cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Risanuri Hidayat
Contact Email
risanuri@ugm.ac.id
Phone
+62274-552305
Journal Mail Official
jnteti@ugm.ac.id
Editorial Address
Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Published by Universitas Gadjah Mada
ISSN : 23014156     EISSN : 24605719     DOI : 10.22146/jnteti
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Topics cover the fields of (but not limited to): 1. Information Technology: Software Engineering, Knowledge and Data Mining, Multimedia Technologies, Mobile Computing, Parallel/Distributed Computing, Artificial Intelligence, Computer Graphics, Virtual Reality 2. Power Systems: Power Generation, Power Distribution, Power Conversion, Protection Systems, Electrical Material 3. Signals, Systems, and Electronics: Digital Signal Processing Algorithm, Robotic Systems and Image Processing, Biomedical Instrumentation, Microelectronics, Instrumentation and Control 4. Communication Systems: Management and Protocol Network, Telecommunication Systems, Wireless Communications, Optoelectronics, Fuzzy Sensor and Network
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 644 Documents
 15   16   17   18   19 
Pemantauan dan Pengendalian Kepekatan Larutan Nutrisi Hidroponik Berbasis Jaringan Sensor Nirkabel Helmy Helmy; Aji Rahmawati; Syahrul Ramadhan; Thomas Agung Setyawan; Arif Nursyahid
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1208.613 KB)

Abstract

Nutrient Film Technique (NFT) is one of hydroponic plant cultivation models. Most of hydroponic farmers are using NFT model to raise the productivity of crops. NFT hydroponic farmers usually use more than one hydroponic table in order to fulfill market needs. Harvest failures can happen when farmers do not have suficient information on monitoring and controlling the nutrition solution concentration. This can be overcome with the existence of monitoring and controlling system of nutrition solution concentration. This paper aims to build and examine system reliability using two NFT hydroponic tables based on wireless sensor network. Each table is installed with monitoring and controlling of nutrition solution concentration devices which transmit the data to server throughwireless sensor network. The result shows that electrical conductivity meter which is used to read nutrition solution concentration has 3.92% of error rate. Node 2 has faster threshold data transmission than node 1, with 34.68 second of node 2 delay and 40.01 second of node 1 delay. Node 1 has better accuracy of nutrition solution concentration control for 96.12% than node 2 which has 92.79% nutrition solution concentration accuracy.
Deteksi Gestur Lengan Dinamis pada Lingkungan Virtual Tiga Dimensi Koleksi Warisan Budaya Adri Gabriel Sooai; Atyanta. N. Rumaksari; Khamid Khamid; Nurul Zainal Fanani; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1256.2 KB)

Abstract

Virtual reality technology can be used to support museum exhibitions. Implementation could be in various platforms. There are many implementation options, for example in smartphones, tablet, and desktop computers. Most objects of museum collections are very fragile. Minimizing the direct touch on a collection object is one of the benefits of this technology. This study aims to prepare gestures suitable for the exploration of virtual objects of cultural heritage collection. Five sets of gestures have been prepared, namely lifting, picking, holding, sweeping from both directions, left and right. Dynamic arm gestures are recorded using the forearm sensor. The recorded data contains coordinates of gestures in form of x, y, z, raw, pitch, and yaw. Gaussian mixture models are used in selecting features to produce good accuracy in the classification process.Two functions are used, namely probability density function and cumulative distribution function for the feature selection process. In this study, two experiments were used to train the gesture model. The accuracy of the two experiments is shown in the form of a confusion matrix. Each of the confusion matrices show excellent results of 99.8% for SVM and k-NN. Furthermore, modeling results are tested using new data. The testing shows 89.25% result for SVM classifier and 90.09% for k-NN. Four other dynamic arm gestures have a very satisfactory rate of 100% for the two classifiers. The five gestures can be used in the development of virtual reality applications.
Deteksi Limfoblas pada Citra Sel Darah Menggunakan Fitur Geometri dan Local Binary Pattern Annisaa Sri Indrawanti; Eka Prakarsa Mandyartha
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1098.356 KB)

