Sistem Pengenal Wicara Menggunakan Mel-Frequency Cepstral Coefficient

Human-machine interaction evolves toward a more adaptive and interactive system. There are several media that can be used in human-machine interaction systems, such as voice signals. The process includes converting analog signals into the appropriate meaning, which depend on the noise and reliability of signal characteristic extraction methods. In fact, variations of pronunciation by different people will result in a diversity of voice signal patterns. This research develops technology that can recognize and translate speech according to data that has been trained and can be modified based on user requirement. The voice signal will be separated from the silent signal using voice activity detection. Then, the voice signal is converted to the frequency domain before it is extracted using mel-frequency cepstral coefficients. Cepstral value from MFCC extraction will be identified as words using artificial neural network. This study utilizes a computer with a microphone as a sound recording device and pascal programming language as the basis for building applications. Based on the experimental results, the accuracy is 87% on the speech recognition process with 28 vocabulary sets. Accuracy decreases with more sets of vocabulary. However, the more pronounced speech variations, the greater the accuracy with an average number around 93%

Sistem Identifikasi Perintah Bahasa Indonesia Pada Prototipe Robot Pelayan Dan Keamanan

Dewasa ini, perkembangan interaksi manusia-mesin semakin mengarah pada sistem interaksi yang lebih natural dan mendekati sistem interaksi manusia. Sistem interaksi manusia menggunakan wicara lebih dominan daripada sarana komunikasi yang lain seperti kontak mata dan bahasa tubuh atau isyarat. Wicara adalah bentuk vokal sarana komunikasi manusia yang lebih dekat dengan sistem bahasa. Permasalahan yang muncul adalah pemaknaan, ambiguitas dan bahasa yang tidak sesuai aturan sintaksis menyebabkan penerjemahan perintah lisan menjadi lebih kompleks. Pada penelitian ini, dikembangkan teknologi kecerdasan buatan yang dapat memahami perintah lisan Bahasa Indonesia untuk aplikasi di bidang robotika, yang direalisasikan pada prototipe robot pelayan dan keamanan. Tujuan dari penelitian ini adalah untuk menerjemahkan perintah suara menjadi perintah bagi robot sehingga robot dapat mengerjakan perintah yang dimaksud dan menghasilkan sistem interksi yang lebih natural. Sinyal suara akan diekstrak menggunakan barkfrequency cepstral coefficients. Cepstral akan diidentifikasi sebagai kata-kata dengan menggunakan neural network. Kata-kata dalam kalimat lengkap akan diproses menggunakan natural language processing sehingga maksud yang diucapkan dapat diterjemahkan menjadi aksi bagi robot. Percobaan pengenalan wicara dengan 28 set sinyal wicara memperoleh akurasi 82 %, sedangkan percobaan pemrosesan bahasa natural menghasilkan akurasi 93 % dengan 100 set kosakata bahasa Indonesia. ========================================================================================================= Human-machine interaction has been growing with the discovery of artificial intelligence technology. The development of human-machine interaction leads to a more natural interaction. In daily interactions, human uses speech, more dominant than the other way such as gestures and eye contact. Speech is the vocalized form of human communication which is closely related to language system. The problem is meaning, ambiguity, and the language that is not according to the rules of syntax, causing the command translation become more complex. We need certain rules of syntax and semantics to understand the meaning of a command. An artificial intelligence technology developed in this research to understand the Indonesian speech commands for applications in the field of robotics, which is realized on the service and security robot prototype. The purpose of this research is to tranpslate voice command into the robots action, to generate human-machine interaction more natural. The voice command will be extracted using bark-frequency cepstral coefficients. Cepstrals identified into words using neural networks. Words in a complete sentences will be processed using natural language processing so that, the meaning can be translated into robot action. Speech recognition experiments with 28 sets of speech signal obtain 82 % accuracy, while natural language processing experiments obtain 93 % accuracy with 100 sets of Indonesian vocabulary

