Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

HOAX DETECTION IN INDONESIA LANGUAGE USING LONG SHORT-TERM MEMORY MODEL

Nowadays, the internet and social media grow fast. This condition has positive and negative effects on society. They become media to communicate and share information without limitation. However, many people use that easiness to broadcast news or information which do not accurate with the facts and gather people's opinions to get benefits or we called a hoax. Therefore, we need to develop a system that can detect hoax. This research uses the neural network method with Long Short-Term Memory (LSTM) model. The process of the LSTM model to identify hoax has several steps, including dataset collection, pre-processing data, word embedding using pre-trained Word2Vec, built the LSTM model. Detection model performance measurement using precision, recall, and f1-measure matrix. This research results the highest average score of precision is 0.819, recall is 0.809, and f1-measure is 0.807. These results obtained from the combination of the following parameters, i.e., Skip-gram Word2Vec Model Architecture, Hierarchical Softmax, 100 as vector dimension, max pooling, 0.5 as dropout value, and 0.001 of learning rate.

Global North-Global South Relations Around a Notional U.S.-Mexico “Border Wall”

In the present political moment, “border walls” between the U.S. and Mexico have become a flashpoint, representing binaries like governed / ungoverned spaces, security / insecurity, morality / immorality, respect / disrespect for human rights, human unity / disunity, North / South, haves / have-nots, citizens / non-citizens, Republicans / Democrats, conservatives / liberals, patriots / traitors, nationalists / internationalists (or globalists), and others. This work explores some of the thematic Global North – Global South implications of a notional “border wall” based on social imagery (in a multi-loop image analysis approach). This work questions how the “other” may be viewed through the limiting slats of a fence or windows in a wall. In addition to the image analyses, topic-related textual data will also be studied from various sources: academia, journalism, and social media (including mass search correlations, big data word search, related tags networks, and #hashtag network analysis).

On Computability of Data Word Functions Defined by Transducers

In this paper, we investigate the problem of synthesizing computable functions of infinite words over an infinite alphabet (data $$\omega $$ ω -words). The notion of computability is defined through Turing machines with infinite inputs which can produce the corresponding infinite outputs in the limit. We use non-deterministic transducers equipped with registers, an extension of register automata with outputs, to specify functions. Such transducers may not define functions but more generally relations of data $$\omega $$ ω -words, and we show that it is PSpace-complete to test whether a given transducer defines a function. Then, given a function defined by some register transducer, we show that it is decidable (and again, PSpace-c) whether such function is computable. As for the known finite alphabet case, we show that computability and continuity coincide for functions defined by register transducers, and show how to decide continuity. We also define a subclass for which those problems are PTime.

Energy Storage with Inclusion of Power Stability Improvement with Advanced Controls Based on VLSI

Multi rate strategy is fundamental for systems with different data and yield taking a gander at rates. Late advances in negligible figuring and correspondence applications deals low power and rapid VLSI DSP structures. This Paper presents Multi rate modules used for binding to offer sign overseeing in remote correspondence structure. Distinctive arranging made for the structure of low multifaceted nature, bit parallel Multiple Constant Multiplications improvement which principles the unusualness of DSP systems. In any case, basic hindrances of present approaches are either outrageously extravagant or not profitable enough. On the other hand, MCM and digit-consecutive snake offer elective low multifaceted nature plans, since digit-dynamic structure consolidate less space and are free of the data word length. Distinctive Constant Multiplications is capable way to deal with oversee decrease the proportion of enlargement and subtraction in poly stage channel execution. This Multi rate structure believing is purposeful and real to various issues. In this paper, thought has given to the MCM and digit dynamic structure with moving and including technique that offers elective low multifaceted nature in exercises. This paper what's more pivoted around Multi rate Signal Processing Modules using Voltage and Technology scaling. Lessening of intensity use is giant for VLSI system and moreover it ends up one of the most fundamental game-plan parameter.

INFORMATION SYSTEM FILE DELIVERY USING BLOWFISH ALGORITHM AND CRYPTOGRAPHY AES ANDROID-BASED ( CASE STUDY : LPPPTK KPTK DISTRICT GOWA )