Abstract

Lymphoblasts are white blood cell types of lymphocytes, which can mark leukemia. To identify lymphoblasts, an analysis of white blood cells is required. In this study, a computer-based automated system was proposed using digital image processing techniques to detect lymphoblasts by analyzing microscopic images of blood cells. This proposed method segments the components of white blood cells, which are cytoplasm and nucleus, using a new approach based on adaptive local thresholding techniques. After each cell component was segmented, the geometry features and texture were extracted. The texture feature used a local binary pattern (LBP) descriptor from the nucleus. The set of features was used to train the support vector machine classification algorithm in detecting lymphoblasts. The proposed method is able to segment correctly 264 of 269 total white blood cells, with 98.14% accuracy, out of 35 acute lymphoblastic leukemia images taken with the same camera with the same lighting conditions. The use of geometry features with 16 dimensional feature vector and LBP features with 256 dimensional feature vector result in accuracy of lymphoblast identification of 88.79% and 89.72% respectively. Better performance is obtained by combining two features, the geometry and the LBP with 272 dimensional feature vector, with classification accuracy of 94.32%.
Implementasi Sistem Notifikasi untuk Pengawasan Pasien Alzheimer Berbasis Bluetooth Low Energy (BLE) Aries Pratiarso; Trisna Agung Mahendra; Mike Yuliana; Prima Kristalina; I Gede Puja Astawa; Arifin Arifin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1726.001 KB)

Abstract

Alzheimer's patients need attention and special treatment due to their inability to remember something. One technology that is widely used for tracking objects or people in an indoor environment is a Bluetooth Low Energy (BLE). In this paper, a surveillance notification system for Alzheimer's patients is proposed using Beacon technology to prevent the lossof patients. Improvement in accuracy of the estimated position of the patient were calculated using a Kalman filter. The reason for using this method was the difficulty of determining the location of objects due to noise and inaccuracy of measurement data.Fromthe results of the tests performed, it can be seen that the system made is able to provide notifications to nurses if the patient exceeds the specified distance with an average success of up to 90%. The use of the Kalman method is also able to increase the accuracy of the estimation of patient position with an estimated error reduction of 69.7%.
Ekstraksi Ciri Produktivitas Dinamis untuk Prediksi Topik Pakar dengan Model Discrete Choice Diana Purwitasari; Chastine Fatichah; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1928.298 KB)

Abstract

Recommendation of active or productive experts is indispensable in supporting collaborations. Activities of publication and citation indicate expert productivity. An expert can be inferred to have an interest in a subject through productivity in that particular topic. Since an expert can change interests over time, the contribution of this paper is a Discrete Choice Model (DCM) based on topic productivities to predict the primary interests of the experts. DCM uses features extracted from bibliographic data of citation relation and title-abstract texts. Before extracting productivity features and dynamicity features to represent interest changes, title clustering with KMeans++ is used to identify research topics. There are six productivity features and five dynamicity values for each productivity feature to demonstrate the expert behavior. Therefore, a clustered topic as a research interest is represented as an expert choice with 30 extracted features in the proposed method. The experiments used multinomial logistic regression for DCM and a log-likelihood indicator for the fitted models of the features. The resulted DCM models showed that productive behavior of the experts by doing many publications and receiving many citations effected to the precision of topic prediction by 80%. Some features were better for predicting primary interests of the expert. It was demonstrated with a lower precision value of 60% by using features that represent the expert behavior of only doing publication or only getting citation.
Estimasi Rapat Spektral Daya Berbasiskan Compressive Sampling Dyonisius Dony Ariananda
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2295.66 KB)

