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

A TDMA-Based MAC Protocol for Mitigating Mobility-Caused Packet Collisions in Vehicular Ad Hoc Networks

An autonomous driving environment poses a very stringent requirement for the timely delivery of safety messages in vehicular ad hoc networks (VANETs). Time division multiple access (TDMA)-based medium access control (MAC) protocols are considered a promising solution because of their time-bound message delivery. However, in the event of mobility-caused packet collisions, they may experience an unpredicted and extended delay in delivering messages, which can cause catastrophic accidents. To solve this problem, a distributed TDMA-based MAC protocol with mobility-caused collision mitigation (MCCM-MAC) is presented in this paper. The protocol uses a novel mechanism to detect merging collisions and mitigates them by avoiding subsequent access collisions. One vehicle in the merging collisions retains the time slot, and the others release the slot. The common neighboring vehicles can timely suggest a suitable new time slot for the vacating vehicles, which can avoid access collisions between their packet transmissions. A tie-breakup mechanism is employed to avoid further access collisions. Simulation results show that the proposed protocol reduces packet loss more than the existing methods. Consequently, the average delay between the successfully delivered periodic messages is also reduced.

FUNCTIONING DISCOURSE MARKERS TO CONSTRUCT A SOCIAL SITUATION IN SPEECH

This study relates the discussion of discourse markers to their functions from a social context. It aims to identify discourse markers and analyze their function to construct a social situation in Steve Jobs’ speech delivered at Stanford Commencement Address. To analyze the data in this study, the researchers used a qualitative descriptive method. This study showed that the dominant discourse markers used were connective, followed by cause result, temporal adverb, and marker of response, respectively, in which the last marker was the least used. Furthermore, all discourse markers functioned to gain coherent message delivery in the speech by considering the “setting and scene," "participants," "ends,” “act sequence,” “key,” “instrumentalities,” “norms of interaction,” and “genre,” all of which were shortened in the acronym of “speaking.” Finally, based on its social situation, the present study is expected to broaden the understanding of discourse markers in a particular text.

Designing Text Message Learning to More Equitably Reach Students Wherever They Go

This chapter describes how TESOL educators can partner with global aid organizations, local communities, and learners themselves to leverage low-tech yet innovative learning solutions like text-message lessons with the goal of more equitably reaching learners, particularly those affected by disruption to their education such as those who are migrants/refugees. Taking such action as advocates committed to closing opportunity gaps arising from social issues affecting language learning not only involves the TESOL educator in the six principles for exemplary teaching of English learners but also UN Sustainable Development Goals as a framework, trauma-informed teaching and learning, and the concept of text messages used as micro-learning. Through a UNICEF case study of practice in action, the author provides a stepwise how-to for redesigning curriculum into micro-learning appropriate for text-message delivery and offers considerations and recommendations for its dissemination, evaluation, and potential application to many other contexts and learner populations at scale.

Evaluation of Logistics Service Quality: Sentiment Analysis of Comment Text Based on Multi-Level Graph Neural Network

The world is severely impacted by the coronavirus (COVID19). During the epidemic, logistics service, an often-overlooked pillar of the modern society, steps into the spotlight. However, the service capability is inevitably weakened by the epidemic. The fatigued service providers are increasingly unable to meet the high expectations of users, who therefore leave harsh comments on logistics services. It is important for managers to find information that helps to improve management, out of the biased and angry comments. Text sentiment analysis is a fundamental work in natural language processing (NLP). In recent years, graph neural network (GNN) has achieved excellent performance in various NLP tasks. Nevertheless, GNN only considers the adjacent words, as it updates graph nodes. The model thereby emphasizes local features over global features, and misses the intent of the comment text. This paper constructs a triple graph neural network (TGNN) to serve the sentiment analysis of service texts. Firstly, the corresponding node connection windows were applied on different network layers to consider both local and global features. Next, the graph attention network (GAT) was adopted as the message delivery mechanism to fuse the features of all word nodes in the graph. Experimental results show that, the TGNN can evaluate the comment texts on logistics service quality more accurately than the other models.

Security Guarantee for Vehicular Message Transmission Based on Dynamic Social Attributes

Effective message forwarding between vehicles can reduce the occurrence of traffic accidents and improve the driving experience. Vehicle clustering can improve message utilization, but attackers in the network pose a serious threat to message forwarding. Based on vehicle clustering, we propose a message forwarding strategy for Vehicular Ad hoc Network. Specifically, the vehicles are clustered based on their directions and speeds. Besides, the friendship of vehicles is evaluated in terms of the interaction friendship and reference friendship. Based on the friendship of vehicles, the optimal vehicle can be selected as the cluster head. Thereafter, the double key technology is designed to encrypt vehicular messages such that the messages can be forwarded more safely and efficiently. The analysis results show that the proposed strategy can effectively improve the message delivery rate, reduce the message leakage rate, and improve the network performance.