Parsing Indonesian Syntactic with Recursive Neural Network

Sentence is a form of human communication which is closely related to language system. Sentence is one of the recursive structures that are often found in daily conversation. Learning syntactic structure is useful to explore the meaning of the sentence contained on it or translated it into another language such as machine language. The problem is meaning, ambiguity, and the language that is not according to the rules of syntax, causing the command translation become more complex. This research is about parsing Indonesian syntax based on natural language rules for applications in the field of human-machine interaction. Each word that is a part of the sentence, is mapped into vector-space model. To estimate the potential connection of two words, we use the recursive neural network. The potential connection of two words translated into a higher structure to obtain a complete sentence structure. We obtain 93% accuracy, with 50 data-set are given in the learning process to represent a hundred vocabularies

University Course Timetabling with Genetic Algorithm: A Case Study

University Course Timetabling Problems is a scheduling problem to allocate some lectures with some constraint, such as the availability of lecturers, number of classrooms and time slot in each day. The schedule of courses is one of important factors before start the semester in order to manage the study process. Generally, the university course scheduling in some universities are usually created manually through administration office. It needs to synchronize for all schedules from all departments in faculty of the university. In addition, the limitations of classroom and timeslot can make collision of the courses, lecturers and also incompatibility between the room capacity and the number of students whom take the course in the class. This paper proposes the university course time tabling systems. Based on some simulations with 93 courses, 18 lecturers and up to six classrooms, the result is that the system will get the best violation if the system adds more number of iteration. This situation also happens in the result of the scheduling lectures, the system will get the best percentage when the number of iteration sets as maximum

Analysis of Steam Power Generators in Fulfilling Electricity Needs: A Case Study at PT Madubaru Yogyakarta, Indonesia

This research was conducted to find out the amount of power produced by steam power generators and the amount of power needed for the operation of production motors in PT Madubaru Yogyakarta. This study also discusses the factors that support and inhibit the fulfillment of electric power in PT Madubaru Yogyakarta. From the results of the analysis that has been done, it can be seen that the power generated by the steam power generator at PT Madubaru Yogyakarta can meet the electricity needs of production motors in the factory. The power generated by the three steam generators at PT Madubaru amounts to 3,000 KW, while the power needed for the operation of production motors at the Milling Station, the Ketel Station, Central Factory Station, and Rear Factory Station is 2,313.54 KW. However, the production motor does not always turn on simultaneously, so the power needed is between 1,500 KW to 2,000 KW. The remaining power is used to anticipate an increase in electricity load

Open Source System for Smart Home Devices Based on Smartphone Virtual Assistant

No doubt, the functionality of smartphones and the internet is now very diverse. Various features and applications in it are present to simplify human life, including the voice assistant feature. This study raises the theme of utilizing voice assistant for the purposes of controlling smart household devices based on the IoT module. The IoT NodeMCU module has an integrated WiFi chip, works with low power settings and supports flexible programming languages using the Arduino IDE. Testing using Android OS 5.0.2 version and 7.1.2 version generate four different results, which are 100% 83.33% accuracy for male voices and 83.33% 58.33% when tested using female voices. The accuracy is determined by several reasons i.e. system version, the quality of the microphone, noise, and voice articulation when giving commands to the Google Assistant

Electrical Design of a Portable Pure Sine Wave Inverter Using Ferrite Core Transformer and Double Stage Technique

The size of the iron core transformer in an inverter is large and heavy because it has more conductor turns and works at low frequencies. In contrast, ferrite core transformers are designed to work at high frequencies, so the number of turns of the conductor is less, and the transformer size is relatively small and light. Device portability is a significant challenge in designing high-power inverters. This research uses a ferrite core transformer to design a portable pure sine wave inverter. A two-stage technique is proposed in designing the inverter so that the dc-link voltage and capacitor size can be flexibly selected, and the device size can be compacted. The design consists of two stages. First, a circuit to generate a 400-Volt DC voltage is designed using IC SG3525, a MOSFET power amplifier, and a ferrite core step-up transformer. Second, a pure sine wave generator circuit is constructed using an EGS002 module, MOSFETs, and a filter circuit. Experiments are performed by measuring the output voltage, monitoring power and frequency, and observing the waveform with an oscilloscope. The results reveal that the designed inverter can generate a 220-volt pure sine wave output, a maximum power of 500 Watts, a frequency of 50 Hz, and an efficiency between 91.4% to 98.1%