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Risanuri Hidayat
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Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
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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
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Metode Power Control sebagai Manajemen Interferensi pada Komunikasi Device to Device Anggun Fitrian Isnawati; Sholihah Larasati; Indak Danil Mabar
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 4: November 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1234.421 KB) | DOI: 10.22146/jnteti.v10i4.2433

Abstract

Today’s communication technology development has entered the 5th generation (5G), where one of the solutions offered is device to device (D2D) communication. The scheme used in this study was cooperative D2D with Mobile User Equipment (MUE) located far from the eNodeB (eNB). The D2D User Equipment (DUE) served as a relay that helped MUE to improve service quality. The effect caused by D2D communication was interference. Therefore, the power control method was used to overcome this problem. This study used three comparative simulations, namely Without Power Control, using Power Control 1, and using Power Control 2. The scheme used in Power Control 1 was a fixed power control, while Power Control 2 used an adaptive power control. Using the Cumulative Distribution Function (CDF), Power Control 1 scheme could improve SINR by 0.124 dB for downlink and 0.0814 dB for uplink. Meanwhile, Power Control 2 scheme could increase SINR by 0.0316 dB for downlink and 0.0627 for uplink. Based on the final results related to SINR, throughput, and CDF, the Power Control 1 method has better results than the Power Control 2 method.
Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia Yudi Widhiyasana; Transmissia Semiawan; Ilham Gibran Achmad Mudzakir; Muhammad Randi Noor
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 4: November 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1150.961 KB) | DOI: 10.22146/jnteti.v10i4.2438

Abstract

Text classification is now a well-studied field, particularly in Natural Language Processing (NLP). The text classification can be carried out using various methods, one of which is deep learning. Deep learning methods such as RNN, CNN, and LSTM are the most frequent methods used for text classification. This research aims to analyze the implementation of two deep learning methods combination, namely CNN and LSTM (C-LSTM), to classify Indonesian news texts. News texts used as data in this study were collected from Indonesian news portals. The obtained data were then divided into three categories based on their scope: "National," "International," and "Regional." Three research variables were tested in this study: the number of documents, the batch size value, and the learning rate value of the built C-LSTM. The experimental results showed that the F1-score obtained from the classification results using the C-LSTM method was 93.27%. The F1-score value generated by the C-LSTM method was higher than that of CNN (89.85%) and LSTM (90.87%). In summary, the combination method of two deep learning methods, namely CNN and LSTM (C-LSTM), outperforms CNN and LSTM.
Pengembangan Onti Measures Berbasis Web dengan Pengujian Data Ontology Virus dan Penyakit Nur Alfi Ekowati; Ika Indah Lestari; Sulistiyasni
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 4: November 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1510.956 KB) | DOI: 10.22146/jnteti.v10i4.2443

Abstract

The use of the ontology document in the COVID-19 case is an example where the inconsistency measure for OWL ontologies is undoubtedly needed. Ontology is a knowledge representation of semantic web technology, which is the extension of a website. The role of inconsistency measure is essential in ensuring that all information on an ontology is consistent. This research aims to develop a web-based application program, namely Onti Measures, which is an expansion of the inconsistency measures program prototype that has been created in the previous research. That prototype has multiple weaknesses, such as it is only in the form of program codes with no user interface so that the public cannot access this program. The data collection method in this research was done through literature study, while the waterfall method was used as the system development method. This research’s testing sample was ontology files for virus and disease cases served as the input of Onti Measures using 3 types of OWL reasoners. The program's outputs were the information of inconsistency values, running times, and the ontology sizes. The testing was done by employing the whitebox and blackbox testing methods.
Autopilot Pesawat Tanpa Awak Menggunakan Algoritme Genetika untuk Menghilangkan Blank Spot Ronny Mardiyanto; Muhammad Ichlasul Salik; Djoko Purwanto
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 1: Februari 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1669.33 KB) | DOI: 10.22146/jnteti.v11i1.2492

