cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yeni Kustiyahningsih
Contact Email
ykustiyahningsih@trunojoyo.ac.id
Phone
+6282139239387
Journal Mail Official
kursor@trunojoyo.ac.id
Editorial Address
Informatics Department, Engineering Faculty University of Trunojoyo Madura Jl. Raya Telang - Kamal, Bangkalan 69162, Indonesia Tel: 031-3012391, Fax: 031-3012391
Location
Kab. bangkalan,
Jawa timur
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 155 Documents
 1   2   3   4   5 
ALGORITHM A* FOR THE NEAREST ROUTE TRACKING SYSTEM IN THE MODE OF TRASNPORTATION Febri Ramanda; Eko Sediyono; Catur Edi Widodo
Jurnal Ilmiah Kursor Vol 10 No 1 (2019)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v10i1.212

Abstract

Transport as a basic services industry as the basis of national economic development, it is the foundation of a nation's progress. With increasing in the standard life society, transportation needs for the whole society have also increased. Online transportation service needs are influenced by various factors such as cost, quality of service, income and ownership that modes of transport used. The purpose of this study is to apply the A* algorithm to find paths or routes, because it is quite flexible and works better than the algorithm dijakstra in all cases (barriers and without obstacles). The results showed that the A* algorithm is able to provide the information than other algorithms that are best finding the shortest path.
PARTICLE SWARM OPTIMIZATION FOR MANAGING AS INJECTION ALLOCATION Hannan Fatoni; Mauridhi Hery P; Ardyono Priyadi
Jurnal Ilmiah Kursor Vol 7 No 3 (2014)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

PARTICLE SWARM OPTIMIZATION FOR MANAGING AS INJECTION ALLOCATION aHannan Fatoni, bMauridhi Hery P, cArdyono Priyadi a Program Studi Magister ManajemenTeknologi, Institut Teknologi Sepuluh Nopember Jl. Cokroaminoto 12A, Surabaya, 60264, Indonesia b JurusanTeknikElektro, InstitutTeknologiSepuluhNopember Email: a hannanfatoni@gmail.com Abstract In oil and gas industry, the size of hydrocarbon reserves and type of the reservoir is crucial to the design methods and lifting the hydrocarbons for further processes. PT. XYZ uses the gas lift injection design to lift the oil content from the reservoir. In some conditions, the production choke valve shall be opened moreto increase the hydrocarbon production rates. However, it causes the reservoir instability, decreasing the reservoir pressure, and reducing the oil production drastically.Therefore, optimization of allocating gas lift injection rate on each of the production is needed to produce maximum oil and to improve the sustainability of oil and gas production on PT.XYZ. This paper proposes optimization technique for managing gas injection allocation using Particle Swarm Optimization (PSO). The procedure optimization can be explained as below; first step uses prosper modeling software to generate the model of production wells. Second, it obtains the curve of the gas lift injection rate against the oil production. Third, each well production model is validated by reference data from the well test result. The best PSO simulationwith limited gas injections which is 17 MMscfdresults of the gas lift injection allocation for each production wells are 0.98, 2.66, 1.39, 0.98, 3.19, 1.61, 1.78, 2.03, 1.40, and 0.98 MMscfd.With these gas injection allocations, the oil production increases to 4908.7 Barrels of oil per day (BPD). Maximum company profit after optimization reaching USD$ 578,004 compare with before optimization. The other optimization using Genetic Algorithm (GA) is also used for comparison. Keywords: Optimization, Prosper Modeling, PSO, GA.
IDENTIFICATION OF DIGITAL EVIDENCE FACEBOOK MESSENGER ON MOBILE PHONE WITH NATIONAL INSTITUTE OF STANDARDS TECHNOLOGY (NIST) METHOD Anton Yudhana; Imam Riadi; Ikhwan Anshori
Jurnal Ilmiah Kursor Vol 9 No 3 (2018)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i3.152

