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All Journal Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Bumigora Information Technology (BITe) Jurnal Ilmiah Matematika Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat STATISTIKA Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Conference on Digital Advanced Tourism, Management, and Technology Radiant : Journal of Applied, Social, and Education Studies
Winita Sulandari
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Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : STATISTIKA

 1 
Perbandingan Algoritma Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) dan Self-Organizing Map (SOM) untuk Clustering Data Gempa Bumi Wati, Rosita Kurnia; Pratiwi, Hasih; Winita Sulandari
Statistika Vol. 24 No. 2 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i2.3645

Abstract

ABSTRAK Gempa bumi merupakan bencana alam yang kerap melanda Indonesia karena letak geografisnya berada pada batas pertemuan tiga lempeng aktif dunia. Dampak kerusakan yang timbul akibat gempa bumi bergantung pada magnitudo dan kedalamannya. Oleh karena itu, perlu upaya mitigasi bencana dan manajemen risiko bencana melalui pengolahan data untuk mengetahui karakteristik dari data gempa tersebut. Penelitian ini bertujuan untuk clustering data gempa bumi di Indonesia berdasarkan magnitudo dan kedalaman dengan menerapkan algoritma Density-Based Spatial Clustering Algorithm With Noise (DBSCAN) dan Self-Organizing Map  (SOM) dengan validasi kebaikan cluster menggunakan koefisien silhouette. Penerapan algoritma DBSCAN dengan nilai Eps dan MinPts optimal sebesar 1,6 dan 12 membentuk dua cluster dan 23 data diidentifikasi sebagai noise, sedangkan menggunakan algoritma SOM dengan learning rate 0,05 dan maksimal epoch 1.000 membentuk dua cluster. Pada analisis ini SOM mampu  melakukan clustering yang lebih baik jika dibandingkan dengan DBSCAN karena memberikan  nilai koefisien silhouette yang lebih besar, yaitu sebesar 0,717 sedangkan DBSCAN sebesar  0,677. Hasil clustering terbaik memiliki karakteristik yaitu cluster 1 dikategorikan sebagai gempa sedang berkekuatan sedang dan cluster 2 dikategorikan sebagai gempa dangkal berkekuatan sedang. ABSTRACT Earthquakes are natural disasters that occur frequently in Indonesia because of the geographical location at the convergence of three active tectonic plates. The severity of an earthquake's impact is influenced by magnitude and depth. Therefore, disaster mitigation efforts and disaster risk management through data mining are needed to understand the characteristics of earthquakes. This research aims to cluster earthquake data in Indonesia based on magnitude and depth by applying a Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) and Self-Organizing Map (SOM) algorithms and cluster results are evaluated using the silhouette coefficient. Using the DBSCAN algorithm with optimal Eps and MinPts values of 1.6 and 12 formed two clusters and 23 data were identified as noise while using the SOM algorithm with a learning rate of 0.05 and a maximum epoch of 1000 formed two clusters. SOM can perform clustering better than DBSCAN because it provides a larger silhouette coefficient value, which is 0.717 while DBSCAN is 0.677. The clustering results obtained show that cluster 1 is categorized as moderate earthquakes of moderate intensity and cluster 2 is categorized as shallow earthquakes of moderate intensity.
Optimizing Train-Test Splits for LSTM and MLP Models in Bitcoin Price Forecasting Accuracy Kamisan, Nur Arina Bazilah; Lee, Muhammad Hisyam; Sulandari, Winita
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.6989

Abstract

Abstract. This study investigates the application and efficiency of two machine learning models, Long-Short Term Memory (LSTM) and Multilayer Perceptron (MLP), for cryptocurrency price forecasting, using Bitcoin as a case study. MLP is a feedforward neural network that learns patterns from independent data, while LSTM is a recurrent network that remembers past information to handle sequential or time-series data. The rapid growth and volatility of cryptocurrencies underscore the need for accurate price predictions to support investor’s and trader’s decision-making. The study aims to identify the optimal train-test splitting ratio for each machine learning model and to forecast Bitcoin prices over a 120 days. The daily Bitcoin price data is obtained from the Bitcoin website recorded from January 2018 until March 2021. Model performance was evaluated using Akaike Information Criterion (AIC), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that both models exhibit strong predictive capabilities; the LSTM model consistently outperforms MLP in accuracy and reliability, achieving lower MAE, MAPE, and AIC values. These findings highlight LSTM’s effectiveness for forecasting volatile financial data and provide insights into selecting appropriate data-splitting ratios to improved model performance.
Co-Authors Adi Wicaksono, Nanda Adigama Tri Nugraha Aji Susanto Ardiyani, Faradilla Fadlila Nurwanda Febrianti, Tri Kartika Fhadilla, Nahdatul Hapsari, Riskhia Husna Afanyn Khoirunissa Ilahi, Esa Permata Sari Putri Indra Raditya , Dionisius Irwan Susanto Irwan Susanto Isnaini, Bayutama Isnandar Slamet Isnandar Slamet, Isnandar Kadar, Jimmy Abdel Kamisan, Nur Arina Bazilah Kayyisa, Alfien Diva Lee, Muhammad Hisyam Marita Saptyani, Marita Mika Asrini Neyun, Ignasia N.G. Noven, Michela Sheryl Nugraha, Titis Jati Pangadi Pangadi Pardede, Hilman Ferdinandus Pramesti, Arsita Anggraeni pratiwi, hasih Respatiwulan, Respatiwulan Santoso Budi Wiyono Setiawan, Crisma Devika Sri Subanti Sucipto, Christy Sheldy Sufia Nur Janah Sugianto Sugianto Sugiyanto - Susilotomoa, Dhestahendra Citra Wati, Rosita Kurnia Wijayanti, Monica Dini Yudho Yudhanto, Yudho Yuliana Susanti Yuliana Susanti, Yuliana Zakya Reyhana Zukhronah, Etik