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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Mnemonic Fidelity : Jurnal Teknik Elektro
Akbar, Jiwa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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PERAMALAN HARGA BITCOIN CASH-USD (BCH-USD) PADA TIME FRAME HARIAN MENGGUNAKAN LSTM Akbar, Jiwa; Ali Setyo Yudono, Muchtar; Lucia Kharisma, Ivana
Jurnal Mnemonic Vol 7 No 2 (2024): Mnemonic Vol. 7 No. 2
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i2.10121

Abstract

Perkembangan teknologi yang pesat, terutama di sektor keuangan, telah mendorong pergeseran dari mata uang konvensional ke mata uang digital seperti Bitcoin Cash (BCH). BCH adalah mata uang kripto yang menggunakan teknologi blockchain, yaitu rantai blok yang berfungsi sebagai tempat pencatatan transaksi tanpa sentralisasi, berbeda dengan sistem bank tradisional. Namun, harga BCH sangat fluktuatif sehingga diperlukan peramalan harga masa depan. Penelitian ini mengusulkan penggunaan algoritma Long Short Term Memory (LSTM) untuk meramalkan harga BCH. LSTM, sebuah algoritma jaringan saraf tiruan, mampu memahami data deret waktu yang kompleks seperti pergerakan harga BCH. Studi sebelumnya menunjukkan bahwa LSTM berhasil digunakan untuk meramalkan harga saham dan umur transformator, dengan penurunan Root Mean Square Error (RMSE) yang signifikan dibandingkan algoritma lain. Berdasarkan pendekatan ini, penelitian bertujuan menyajikan proyeksi harga BCH yang bermanfaat bagi pelaku pasar keuangan. Penelitian ini melibatkan lima skenario peramalan. Skenario pertama menggunakan epoch 10 dan hidden layer 10, skenario kedua epoch 20 dan hidden layer 20, skenario ketiga epoch 30 dan hidden layer 30, skenario keempat epoch 40 dan hidden layer 40, dan skenario kelima epoch 50 dan hidden layer 50. Panjang sequence di setiap skenario yaitu 30. Hasil terbaik diperoleh pada skenario kedua menghasilkan nilai Mean Squared Error (MSE) sebesar 1654,40 dan RMSE sebesar 40,67
Water Level Classification for Early Flood Detection Using KNN Method Akbar, Jiwa; Yudono, Muchtar Ali Setyo
Fidelity : Jurnal Teknik Elektro Vol 6 No 2 (2024): Edition for May 2024
Publisher : Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/fidelity.v6i2.227

Abstract

Floods occur when water levels exceed normal limits, causing rivers to overflow and inundate low-lying areas. Early warning systems for flood disasters are crucial to mitigate the damage caused, such as loss of life and property. A flood classification system can be developed by utilizing water level data from the Department of Water Resources to predict the likelihood of flooding using the K-Nearest Neighbors (KNN) algorithm. This study aims to determine the flood status classification based on water levels using the KNN method in the Ciliwung River. The research data were obtained from the DKI Jakarta open data site, consisting of 564 samples. The study evaluated K values ranging from 1 to 10. The average accuracy across all K scenarios was 99%, with the best K value being 1, which provided 100% accuracy, sensitivity, and specificity. These results indicate that the KNN method is effective in classifying flood status based on water level data, making it a reliable tool for early warning systems. This system is expected to help reduce the negative impacts of floods by providing accurate and timely information to the public and authorities. This research makes a significant contribution to the development of disaster mitigation technology, particularly in flood risk management in urban areas.
Water Level Classification for Detect Flood Disaster Status Using KNN and SVM Akbar, Jiwa; Setyo Yudono, Muchtar Ali
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2166

Abstract

Flooding occurs when the water's surface elevation exceeds the average level, overflowing river water and creating inundation in low-lying areas. Early warning for potential floods significantly reduces losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The study results indicate that the SVM algorithm outperforms the KNN algorithm. The SVM algorithm used parameter C ranging from 1 to 10 in the scenarios, and the RBF kernel achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10.
Water Level Classification for Detect Flood Disaster Status Using KNN and SVM Akbar, Jiwa; Setyo Yudono, Muchtar Ali
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2166

Abstract

Flooding occurs when the water's surface elevation exceeds the average level, overflowing river water and creating inundation in low-lying areas. Early warning for potential floods significantly reduces losses, such as human casualties and property damage. In this context, the flood disaster classification system uses water surface elevation data from the Water Resources Agency to predict the likelihood of floods using the K-Nearest Neighbors (KNN) Algorithm. This research aims to classify flood status based on water surface elevation using the K-Nearest Neighbors and Support Vector Machine(SVM) methods in the Ciliwung River. The study results indicate that the SVM algorithm outperforms the KNN algorithm. The SVM algorithm used parameter C ranging from 1 to 10 in the scenarios, and the RBF kernel achieved 100% accuracy. On the other hand, the KNN algorithm achieved 100% accuracy only for K values of 1, 2, 3, 4, and 5 in scenarios where K ranged from 1 to 10.
Co-Authors Lucia Kharisma, Ivana Muchtar Ali Setyo Yudono