Jurnal Teknologi dan Sistem Komputer

Journal Information
ISSN / EISSN : 26204002 / 23380403
Current Publisher: Diponegoro University (10.14710)
Total articles ≅ 182
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Latest articles in this journal

Dody Ichwana, Surya Dwi Saputra, Shelvi Ekariani
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 64-70; doi:10.14710/jtsiskom.7.2.2019.64-70

Abstract:The increasing use of vehicles at campus locations makes it more difficult to find an empty parking lot. This paper develops a system for determining parking locations on campus areas using cloud-based fuzzy logic and Internet of Things (IoT). NFC is used to confirm the order code of the location that has been generated by the system. At the parking location, a sensor is installed to detect parking availability. The concept of IoT has been applied to build this system. Applications on smartphone devices are used for reservations at desired parking locations via the internet. The results show that the system has been able to detect the location of empty parking lots and make reservations in the Andalas University campus environment. The application of fuzzy logic has succeeded in obtaining parking location sequences based on distance and total capacity to find the best parking location.
Ahmad Rizaqu Muttaqi, Sri Wahjuni, Shelvie Nidya Neyman
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 71-76; doi:10.14710/jtsiskom.7.2.2019.71-76

Abstract:The technical problems faced in e-government implemented by the Ministry of Religion of Indonesia since 2015 are minimal bandwidth requirements to provide information services and behavior of users who access entertainment sites. When peak hours occur, the congested network often occurs which becomes a significant bottleneck. This study aims to implement bandwidth management using the fractional knapsack problem method by limiting access to entertainment services. The QoS parameters used in this management are throughput, delay, and jitter. The method was tested using a paired t-test using throughput, jitter, and delay test parameters by comparing test parameters before and after bandwidth management applied. The significance value produced is between 75-85%. The method used can control the amount of traffic for each service, but on the other, hand the delay and jitter are still high. It is necessary to add additional free space to each service that can be used when needed to reduce the delay and jitter.
I Made Gede Sunarya, Tita Karlita, Joko Priambodo, Rika Rokhana, Eko Mulyanto Yuniarno, Tri Arief Sardjono, Ismoyo Sunu, I Ketut Eddy Purnama
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 56-63; doi:10.14710/jtsiskom.7.2.2019.56-63

Abstract:Detection of vascular areas (blood vessels) using B-Mode ultrasound images is needed for automatic applications such as registration and navigation in medical operations. This study developed the detection of the carotid artery area using Convolution Neural Network Single Shot Network Multibox Detector (SSD) to determine the bounding box ROI of the carotid artery area in B-mode ultrasound images. The data used are B-Mode ultrasound images on the neck that contain the carotid artery area (primary data). SSD method result is 95% of accuracy which is higher than the Hough transformation method, Ellipse method, and Faster RCNN in detecting carotid artery area in the B-Mode ultrasound image. The use of image enhancement with Gaussian filter, histogram equalization, and Median filters in this method can increase detection accuracy. The best process time of the proposed method is 2.09 seconds so that it can be applied in a real-time system.
Nurajijah Nurajijah, Dwiza Riana
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 77-82; doi:10.14710/jtsiskom.7.2.2019.77-82

Abstract:The decision on financing approval in sharia cooperatives has a high risk of the inability of customers to pay their credit obligations at maturity or referred to as bad credit. To maintain and minimize risk, an accurate method is needed to determine the financing agreement. The purpose of this study is to classify sharia cooperative loan history data using the Naïve Bayes algorithm, Decision Tree and SVM to predict the credibility of future customers. The results showed the accuracy of Naïve Bayes algorithm 77.29%, Decision Tree 89.02% and the highest Support Vector Machine (SVM) 89.86%.
Hadha Afrisal
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 47-55; doi:10.14710/jtsiskom.7.2.2019.47-55

Abstract:Place recognition algorithm based-on visual sensor is crucial to be developed especially for an application of indoor robot navigation in which a Ground Positioning System (GPS) is not reliable to be utilized. This research compares the approach of place recognition of using learned-features from a model of Convolutional Neural Network (CNN) against conventional methods, such as Bag of Words (BoW) with SIFT features and Histogram of Oriented Uniform Patterns (HOUP) with its Local Binary Patterns (LBP). This research finding shows that the performance of our approach of using learned-features with transfer learning method from pre-trained CNN AlexNet is better than the conventional methods based-on handcrafted-features such as BoW and HOUP.
Tekad Matulatan, Martaleli Bettiza, Muhamad Radzi Rathomi, Nola Ritha, Nurul Hayaty
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 83-88; doi:10.14710/jtsiskom.7.2.2019.83-88

