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JSAI (Journal Scientific and Applied Informatics)
Published by Universitas Muhammadiyah Bengkulu
ISSN : 26143062     EISSN : 26143054     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal terbitan dibawah fakultas teknik universitas muhammadiyah bengkulu. Pada jurnal ini akan membahas tema tentag Mobile, Animasi, Computer Vision, dan Networking yang merupakan jurnal berbasis science pada informatika, beserta penelitian yang berkaitan dengan implementasi metode dan atau algoritma.
Arjuna Subject : -
Articles 471 Documents
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Evaluasi Pengalaman Pengguna Aplikasi PLN Mobile Menggunakan Metode User Experience Questionnaire Plus (UEQ+) Zulpa Salsabila; Fandi Halim; Viviyanty; Regina Ave Rameyana Berutu; Jekson Tua Sinamo
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6379

Abstract

This research aims to explain the User Experience Questionnaire Plus (UEQ+) Method for Analyzing and Evaluating User Experience on the PLN Mobile Application. PLN Mobile is an online-based PLN application that has been downloaded by 10 million users until research was conducted from 10 May 2023 – 06 June 2023. The results of the PLN Mobile review received positive scores as well as negative reviews. Seeing positive and negative comments by application users, this prompted this research. This research is aimed at obtaining the level of user experience using scientific methods. This research will use the User Experience Questionnaire Plus (UEQ+) method which consists of 8 UEQ+ and also uses Microsoft Excel to analyze the questionnaire data obtained. As an online questionnaire, the scale that will be used in UEQ+ is in accordance with the relevant scale recommendations in the web shop product category. Based on the results of data processing from 404 respondents on each scale, the PLN Mobile application received a positive evaluation value on the scale (intuitive use, dependability, trust, content appropriateness, content quality, clarity, visual aesthetics, value) and received a mean value (1.94, 1.94, 2.03, 2.14, 2.11, 2.12, 2.01, 2.09). The results of all the important rating graph values ​​get positive values. This shows that every scale measured in the PLN Mobile application is important.
Analisis Perbandingan Metode SAW (Simple Additive Weighting), WP (Weight Product) dan SMART (Simple Multi Attribute Rating Technique) Untuk Pemilihan Domba Kurban M Lutfi MA; Kapti; Yeza Febriani
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6380

Abstract

In implementing urban worship, it is often difficult for shohibul qurban to determine the quality of urban animals because it has several criteria/requirements that must be met so that the animals are sacrificed according to sharia. This study aims to analyze the decision support system method for selecting qurban animals using the SAW (Simple Additive Weighting), WP (Weight Product), and SMART (Simple Multi-Attribute Rating Technique) methods. The results showed that the WP method has an accuracy rate of 99.998% so this method is the most feasible to use for the selection of sacrificial sheep when compared to the SMART and SAW methods with the calculation results in the level of suitability at 99.994% for the SAW method and 99.876% for the SMART method. Sheep 4 has the highest weight ranking of other sheep in all methods, scoring 0.923 in the SAW method, 0.1727 in the WP method, and 17.8 in the SMART method. Sheep 4 criteria is the ideal criteria for a sacrificial animal.
Komparasi Hasil Algoritma Machine Learning Berbasis HSV Color Model Untuk Klasifikasi Citra Jenis Sayuran Umniy Salamah
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6392

Abstract

Currently, research on the classification of vegetables has made many advances. Machine learning has been proposed in recent years and has been created in image recognition, computer vision, and other fields. This study aims to classify vegetable products as part of the research of the classification of objects in charge that are inherently more complex than other subsets of object classification. This study will use the K-Nearest Neighbor (KNN) model to classify vegetable species, but with the addition of HSV color space model features. To see the performance of K-Nearest Neighbor (KNN) against other machine learning algorithms, a comparison will be made with support vector machine algorithms, logistic regression and naïve bayes. From the experimental results, the KNN algorithm got an accuracy of 80.67%, SVM got an accuracy of 72.23%, LR got an accuracy of 61.19%, NB got an accuracy of 48.77% and HSV-KNN got an accuracy of 84.33%.
Perbaikan Kualitas dan Kinerja Klasifikasi Citra Bawah Air dengan Metode CLAHE-CNN Asri, Sri Dianing
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6417

Abstract

Research on underwater image analysis is critical because of challenges such as color distortion, low contrast, and noise in images. Various methods have been proposed to overcome this problem. To improve the quality and classification of underwater photos, this study aims to ensure improved performance using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Convolutional Neural Networks (CNN) on underwater image datasets. The dataset consists of 500 JPG image with RGB channel and dimensions of 512 × 512 collected from online sources. There are classifications of sharks, eels, dolphins, sea rays, and whales in the underwater imagery dataset. The experiment was conducted using Python programming language on a computer that had 24GB RAM, Intel® Core™ i7-10510U CPU and hardware properties of Intel® HD Graphics graphics card. The results of this study show how CLAHE improved the CNN classification of underwater imagery by 0.91% in training data, 0.45% in validation data, and 2.02% in test data.
Segmentasi Citra Bawah Air dengan Algoritma GMM (Gaussian Mixture Model) Asri, Sri Dianing
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6418

