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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 64 Documents
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Search results for , issue "Vol 26, No 1: April 2022" : 64 Documents clear
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue Mashitah Mohd Hussain; Zuhaina Zakaria; Nofri Yenita Dahlan; Nur Iqtiyani Ilham; Zhafran Hussin; Noor Hasliza Abdul Rahman; Md Azwan Md Yasin
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp56-66

Abstract

This article aims to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each point. Nowadays, utility providers claim that natural sources are used to generate power by rising consumer demands for energy. However, occasionally utility workers need to know the demand at certain location, corresponding to maintenance issues or for any shutdown area involved. A distribution pattern based on the data can be predicted based on the incoming data profile without having detailed information of certain load bus, the concept of derivatives was relevant to forecast the types of distribution data. The model was constructed with load profile information based on three different locations, and the concept of derivative was recognized, including the type of incoming data. Historical data were captured from a selected location in Malaysia that was proposed to train the JADE algorithm from three different empirical distributions of consumers, recording every 15 minutes per day. The results were analyzed based on the error measurement and compared with the real specific load distribution feeder information of needed profiles.
Datasets design of gate diffusion input based pipeline architecture for numerically controlled oscillator Ramana Reddy Gujjula; Chitra Perumal; Prakash Kodali; Bodapati Venkata Rajanna
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp253-260

Abstract

Gate diffusion input (GDI) is a technique, which enables reducing power consumption, area and delay in the digital circuits significantly, at the same time maintains low complexity of the logical design. This paper focuses on the analysis and interpretation of the design and implementation of GDI-based pipeline architecture for numerically controlled oscillator (NCO) using look up table (LUT). Based on the input signal and the alternate signal, this phase separation will separate the phase difference signal. The NCO generates a frequency and phase harmonized output signal with an antecedence fixed frequency clock. The 32-bit counter then compares the current count to the value stored in the compare register. Here the Coherent control comes into picture. It controls the carrier synchronizer by employing data from the 32-bit counter and the obtained data will be saved. It is updated and advanced using the third peer group of frequency synthesis technology. The test outcomes are accompanied with the theoretical concept and reproduced the results. The main objective of GDI-based pipeline architecture for NCO using LUT is to reduce the usage of metal oxide semiconductor field effect transistors (MOSFET’s). NCO is an indispensable component in many digital communication systems linked to modems, software-defined radios, and digital radio, digital down/upconverters for cellular and personal communications service base stations.
A novel method for prediction of diabetes mellitus using deep convolutional neural network and long short-term memory Gorli L Aruna Kumari; Poosapati Padmaja; Jaya G Suma
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp404-413

Abstract

Hyperglycemia arises due to diabetes mellitus, which is a persistent and life-threatening ailment. In this paper deep convolution neural network can be embedded to long short-term memory networks to recognize early prediction of diabetes and to decrease the complications that can be occurred through diabetes irrespective to the age. Diabetes problem is being gradually growing and presently, it is reported as a significant cause of death in the top spot. According to the recent studies 48% of overall world population will be affected by diabetes by 2045. If diabetes unidentified in early stages, it may cause other additional cardiac problems. In the proposed based work, a deep learning framework deep combination of convolution neural network and long short-term memory is proposed by embedding both to leverage their respective advantages for diabetes recognition and to allow early prediction of diabetes to avoid other complications. The experimental evolution on the bunch mark of diabetes data set demonstrates the proposed model embedded deep long short-term memory outperforms other machine learning and conventional deep learning approaches. The proposed algorithm in this paper outperforms existing techniques and evaluates total effectiveness and accuracy of predicting whether a person will suffer from diabetes.
Sentiment analysis using global vector and long short-term memory Kusum Kusum; Supriya P. Panda
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp414-422

Abstract

Tweet sentiment analysis is a Deep Learning study that is beneficial for automatically determining public opinion on a certain topic. Using the Long Short-Term Memory (LSTM) algorithm, this paper aims to proposes a Twitter analysis technique that divides Tweets into two categories (positive and negative). The Global Vector (GloVe) word embedding score is used to rate many selected words as network input. GloVe converts words into vectors by building a corpus matrix. The GloVe outperforms its prior model, owing to its smaller vector and corpora sizes. GloVe has a higher accuracy than the model word embedding word2vec, Continuous Bag of Word(CBoW), and word2vec Skip-gram. The preprocessed term variation was conducted to test the performance of sentiment classification. The test results show that this proposed method has succeeded in classifying with the best results with an accuracy of 95.61%.
Sentiment classification of delta robot trajectory control using word embedding and convolutional neural network Zendi Iklima; Trie Maya Kadarina; Muhammad Hafidz Ibnu Hajar
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp211-220

Abstract

Sentiment classification (SC) is an important research field in natural language processing (NLP) that classifying, extracting and recognizing subjective information from unstructured text, including opinions, evaluations, emotions, and attitudes. Human-robot interaction (HRI) also involves natural language processing, knowledge representation, and reasoning by utilizing deep learning, cognitive science, and robotics. However, sentiment classification for HRI is rarely implemented, especially to navigate a robot using the Indonesian Language which semantically dynamics when written in text. This paper proposes a sentiment classification of Bahasa Indonesia that supports the delta robot to move in particular trajectory directions. Navigation commands of the delta robot were vectorized using a word embedding method containing two-dimensional matrices to propose the classifier pattern such as convolutional neural network (CNN). The result compared the particular architecture of CNN, GloVe-CNN, and Word2Vec-CNN. As a classifier method, CNN models trained, validated, and tested with higher accuracy are 98.97% and executed in less than a minute. The classifier produces four navigation labels: right means 'kanan', left means 'kiri', top means 'atas', bottom means 'bawah', and multiplier factor. The classifier result is utilized to transform any navigation commands into direction along with end-effector coordinates.
Efficient carry select 16-bit square root adder with complementary metal-oxide semiconductor implementation Pavitha Uppinakere Sathyanarayan; Mamtha Mohan; Sandeep Kakde; Annam Karthik
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp172-183