Abstract:Computer Assisted Testing (CAT) system in Indonesia has been commonly used but only to displaying random exam questions and unable to detect the maximum performance of the test participants. This research proposes a simple way with a good level of accuracy in identifying the maximum ability of test participants. By applying the Bayesian probabilistic in the selection of random questions with a weight of difficulties, the system can obtain optimal results from participants compared to sequential questions. The accuracy of the system measured on the choice of questions at the maximum level of the examinee alleged ability by the system, compared to the correct answer from participants gives an average accuracy of 75% compared to 33% sequentially. This technique allows tests to be carried out in a shorter time without repetition, which can affect the fatigue of the test participants in answering questions.
Muhammad Zulfikri, Erni Yudaningtyas, Rahmadwati Rahmadwati
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 12-18; doi:10.14710/jtsiskom.7.1.2019.12-18

Abstract:Driving at high speed is among the frequent causes of accidents. In this research, a warning system was developed to warn drivers when their speed beyond the safety limit. Haar cascade classifier was proposed for the detection system which comprises Haar features, integral image, AdaBoost learning, and cascade classifier. The system was implemented using Python OpenCV library and evaluated on road traffic video collected in one way traffic. As a result, the proposed method yields 97.92% of car detection accuracy in daylight and MSE of 2.88 in speed measurement.
Tyas Panorama Nan Cerah, Oky Dwi Nurhayati, R. Rizal Isnanto
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 31-37; doi:10.14710/jtsiskom.7.1.2019.31-37

Abstract:This study aims to examine the k-means clustering and region growing segmentation methods to identify and measure the area of mangrove forests in the Southeast Sulawesi province. The image of the area of this study used Landsat 8 satellite imagery. The area of mangrove forest was carried out by calculating the number of pixels identified as mangrove forests with an area density of 900 m2/pixel. The accuracy of the two segmentation methods in calculating the area was compared based on the same area calculated by LAPAN. The overall accuracy of k-means clustering segmentation method has better accuracy, which is 59.26%, than region growing with 33.33% of accuracy. Both image segmentation methods, k-means clustering and region growing, can be used to calculate the area of mangrove forests in the Southeast Sulawesi region using Landsat 8 satellite imagery.
Olaonipekun Oluwafemi Erunkulu, Elizabeth Nnonye Onwuka, Okechukwu Ugweje, Lukman Adewale Ajao
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 38-46; doi:10.14710/jtsiskom.7.1.2019.38-46

Abstract:Global System for Mobile communication is a digital mobile system that is widely used in the world. Over the years, the number of subscribers has tremendously increased, the quality of service (Call Drop Rate) became an issue to consider as many subscribers were not satisfied with the services rendered. In this paper, we present the Artificial Neural Network approach to predict call drop during an initiated call. GSM parameters data for the prediction were acquired using TEMS Investigations software. The measurements were carried out over a period of three months. Post analysis and training of the parameters was done using the Artificial Neural Network to have an output of “0” for no-drop calls and “1” for drop calls. The developed model has an accuracy of 87.5% prediction of drop call. The developed model is both useful to operators and end users for optimizing the network.
Anggi Mahadika Purnomo, Davia Werdiastu, Talitha Raissa, Restu Widodo, Vivi Nur Wijayaningrum
Jurnal Teknologi dan Sistem Komputer, Volume 7, pp 1-6; doi:10.14710/jtsiskom.7.1.2019.1-6

Abstract:Hypertension can be prevented and handled by eating nutritious foods with the right composition. The genetic algorithm can be used to optimize the food composition for people with hypertension. Data used include sex, age, weight, height, activity type, stress level, and patient hypertension level. This study uses a reproduction method that is good enough to be applied to integer chromosome representations so that the search results provided are not local optimum solutions. The testing results show that the best genetic algorithm parameters are as follows population size is 15 with average fitness 20.97, the generation number is 40 with average fitness 50.10, and combination crossover rate and mutation rate are 0.3 and 0.7 with average fitness 41.67. The solution obtained is the optimal food composition for people with hypertension.