Lembaga Pengembangan dan Pemberdayaan Tenaga Pendidik Bidang Kelautan, Perikanan, Teknologi Informasi dan Komunikasi (LPPPTK KPTK) merupakan salah satu unit pelaksana teknis Kementerian Pendidikan dan Kebudayaan dibidang pengembangan dan pemberdayaan pendidik dan tenaga kerja pendidik dibidang kelautan, perikanan, teknologi informasi dan komunikasi yang terletak di kabupaten Gowa Provinsi Sulawesi Selatan. Adapun masalah pada penelitian ini adalah bagaimana agar file-file berupa soal untuk uji kompetensi guru dan surat tugas pegawai dalam bentuk word dan excel tidak mudah terbaca oleh pihak yang berkepentingan, bagaimana menerapkan algoritma bowfish untuk file word dan kriptografi AES pada file excel. Tujuan dari penelitian ini adalah membuat sistem yang dapat memberikan keamanan data terhadap proses pengiriman file-file word dan excel pada LPPPTK KPTK dari pihak yang tidak berkepentingan. Adapun metode yang digunakan adalah metode algoritma Blowfish dan kriptografi AES, algoritma Blowfish adalah Blowfish merupakan metoda enkripsi yang mirip dengan DES (DES-like cipher) dan kriptografi AES adalah algoritma simetri yaitu mengunakan kunci yang sama untuk proses enkripsi dan deskripsi. Hasil dari penilitian ini adalah dengan adanya sistem informasi berbasis android dan menerapkan algoritma blowfish pada pengiriman file word maka data-data file data word telah di enkripsi begitu pula dengan menerapkan kritografi AES untuk pengiriman data excel telah di enkripsi sehingga terhinndar dari gangguan pihak-pihak yang tidak berkrpentingan dan proses pengiriman file pada LPPPTK KPTK Gowa keamanan datanya lebih terjamin.

Novel Approaches for Efficient Delay-Insensitive Communication

The increasing complexity and modularity of contemporary systems, paired with increasing parameter variabilities, makes the availability of flexible and robust, yet efficient, module-level interconnections instrumental. Delay-insensitive codes are very attractive in this context. There is considerable literature on this topic that classifies delay-insensitive communication channels according to the protocols (return-to-zero versus non-return-to-zero) and with respect to the codes (constant-weight versus systematic), with each solution having its specific pros and cons. From a higher abstraction, however, these protocols and codes represent corner cases of a more comprehensive solution space, and an exploration of this space promises to yield interesting new approaches. This is exactly what we do in this paper. More specifically, we present a novel coding scheme that combines the benefits of constant-weight codes, namely simple completion detection, with those of systematic codes, namely zero-effort decoding. We elaborate an approach for composing efficient “Partially Systematic Constant Weight” codes for a given data word length. In addition, we explore cost-efficient and orphan-free implementations of completion detectors for both, as well as suitable encoders and decoders. With respect to the protocols, we investigate the use of multiple spacers in return-to-zero protocols. We show that having a choice between multiple spacers can be beneficial with respect to energy efficiency. Alternatively, the freedom to choose one of multiple spacers can be leveraged to transfer information, thus turning the original return-to-zero protocol into a (very basic version of a) non-return-to-zero protocol. Again, this intermediate solution can combine benefits from both extremes. For all proposed solutions we provide quantitative comparisons that cover the whole relevant design space. In particular, we derive coding efficiency, power efficiency, as well as area effort for pipelined and non-pipelined communication channels. This not only gives evidence for the benefits and limitations of the presented novel schemes—our hope is that this paper can serve as a reference for designers seeking an optimized delay-insensitive code/protocol/implementation for their specific application.

Prosodically Conditioned Consonant Duration in Djambarrpuyŋu

Cross-linguistically, segments typically lengthen because of proximity to prosodic events such as intonational phrase or phonological phrase boundaries, a phrasal accent, or due to lexical stress. Australian Indigenous languages have been claimed to operate somewhat differently in terms of prosodically conditioned consonant lengthening and strengthening. Consonants have been found to lengthen after a vowel bearing a phrasal pitch accent. It is further claimed that this post-tonic position is a position of prosodic strength in Australian languages. In this study, we investigate the effects of proximity to a phrasal pitch accent and prosodic constituent boundaries on the duration of stop and nasal consonants in words of varying lengths in Djambarrpuyŋu, an Australian Indigenous language spoken in northeast Arnhem Land, Northern Territory, Australia. Our results suggest that the post-tonic consonant position does not condition longer consonant duration compared with other word-medial consonants, with one exception: Intervocalic post-tonic consonants in disyllabic words are significantly longer than word-medial consonants elsewhere. Therefore, it appears that polysyllabic shortening has a strong effect on segment duration in these data. Word-initial position did not condition longer consonant duration than word-medial position. Further, initial consonants in higher-level prosodic domains had shorter consonant duration compared with domain-medial word-initial consonants. By contrast, domain-final lengthening was observed in our data, with word-final nasals preceding a pause found to be significantly longer than all other consonants. Taken together, these findings for Djambarrpuyŋu suggest that, unlike other Australian languages, post-tonic lengthening is not a cue to prosodic prominence, whereas prosodic domain-initial and -final duration patterns of consonants are like those that have been observed in other languages of the world.

Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis. The Facebook analysis shows that posts with photos and positive comments have the highest engagement. We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue. Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day.