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TELKOMNIKA (Telecommunication Computing Electronics and Control)
Published by Universitas Ahmad Dahlan
ISSN : 16936930     EISSN : 23029293     DOI : 10.12928
Core Subject : Science,
Computer Science & IT
Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of submissions that TELKOMNIKA has received during the last few months the duration of the review process can be up to 14 weeks. Communication Engineering, Computer Network and System Engineering, Computer Science and Information System, Machine Learning, AI and Soft Computing, Signal, Image and Video Processing, Electronics Engineering, Electrical Power Engineering, Power Electronics and Drives, Instrumentation and Control Engineering, Internet of Things (IoT)
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 2,614 Documents
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An improvement of Gram-negative bacteria identification using convolutional neural network with fine tuning Budi Dwi Satoto; Imam Utoyo; Riries Rulaningtyas; Eko Budi Khoendori
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14890

Abstract

This paper proposes an image processing approach to identify Gram-negative bacteria. Gram-negative bacteria are one of the bacteria that cause lung lobe damage-bacterial samples obtained through smears of the patient's sputum. The first step bacterium should pass the pathogen test process. After that, it bred using Mc Conkey's media. The problem faced is that the process of identifying bacterial objects is still done manually under a fluorescence microscope. The contributions offered from this research are focused on observing bacterial morphology for the operation of selecting shape features. The proposed method is a convolutional neural network with fine-tuning. In the stages of the process, a convolutional neural network of the VGG-16 architecture used dropout, data augmentation, and fine-tuning stages. The main goal of the current research was to determine the method selection is to get a high degree of accuracy. This research uses a total sample of 2520 images from 2 different classes. The amount of data used at each stage of training, testing, and validation is 840 images with dimensions of 256x256 pixels, a resolution of 96 points per inch, and a depth of 24 bits. The accuracy of the results obtained at the training stage is 99.20%.
Factors influencing the success of information systems in flood early warning and response systems context Waleed A. Hammood; Salwana Mohamad @Asmara; Ruzaini A. Arshah; Omar A. Hammood; Hussam Al Halbusi; Mohammed Abdullah Al-Sharafi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 6: December 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i6.14666

Abstract

Flash flood is a natural disaster that often occurs after heavy rain, and it is getting more common nowadays. The  flood early warning and response system (FEWRS) can be installed to minimize the level of damage and the number of casualties due to flood by providing accurate and reliable flood data. Unfortunately, the existing number of studies detailing on the factors affecting the efficiency of FEWRS in flood disaster is quite limited. The above issue is addressed in the current work, which involves conducting a comprehensive literature review on the factors that drive the effectiveness of information systems (IS) in FEWRS. The current analysis was based on the Wymer and Regan's standards. From the 66 factors identified from the previous studies on IS adoption, the most significant factors affecting the effectiveness of FEWRS are: system quality, information quality, user satisfaction, service quality, use, perceived usefulness, intention to use,net benefits, perceived ease of use,compatibility, user experience, relative advantage, complexity, perceivedrisks, educational quality, and confirmation, these factors can be constructed to the success model to address the effectiveness of FEWRS in disaster management.
PENGENDALIAN KECEPATAN MOTOR DC DENGAN METODE LOOK UP TABLE BERBASIS MIKROKONTROLER AT89C51 Muchlas Muchlas; Sunardi Sunardi; Tri Antoro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 4, No 1: April 2006
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v4i1.1238

