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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. 4 (2024)" : 5 Documents clear
IDENTIFYING THE CLUSTER OF FAMILIES AT RISK OF STUNTING IN YOGYAKARTA USING HIERARCHICAL AND NON-HIERARCHICAL APPROACH Ersa Riga Puspita; Mujiati Dwi Kartikasari
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.358

Abstract

Stunting, or short stature, is a growth disorder usually caused by chronic dietary deficiencies from the prenatal stage to early childhood, typically becoming evident in children after the age of 2. Stunting cases in Yogyakarta Province experienced a decline in 2020. With this development, the government aims to achieve zero stunting in Yogyakarta Province by 2024. To support this goal, a research study was conducted in 2021 to analyze family factors associated with stunting risks in Yogyakarta Province. The study aimed to assist the government in addressing the issue and achieving the target. In this research, a hierarchical clustering algorithm using the Ward technique and a non-hierarchical clustering algorithm using the Fuzzy C-Means (FCM) approach were applied. The optimal number of clusters was determined using the average distance and figure of merit approach. Stability validation, which also used the average distance and figure of merit approach, demonstrated that the best results were achieved by the non-hierarchical clustering algorithm employing FCM. As a result, six clusters were identified: cluster 1 with 5 sub-districts, cluster 2 with 18 sub-districts, cluster 3 with 21 sub-districts, cluster 4 with 17 sub-districts, cluster 5 with 14 sub-districts, and cluster 6 with 3 sub-districts.
IMPLEMENTATION OF BALANCING DATA METHOD USING SMOTETOMEK IN DIABETES CLASSIFICATION USING XGBOOST Ratantja Kusumajati, Fatwa; Rahmat, Basuki; Junaidi, Achmad
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.410

Abstract

In this research, XGBoost algorithm and the SMOTETomek approach are employed with the objective of enhancing the accuracy of diabetes classification. The study utilises 2,000 patient data points, comprising demographic and medical information, sourced from Kaggle. The dataset employed in this study comprises a number of variables, including pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, Body Mass Index (BMI), diabetes pedigree function, age, and an outcome variable. The latter is a binary classification label, taking on the values 0 and 1. A value of 0 indicates that the patient is not affected by diabetes, whereas a value of 1 indicates that the patient has diabetes. Diabetes represents a significant public health concern in Indonesia. A significant challenge in this study was the imbalanced nature of the dataset, which included a disproportionate number of non-diabetic samples relative to diabetic samples. To address this class imbalance, the researchers employed the SMOTETomek method. SMOTETomek integrates the SMOTE (Synthetic Minority Over-sampling Technique) and Tomek links algorithms to oversample the minority class and remove borderline samples, thereby balancing the class distributions. The SMOTETomek method achieved higher accuracy (95.01%) than SMOTE and the original data (both 92.13%), highlighting the benefits of combining SMOTE with Tomek Links for XGBoost. During testing, SMOTETomek slightly reduced the minority class accuracy (0.97 vs. 0.99 for SMOTE and original data) but maintained strong F1-score and precision, indicating effective handling of data imbalance despite minor trade-offs.
EVALUATION OF PARTICLE SWARM ALGORITHM MODIFICATIONS ON SUPPORT VECTOR MACHINE HYPERPARAMETER OPTIMIZATION TUNING FOR RAIN PREDICTION Putri, Aina Latifa Riyana; Riyono, Joko; Pujiastuti, Christina Eni; Supriyadi
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.411

Abstract

The Particle Swarm Optimization (PSO) algorithm, though simple and effective, faces challenges like premature convergence and local optima entrapment. Modifications in the PSO structure, particularly in acceleration coefficients (  and ), are proposed to address these issues. Techniques like Time Varying Acceleration Coefficients (TVAC), Sine Cosine Acceleration Coefficients (SCAC), and Nonlinear Dynamics Acceleration Coefficients (NDAC) have been implemented to enhance convergence speed and solution quality. This research evaluates various PSO modifications for improving convergence and robustness in rainfall potential prediction using Support Vector Machine (SVM) classification. The UAPSO-SVM algorithm C=0.82568 and γ=0.01960 excels in initial exploration, discovering more optimal global solutions with smaller variability. In contrast, TVACPSO-SVM shows gradual improvement but requires more iterations for stability, while SBPSO-SVM achieves the fastest convergence at iteration 14 but risks overfitting. Robustness analysis reveals all PSO-SVM variants maintain stable performance despite variations in dataset subset sizes, with accuracy stabilizing after a spike at 20%.. Therefore, PSO modifications enhance convergence speed and resilience to data fluctuations, improving their effectiveness for rainfall prediction.  
SKIN RASH CLASSIFICATION SYSTEM USING MODIFIED DENSENET201 THROUGH RANDOM SEARCH FOR HYPERPARAMETER TUNING Riyana Putri, Fayza Nayla; Isnanto, R.Rizal; Sugiharto, Aris
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.418

Abstract

Skin rashes caused by various diseases, such as monkeypox, cowpox, chickenpox, measles, and HFMD, often present similar symptoms, making accurate diagnosis challenging. This study aims to improve the classification of skin diseases through the application of a modified DenseNet-201 architecture combined with hyperparameter optimization using Random Search. The base DenseNet-201 model, with pre-trained weights, was first tested, achieving an accuracy of 63%, with the highest performance in the Healthy and HFMD classes. The proposed modified model, optimized using Random Search, improved overall accuracy to 80%, with enhanced precision, recall, and F1-score across most classes. The model’s performance was particularly notable in the HFMD and normal skin classes, although further improvements are needed for challenging classes like Cowpox and Measles. The findings highlight the potential of Random Search for hyperparameter tuning to enhance the performance of deep convolutional neural networks in the medical image classification domain, offering a promising tool for efficient and accurate skin disease detection.
CLOUD-BASED PREDICTIVE MOBILE APPLICATION FOR ASSESSING HONEY PURITY FROM STINGLESS BEES Maulana, Hata; Purwanto, Y Aris; Hartono W, Sony; Sukoco, Heru; Diding Suhandy
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.420

Abstract

Honey bees have various types and characteristics, one of which is the stingless bee. This type has limitations in producing honey, the selling value of its nest is quite expensive, and the water content in the honey produced is relatively high. The high water content affects the shelf life of this type of honey product, making it a challenge for honey farmers in marketing it. In addition, the dominant sour taste also makes its market increasingly limited or vulnerable to falsification of its purity by irresponsible producers. The use of spectrophotometers is increasingly developing in the food sector, especially in detecting the purity of a food product. The portable type of spectrophotometer also makes it easier to obtain spectrum data for a particular product. A simpler technique that is directly connected to a computer device allows it to be developed into a cloud-based application by providing minimal raw data processing (pre-processing). This study produces an android-based application and a simple cloud-based application architecture, which aims to facilitate the application of a honey purity prediction model from stingless bees. The Android-based application was successfully created by applying 'raw' spectrum data processing from the results of scanning a portable spectrophotometer, and data experiments with the SVM classification model produced an accuracy of 95%. The application of PCA techniques to cloud-based mobile application architecture results in efficient preprocessing of spectrum data.

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