Abstract

Facebook Messenger is a popular social media. The increasing number of Facebook Messenger users certainly has a positive and negative impact, one of the negative effects is being used for digital crime. One of the sciences to get digital evidence is to do Digital forensics. Digital forensics can be done on a smartphone used by criminals. This research will carry out as much evidence of digital crime as possible from Facebook Messenger. In this study the forensic devices, Magnet AXIOM and Oxygen Forensics Suite 2014 were used using the National Institute of Standards Technology (NIST) method. NIST has work guidelines for both policies and standards to ensure that each examiner follows the same workflow so that their work is documented and the results can be repeated and maintained. The results of the research in the Magnet AXIOM and Oxygen Forensics Suite 2014 get digital evidence in the form of accounts, conversation texts, and images. This study successfully demonstrated the results of an analysis of forensic devices and digital evidence on Facebook Messenger. The results of the performance evaluation of forensic tools in the acquisition process using AXIOM Magnets are considered the best compared to Oxygen Forensics Suite 2014.
OPTIMASI FUNGSI MULTI-OBYEKTIF BERKENDALA MENGGUNAKAN ALGORITMA GENETIKA ADAPTIF DENGAN PENGKODEAN REAL Wayan Firdaus Mahmudy; Muh. Arif Rahman
Jurnal Ilmiah Kursor Vol 6 No 1 (2011)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Multi-objective optimization problem is difficult to be solved as its objectives generally conflict with each other and its solution is not in the form of a single solution but a set of solutions. Genetic algorithms (GAs) is one of meta heuristic algorithms that may be used to solve this problem. However, a standard GAs is easily trapped in local optimum areas and searching process rate will be lower around the optimum points. This paper proposes a GAs with an adaptive mutation rate to balance the exploration and exploitation on the search space. A simple rule has been developed to determine wheter the mutation rate is increased or decreased. If a significant improvment of the fitness value is not achieved, the mutation rate is increased to enable the GAs exploring search space and escaping the local optimum area. In contrast, the mutation rate is decreased if significant improvment of the fitness value is achieved. This mechanism guide the GAs to exploit the local search area. The experiments show that by using the adaptive mutation, the GAs will move faster toward a feasible search space and achieving solutions on sorter time.
MODELLING AND SIMULATION OF INDUSTRIAL HEAT EXCHANGER ETWORKS UNDER FOULING CONDITION USING INTEGRATED NEURAL NETWORK AND HYSYS Totok R. Biyanto; Roekmono Roekmono; Andi Rahmadiansyah; Aulia Siti Aisyah; Purwadi A. Darwito; Tutug Dhanardono; Titik Budiati
Jurnal Ilmiah Kursor Vol 8 No 1 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i1.70

Abstract

Fouling is a deposit inside heat exchanger network in a refinery has been identified as a major problem for efficient energy recovery. This heat exchanger network is also called Crude Preheat Train (CPT). In this paper, Multi Layer Perceptron (MLP) neural networks with Nonlinear Auto Regressive with eXogenous input (NARX) structure is utilized to build the heat exchanger fouling resistant model in refinery CPT and build predictive maintenance support tool based on neural network and HYSYS simulation model. The complexity and nonlinierity of the nature of the heat exchanger fouling characteristics due to changes in crude and product operating conditions, and also crude oil blends in the feed stocks have been captured very accurate by the proposed software. The RMSE is used to indicate the performance of the proposed software. The result shows that the average RMSE of integrated model in predicting outlet temperature of heat exchangerTH,out and TC,out between the actual and predicted values are determined to be 1.454 °C and 1.0665 °C, respectively. The integrated model is ready to usein support plant cleaning scheduling optimization, incorporate with optimization software.
BASIS PATH TESTING OF ITERATIVE DEEPENING SEARCH AND HELD-KARP ON PATHFINDING ALGORITHM I Gede Surya Rahayuda; Ni Putu Linda Santiari
Jurnal Ilmiah Kursor Vol 9 No 2 (2017)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v9i2.129

Abstract

This research is a continuation of previous research, where in previous research discussed about the implementation of both methods. Implementation is done using visual basic programming language. Both methods are compared based on the results obtained. While in the current study, research is more focused on the analysis of the program flow that has been made. Evaluation is done by using basis path method, there are several processes performed on the method, such as: flowgraph, independent path, cyclomatic complexity and graph matrix. In addition to the evaluation of program flow, evaluation is also done based on program performance. Performance tests are based, time, cpu and memory. Based on the evaluation using the base path, obtained flowgraph structure and independent path different, but obtained the result of CC and Graph Matrix calculation of the same between IDS and HK method is 4. Based on evaluation in terms of performance, process the program from entering data and until getting the result, the HK method takes a longer time than the IDS method. The IDS method takes 2.7 seconds while the HK method takes 2.8 seconds.
SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS Felisia Handayani; Metty Mustikasari
Jurnal Ilmiah Kursor Vol 10 No 4 (2020)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v10i4.233