Abstract

Pengaturan kecepatan motor DC dapat dilakukan dengan tiga cara, yaitu: dengan mengubah tahanan rangkaian jangkar (Ra), mengubah fluks magnit () dan mengubah tegangan (V). Tujuan penelitian ini adalah merancang sistem pengendali kecepatan motor DC dengan mengubah tegangan catu motor secra look up table berbasis mikrokontroler AT89C51 yang diharapkan bisa memberikan fleksibilitas yang lebih baik. Perancangan sistem dimulai dari perancangan rangkaian konversi digital ke analog, pengendali motor, sensor kecepatan, tombol keypad, driver LCD, catu daya, interkoneksi hardware dan dilanjutkan perancangan perangkat lunak pengatur sistem kerja pengendali motor. Sensor yang digunakan adalah optocoupler, konversi digital ke analog menggunakan DAC 0808, driver motor menggunakan  transistor BD 137 dan pengatur sistem kerja menggunakan mikrokontroler AT89C51. Hasil penelitian menunjukkan bahwa pengendali motor yang dirancang dapat bekerja dengan akurasi 97,4%.
Comparative Analysis of Spatial Decision Tree Algorithms for Burned Area of Peatland in Rokan Hilir Riau Putri Thariqa; Imas Sukaesih Sitanggang; Lailan Syaufina
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.3540

Abstract

 Over one-year period (March 2013 – March 2014), 58 percent of all detected hotspots in Indonesia are found in Riau Province. According to the data, Rokan Hilir shared the greatest number of hotspots, about 75% hotspots alert occur in peatland areas. This study applied spatial decision tree algorithms to classify classes before burned, burned, and after burned from remote sensed data of peatland area in Kubu and Pasir Limau Kapas subdistrict, Rokan Hilir, Riau. The decision tree algorithm based on spatial autocorrelation is applied by involving Neigborhood Split Autocorrelation Ratio (NSAR) to the information gain of CART algorithm. This spatial decision tree classification method is compared to the conventional decision tree algorithms, namely, Classification and Regression Trees (CART),  C5.0, and C4.5 algorithm. The experimental results showed that the C5.0 algorithm generate the most accurate classifier with the accuracy of  99.79%. The implementation of spatial decision tree algorithm succesfuly improve the accuracy of CART algorithm.
Gamelan Music Onset Detection based on Spectral Features Diah P. Wulandari; Aris Tjahyanto; Yoyon K. Suprapto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 1: March 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i1.3162

Abstract

This research detects onsets of percussive instruments by examining the performance on the sound signals of gamelan instruments as one of  traditional music instruments in Indonesia. Onset plays important role in determining musical rythmic structure, like beat, tempo, measure, and is highly required in many applications of music information retrieval. Four onset detection methods that employ spectral features, such as magnitude, phase, and the combination of both are compared in this paper. They are phase slope (PS), weighted phase deviation (WPD), spectral flux (SF), and rectified complex domain (RCD). Features are extracted by representing the sound signals into time-frequency domain using overlapped Short-time Fourier Transform (STFT) and by varying the window length. Onset detection functions are processed through peak-picking using dynamic threshold. The results showed that by using suitable window length and parameter setting of dynamic threshold, F-measure which is greater than 0.80 can be obtained for certain methods.
Deep Learning for Tuning Optical Beamforming Networks Herminarto Nugroho; Wahyu Kunto Wibowo; Aulia Rahma Annisa; Hanny Megawati Rosalinda
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.8176

Abstract

In communication between planes and satellites, Optical Beamforming Networks (OBFNs), which rely on many small and flat Phased Array Antennas (PAAs), need to be tuned in order to receive signals from specific angles. In this paper, we develop a deep neural network representation of tuning OBFNs. The problem of tuning an OBFN is in many aspects similar to training a deep neural network. We present a way to exploit the special structure of OBFNs into deep neural network and an algorithm for tuning OBFNs based on feedback that can be easily measured in real system. Training data, which consists of full signals, can be measured, and therefore is used in this paper. For pilot signals, the desired signal is known explicitly. Given the configuration of OBFNs and all nominal parameters required, it was verified in simulation that the deep neural network can be used to tune large scale OBFNs for any desired delays.
Evaluation of network security based on next generation intrusion prevention system Gilang Intan Permatasari Duppa; Nico Surantha
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.9191

