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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmiah Media Sisfo Indonesian Journal of Electrical Engineering and Computer Science Jurnal Informatika dan Rekayasa Komputer Jurnal Manajemen Teknologi dan Sistem Informasi Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
WILLY RIYADI
Universitas Dinamika Bangsa, Jambi, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Comparative Analysis of Optimizer Effectiveness in GRU and CNN-GRU Models for Airport Traffic Prediction Willy Riyadi; Jasmir Jasmir
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29659

Abstract

The COVID-19 pandemic has posed significant challenges to airport traffic management, necessitating accurate predictive models. This research evaluates the effectiveness of various optimizers in enhancing airport traffic prediction using Deep Learning models, specifically Gated Recurrent Units (GRU) and Convolutional Neural Network-Gated Recurrent Units (CNN-GRU). We compare the performance of optimizers including RMSprop, Adam, Nadam, AdamW, Adamax, and Lion, and analyze the impact of their parameter tuning on model accuracy. Time series data from airports in the United States, Canada, Chile, and Australia were used, with preprocessing steps like filtering, cleaning, and applying a MinMax Scaler. The data was split into 80% for training and 20% for testing. Our findings reveal that the Adam optimizer paired with the GRU model achieved the lowest Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) in the USA. The study underscores the importance of selecting and tuning optimizers, with ReduceLROnPlateau used to adjust the learning rate dynamically, preventing overfitting and improving model convergence. However, limitations include dataset imbalance and region-specific results, which may affect the generalizability of the findings. Future research should address these limitations by developing balanced datasets and exploring optimizer performance across a broader range of regions and conditions. This study lays the groundwork for further investigating sustainable and accurate airport traffic prediction models.
Perbandingan Algoritma Naïve Bayes Classifier (NBC) dengan Random Forest Untuk Klasifikasi Penyakit Ginjal Kronis (PGK) Caterina Paras Dewi; Jasmir Jasmir; Willy Riyadi; Alya Rafina
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.72

Abstract

Chronic Kidney Disease (CKD) is a heterogeneous disorder that gradually affects the structure and function of the kidneys, is difficult to recover, and causes the body to be unable to maintain metabolism and fail to maintain fluid and electrolyte balance, leading to increased urea levels. Chronic kidney disease data was obtained from Kaggle, in this study a comparison was made between two classification algorithms, namely Naïve Bayes Classifier (NBC) and Random Forest because it is not yet known what algorithm is best in classifying chronic kidney disease (CKD). Both algorithms are evaluated based on performance metrics such as accuracy, precision, recall, and confusion matrix. The results of the evaluation showed that in a dataset of 400 samples, the performance  of the Naïve Bayes Classifier (NBC) algorithm obtained an accuracy of 94%, while Random Forest had an accuracy of 93%. Then in the small dataset (158 data), Random Forest got a better accuracy score with 87% compared to the Naïve Bayes Classifier (NBC) of 78%. Based on the results of the evaluation, Random Forest has a more stable performance on small datasets, while Naïve Bayes Classifier (NBC) provides higher performance on larger datasets in the context of chronic kidney disease classification.
Co-Authors Abdul Harris Abdul Harris Abidin, Dodo Zaenal Adi Kusuma Ahmad Ahmad Ali, Edi Alya Rafina Amroni Amroni Arvita, Yulia Caterina Paras Dewi Desi Kisbianty, Desi Despita Meisak Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin Ibnu Sani Wijaya ISW Ido Fitra Ramadhan Igra Soliqin Ika Riski Monika Irawan Irawan Irawan, Beni Jasmir Jasmir Jasmir Jasmir Jasmir Jasmir Jasmir, Jasmir Junaidi, Feri Khairuldi Kiki Fauzi Kurniabudi Kurniabudi, Kurniabudi Lies Aryani Lina Juriatun Nasirin M Irwan Bustami Marrylinteri Istoningtyas Muhammat Andri Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Randa, Wahidul Riko Ardiyanto Rohaini, Eni Rohaini, Eni Selphia , Megah SIKA, XAVERIUS Sika, Xaverius Silvia Rianti Agustini Sutoyo, Mochammad Arief Hermawan Xaverius Sika Yessi Hartiwi YOSE, IMELDA Yovi Pratama Yulia Arvita Zulia, Restutik