Abstract

This paper focus on spectrum sensing based on power spectral density (PSD) reconstruction from sub-Nyquist-rate samples. In the existing works on PSD reconstruction from sub-Nyquist-rate samples, the resulting system of linear equations (SLE) is generally overdetermined, which allows the PSD reconstruction using least-squares (LS). Note that there is a lower bound for the achievable sampling rate ensuring that the resulting SLE is overdetermined. This paper aims for a further sampling rate reduction, which results in an underdetermined SLE. However, when the resulting SLE is underdetermined, the LS method cannot be used to reconstruct PSD and additional constraints are required. Under this circumstance, a sparsity assumption (which is applicable for some applications) can be applied on the PSD. The use of the orthogonal matching pursuit (OMP) and the least absolute shrinkage and selection operator (LASSO) algorithms to reconstruct the PSD for the case of underdetermined SLE is examined. The simulation study shows that if an appropriate regularization parameter is used, the quality of the PSD reconstructed using LASSO is only slightly below the one produced using Nyquist-rate sampling. From the detection point of view, the PSD reconstructed using LASSO can accurately locate the occupied frequency band when the user signal power is sufficiently high compared to the noise power. Meanwhile, OMP can be used only in the noiseless scenario. These results indicate that the sampling rate alleviation up to a very low rate is possible while maintaining the quality of the spectrum sensing results at the acceptable level.
Peningkatan Akurasi Pengenalan Emosi pada Sinyal Electroencephalograpy Menggunakan Multiclass Fisher Evi Septiana Pane; Adhi Dharma Wibawa; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1624.293 KB)

Abstract

EEG signals have a significant correlation to emotions when compared to other external appearances such as face and voice. Due to the low accuracy of emotional recognition through EEG signals, this study proposes a dimensional reduction method for EEG data to address that problem using Multiclass Fisher Discriminant Analysis (MC-FDA). In this study, the experiment was applied on public EEG dataset with three classes of emotions, namely positive, negative, and neutral. Differential entropy features were extracted from the decomposed EEG signals in five frequency band of the delta, theta, alpha, beta, and gamma. The accuracy of emotion recognition was measured using two prevalent classifiers on EEG identification, such as LDA and SVM. To demonstrate the superiority of the MC-FDA method, the PCA dimension reduction method was applied as a comparison. Classification accuracy results from all experiment scenario showed the advantages of the MC-FDA compared to the PCA.The best emotion classification accuracy was obtained from trials on all data from twelve electrodes using the MC-FDA and LDA methods, namely 93.3%. These results show a mean increase in accuracy of 3.5 points from the original feature vector dataset.
Manajemen dan Pemantauan Energi Motor BLDC pada Mobil Listrik Berbasis IoT Aditya Ilham Pradana; Eka Prasetyono; Ony Asrarul Qudsi; Era Purwanto; Sutedjo Sutedjo; Syechu Dwitya Nugroho; Lucky Pradigta S.R.; Diah Septi Y.
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 4: November 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1103.969 KB)

Abstract

This paper presents a system design as a management and monitoring of energy consumption in BLDC motors that are applied to electric vehicle. Energy consumption settings are applied using the Pulse Amplitude Modulation principle by adjusting the input voltage on a BLDC motor. This setting uses a DC-DC converter with Buck Converter topology. This converter is designed with a maximum current capability of 20 A and an output voltage that varies from a range of 24 V - 56 V. To ensure the output voltage is always on the set point, the duty cycle of Buck Converter is set using proportional controls. The regulated energy consumption is monitored with modern technology, namely by using low energy components and with the IoT Devices principle. Based on the results obtained, this method can reduce energy consumption up to 36%, as well as monitoring stable energy consumption at reading sensor.
Ekstraksi Frasa Kunci pada Penggabungan Klaster berdasarkan Maximum-Common-Subgraph Adhi Nurilham; Diana Purwitasari; Chastine Fatichah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1771.664 KB)

