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Yeni Kustiyahningsih
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kursor@trunojoyo.ac.id
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Informatics Department, Engineering Faculty University of Trunojoyo Madura Jl. Raya Telang - Kamal, Bangkalan 69162, Indonesia Tel: 031-3012391, Fax: 031-3012391
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INDONESIA
Jurnal Ilmiah Kursor
Published by Universitas Trunojoyo Madura
ISSN : 02160544     EISSN : 23016914     DOI : https://doi.org/10.21107/kursor
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah Kursor is published in January 2005 and has been accreditated by the Directorate General of Higher Education in 2010, 2014, 2019, and until now. Jurnal Ilmiah Kursor seeks to publish original scholarly articles related (but are not limited) to: Computer Science. Computational Intelligence. Information Science. Knowledge Management. Software Engineering. Publisher: Informatics Department, Engineering Faculty, University of Trunojoyo Madura
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 11 No 3 (2022)" : 5 Documents clear
APPLICATION OF COST-SENSITIVE CONVOLUTIONAL NEURAL NETWORK FOR PNEUMONIA DETECTION Rizki Anantama
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.264

Abstract

Pneumonia is a disease caused by a viral, bacterial, or fungal infection. In the diagnostic process of pneumonia, one approach is to use X-ray images. One of the existing problems is the lack of qualified and experienced medical personnel to recognize the X-ray images that have been taken. For this reason, an alternative is needed to detect pneumonia. Existing research shows that the use of convolutional neural networks can effectively detect pneumonia X-ray images. However, one of the problems is that this approach focuses a lot on accuracy without considering performance criteria such as sensitivity and specificity. To solve this problem, a cost-sensitive based approach has been proposed. In this study, a convolutional neural network-based model was created and trained using a cost-sensitive and non-cost sensitive approach. From the results obtained, it is seen that the model made still has a comparatively low level of accuracy. However, it is found that training with a cost-sensitive approach is able to improve performance on the specificity side, although at the expense of performance on the sensitivity side.
OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD Hendy Prasetyo; Trihastuti Agustinah
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.267

Abstract

Quadcopter is a type of Unmanned Aerial Vehicle (UAV) technology, characterized by simple mechanical structure, ease of flying and good maneuvering. In its usage, the quadcopter is required to evade obstacles in its path. Thus, an obstacle avoidance system in a 3D space with both static and dynamic obstacles is. Avoidance direction is determined by considering nearest distance based on the dimensions of the obstacle. Due to limited battery capacity, the quadcopter also needs to consider energy efficiency in obstacle avoidance. The obstacle’s properties and movement direction are also needed in considering the correct avoidance direction. Using a modified Local Mean K-Nearest Centroid Neighbor (LMKNCN) algorithm results in a 97.5% accuracy for avoidance direction decision. The learning process between training data and testing data yielded a computation duration of 0.142341 seconds. The simulations showed that the quadcopter is able to avoid static and dynamic obstacles to reach its destination without collisions.
DEVELOPMENT OF AUTONOMOUS UNDERWATER VEHICLE (AUV) BASED ON ROBOTIC OPERATING SYSTEM FOR FOLLOWING UNDERWATER CABLE Mardiyanto Ronny
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.274

Abstract

In addition to satellites, the Marine Cable Communication Channel (SKKL) which is located under the sea is also one of the backbones of the communication network to connect from one island to another. However, there are often irresponsible parties who damage or commit acts of vandalism. This action resulted in the disruption of the communication process on the submarine cable network. So, it is necessary to periodically check the condition of the underwater communication network cables. Regular checking of underwater cables is very risky, so we propose an underwater robot to handle it. This paper presents the development of Autonomous Underwater Vehicle (AUV) based on Robotic Operating System (ROS) for Following Underwater Cable to monitor the condition of the cables automatically. The AUV is equipped with a camera, Nvidia Jetson Nano, Arduino, Flight Controller, ESC, and brushless DC motors that used to assist the tracking process on the cable. The camera is used as the main visual sensor. Visual image processing methods are carried out using thresholding and contours detection methods, then the obtained data are processed to drive the motors on the AUV so that they can move on the direction of the cable. The experiment results show that the object detection method can be used under conditions with light intensity more than 25 lux. It works optimally at speeds of 0.27 m/s to 0.42 m/s. In the horizontal motion control test, the overshoot parameter value is ±60%, rise time is 2s, settling time is 16s, and steady state error is ±20%. The AUV can track on a straight and winding path of 2 meters with a bright light intensity of ±493 lux, a dim light of ±107 lux and a dark light intensity of ±25 lux with the help of an LED beam with a light intensity of ±773 lux. The percentage of success of scoping experiments on a straight track and a winding track with three trials is 75%. This performance shows that the developed AUV works well to follow underwater cable.
ANT COLONY OPTIMIZATION TO DETERMINE THE SHORTEST ROUTE OF TOURIST DESTINATIONS IN BALI : A CASE STUDY I Gede Susrama Mas Diyasa
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.279

Abstract

Bali is one of the popular tourist destinations in Indonesia. As technology develops, the tourism sector also gets support in the process. The problem we address in this research is to determine the closest distance to travel in Bali. The purpose of this study is to assist users in deciding which route is better to visit first so that it is more effective in terms of energy, gasoline usage, and time. We proposed a shortest route system using ant colony optimization (ACO). ACO then compared with other optimization method. ACO produces a great result with the optimal distance and reasonable amount of search time.
EFFECTIVENESS OF DEEP LEARNING APPROACH FOR TEXT CLASSIFICATION IN ADAPTIVE LEARNING Umi Laili Yuhana; Imamah Imamah; Chastine Fatichah; Bagus Jati Santoso
Jurnal Ilmiah Kursor Vol 11 No 3 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i3.285

Abstract

Klasifikasi text sangat bermanfaat dan dibutuhkan diberbagai bidang. Salah satu bidang yang membutuhkan klasifikasi text adalah E-learning yang bersifat adaptive atau disebut sebagai adaptive learning sistem. Adaptive learning sytem adalah sistem pembelajaran online yang dapat memberikan rekomendasi pembelajaran berdasarkan kebutuhan pengguna. Adaptive learning memiliki dua bagian, yaitu modul learning dan modul testing. Modul learning adalah bagian dari sistem yang bertugas untuk memberikan rekomendasi pembelajaran bagi pengguna, sedangkan modul testing bertugas untuk menguji dan memberikan penilaian terhadap hasil pembelajaran yang diperoleh dari modul learning. Materi pembelajaran pada modul learning memerlukan klasifikasi text berdasarkan tingkat kesulitannya untuk memastikan bahwa pengguna dengan level kemampuan rendah juga mendapatkan materi pembelajaran yang mudah, dan rekomendasi ini akan dinamis mengikuti perkembangan kemampuan pengguna. Pada penelitian ini, akan dibahas bagian kecil dari sistem pembelajaran adaptive pada modul learning yaitu tahap klasifikasi text. Dataset yang digunakan dalam penelitian ini adalah mata pelajaran IPA untuk tingkat SMP yang didapatkan dari Ruang Guru dan merupakan salah satu platform E-learning di Indonesia. Metode yang digunakan dalam penelitian ini adalah CNN, RNN dan HAN dengan menggunakan word embedding Word2Vec

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