Multiclass Classification of Brain Cancer with Multiple Multiclass Artificial Bee Colony Feature Selection and Support Vector Machine

Kharis, Selly Anastassia Amellia; Hadi, Ibnu; Hasanah, K A

doi:10.1088/1742-6596/1417/1/012015

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Pendidikan merupakan aspek krusial dalam pembentukan individu dan masyarakat. Di era dimana teknologi semakin meresap ke dalam berbagai bidang kehidupan (Kharis et al, 2019(Kharis et al, , 2024Kharis, Robiansyah, Maulana, et al, 2023;Kharis, Tarigan, et al, 2023;Kharis, Zili, Putri, et al, 2023;Rustam & Kharis, 2018, 2020Zili et al, 2022), pendidikan tidak luput dari dampaknya. Salah satu perkembangan teknologi yang semakin menonjol dalam konteks pendidikan adalah kecerdasan buatan (artificial intelligence, AI) Kharis, Zili, Zubir, et al, 2023).…”

Section: Pendahuluanunclassified

Pengalaman dan Perspektif Pendidik terhadap Penggunaan ChatGPT dalam Pengajaran

Kharis,

Arisanty,

Zili

2024

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Perkembangan teknologi khususnya pada kecerdasan buatan semakin berkembang. Salah satu kecerdasan buatan adalah Chat Generative Pre-trained Transformer (ChatGPT). ChatGPT digunakan dalam berbagai aspek termasuk dalam bidang pendidikan. Penelitian ini bertujuan untuk mengeksplorasi pengalaman dan perspektif para pendidik terhadap penerapan ChatGPT dalam proses pengajaran. Metode penelitian kualitatif digunakan untuk mendapatkan wawasan melalui kuesioner yang disebarkan kepada pendidik baik dosen maupun guru. Hasil penelitian menunjukkan bahwa pendidik memiliki berbagai pandangan terkait dengan penggunaan ChatGPT dalam pembelajaran. Sebanyak 54,3% pendidik setuju bahwa ChatGPT merupakan chatbot yang mudah digunakan dan membuat perbedaan signifikan dalam produktivitas atau efisiensi dalam pekerjaan pendidik. Sebanyak 48,6% pendidik juga setuju bahwa ChatGPT efektif dalam menghasilkan konten atau materi pembelajaran. Penelitian menunjukkan bahwa meskipun ChatGPT dapat menjadi alat yang berguna untuk memberikan bantuan dalam pengajaran namun sebagian besar guru tetap melakukan verifikasi atau mencari ulang informasi yang diberikan oleh ChatGPT dengan sumber lainnya. Beberapa cara telah dilakukan pendidik untuk mencegah penyalahgunaan ChatGPT oleh peserta didik dalam proses pembelajaran, mulai dari dengan membiasakan peserta didik menjawab dengan menggunakan penjelasan sendiri disertai dengan sumber yang valid, menginformasikan di awal pembelajaran bahwa ChatGPT boleh dipergunakan namun bukan sebagai satu-satunya sumber dalam memberikan jawaban, menanamkan pentingnya critical thinking, dan melakukan ujian secara lisan untuk menguji jawaban peserta didik.

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Section: Pendahuluanunclassified

Pengalaman dan Perspektif Pendidik terhadap Penggunaan ChatGPT dalam Pengajaran

Kharis,

Arisanty,

Zili

2024

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“…In the case of multiclass classification, the metrics specified for binary classification, do not fully apply [30]. Table 1 shown the confusion matrix for multi-class problems with k class numbers [31].…”

Section: Accuracy Measurement With Confusion Matrixmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Classification of Ultraviolet Index

Syahab

Widiyanto

Anuggilarso

et al. 2023

J. teknol. inf. pendidik.

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Indonesia is a tropical climate country which has the potential for high intensity of sunlight exposure. Several types of exposure that receives are ultraviolet rays. According to the National Agency for Meteorology, Climatology and Geophysics, it provides information regarding the impact on human activities in an ultraviolet index which has a risk scale. The aim of this research was creating a recommender system based on the ultraviolet index category in providing daily activity advice to users. The methods used the K-nearest Neighbor algorithm and Support Vector Machine with a Collaborative Filtering Model-Based approach that could recommend items based on the results of a model trained to identify input data patterns. The stages carried out in this study included data collection, data pre-processing, data division into test data and train data, dataset testing, analysis of the results of models that had been trained in the accuracy values using the algorithm tested. The results of the confusion matrix calculation produced test evaluations in accuracy values, precision values, and recall values. The comparison of result had the highest performance in K-nearest Neighbor with an accuracy value of 99.69%, a precision value of 99.00%, and a recall value of 96.20%. In research used the Support Vector Machine showed the lowest performance with an accuracy value of 97.91%, a precision value of 93.20%, and a recall value of 86.40%.

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“…A multiclass classification issue may be used to describe this situation. [ 15 ]. As a feature selection approach, Multiple Multiclass Artificial Bee Colony (MMABC) was used, and as a classification method, Support Vector Machine (SVM) was used.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm-Based Strategy for Brain Tumor Segmentation

Dahiya

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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Medical image segmentation is a technique for detecting boundaries in a 2D or 3D image automatically or semiautomatically. The enormous range of the medical image is a considerable challenge for image segmentation. Magnetic resonance imaging (MRI) scans to aid in the detection and existence of brain tumors. This approach, however, requires exact delineation of the tumor location inside the brain scan. To solve this, an optimization algorithm will be one of the most successful techniques for distinguishing pixels of interest from the background, but its performance is reliant on the starting values of the centroids. The primary goal of this work is to segment tumor areas within brain MRI images. After converting the gray MRI image to a color image, a multiobjective modified ABC algorithm is utilized to separate the tumor from the brain. The intensity determines the RGB color generated in the image. The simulation results are assessed in terms of performance metrics such as accuracy, precision, specificity, recall, F-measure, and the time in seconds required by the system to segment the tumor from the brain. The performance of the proposed algorithm is computed with other algorithms like the single-objective ABC algorithm and multiobjective ABC algorithm. The results prove that the proposed multiobjective modified ABC algorithm is efficient in analyzing and segmenting the tumor from brain images.

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Multiclass Classification of Brain Cancer with Multiple Multiclass Artificial Bee Colony Feature Selection and Support Vector Machine

Cited by 6 publications

References 6 publications

Pengalaman dan Perspektif Pendidik terhadap Penggunaan ChatGPT dalam Pengajaran

Pengalaman dan Perspektif Pendidik terhadap Penggunaan ChatGPT dalam Pengajaran

Comparison of Machine Learning Algorithms for Classification of Ultraviolet Index

Modified Artificial Bee Colony Algorithm-Based Strategy for Brain Tumor Segmentation

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