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Yeni Kustiyahningsih
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ykustiyahningsih@trunojoyo.ac.id
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+6282139239387
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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
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Search results for , issue "Vol. 12 No. 2 (2023)" : 5 Documents clear
IMPLEMENTATION OF PROBLEM-BASED LEARNING MULTIMEDIA WITH FIND AND SORT QR CODE GAMES TO IMPROVE STUDENT'S COMPUTATIONAL THINKING SKILLS Al Husaeni, Dwi Fitria; Rahman, Eka Fitrajaya; Piantari, Erna
Jurnal Ilmiah Kursor Vol. 12 No. 2 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i2.343

Abstract

This study aims to evaluate the effectiveness of using and developing problem-based learning multimedia with find and sort QR code games to improve students' computational thinking (CT) skills in learning object-oriented programming using a web-based digital platform. The Research and Development (R&D) method and One-Group Pretest-Posttest design was used in this study. The subjects of this study were 35 students of SMK Negeri 1 Cimahi, Indonesia. There are three stages in conducting research 1) analysis of problems, 2) learning multimedia development, and 3) evaluation. The findings show there is an increase in students' CT skills after implementing the find and sort QR Code Game problem-based learning multimedia during the learning process. Student learning outcomes have increased from 45.71 (pretest) to 89.50 (posttest). The average increase in student learning outcomes occurred significantly based on the results of the t-test. In addition, the students' CT average score increased from 65.43 (pretest) to 85.29 (posttest). The order of increasing the CT component based on the n-gain value is 1) abstraction (0.66); 2) pattern recognition (0.63); 3) decomposition (0.48); and 4) algorithm design (0.39). Student responses to multimedia learning in this study were obtained very well with a score of 84.95%.
OPTIMIZING LANTANA CLASSIFICATION: HIGH-ACCURACY MODEL UTILIZING FEATURE EXTRACTION Sooai, Adri Gabriel; Mau, Sisilia Daeng Bakka; Siki, Yovinia Carmeneja Hoar; Manehat, Donatus Joseph; Sianturi, Shine Crossifixio; Mondolang, Alicia Herlin
Jurnal Ilmiah Kursor Vol. 12 No. 2 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i2.347

Abstract

As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers. This study used five types of lantana colors, red, white, yellow, purple, and orange, as the primary dataset, which had 411 instances. VGG16 assisted feature extraction in preparing datasets for the data training using three classifiers: decision tree, AdaBoost, and k-NN. 2-fold cross-validation, 5-fold cross-validation, and a self-organizing map are used to help validate each process. The experiment to measure the classifier's performance resulted in a good figure of 99.8% accuracy for 2-fold cross-validation, 100% for 5-fold cross-validation, and a primary dataset of lantana interest that can be accessed freely on the IEEE Data port. This study outperformed other related studies in terms of classifier accuracy.
SHORT-TERM FORECASTING DAILY ELECTRICITY LOADS USING SEASONAL ARIMA PATTERNS OF GENERATION UNITS AT PT. PLN (PERSERO) TARAKAN CITY Mado, Ismit; Budiman, Achmad; Triwiyatno, Aris
Jurnal Ilmiah Kursor Vol. 12 No. 2 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i2.348

Abstract

Electrical power requirements at load centers tend to change over time, so the State Electricity Company (PLN) as a provider of electrical energy must be able to predict electrical load requirements every day. The city of Tarakan as a reference center in the northern region of Indonesia is developing rapidly. Along with this growth, the need for electric power is of course also increasing, so we must be able to provide an economical and reliable electric power supply system. This research aims to predict the electricity load at PT. PLN (Persero) Tarakan City. The author will carry out short-term forecasting using time series data in the form of daily electrical power usage data using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method or often called the Box-Jenkins technique shows that this method is suitable for predicting a number of variables quickly, simply and cheaply because it only requires variable data to be predicted. Analysis based on the Box-Jenkins time series taking into account the influence of seasonal patterns. The prediction results show that the data contains seasonal elements with the best model being SARIMA with a MAPE of 3 percent.
DEEP LEARNING ARCHITECTURE BASED ON CONVOLUTIONAL NEURAL NETWORK (CNN) IN IMAGE CLASSIFICATION Fawaidul Badri; M. Taqijuddin Alawiy; Eko Mulyanto Yuniarno
Jurnal Ilmiah Kursor Vol. 12 No. 2 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i2.349

Abstract

In current technological developments, Deep Learning is one of the most popular studies today, especially in the fields of machine learning and computer vision, GPU Acceration Technology is one of the reasons for the development of Deep Learning. Deep Learning has a very good ability to solve classic problems in the field of computer vision, one of which is in the case of object classification in images. one of the deep learning methods that is often used in image processing is the Convolution Neural Network (CNN) which is a development of the Multi Layer Perceptron method. This study uses the CNN architecture which consists of a convolution layer, as well as a fully connected layer, and will also determine the appropriate Optimizer and Loss function for CNN. The implementation of this method uses Google Colab (Tensorflow and Keras) with the Python programming language. In the training process using CNN, setting the number of epochs is done to improve accuracy in image classification, in the first scenario using epoch 20 produces an average accuracy of 99.45 with a loss value of 1.66. In the second scenario using epoch 15 produces an average accuracy value of 99.00 with a loss value of 2.92. then in the third scenario with a number of epochs 10 it produces an average accuracy value of 95.55 with a loss value of 95.55, while in the last scenario with a number of epochs 5 it produces an average accuracy value of 73.6 with a loss value of 51.92. From the 4 trial scenarios using the CNN method gives effective results and produces a fairly good accuracy value with an average accuracy and loss value of 99.99%. As well as the results of an average loss of 4.
LONG SHORT-TERM MEMORY FOR PREDICTION OF WAVE HEIGHT AND WIND SPEED USING PROPHET FOR OUTLIERS Baihaqi, Galih Restu; Mulaab
Jurnal Ilmiah Kursor Vol. 12 No. 2 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i2.351

Abstract

The reason fishermen lose control is wave height and wind speed. The impact is also felt by all users of the marine sector. This research uses the Long Short Term Memory (LSTM) method because this method has accurate values in the forecasting process with a lot of historical data and uses the Prophet method to detect outliers with Newton interpolation to replace the detected outlier data. The total number of data was 2074 obtained from BMKG Perak Surabaya from January 2020 to November 2022 at four research points, namely north, northeast, east and south points. The test results provide varying error values with MAPE as the model evaluation value. The error value for sea wave height at the north, northeast, east and south points is 13.32 respectively; 13.32; 9.32 and 8.85 with data without interpolation. Meanwhile, the error value in the wind speed data is 14.74; 14.85; 15.14 and 14.52 with a 3rd order Newton interpolation process at the northeast and east points. MAPE values below 20% prove that the LSTM model is good for predicting wave height and wind speed data at four points in Sumenep Regency. The system implementation is made into a web-based application.

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