Abstract

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20
FACIAL EXPRESSIONS RECOGNITION USING BACKPROPAGATION NEURAL NETWORK FOR MUSIC PLAYLIST ELECTIONS Setiawardhana Setiawardhana; Nana Ramadijanti; Peni Rahayu
Jurnal Ilmiah Kursor Vol 6 No 3 (2012)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini dibuat untuk mengenali ekspresi wajah sebagai indikator untuk menjalankan playlist musik. Sistem pengenalan ekspresi wajah berasal dari data masukan seseorang yang diambil secara offline, dengan posisi terdekat dengan kamera, dimana posisi wajah tidak boleh miring. Prosesnya dengan pengambilan citra wajah secara offline yang dikenali dengan kombinasi warna, dan mengekstrak fitur penting dari wajah berdasarkan lokasi alis, mata, dan bentuk mulut kemudian mengenali ekspresi wajah menggunakan Jaringan Saraf Tiruan Propagasi Balik (Backpropagation Neural Network). Ekspresi yang akan dikenali Data keluaran dari pengenalan ekspresi wajah berupa indek yang secara otomatis akan digunakan sebagai indikator untuk menjalankan musik, sehingga musik akan berubah mengikuti perubahan ekspresi wajah seseorang. Sistem yang telah dibuat dapat mengenali tiga jenis ekspresi yaitu: normal, marah, dan bahagia. Pengujian dengan pengambilan gambar wajah secara offline sebagai data masukan untuk Jaringan Saraf Tiruan Propagasi Balik, dimana pada saat pembelajaran diperoleh hasil yang konvergen dengan error terendah dengan jumlah neuron pada lapisan hidden sebanyak 10 unit, nilai laju pembelajaran sebesar 0.0625325 dan nilai mean square error sebesar 0.0135. Kata Kunci: Ekspresi Wajah, Backpropagation, Music Playlist. Abstract The objective of the research is to detect facial expression as indicator to cast a music playlist. Facial expression detection system input is performed offline by taking photograph of a subject with nearest position from the camera and facial position should not be tilted. The image is identified as a combination of color and feature extraction is performed based on location of eyebrow, eye, and mouth. Facial expression is detected with Artificial Neural Network Backpropagation method. The output data is an index, which automatically select and play the music. In this way, the music is modified according to the changes of facial expression. The system is designed to detect three facial expressions: normal, angry, and happy expression. The similarity between features values from each expression influence the ability to differentiate each expression. Offline system evaluation is performed with backpropagation neural network method,for learning process, it reaches convergent value with lowest error value when using 10 unit neuron on hidden layer, learning rate value is 0.0625325 and mean square error value is 0.0135.
NEAR-DUPLICATE REAL-LIFE FACE IMAGE Intan Yuniar Purbasari; Budi Nugroho
Jurnal Ilmiah Kursor Vol 7 No 1 (2013)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

