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All Journal Proceeding SENDI_U TIN: TERAPAN INFORMATIKA NUSANTARA
Rachmatullah, Muhammad Ibnu Choldun
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PENGKLASTERAN TERHADAP NEGARA-NEGARA DENGAN JUMLAH KASUS COVID 19 TERBESAR Rachmatullah, Muhammad Ibnu Choldun
Proceeding SENDI_U 2021: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

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Abstract

Pandemi Covid 19 telah menyebar di seluruh negara di dunia. Berdasarkan data pada tanggal 11 Juli 2021 telah menyebabkan 187.393.771 orang terinfeksi di seluruh dunia. Paper ini bertujuan untuk mengelompokkan negara dengan jumlah kasus terbesar berdasar karakteristik kesamaan dan perbedaan dengan mempertimbangkan atribut jumlah kasus per 1 juta penduduk, jumlah kematian per 1 juta penduduk, dan jumlah tes per 1 juta penduduk. Metode pengklasteran yang digunakan adalah K-Means, sedangkan penentuan jumlah klaster optimal dengan menerapkan kriteria Elbow. Dari penerapan kriteria Elbow ini dari hasil eksperimen diperoleh jumlah klaster optimal=5, sehingga akan membagi 30 negara dengan jumlah kasus terbesar ke dalam lima kelompok/kategori. Dari pengelompokan ini dapat diketahui karakteristik 30 negara dengan jumlah kasus Covid 19 terbesar, berdasar jumlah kasus dan jumlah pengetesan per 1 juta penduduk.
Forecasting Data Time Series Menggunakan MLP dan LSTM untuk Memprediksi Jumlah Produksi Bir Rachmatullah, Muhammad Ibnu Choldun
TIN: Terapan Informatika Nusantara Vol 6 No 4 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i4.7755

Abstract

Time series data forecasting is an important approach in various sectors such as finance, energy, and healthcare. As technology advances, deep learning methods such as Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) are increasingly being used to improve prediction accuracy. This study compares the performance of these two methods in forecasting a time series dataset of monthly beer production in Australia. The model was trained and tested using a 70% training and 30% testing data split. Performance evaluation was based on the Root Mean Square Error (RMSE) value after 10 experimental repetitions. The results show that MLP has a lower RMSE value and a smaller standard deviation than LSTM, both on the training and testing data. This indicates that MLP is more stable and efficient in handling datasets with simple patterns and low complexity, while LSTM tends to require more intensive tuning and has a higher risk of overfitting. Therefore, MLP is recommended as a lighter and more consistent alternative forecasting method for similar data scenarios.
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