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INDONESIA
Jiko (Jurnal Informatika dan komputer)
Published by Universitas Khairun
ISSN : 26148897     EISSN : 26561948     DOI : -
Core Subject : Science,
Computer Science & IT
Jiko (Jurnal Informatika dan Komputer) Ternate adalah jurnal ilmiah diterbitkan oleh Program Studi Teknik Informatika Universitas Khairun sebagai wadah untuk publikasi atau menyebarluaskan hasil - hasil penelitian dan kajian analisis yang berkaitan dengan bidang Informatika, Ilmu Komputer, Teknologi Informasi, Sistem Informasi dan Sistem Komputer. Jurnal Informatika dan Komputer (JIKO) Ternate terbit 2 (dua) kali dalam setahun pada bulan April dan Oktober
Arjuna Subject : -
Articles 13 Documents
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Search results for , issue "Vol 7 No 2 (2024)" : 13 Documents clear
IMPLEMENTATION OF THE FUZZY TIME SERIES METHOD FOR FORECASTING BLOOD NEEDS IN THE INDONESIAN RED CROSS (PMI) MEDAN Rina Syafiddini Harahap; Rakhmat Kurniawan R
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8614

Abstract

The primary issue faced by PMI (Indonesian Red Cross) about blood requirements is often associated with insufficient blood supplies to satisfy the demand of patients, particularly during emergencies or significant catastrophes such as natural calamities. Hence, it is essential to use appropriate methodologies to forecast blood requirements accurately and determine the quantity of blood bags required in the future. When forecasting calculations using fuzzy time series, the interval length is established at the start of the calculation procedure. The duration of the gap significantly affects the establishment of fuzzy associations, which in turn affects the difference in forecast computation outcomes. The investigation reveals that Group AB has the lowest Root Mean Square Error (RMSE) value of 136.90, indicating that your model demonstrates superior accuracy in predicting blood group AB compared to other blood groups. The RMSE score for Group O is 819.5, which suggests that your model's accuracy in predicting blood group O is lower compared to other blood groups
CLASSIFICATION OF DURIAN LEAF IMAGES USING CNN (CONVOLUTIONAL NEURAL NETWORK) ALGORITHM Lely Mustikasari Mahardhika Fitriani; Yovi Litanianda; Adi Fajaryanto Cobantoro
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8576

Abstract

This research investigates the classification of durian leaf images using Convolutional Neural Network (CNN) algorithms, specifically focusing on the architectures AlexNet, InceptionNetV3, and MobileNet. The study begins with the collection of a dataset comprising 1604 images for training, 201 images for validation, and 201 images for testing. The dataset includes five classes of durian leaves: Bawor, Duri Hitam, Malica, Montong, and Musang King, chosen for their varied characteristics such as taste, texture, and aroma. Data preprocessing involved several steps to ensure the images were suitable for model training. These steps included data augmentation to increase variability, pixel normalization to standardize the images, and resizing to 150x150 pixels to match the input requirements of the CNN models. After preprocessing, the CNN models were implemented and trained using deep learning frameworks such as TensorFlow and PyTorch. Model performance was evaluated using a Confusion Matrix, which provided detailed insights into classification accuracy, precision, sensitivity, specificity, and F-score. The results indicated that InceptionNetV3 and AlexNet achieved near-perfect classification accuracy, with no misclassifications, demonstrating their robustness and precision in identifying durian leaf images. The training accuracy for both models rapidly approached 100% within the first few epochs and stabilized, while the loss values decreased sharply, indicating effective learning without overfitting. In contrast, MobileNet, while showing high accuracy and low loss during training, exhibited several misclassifications across all classes. The training accuracy of MobileNet also approached 100%, but the presence of misclassifications suggested that further tuning and improvements were necessary. Specifically, MobileNet's Confusion Matrix revealed errors in correctly identifying samples from each class, indicating potential areas for enhancement in the model's architecture or preprocessing techniques. In conclusion, InceptionNetV3 and AlexNet proved to be highly efficient and accurate architectures for classifying durian leaf images, making them suitable for practical applications. MobileNet, although performing well, requires further refinement to achieve the same level of accuracy and reliability. This study highlights the importance of selecting appropriate CNN architectures and the need for thorough preprocessing to optimize model performance in image classification tasks.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Nova Auliyatul Maulidiyyah; Trimono Trimono; Aviolla Terza Damaliana; Dwi Arman Prasetya
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.

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