Abstract

The adder is the maximum usually used mathematics block in programs inclusive of central processing unit (CPU) and virtual sign processing. As a result, it is important to expand a space-saving, low-strength, high-overall performance adder circuit. The hassle is diagnosed to layout mathematics sub structures with minimized strength dissipation, low area, and minimal time postpone of common-sense circuits. In conventional carry select adder (CSA), the time required to generate the sum output is less than other basic adder circuits but the principal difficulty is the location because the variety of transistors used to put in force the CSA circuit is fairly more. So, the area increases because of which the overall power consumption of the circuit will be more. If it's far viable to lessen the variety of transistors used withinside the structure of CSA adder, then, the strength intake of the circuit may be decreased or even the reaction time will improve. By lowering the area of the adder circuit, the suggested solution intends to reduce power consumption and latency.
The development of smart flowerpot based on internet of things and mobile and web application technology Satien Janpla; Chaiwat Jewpanich; Nisanart Tachpetpaiboon; Waraporn Prongsanthia; Butsirin Jewpanich
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp423-433

Abstract

Cultivation of ornamental plants in the office is popular among office workers and the general public because it can create a good environment in the area, but the plant growers must pay attention to watering the plants, because it may cause the plant to die. With the advancement of internet of things (IoT) technology, used to control devices wirelessly, this research developed the smart flowerpot system that works through mobile and web applications, using a microcontroller to control the system and connect to users via mobile and web application that can monitor the system, and control the operation both directly and automatically. When soil moisture is reduced to a predetermined value, the system will order the plants to be watered automatically, and when the water level is almost completely reduced, the ultrasonic sensor will send a notification to the mobile application to let the user know, after testing the system, it was found that the smart plant pot can work efficiently and can automatically water the plants.
Classification based topic extraction using domain-specific vocabulary: a supervised approach Vandana Kalra; Indu Kashyap; Harmeet Kaur
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp442-449

Abstract

Recently, a probabilistic topic modelling approach, latent dirichlet allocation (LDA), has been extensively applied in the arena of document classification. However, classical LDA is an unsupervised algorithm implemented using a fixed number of topics without prior domain knowledge and generates different outcomes with the change in the order of documents. This article presents a comprehensive framework to evade the order effect and unsupervised probabilistic nature. First, the framework creates the vocabulary specific to the category using a weight-dependent model that extracts distinctive features suitable for supervised classification. Then, it transforms a classified cluster of documents from the domain corpus to the relevant topic making it more robust to noise. The framework was tested on a comprehensive collection of benchmark news datasets that vary in sample size, class characteristics, and classification tasks. In contrast to the conventional classification methods, the proposed framework achieved 95.56% and 95.23% accuracy when applied on two datasets, indicating that the proposed algorithm has a better classification capability. Furthermore, the topics extracted from the classified clusters are highly relevant to domain categories.
A cluster validity for optimal configuration of Kohonen maps in e-learning recommendation Jamal Mawane; Abdelwahab Naji; Mohamed Ramdani
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp482-492

Abstract

the first block of our unsupervised deep collaborative recommendation (UDCF) system and proposes a platform whose goal is to try to find the adequate parameters of the Kohonen maps, to create homogeneous clusters in profile data and results, the homogeneity is verified thanks to the very low variance rate of the results obtained by the cluster population and a second criterion which is the high prediction rate of collaborative recommendation. Although the revision concerns only the clustering block, and the use of a symmetrical autoencoder without searching for its optimization, the result obtained (82.33%) for the optimal configurations with high homogeneity of the Kohonen map is equivalent to the optimized result of the UDCF and even better than the classical recommendation methods
Development of mini smart multipurpose vehicle for organic rice harvesting Kanchana Daoden; Sureeporn Sringam; Supanat Nicrotha; Thanawat Sornnen
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp152-159

Abstract

This research aimed to develop the mini smart multipurpose vehicle (MSMPv) innovative from the conventional agriculture tractor for three objectives. The novel automatic gear modified technique for the MSMPv is proposed, then an idea to enhance peripheral capability through a hitch system. The final purpose is to support the farmer's ability to follow organic agriculture regulations on the issue of contaminated tools and machinery, especially in the rules related to contamination of equipment or machines that cannot share with conventional agricultural production. The organic rice crop plot of Nong Bua Lamphu Province in Thailand has been set to the case study. Here, farmers faced problems; lack of labour, production under an organic system that does not permit chemicals, and limited harvesting. According to the existing technology, this research has developed a typical farm tractor used in the country by inventing a manual transmission engine to an automatic transmission and accessories such as remote control, GPS, camera, and sensors. Thus, the development of this organic rice harvesting prototype should be an approach that provides both the opportunity to raise the self-reliance concept and enhance the knowledge of the development of innovative tools for farmers simultaneously.

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