Abstract

Document clustering based on topic similarities helps users in searching from a collection of scientific articles. Topic labels are necessesary for describing subjects of the document clusters. Clusters with related subjects or contextual similarities can be merged to produce more descriptive labels. Relations between those words in one context can be modelled as a graph. Instead of single word, this paper proposed cluster labeling of phrases from scientific articles withcluster merging based on graph. The proposed method begins with K-Means++ for clustering the scientific articles. Then, the candidates of word phrases from document clusters are extracted using Frequent Phrase Mining which inspired by Apriori algorithm. Each cluster result has a representation graph from those extracted word phrases. An indicator value from each graph shows any similarities of graph structures which is calculated with Maximum Common Subgraph (MCS). Those clusters are merged if there are any structure similarities between them. Topic labels of clusters are keyword phrases extracted from a representation graph of previous merged clusters using TopicRank algorithm. The merging process which becomes the contribution of this paper is considering topic distribution within clusters for phrase extraction. The proposed method evaluationis performed based on topic coherence of the merged clusterslabel. The results show that proposed method can improve topic coherence on the merged clusters with MCS graph size percentage as the key factor.Further observation shows that merged cluster labels consistent to MCS graph.
Pengenalan Viseme Dinamis Bahasa Indonesia Menggunakan Convolutional Neural Network Aris Nasuha; Tri Arief Sardjono; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1057.97 KB)

Abstract

There has been very little researches on automatic lip reading in Indonesian language, especially the ones based on dynamic visemes. To improve the accuracy of a recognition process, for certain problems, choosing suitable classifiers or combining of some methods may be required. This study aims to classify five dynamic visemes of Indonesian language using a CNN (Convolutional Neural Network) and to compare the results with an MLP (Multi Layer Perceptron). Varying some parameters theoretically improving the recognition accuracy was attempted to obtain the best result. The data includes videos on pronunciation of daily words in Indonesian language by 28 subjects recorded in frontal view. The best recognition result gives 96.44% of validation accuracy using the CNN classifier with three convolution layers.

Page 17 of 65 | Total Record : 644

 15   16   17   18   19 

Filter by Year

2012 2025


Reset
Filter By Issues
All Issue Vol 14 No 4: November 2025 Vol 14 No 3: Agustus 2025 Vol 14 No 2: Mei 2025 Vol 14 No 1: Februari 2025 Vol 13 No 4: November 2024 Vol 13 No 3: Agustus 2024 Vol 13 No 2: Mei 2024 Vol 13 No 1: Februari 2024 Vol 12 No 4: November 2023 Vol 12 No 3: Agustus 2023 Vol 12 No 2: Mei 2023 Vol 12 No 1: Februari 2023 Vol 11 No 4: November 2022 Vol 11 No 3: Agustus 2022 Vol 11 No 2: Mei 2022 Vol 11 No 1: Februari 2022 Vol 10 No 4: November 2021 Vol 10 No 3: Agustus 2021 Vol 10 No 2: Mei 2021 Vol 10 No 1: Februari 2021 Vol 9 No 4: November 2020 Vol 9 No 3: Agustus 2020 Vol 9 No 2: Mei 2020 Vol 9 No 1: Februari 2020 Vol 8 No 4: November 2019 Vol 8 No 3: Agustus 2019 Vol 8 No 2: Mei 2019 Vol 8 No 1: Februari 2019 Vol 7 No 4: November 2018 Vol 7 No 3: Agustus 2018 Vol 7 No 2: Mei 2018 Vol 7 No 1: Februari 2018 Vol 6 No 4: November 2017 Vol 6 No 3: Agustus 2017 Vol 6 No 2: Mei 2017 Vol 6 No 1: Februari 2017 Vol 5 No 4: November 2016 Vol 5 No 3: Agustus 2016 Vol 5 No 2: Mei 2016 Vol 5 No 1: Februari 2016 Vol 4 No 4: November 2015 Vol 4 No 3: Agustus 2015 Vol 4 No 2: Mei 2015 Vol 4 No 1: Februari 2015 Vol 3 No 4: November 2014 Vol 3 No 3: Agustus 2014 Vol 3 No 2: Mei 2014 Vol 3 No 1: Februari 2014 Vol 2 No 4: November 2013 Vol 2 No 3: Agustus 2013 Vol 2 No 2: Mei 2013 Vol 2 No 1: Februari 2013 Vol 1 No 3: Agustus 2012 Vol 1 No 2: Mei 2012 Vol 1 No 1: Februari 2012 More Issue