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Yeni Kustiyahningsih
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ykustiyahningsih@trunojoyo.ac.id
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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 157 Documents
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E-LEARNING ADOPTION READINESS IN SECONDARY EDUCATION OF DEVELOPED AND DEVELOPING COUNTRIES: A SYSTEMATIC LITERATURE REVIEW Ratu Syafianisa Nuzulismah; Harry Budi Santoso; Panca O. Hadi Putra
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.301

Abstract

Situation in COVID-19 pandemic forced educational activity to shift from face-to-face to blended learning or to full online learning. This situation becomes a problem in different academic levels, especially secondary education as some teachers and students were not ready. This “E-Learning Adoption Readiness in Secondary Education: A Systematic Literature Review” article summarized the influencing factors and issues of readiness in adopting e-Learning in high school, including the technologies and communication tools, as the foundation of analysis. The research objective is to identify and compare e-Learning adoption between developed and developing countries during pandemic. This article used Kitchenham and Charter method which extract data research published in databases such as Scopus and Science Direct. This research found distinct gaps between developed and developing countries in the context of e-learning readiness adoption and factors that influenced the said adoption. We conclude that there are still a numbers of basic internal and external factors that need to be considered in e-Learning adoption, especially in developing countries. The implication and recommendation for the adoption during pandemic is hoped to be insightful for future research in the same field, as there are significant differences in developing and developed countries, especially regarding IT literacy.
SENTIMENT ANALYSIS OF LEAGUE OF LEGENDS: WILD RIFT REVIEWS ON GOOGLE PLAY USING NAÏVE BAYES CLASSIFIER Khadijah Khadijah; Nur Sabilly; Fajar Agung Nugroho
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.328

Abstract

League of Legends: Wild Rift is a mobile game with more than 48 million downloads worldwide. The game publishers could earn profit from selling item in the game (in-app purchases). Performance of player and players' impressions during the first week usually determine whether players would made in-app purchases or not. Therefore, it is important to understand the player opinions so that the game publisher could encourage the players to increase the in-app purchases. Therefore, this research utilized sentiment analysis to study the player opinions about the League of Legends: Wild Rift game based on the reviews given by the players on the Google Play Store. The sentiment analysis was applied by using Naive Bayes Classifier (NBC) algorithm which was well known for achieving good accuracy in the sentiment analysis task. In addition, data preprocessing and feature extraction should be carried out properly to increase the accuracy of the classifier. Therefore, this research investigated the impact of using stemming and transformation of informal words into formal words in the preprocessing stages, then compared two feature extraction algorithms, namely Term Frequency – Inverse Document Frequency (TF-IDF) and Bag of Words (BOW). From the experiment, it was found that the use of stemming could decrease the accuracy of the classifier, but the use of transformation of non-standard words into standard words could improve the performance of the classifier, for both feature extractions, BOW and TF-IDF. In this case, BOW feature extraction was able to achieve better performance, compared to TF-IDF. The best model was achieved when not using stemming, applying the transformation of informal words into formal words, and using BOW bigram feature extraction, with the accuracy of 79,3%, precision of 82.10%, recall of 83.50%, and f1-score of 82,8.10%.
DESIGN AND DEVELOPMENT OF BACKEND APPLICATION FOR THESIS MANAGEMENT SYSTEM USING MICROSERVICE ARCHITECTURE AND RESTFUL API Ach. Khozaimi; Yoga Dwitya Pramudita; Firdaus Solihin
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.313

Abstract

A thesis is a scientific work completed by students with the aim of developing the knowledge gained during the lecture period. Students at Universitas Trunojoyo Madura (UTM), Faculty of Engineering, particularly Informatics Engineering, carry out their theses manually and on paper. Thesis Management System (TMS) is software designed to help with the thesis execution process by reducing paper usage and increasing time efficiency. Monolithic system development can disrupt the service process if improvements are being made to the system. Therefore, in this research, a Thesis Management System (TMS) will be built using a microservice approach to make it easier to maintain and develop the system, for example, system scalability. As a means of communication between services, TMS is designed and developed using the REST API. TMS has undergone system performance testing to verify that it performs well under certain conditions. The results show that the number of requests increases the performance response time, CPU usage, and memory consumption, with an average resource usage of each service based on a response time of 61.64 ms, CPU usage of 8.64%, and memory usage of 89.47 Mb. As the number of requests on the service increases, so does resource usage in each service, but this has no effect on device performance because the increase is so low.
SEGMENTATION OF LUNG CANCER IMAGE BASED ON CYTOLOGIC EXAMINATION USING THRESHOLDING METHOD Rulisiana Widodo; Tessy Badriyah; Iwan Syarif; Willy Sandhika
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.277