Abstract

This paper presents the autopilot of unmanned aerial vehicles (UAV) with the ability to minimize blank spots on aerial mapping using the genetic algorithm. The purpose of the developed autopilot is to accelerate the times required for aerial mapping and save battery consumption. Faster time in conducting aerial mapping saves operational costs, saves battery consumption, and reduces UAV maintenance costs. The proposed autopilot has the ability to analyze blank spots from aerial shots and optimize flight routes for re-photography. The genetic algorithm was applied to obtain the shortest distance, which was done to save battery consumption and flight time. When developing the autopilot, the operator would manually set the flight route, then the aircraft would fly according to that route. The unstable wind factor has caused a shift in the flight route, which correspondingly caused blank spots. After all flight routes were traversed, the system developed would analyze the location of the blank spots. The new flight route was calculated using the genetic algorithm to determine the shortest distance from all the blank spot locations. The system developed consisted of a UAV equipped with autopilot and a ground control station (GCS). At the time of flight, the UAV would send the coordinates of the path traversed to the GCS to calculate the blank spot analysis. After the flight mission has been completed, the GCS would create a new route and send it to the UAV. The test carried out was an aircraft with a height of 120m using a 4S 4,200 mAh 25C lipo battery, and the percentage of throttle when flying straight was 30%. The results obtained are that the developed autopilot saves 46.4% of the time and saves 41.18% of battery capacity compared to conventional autopilots.
Kinerja Algoritme Pengelompokan Fuzzy C-Means pada Segmentasi Citra Leukosit Khakim Assidiqi Nur Hudaya; Budi Sunarko; Anan Nugroho
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 1: Februari 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1205.699 KB) | DOI: 10.22146/jnteti.v11i1.2493

Abstract

Image segmentation is one of the most critical steps in computer-aided diagnosis that potentially accelerate leukemia diagnosis. Leukemia is categorized as blood cancer known as a deadly disease. Generally, acute lymphoblastic leukemia (ALL) detection can be done manually by counting the leukocytes contained in the stained peripheral blood smear image using the immunohistochemical (IHC) method. Unfortunately, the manual diagnosis process takes 3−24 hours to complete and is most likely inaccurate due to operator fatigue. An image segmentation method proposed by Vogado can achieve an accuracy of 98.5%. However, this method uses a K-means clustering algorithm that is not optimal for input images containing mostly noise. In this research, fuzzy c-means were applied to solve this problem. The dataset used in this study was ALL-IDB2, which consisted of 260 images, with each image having the size of 257×257 pixels in tagged image file (TIF) format. The initial stage of this method was to divide the ALL-IDB 2 acute leukemia dataset image into cyan, magenta, yellow, key (CMYK) and L*a*b color schemes which then subtract the M component subtracted by component *b. The subtraction results were then splits using the FCM algorithm, resulting in the nucleus and background sections. The output of this method was then evaluated and measured using the metrics accuracy, specificity, sensitivity, kappa index, dice coefficient, and time complexity. The results showed that changing the clustering algorithm in the image segmentation method did not provide a significant change in results; an increase occurred in the specificity and precision metrics with an average of 0.1−0.4%, the execution time also increased by an average of 23.10%. The decrease in the accuracy metric was down to 95.4238%, and the dice coefficient value was 79.3682%. From the explanation above, it can be concluded that the application of the FCM algorithm to the segmentation method does not provide optimal results.
Integrasi Gradient Boosted Trees dengan SMOTE dan Bagging untuk Deteksi Kelulusan Mahasiswa Achmad Bisri; Rinna Rachmatika
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 4: November 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (954.179 KB) | DOI: 10.22146/jnteti.v8i4.2554

Abstract

Education has an important role in life. Pamulang University is a university which provides education at affordable cost. However, based on student academic performance data, there is imbalance in class between the number of students who graduate on time and students who can not graduate on time, on various study programs. In this paper, an implementation of SMOTE and bagging techniques was conducted on the Gradient Boosted Trees (GBT) classification method for handling the class imbalance problem. The proposed method is able to provide significant results with an accuracy of 80.57% and an AUC of 0.858, in the category of good classification.
Mendeteksi Cyberhate pada Twitter Menggunakan Text Classification dan Crowdsourced Labeling Dana Sulistyo Kusumo; Hadi Kurniawan Sidiq; Indra Lukmana Sardi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 4: November 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1157.974 KB)