Abstract

The purpose of this study was to measure the performance of Gaussian Mixture Model (GMM) technique for underwater image segmentation of seagrass objects based on datasets from autonomous surface vehicles (ASV from the Faculty of Fisheries and Marine Sciences, Bogor Agricultural University. The dataset is 640 x 480 pixel image data to support image segmentation research. There are three categories of underwater imagery: (a) underwater imagery featuring seagrass and seawater backgrounds; (b) underwater imagery featuring seagrasses, clear fish, and seawater backgrounds; and (c) underwater imagery featuring seagrasses, faint fish, and seawater backgrounds. Based on the experimental results, seagrass objects in image type (a) have almost identical colors to each pixel in the underwater image, the GMM model was able to distinguish them from the background and seawater background. The GMM model can distinguish between the background and the seawater background in image type (b), but cannot eliminate fish objects in the image. The segmentation results in image type (c) are not perfect because the GMM model removes seagrass objects that have green pixel color.
Klasifikasi Citra Tumor Otak Menggunakan Gaussian Model Berbasis Machine Learning Berdasarkan MRI Dataset Anita Ratnasari
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6419

Abstract

Early detection of brain tumors using brain magnetic resonance imaging is needed to prevent benign tumors from developing into malignant tumors. This study aims to classify brain tumors using thresholding and support vector machine (SVM) methods. The thresholding methods used in this study are global thresholding, adaptive thresholding and gaussian thresholding. The evaluation methods used are accuracy, recall, precision, and specificity. This study has used magnetic resonance imaging (MRI) based image datasets totaling 3,079 data. Overall, the accuracy of the support vector machine (SVM) algorithm and adaptive thresholding method got the best accuracy of 84.25%, while the gaussian thresholding method got 82.81% accuracy and global thresholding got 81.25% accuracy.
Retinal Optical Coherence Tomography (OCT) Analysis for Retinal Damage Detection Using Machine Learning Methods Anita Ratnasari
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6420

Abstract

This study attempts to use support vector machine and otsu thresholding as proposed algorithm models to classify Retinal optical coherence tomography (OCT) images. In this study, there are two types implemented in classifying retinal image datasets. The first scenario is to classify using the support vector machine algorithm without the otsu thresholding method and the second scenario is to classify using the support vector machine algorithm with the otsu thresholding method with various parameter values. Based on the experimental results, classification of retina image datasets using the support vector machine algorithm without the otsu thresholding method obtained an accuracy of 63.00% while classification using the support vector machine algorithm with the otsu thresholding method with parameter values (0, 255), (50, 255), (100, 255), (150, 255) obtained an accuracy of 59.30%.
Perbandingan Kinerja Activation Function pada Algoritma Resnet untuk Klasifikasi Varietas Beras Ayumi, Vina
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6421

Abstract

Quality checking of rice seed varieties (Oryza sativa) is an important procedure for quality assessment in the agricultural sector. The application of transfer learning algorithms has shown good results in image recognition tasks, so this algorithm is suitable for classifying rice variety images automatically. The data classes to be analyzed are Arborio, Basmati, Ipsala, Jasmine and Karacadag based on morphological, shape and color features analysis using the ResNet algorithm. The experiment used three types of models, namely ResNet-TopHat-ReLU, ResNet-TopHat-LeakyReLU and ResNet-TopHat-eLU. The ResNet-TopHat-eLU model is the best model with training accuracy of 96.61%, validation accuracy of 95.12% and testing accuracy of 78.17%.
Identifikasi Penyakit Kelainan Tulang Belakang Berdasarkan Pengolahan Dataset Spine X-ray Mengunakan Algoritma LBP dan CNN Noprisson, Handrie
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6422

Abstract

This research will use deep learning in conducting spinal x-ray image analysis but computational time problems are a problem of this study. Computations on deep learning across multiple nodes can increase training time and longer computation time compared to machine learning models. Based on experimental results, the best spine x-ray image classification results when using the CNN model with accuracy at the training stage, evaluation stage and test stage were 69.00%, 83.33% and 81.16% respectively. CNN models optimized with LBP get the lowest accuracy, with results at the training stage of 62.64%, validation stage of 75.00% and testing stage of 65.22%. LBP feature extraction turns out to have several drawbacks when combined with the CNN model, one major drawback is its inability to process global spatial information while retaining local texture information which causes LBP to be unable to capture the entire structure or context of the image, focusing only on local patterns so that many features of the image are lost. Another issue is the sensitivity of CNNs to image data, which can affect classification accuracy.
Analisis Performa Metode Klasifikasi Dataset Multi-Class Kanker Kulit Menggunakan KNN dan HOG Rahayu, Sarwati; Sandiwarno, Sulis; Dwika Putra, Erwin; Utami, Marissa; Setiawan, Hadiguna
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6423

Abstract

Detection of skin cancer in its early phase is a challenge even for dermatologists. This study aims to analyze the performance of classification methods on multiclass skin cancer datasets using K-nearest neighbor (KNN) and histogram of oriented gradients (HOG). The dataset is taken publicly under the name Skin Cancer MNIST dataset: HAM10000 dataset totaling 10,015 data. The first experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The second experiment used the pixels per cell parameter of 8.8 and cells per block of 2.2 to get an accuracy of 60.58%. The last experiment using the pixels per cell parameter of 8.8 and cells per block of 2.2 got the best accuracy of 61.43%.

Page 33 of 48 | Total Record : 471

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