NEAR-DUPLICATE REAL-LIFE FACE IMAGE a Intan Yuniar Purbasari, bBudi Nugroho a,bTeknik Informatika, Fakultas Teknologi Industri, UPN “Veteran” Jawa Timur, Indonesia Jl. Raya Rungkut Madya, Gunung Anyar, Surabaya 60294 E-Mail: intan.yuniar@gmail.com Abstrak Content-based Image Retrieval (CBIR) merupakan metode temu kembali citra berdasarkan karakteristik numerik pada citra. Pencarian similaritas yang efisien pada ruang dimensi ultra-high telah diajukan menggunakan two-tier inverted file dan Local Derivative Patterns (LDP) sebagai metode ekstraksi fitur dengan tingkat keakuratan dan kinerja yang tinggi pada data set citra wajah eksperimental. Namun demikian, citra real-life memiliki ukuran dan resolusi yang berbeda serta noise bawaan. Masih belum diketahui apakah LDP dapat menunjukkan hasil yang sama memuaskan jika diberikan data set citra real-life. Penelitian ini merancang dan membangun search engine citra wajah untuk mencari citra nyaris duplikat pada citra real-life menggunakan metode LDP untuk ekstraksi fitur dan two-tier inverted file untuk pengindeks-an multidimensi. Sebuah metode ekpansi state juga diperkenalkan untuk lebih menangkap banyak detil dari histogram citra dengan mempertimbangkan informasi piksel tetangga. Eksperimen ini dilakukan pada 8083 citra wajah real-life dari berbagai ukuran antara 20x20 dan 80x80. Data set berisi kopi duplikat dari citra wajah setelah melalui beberapa proses transformasi. Hasil pencarian mengembalikan 20 citra yang memiliki kemiripan paling tinggi dengan citra query dan memiliki nilai presisi 0.75 atau 75%. Kata kunci: Content-Based Image Retrieval, Local Derivative Pattern, Two-tier Inverted File, Real-life Face Image. Abstract Content-based image retrieval (CBIR) is an image retrieval method based on the analysis of numerical characteristics of the image at the absence of text information. An efficient similarity search in ultra-high dimensional space has been proposed using two-tier inverted file and Local Derivative Patterns (LDP) as feature extraction method with high accuracy and high performance on experimental face image data sets. However, real-life images have different size, resolution and a potential noise. It is unknown whether LDP would show the same satisfactory result given real-life image data sets. This research designed and developed a face search engine to find near-duplicate face in real life images using LDP method to extract image features and two-tier inverted file for multidimensional indexing process. A state expansion method was also introduced to capture more detailed description of image histogram by considering neighbor information. The experiment was performed on 8,083 reallife face images of various sizes between 20x20 to 80x80. The data set contained duplicate copies of face images with some transformation processes. The search result returned top 20 images which had the most similarity with the query images and had an average precision rate of 0.75 or 75%. Keywords: Content-Based Image Retrieval, Local Derivative Pattern, Two-tier Inverted File, Real-life Face Image
A DATA ANALYSIS OF THE IMPACT OF NATURAL DISASTER USING K-MEANS CLUSTERING ALGORITHM Prihandoko Prihandoko; Bertalya Bertalya
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.109

Abstract

Indonesia is one of the country with a lot of natural disasters occurred every year. The victims of natural disasters, are quite high in terms of the number of deaths, missing people, injuries, sufferings and the number of refugees. Unfortunately, the number of victims is growing from year to year in the last ten years. Thus, based on this condition, this research is carried out in order to analyze the data of the natural disasters and their victims for the last five years. The analysis is intended to know what is the main cause of natural disaster. The series of data about the natural disaster and the weather condition is collected from the government office website. The analysis was carried out by implementing clustering technique to the data, by using k-means algorithm, after data preprocessing completed. The result of the research shows that the weather condition is not the main cause of the occurrence of natural disaster, but the geographical condition is the main trigger of the problem. In addition, this research also found that the data published by the government need to be updated regularly.

Page 3 of 16 | Total Record : 155

 1   2   3   4   5 

Filter by Year

2011 2025


Reset
Filter By Issues
All Issue Vol. 13 No. 1 (2025) Vol. 12 No. 4 (2024) Vol. 12 No. 3 (2024) Vol. 12 No. 2 (2023) Vol 12 No 1 (2023) Vol 11 No 4 (2022) Vol 11 No 3 (2022) Vol 11 No 2 (2021) Vol 11 No 1 (2021) Vol 10 No 4 (2020) Vol 10 No 3 (2020) Vol 10 No 2 (2019) Vol 10 No 1 (2019) Vol 9 No 4 (2018) Vol 9 No 3 (2018) Vol 9 No 2 (2017) Vol 9 No 1 (2017) Vol 8 No 4 (2016) Vol 8 No 3 (2016) Vol 8 No 2 (2015) Vol 8 No 1 (2015) Vol 7 No 4 (2014) Vol 7 No 3 (2014) Vol 7 No 2 (2013) Vol 7 No 1 (2013) Vol 6 No 4 (2012) Vol 6 No 3 (2012) Vol 6 No 2 (2011) Vol 6 No 1 (2011) More Issue