Abstract

During the 2019 presidential election campaign in Indonesia, a lot of support was made by the community with various forms of support, such as poster distribution or even content on social media. For example, in social media such as Twitter, there were many support tags during the presidential election, such as #2019gantipresiden, #2019tetapjokowi, and other hashtags related to the Indonesian presidential election. However, many hate speeches are contained in tweets with the related hashtag. Hate speech on the internet (cyberhate) could cause disputes between support groups of the two presidential candidates which cause conflicts such as riots and other actions that harm the country. This study uses the SVM algorithm to detect cyberhate that produces the best accuracy of 97%. Also, this study applies crowdsourced labeling in dataset labeling which results in 98% valid data.
Asosiasi Single Nucleotide Polymorphism pada Diabetes Mellitus Tipe 2 Menggunakan Random Forest Regression Lina Herlina Tresnawati; Wisnu Ananta Kusuma; Sony Hartono Wijaya; Lailan Sahrina Hasibuan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 4: November 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1046.657 KB)

Abstract

Precision medicine can be developed by determining association between genomic data, represented by Single Nucleotide Polymorphism (SNP), and phenotype of diabetes mellitus type 2 (T2D). The number of SNP is actually very abundance. Thus, sorting and filtering the SNP is required before conducting association. The purpose of this paper was to associate SNP with T2D phenotypes. SNP ranking was conducted to choose significant SNPs by calculating importance score. Selected SNPs were associated with T2D phenotype using random forest regression. Moreover, the epistasis was also examined to show the interactions among SNPs affecting phenotype. This paper obtained 301 importance SNPs. Top ten SNPs have association with five T2D protein candidates. The evaluation results of the proposed models showed the Mean Absolute Error (MAE) of 0.062. This results indicate the success of random forest regression in conducting SNP and phenotype association and epistatic examination between two SNPs.
Implementasi Bellman-Ford untuk Optimasi Rute Pengambilan Sampah di Kota Palembang Rezania Agramanisti Azdy; Febriyanti Darnis
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 4: November 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1292.273 KB)

Abstract

The problem of waste in big cities in Indonesia is still the main focus of the related department. Generally, the problem is the lack of garbage transport facilities that can beused to transport waste from the temporary shelter (Tempat Penampungan Sementara, TPS) to the final processing site (Tempat Pemrosesan Akhir, TPA). This lack of garbage transport makes the garbage contained in TPS not fully transportable to TPA due to limited capacity and operational time of the relatedservice. In overcoming this problem, optimization of garbage transport facility utilization can be carried out to optimize the capacity of the garbage transported by finding the shortest path that can be traversed by the garbage transporter. This paper aims to apply the Bellman-Ford algorithm for determining waste collection routes. The Bellman-Ford algorithm allows a negative weighting for its edge value, so that it can anticipate the possible costs to be incurred in the selection of the garbage collection path. The stages of this paper were data requirements analysis, design, implementation, and testing. The results of the study are trajectories with a minimum cost from the origin location to the destination location, although it does not pass through all TPS that must be visited.
Dataset Indonesia untuk Analisis Sentimen Ridi Ferdiana; Fahim Jatmiko; Desi Dwi Purwanti; Artmita Sekar Tri Ayu; Wiliam Fajar Dicka
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 4: November 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1026.557 KB)

Abstract

This paper present a text dataset which can be used in the field of text analysis, especially sentiment analysis. This dataset covers the primary data which consists of 10,806 lines of Indonesian text data originated from Twitter social media, which categorized into three categories that are positive, negative, and neutral; and the raw data which consists of 454,559 lines of unprocessed data. Other than that, on the labeled data, the data is cleaned by removing many kind of noises in the data, such as symbols or urls. In this paper, the presented dataset is tested using a sentiment analysis model to make sure that this dataset is suitable to be used in the field of text analysis. The testing is done by measuring the model accuracy which is trained using this dataset and then comparing it to other model which is trained using already published dataset. After testing the data using various algorithm, such as SVM, KNN, and SGD, the accuracy result between our data and the comparison data are more or less equal with around 4% to 12% differences in accuracy, and prove that the dataset presented in this paper is feasible to be used in sentiment analysis. Dataset can be downloaded from link at conclusion section.

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