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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah KOMPUTASI JIKA (Jurnal Informatika) Insearch: Information System Research Journal
Bayangkari Karno, Adhitio Satyo
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences Other

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Identification of 29 Types of Plant Diseases using Deep Learning EfficientNetB3 Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Kusuma Wardhana, Indra Sari; Sutarno, Sutarno; Arif, Dodi
Insearch: Information System Research Journal Vol 2, No 02 (2022): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v2i02.4389

Abstract

To supply the world's food needs in the midst of the existing food crisis, farmers urgently need to expand crop production. By establishing it simple to recognize the kind of plant disease so that earlier control efforts could be conducted, farmers' harvest failures driven on by disease attacks must be prevented. In this study, one of the Convolutional Neural Network (CNN) architectures known EfficeintNetB3 is applied to generate a classification model for 29 different types of plant diseases. A model is created after 3,170 image data are used for validation and 57,067 image data were utilized for training. 3,171 image data tests were conducted as part of the model testing phase, and the total test results were produced an extraordinarily high accuracy score of 0.99 percentage and an F1-score
Classification of cervical spine fractures using 8 variants EfficientNet with transfer learning Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Surawan, Tri; Lamandasa, Serlia Raflesia; Usuli, Sudarto; Kapuy, Holmes Rolandy; Digdoyo, Aji
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7065-7077

Abstract

A part of the nerves that govern the human body are found in the spinal cord, and a fracture of the upper cervical spine (segment C1) can cause major injury, paralysis, and even death. The early detection of a cervical spine fracture in segment C1 is critical to the patient’s life. Imaging the spine using contemporary medical equipment, on the other hand, is time-consuming, costly, private, and often not available in mainstream medicine. To improve diagnosis speed, efficiency, and accuracy, a computer-assisted diagnostics system is necessary. A deep neural network (DNN) model was employed in this study to recognize and categorize pictures of cervical spine fractures in segment C1. We used EfficientNet from version B0 to B7 to detect the location of the fracture and assess whether a fracture in the C1 region of the cervical spine exists. The patient data group with over 350 picture slices developed the most accurate model utilizing the EfficientNet architecture version B6, according to the findings of this experiment. Validation accuracy is 99.4%, whereas training accuracy is 98.25%. In the testing method using test data, the accuracy value is 99.25%, the precision value is 94.3%, the recall value is 98%, and the F1-score value is 96%.
ENHANCING SOLAR ENERGY EFFICIENCY: PREDICTIVE MODELING WITH XGBOOST AND LINEAR REGRESSION Hastomo, Widi; Digdoyo, Aji; Bayangkari Karno, Adhitio Satyo; Arif, Dodi
Jurnal Informatika Vol 9, No 1 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i1.12713

Abstract

Abstract Improving the reliability of the power grid system and operational efficiency is essential to facing future energy challenges. This study aims to provide added value to the management of the power grid, especially solar photovoltaic power plants (PLTS), by developing a more accurate predictive model for estimating energy output. By utilizing two real-time data sets, namely weather data and PLTS data, as well as machine learning methods, this study compares the performance of the XGBoost and Linear Regression (LR) models. We built the model to overcome high variability in energy output and maintain the stability of the power grid. The results show that XGBoost has a better performance with an MAE value of 38.08 compared to linear regression, which has an MAE of 80.23, indicating the superiority of XGBoost in predicting PLTS energy output. This study also opens up opportunities for further research with a focus on the application of other models such as random forests and neural networks, as well as improving data quality and parameter optimization to further improve prediction reliability and operational efficiency. The best-performing XGBoost model enables more efficient energy utilization and enhances the operational efficiency of PV solar power plants.
Prediksi Cacat Lempeng Baja Menggunakan Algoritma Bagging: Pendekatan Machine Learning untuk Peningkatan Kualitas Produksi Digdoyo, Aji; Bayangkari Karno, Adhitio Satyo; Hastomo, Widi; Sestri, Elliya; Fitriansyah, Reza
Jurnal Ilmiah Komputasi Vol. 24 No. 1 (2025): Jurnal Ilmiah Komputasi : Vol. 24 No 1, Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32409/jikstik.24.1.3654

Abstract

Industri baja memiliki peran krusial dalam berbagai sektor, menjadi faktor kunci dalam memastikan integritas struktural produk akhir. Penelitian ini bertujuan untuk mengatasi masalah ini dengan menerapkan algoritma Bagging dalam prediksi cacat lempeng baja. Hasil model training dengan kurva ROC dengan nilai AUC 99% dab logloss 0,14. Pengukuran precision, recall, dan f1 score untuk 7 jenis cacat baja memperoleh prosentase yang sangat baik (lebih dari 90%). Confusion Matrix menunjukan korelasi yang kuat antara jenis cacat ke 6 dan ke 5. Sedangkan validasi, antara jenis cacat ke 4 dan ke 0 terdapat hubungan yang sangat kuat. Classification report menunjukan nilai precision, recall, dan f1 score terbaik (lebih dari 80%) untuk jenis cacat ke 1, 2, dan 3. Nilai AUC yang cukup baik yaitu 88% dan Logloss yang cukup besar yaitu 3,13. Penelitian selanjutnya dapat fokus untuk meningkatkan nilai logloss yang masih harus diperbaiki untuk proses validasi.
Co-Authors Digdoyo, Aji Dodi Arif Holmes Rolandy Kapuy Kusuma Wardhana, Indra Sari Reza Fitriansyah Sestri, Elliya Sudarto Usuli Surawan, Tri Sutarno Sutarno Widi Hastomo