantic differences between different modalities. However, most of the existing GAN-based cross-modal hashing methods have some issues such as network training is unstable and gradient disappears, which affect the elimination of modal differences. To solve this issue, this paper proposed a novel Semantic-guided Autoencoder Adversarial Hashing method for cross-modal retrieval (SAAH). First of all, two kinds of adversarial autoencoder networks, under the guidance of semantic multi-labels, maximize the semantic relevance of instances and maintain the immutability of cross-modal. Secondly, under the supervision of semantics, the adversarial module guides the feature learning process and maintains the modality relations. In addition, to maintain the inter-modal correlation of all similar pairs, this paper use two types of loss functions to maintain the similarity. To verify the effectiveness of our proposed method, sufficient experiments were conducted on three widely used cross-modal datasets (MIRFLICKR, NUS-WIDE and MS COCO), and compared with several representatives advanced cross-modal retrieval methods, SAAH achieved leading retrieval performance.