Abstract

Next Generation Intrusion Prevention System (NGIPS) is a system that works to monitor network traffic, to detect suspicious activity, and to conduct early prevention toward intrusion that can cause network does not run as it supposed to be, NGIPS provides vulnerability protection broader compared to the traditional IPS, especially in the application layer that has ability to detect and learn vulnerability asset and carried out layering inspection until layer 7 packet. This paper intended to analyze and evaluate the NGIPS to protect network from penetration system that utilize the weakness from firewall, that is exploitation to HTTP port. By the existence of NGIPS, it is expected can improve the network security, also network administrator could monitor and detect the threats rapidly. Research method includes scenario and topology penetration testing plan. The result of this research is the evaluation of penetration testing that utilizes HTTP port to exploit through malicious domain. The evaluation conducted to ensure the NGIPS system can secure the network environment through penetration testing. This study can be concluded that it can become reference to optimize network security with NGIPS as network security layer.
Neurocomputing fundamental climate analysis Rezzy Eko Caraka; Sakhinah Abu Bakar; Muhammad Tahmid; Hasbi Yasin; Isma Dwi Kurniawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.11788

Abstract

Rainfall is a natural phenomenon that needs to be studied more deeply and interesting to be analyzed. It involves numbers of human activities such as aviation, agriculture, fisheries, and also disaster risk reduction. Moreover, the characteristics of rainfall data follows seasonality, fluctuation, not normally distributed and it makes traditional time series challenging to use. Therefore, neurocomputing model can be used as an alternative to extraction information from rainfall data and give high performance also accuracy. In this paper, we give short preview about SST Anomalies in Manado, Northern Sulawesi and at the same time comparing the performance of rainfall forecasting by using three types of neurocomputing methods such as Generalized Regression Neural Network (GRNN), Feed forward Neural Network (FFNN), and Localized Multi Kernel Support Vector Regression (LMKSVR). In a nutshell, all of neurocomputing methods give highly accurate forecasting as well as reach low MAPE FFNN 1.65%, GRNN 2.65% and LMKSVR 0.28%, respectively.
Buck converter controlled with ZAD and FPIC for DC-DC signal regulation Fredy E. Hoyos Velasco; Yeison Alberto Garcés Gómez; John E. Candelo-Becerra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.14108

Abstract

This paper presents the performance of a fixed-point induction control (FPIC) technique working in conjunction with the non-linear control technique called zero average dynamics (ZAD) to control chaos in a buck converter. The control technique consists of a sliding surface in which the error tends to zero at each sampling period. A switch is controlled by using centered pulse width modulation (CPWM) control signal. The converter controlled with ZAD-FPIC has been simulated in Matlab and implemented using rapid control prototyping (RCP) in a DSP to make comparisons between simulation and experimental tests. To perform this comparison, some variations in the control parameter and the voltage reference are made in order to evaluate the performance of the system. Results are obtained with errors lower than 1 % which demonstrates the good performance of the control techniques.
Ananas comosus crown image thresholding and crop counting using a colour space transformation scheme Wan Nurazwin Syazwani Rahimi; Muhammad Asraf H.; Megat Syahirul Amin Megat Ali
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.13895

Abstract

The implementation of unmanned aerial vehicle (UAV) technology having image processing capabilities provides an alternative way to observe pineapple crowns captured from aerial images. In the majority of pineapple plantations, an agricultural officer will physically count the crop yield prior to harvesting the Ananas Comosus, also known as pineapple. This process is particularly evident in large plantation areas to accurately identify pineapple numbers. To alleviate this issue, given it is both time-consuming and arduous, automating the process using image processing is suggested. In this study, the possibilities and comparisons between two techniques associated with an image thresholding scheme known as HSV and L*A*B* colour space schemes were implemented. This was followed by determining the threshold by applying an automatic counting (AC) method to count the crop yield. The results of the study found that by applying colour thresholding for segmentation, it improved the low contrast image due to different heights and illumination levels on the acquired colour image. The images that were acquired using a UAV revealed that the best distance for capturing the images was at the height of three (3) metres above ground level. The results also confirm that the HSV colour space provides a more efficient approach with an average error increment of 47.6% when compared to the L*A*B*colour space.

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