LRDC: Learning Resource Diffusion Based on Credibility for Computer-Supported Collaborative Learning

Computer-supported collaborative learning (CSCL) is a learning strategy that gathers students together on campus through mobile application software on intelligent handheld devices to carry out creative exploration learning activities and social interaction learning activities. Learning resource diffusion is a very important constituent part of CSCL mobile software. However, learners will receive or forward a large number of learning resources such that short video, images, or short audio which will increase the energy consumption of forwarding nodes and reduce the message delivery success rate. How to improve the message delivery success rate is an urgent problem to be solved. To solve the aforementioned problem, this paper mainly studies the diffusion of learning resources in campus opportunistic networks based on credibility for CSCL. In campus opportunistic networks, learners who participate in collaborative learning can obtain the desired learning resources through the distribution and sharing of learning resources. Learning resource diffusion depends on the credibility of learners who participate in collaborative learning. However, the existing classical algorithms do not take into account the credibility between learners. Firstly, the concept of credibility in campus opportunistic networks is proposed, and the calculation method of credibility is also presented. Next, the problem of node initialization starvation is solved in this paper. The node initialization starvation phase of collaborative learning is defined and resolved in campus opportunistic networks. Based on the information of familiarity and activity between nodes formed in the process of continuous interaction, a learning resource diffusion mechanism based on node credibility is proposed. Finally, the paper proposes a complete learning resource diffusion algorithm based on credibility for computer-supported collaborative learning (LRDC for short) to improve the delivery success rate of learning resources on the campus. Extensive simulation results show that the average message diffusion success rate of LDRC is higher than that of classical algorithms such as DirectDeliver, Epidemic, FirstContact, and SprayAndWait under the different transmission speed, buffer size, and initial energy, which is averagely improved by 46.83%, 44.43%, and 45.6%, respectively. The scores of LRDC in other aspects are also significantly better than these classical algorithms.

An Adaptive Routing Algorithm Based on Relation Tree in DTN

It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.

Biblical Concept of “Amen” Translated as “Ase” in Okun Language

The concept of “Amen” translated as “Ase” in Okun language is a term that cuts across nations, cultures and religions, even though it may be translated or transliterated differently in terms of linguistic and discourse structures. Amen could probably be assumed to be the commonest word of human speech. Its use among the three great religions—Christianity, Islam and Traditional religion—cannot be overemphasized. For instance, the Old Testament usage of it is always at the end of a speech, while in the New Testament, Jesus used it mostly at the beginning of his message to his audience. Paul the Apostle oftentimes used it at the end of his letters to close his doxological statements and greetings to the churches. And also, the term is being used as a seal of an oath for legal backing or pronouncement of a curse as it is in the book of Deuteronomy 27 and Nehemiah 5:13. Jeremiah at another instance used Amen as an affirmation of God’s statements of the blessings and the curses of the covenant in Jeremiah 11:5. In our contemporary time, the Christians use it intermittently in the middle of a message delivery to show complete agreement with the minister. Similarly, in the socio-cultural and religious settings of the Okun speakers, Ase is an African philosophical concept through which the Okun people affirm the power to make things happen and produce change. It is used most often at the end of the pronouncement of a blessing, prayers offered, declaration of curse on certain norms or laws if broken, or to prove the innocence of a person in a controversial issue. In any case, the meanings are similar or the same in accordance to the context in which they are being used culturally and religiously. As the saying goes, “As it was in the beginning, it is now and ever shall be, world without end. Amen, amen”; in Okun language it is Ase, ase. “Blessed be the Lord, the God of Israel, from everlasting to everlasting. Amen and amen” (Psalm 41:13).

Infographics as a campaign medium for goat milk drinking culture in Sragen regency, Central Java, Indonesia

Milk is a dairy product that contains excellent nutritional value for human health and growth. In addition to having a strategic selling value, milk can be processed into various products such as UHT milk, milk candy, milk soap and etc. Cow milk and goat milk are types of milk that are often consumed daily by people. Although cow milk is more popular than goat milk, there are many advantages and benefits of consuming goat milk. The “Taruna Mukti” Goat Farmer Group in Sragen Regency, Central Java, in collaboration with the Visual Communication Design Undergraduate Study Program, the Faculty of Fine Arts and Design of Universitas Sebelas Maret, designed infographics that were distributed through social media platforms to campaign for the culture of drinking goat milk. Research and Development method and ADDIE model were used in the media development. The expected results of this research are an effective message delivery about the importance of consuming goat milk and a positive response from the community.