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All Journal Jurnas Nasional Teknologi dan Sistem Informasi PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Rahmadeyan, Akhas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Performance Comparison Between Artificial Neural Network, Recurrent Neural Network and Long Short-Term Memory for Prediction of Extreme Climate Change Luchia, Nanda Try; Tasia, Ena; Ramadhani, Indah; Rahmadeyan, Akhas; Zahra, Raudiatul
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.864

Abstract

Extreme climate change is the most common problem in Indonesia. Extreme climate change for months can cause various natural disasters. Therefore, it is necessary to make predictions about climate change that will occur in order to avoid the risk of future conflicts. This study uses the Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) algorithms by comparing the performance of the three using Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) evaluations. The results of this study indicate that RNN is better at predicting temperature in Indonesia compared to ANN and LSTM. This is evidenced by the MAPE value generated by the RNN which is smaller than the ANN and LSTM, which is 1.852 %, the RMSE value is 1,870, and the MSE value is 3,497.
Seleksi Fitur pada Supervised Learning: Klasifikasi Prestasi Belajar Mahasiswa Saat dan Pasca Pandemi COVID-19 Rahmadeyan, Akhas; Mustakim, Mustakim
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 1 (2023): April 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i1.2023.21-32

Abstract

Dampak pandemi COVID-19 membuat lembaga pendidikan mengubah metode belajar menjadi pembelajaran jarak jauh secara daring. Banyak perguruan tinggi menyatakan keprihatinannya pada prestasi akademik mahasiswanya selama selama periode tersebut, namun disisi lain terdapat mahasiswa yang merasa puas dan nyaman. Di masa pasca pandemi terjadi transisi bertahap untuk kembali ke pembelajaran tatap muka.  Ini dilakukan karena pembelajaran tatap muka dianggap lebih efektif dibandingkan pembelajaran daring. Untuk meningkatkan dan memantau kemajuan prestasi akademik mahasiswa demi menghasilkan lulusan yang berkualitas, maka diperlukan analisis terkait perilaku dan prestasi belajar mahaiswa, salah satunya dengan menggunakan teknik data mining. Penelitian ini bertujuan untuk menemukan pola dan faktor yang mempengaruhi prestasi akademik mahasiswa saat dan pasca pandemi COVID-19. Chi-Square dan Mutual Information diterapkan sebagai seleksi fitur untuk menentukan fitur paling berpengaruh pada data. Data dengan fitur optimal akan dilakukan klasifikasi dengan algoritma NBC, CART, RF, dan SVM. Berdasarkan hasil dan analisis yang dilakukan, dapat disimpulkan seleksi fitur efektif dalam meningkatkan kemampuan model dan mempercepat waktu komputasi. Pemodelan dengan 4 algoritma dan 2 metode seleksi fitur menghasilkan CART dengan Chi-Square pada 2 fitur sebagai model terbaik dengan akurasi 89,00%, precision 87,72%, recall 93,57% dan waktu komputasi 0,01814s. Dibandingkan tanpa seleksi fitur, performa CART dengan Chi-Square mengalami peningkatan akurasi sebesar 3% dan waktu komputasi 0,00629s. Chi-Square menjadi seleksi fitur yang efektif pada penelitian ini, yang mana Chi-Square unggul pada rara-rata recall dan waktu komputasi dibandingkan Mutual Information.
Random Forest Optimization Using Particle Swarm Optimization for Diabetes Classification Pratama, Pangeran Fadillah; Rahmadani, Desvita; Nahampun, Rahma Sani; Harmutika, Della; Rahmadeyan, Akhas; Evizal, Muhammad Fikri
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.809

Abstract

Diabetes mellitus is a chronic degenerative disease caused by a lack of insulin production in the pancreas or the body's ability to use insulin less effectively. According to a report by the World Health Organization (WHO), 4% of the total deaths in the world are caused by diabetes. The International Diabetes Federation (IDF) notes that in 2013 there has been an increase in diabetes sufferers. Indonesia is the seventh place with the largest number of cases of diabetes mellitus. In this study, the method used to classify diabetes is using a random forest algorithm with Particle Swarm Optimization (PSO) optimization. This study resulted in an accuracy of the random forest classification algorithm of 78.2% and 82.1 using PSO optimization with an increase in value of 3.9%. It can be concluded that PSO optimization can provide a better increase in classification accuracy values when compared to the random forest algorithm without PSO optimization
Co-Authors Evizal, Muhammad Fikri Harmutika, Della Luchia, Nanda Try Mustakim Mustakim Nahampun, Rahma Sani Pratama, Pangeran Fadillah Rahmadani, Desvita Ramadhani, Indah Tasia, Ena Zahra, Raudiatul