Abstract

Lung cancer is the most dangerous cases which mostly attacks the man with the biggest causes of smoking. This cancer threatens the second largest death after heart attack, lung cancer cases increase significantly every year in various countries. Several methods have been established to detect lung cancer, including Computed Tomography of the thorax, sputum examination and cytology examination. The most decisive examination is through cytologic examination of the pleural fluid. However, the current state of biopsy performed by doctors does not always get a lot of specimens, making it difficult to determine the presence of cancer cells in the lungs. Cytological examination through the pleural fluid has difficulty in detecting cell images. The image of pleural fluid that has a high density between cells will produce an image with low detail, while an image with a low density will produce an image with high detail. Image segmentation is an important part in determining the cellular anatomy of pleural fluid to characterize images with cancer or normal categories. We propose the methodology of research by using group images to separate objects from other objects by highlighting important parts using image segmentation on pleural fluid of patients suspected of having lung cancer. Thresholding method used to see the comparison is Adaptive Thresholding, binary thresholding and Otsu Thresholding. The classification results of the three methods show a high accuracy of 99% on binary thresholding, then 97% accuracy on otsu thresholding and the lowest accuracy of 96% on adaptive thresholding, the three methods are considered to increase in proportion to the addition of the epoch parameter.
THE PARAMETRIC AND NONPARAMETRIC ESTIMATOR IN SEMIPARAMETRIC REGRESSION FOR LONGITUDINAL DATA WITH SPLINE APPROACH Tony Yulianto; Kuzairi Kuzairi; Noer Azizah; M. Fariz Fadillah Mardianto; Ira Yuditira; Faisol Faisol; Rica Amalia
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.316

Abstract

Regression analysis aims to determine the relationship between response variables and predictor variables. There are three approaches to estimate regression curves, there are parametric, nonparametric, and semiparametric regression. In this study, the form of spline semiparametric regression curve estimator for longitudinal data assessed. Based on the estimator that be obtained by using Weighted Least Square (WLS) optimization applied to model electricity consumption in Madura by choosing a model for longitudinal data based on linear spline estimator with two knot. The good criterion of the model is using the GCV value, the coefficient of determination and the value of MSE. The best model is a model that has a high coefficient of determination and a small MSE value. This spline model has a determination coefficient value of 99,72911% and MSE 32,50458.
APPLYING FUZZY LOGIC AND IOT FOR INTELLIGENT AUTOMATION IN CRAYFISH WATER QUALITY CONTROL Suwardi Ansyah, Adi Surya; Arifin, Miftahol; Laili, Umi
Jurnal Ilmiah Kursor Vol. 12 No. 3 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i3.334

Abstract

Crayfish, known for their high market value due to their substantial meat volume compared to other freshwater shrimp, necessitate improved cultivation efficiency, which can be significantly enhanced with advanced technology. In this study, we designed a highly effective automatic water quality control system specifically for crayfish cultivation that strategically integrates an Internet of Things (IoT)-based control system and a smartphone application. Uniquely, the system incorporates fuzzy logic within the decision-making algorithm, which maintains water quality by adaptively adjusting drainage and temperature control parameters based on dynamic pH and turbidity conditions. This seamless and responsive mechanism ensures optimal cultivation conditions are maintained efficiently. This study manifests that this novel IoT and fuzzy logic technology integration proved effective for automatic water quality control and monitoring. The research contribution is the pioneering integration of fuzzy logic and IoT technologies to devise an intelligent automation system for crayfish water quality control. This system offers real-time remote monitoring and control from a smartphone application and automatically adapts to varying pH and turbidity conditions, ensuring consistently optimal water quality for crayfish cultivation. Such a system holds the potential to set a new standard for precision aquaculture, elevating productivity and sustainability within the crayfish farming sector.
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.

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