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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTECOMS: Journal of Information Technology and Computer Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Dhani Ariatmanto
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Transportation

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model Rizki Firdaus Mulya; Ema Utami; Dhani Ariatmanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5182

Abstract

Acute lymphoblastic leukemia (ALL) is the most common form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can help with the prognosis and appropriate treatment. In this study, the author proposes an approach to classifying ALL based on white blood cell images using a convolutional neural network (CNN) model called InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service that stores large-scale cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition accuracy of 0.9896 with a loss of 0.031. The use of CNN models such as InceptionV3 in medical image analysis has the potential to improve the efficiency and precision of image-based disease diagnosis.
Cattle Weight Estimation Using Linear Regression and Random Forest Regressor Anjar Setiawan; Ema Utami; Dhani Ariatmanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5494

Abstract

The global cattle farming industry has benefits as a food source, livelihood, economic contribution, land environmental restoration, and energy source. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. The authors propose estimating cattle weighting linear regression and random forest regression. Linear regression can interpret the linear relationship between dependent and independent variables, and random forest regression can generalize the data well. The data set used in this study consisted of ten variables: live body weight, withers height, sacrum height, chest depth, chest width, maclocks width, hip joint width, oblique body length, oblique back length and chest circumference. Find the model that produces the smallest MAE value. The results show that the linear regression algorithm can produce estimated weight values for cattle with the best performance. This model produces a mean absolute error (MAE) of 0.35 kg, a mean absolute percentage error (MAPE) of 0.07%, a root mean square error (RMSE) of 0.5 kg, and an R² of 0.99. Each variable has excellent correlation performance results and contributes to computer vision and machine learning.
Co-Authors Adrianto Mahendra Wijaya Agus Fatkhurohman Alva Hendi Muhammad Anjar Setiawan annisa gatri zakinah Ari Eka Prasetiyanto Bambang Soedijono Bima Widianto Dendi Anggriandi Dewi Yustika Lakoro Elisa Tikasni Ema Utami Emanuel Minggu Fatihatul Khairani Ferda Ernawan Kusrini M Suyanto M. Iqbal Abdullah Sukri2 Maulana Brama Shandy Ma’ruf Aziz Muzani Muhammad Rusdi Rahman Muhammad Salimy Ahsan Rizki Firdaus Mulya Syifa Nur Fauziah