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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. 12 No. 3 (2024)" : 5 Documents clear
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.
STUDENT ACADEMIC PERFORMANCE PREDICTION FRAMEWORK WITH FEATURE SELECTION AND IMBALANCED DATA HANDLING Wijayaningrum, Vivi Nur; Kirana, Annisa Puspa; Putri, Ika Kusumaning
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.v12i3.356

Abstract

Various factors cause the low scores of students in practicum courses. If these factors cannot be identified, more and more students will drop out of the study due to low scores, especially Vocational College students who do not have the opportunity to improve their scores in the short semester. Students with the potential to drop out must be identified as soon as possible because the number of dropouts can have an impact on a university's accreditation value. In this study, the prediction of student academic performance was carried out using a framework consisting of imbalanced data handling using SMOTE and feature selection using Random Forest, as well as the application of Multi-Layer Perceptron (MLP) for the formation of a classification model. The MLP architecture consists of some neurons in the input layer, two hidden layers with five neurons each, and two neurons in the output layer. SMOTE succeeded in selecting ten significant parameters from 22 initial parameters, which produced the most accurate predictions. According to the test results, the proposed framework offers the best accuracy of 0.8889 and an F1-Score of 0.9032. These results prove that the proposed framework can be used as an alternative for the Department to take action to prevent students from dropping out.
THE INFLUENCE OF DATA CATEGORIZATION AND ATTRIBUTE INSTANCES REDUCTION USING THE GINI INDEX ON THE ACCURACY OF THE CLASSIFICATION ALGORITHM MODEL Willy Fernando; Jollyta, Deny; Dadang Priyanto; Dwi Oktarina
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.v12i3.372

Abstract

Numerical data problems are typically caused by a failure to comprehend the data and the outcomes of its processing. In order to give richer context and a deeper understanding of the facts, numerical data must be transformed into categories. On the other hand, changes in data have a significant impact on the analysis's outcomes. The purpose of this study is to see how transforming numerical data into categories affects the model produced by the classification algorithms. The dataset used in this study is the Maternal Health Risk. Categorization refers to formal arrangements. Categorization is also accomplished by using the Gini Index to limit the number of instances of an attribute. The classification is carried out using the Random Forest (RF), K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) algorithms to produce a model. The influence of data modifications to model can be observed in the confusion matrix with 5 different data splitting. The study results suggested that changing numerical data to categories data significantly improved the performance of the SVM model from 76.92% to 80.77% at a data splitting percentage of 95/5.
IMAGE CAPTIONING USING TRANSFORMER WITH IMAGE FEATURE EXTRACTION BY XCEPTION AND INCEPTION-V3 Pardede, Jasman; Fandi
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.v12i3.376

Abstract

Image captioning is a task in image processing that involves creating text descriptions that can describe the image content. The formation of the image captioning system model is influenced by image interpretation related to the given image caption. Image interpretation is influenced by the feature extraction used. This research proposes feature extraction with Xception and Inception-V3 by generating an image captioning model using Transformer. Model performance is measured based on BLUE and METEOR values. Based on the results of research conducted on the Flickr8k Dataset, it shows that the best model performance is using Xception feature extraction and batch_size = 256. The image captioning performance of Xception feature extraction for BLUE-1, BLUE-2, BLUE-3, BLUE-4, and METEOR when compared with Inception-V3 achieves increasing of 13.15%, 18.03%, 18.71%, 27.27%, and 15.43% respectively. The performance for Xception feature extraction with batch_size = 256 compared with batch_size = 128, increasing BLUE-1, BLUE-2, BLUE-3, BLUE-4, and METEOR namely 19.81%, 41.84%, 52.23%, 53.14%, and 31.56% respectively.
The Multiple Brain Tumor with Modified DenseNet121 Architecture Using Brain MRI Images: Classification Multiclass Brain Tumor Using Brain MRI Images with Modified DenseNet121 Sal Sabila, Syaqila; Peni Agustin Tjahyaningtyas, Hapsari
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.v12i3.379

Abstract

Brain tumors are capable of developing in individuals of all ages and can originate from brain tissue in various shapes and sizes. As a result, it is critical to quickly identify patients in order to expedite treatment. Magnetic Resonance Imaging (MRI) of the brain is an appropriate technique for identifying chronic conditions, including tumors. Deep learning methodologies have suggested numerous medical analysis strategies for health monitoring and brain tumor identification. This study used a modified version of DenseNet121 to accurately categorize three different forms of brain tumors: meningioma, pituitary, and glioma. Following the last transition layer, the DenseNet121 modification adds DropOut and GlobalAveragePooling layers. We determine the optimal hyperparameters that yield the highest performance by comparing several factors, including dropout, epoch, optimizer, and activation function. Evaluation of classification performance involves a comparison between Basic CNN and Basic DenseNet. Results of the analysis show that the modified DenseNet121 model works best with the following ideal hyperparameters: ADAM optimizer, Softmax activation, 150 epochs of training, and an 0.8 dropout rate. The performance results show an accuracy value of 0.9782, exceeding previous research findings.

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