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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Saintia Matematika Computatio : Journal of Computer Science and Information Systems Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Syntax Literate: Jurnal Ilmiah Indonesia KOMPUTIKA - Jurnal Sistem Komputer Syntax Idea Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Syntax Transformation Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika International Journal of Business, Economics, and Social Development FARABI: Jurnal Matematika dan Pendidikan Matematika Jurnal Sisfotek Global Jurnal Multidisiplin Madani (MUDIMA) Indonesian Journal of Business Analytics (IJBA) Jurnal Pengabdian Masyarakat Bestari (JPMB) Formosa Journal of Applied Sciences (FJAS) Mutiara: Multidiciplinary Scientifict Journal Journal of Mathematics, Computation and Statistics (JMATHCOS)
Bakti Siregar
Jurusan Statistika Universitas Matana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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Pelatihan Visualisasi Data Menggunakan Excel di SMA Erenos Tangerang Siregar, Bakti; Gunawan, Naftali Brigitta; Riswandi, Calvin
Abdimas Galuh Vol 6, No 1 (2024): Maret 2024
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v6i1.13358

Abstract

Visualisasi Data merupakan bidang terapan yang meliputi statistika, ilmu komputer, dan bisnis. ilmu dan menganalisis data, khususnya data kuantitatif (numerik), baik yang terstruktur maupun tidak terstruktur. Saat ini kebutuhan terhadap profesi data scientist (Analis Data) sangat tinggi seiring pengkembangan teknologi digital.  Banyak siswa-siswi SMA/SMU sangat tertarik tetapi belum mengetahuai bagaimana mereka memulai pembelajaran Visualisasi Data menggukan Excel. Olehkarena itu, Universitas Matana melalui prodi Statistika komitmen dalam melakukan pengenalan dasar mengenai pemanfaat Excel dalam mengolah data (sains data).  Adapun mitra dalam Pengabdian Kepada Masyarakat (PKM) ini adalah SMA Erenos.  Pelaksaan pelatihan ini terbukti memberikan kenaikan pengetahuan sebanyak 19% terkait Visualisasi data berdasarkan evaluasi nilai post-test dan nilai pre-test yang diberikan kepada peserta. Sehingga, konsep pembelajaran praktikum ini dapat diterapkan lebih banyak pembelajaran di Sekolah.
Implementasi Metode Random Forest dalam Analisis Prediksi Dogecoin Siregar, Bakti; Jimy, Valensius
Komputika : Jurnal Sistem Komputer Vol. 14 No. 2 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i2.16486

Abstract

Perkembangan teknologi digital telah mendorong munculnya mata uang kripto seperti Dogecoin yang memiliki volatilitas harga tinggi, sehingga menarik perhatian investor namun juga menimbulkan risiko prediksi harga yang kompleks. Penelitian ini menawarkan solusi prediksi harga Dogecoin dengan menerapkan metode Random Forest, salah satu algoritma machine learning berbasis ensemble learning yang unggul dalam menangani data berpola nonlinier. Data yang digunakan adalah data historis harian harga Dogecoin periode 1 Januari–31 Desember 2024 yang diperoleh dari investing.com. Tahapan penelitian meliputi pengunduhan dan pra-pemrosesan data untuk mengatasi missing values, pembagian dataset menjadi data latih (90%) dan data uji (10%), pembangunan model Random Forest menggunakan perangkat lunak R, serta evaluasi kinerja model menggunakan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa model mampu memprediksi harga Dogecoin dengan nilai MAPE 2,59% dan R-squared 99,37%, yang menandakan tingkat akurasi tinggi. Kontribusi penelitian ini adalah memberikan model prediksi harga kripto yang andal dan terukur, yang dapat dimanfaatkan investor maupun peneliti sebagai acuan pengambilan keputusan, sekaligus memperluas literatur metode Random Forest pada analisis harga aset digital.
The Comparative Analysis of Integrated Moving Average and Autoregressive Integrated Moving Average Methods for Predicting Bitcoin Returns Brigita Tiara Elgityana Melantika; Kalfin; Bakti Siregar; Wiwik Wiyanti
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.3788

Abstract

The rise in popularity of cryptocurrencies such as Bitcoin across various platforms has attracted the attention of young investors, making it easier for them to invest. However, due to the volatile nature of Bitcoin, this type of investment carries a high risk. Therefore, this research conducts an analysis of stock return prices to minimize losses and help investors make effective investment decisions through stock price prediction. The focus of this study is on predicting Bitcoin stock returns by analyzing closing price data over the past five years (2019-2024).  The methods used are a comparison between Integrated Moving Average (IMA) and Autoregressive Integrated Moving Average (ARIMA) with a quantitative approach using R Studio software. One of the main focuses of this research is the comparison of error estimation values between the two methods, namely Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The data analyzed comprises the daily closing prices of Bitcoin over the last five years, which is publicly accessible data. The best model for predicting the daily return of Bitcoin stock is the ARIMA (1,0,1) model. The predicted values for the next five days, from May 27, 2024, to May 31, 2024, are 0.0016632438, 0.0007991618, 0.0013415932, 0.0010010794, and 0.0012148386. The ARIMA (1,0,1) model has error measurement values with an MAE of 2.3% and an RMSE of 3.5%. It is hoped that this research will provide a better understanding of the effectiveness and relative advantages of the IMA and ARIMA methods in forecasting cryptocurrency returns, thereby offering more accurate guidance for investors in making investment decisions.
Comparative Analysis of K-Means and K-Medoids Algorithms for Product Sales Clustering and Customer Yosia; Bakti Siregar
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4053

Abstract

In today's rapidly evolving business landscape, effective product management is crucial for maintaining a company's competitive advantage. Comprehensive analysis is essential for providing insights that inform strategic business development decisions. This study examines the sales data of PT XYZ from July 2020 to May 2024 using the K-Medoids algorithm, with dimensionality reduction applied through Principal Component Analysis (PCA). The clustering results identified three customer segments: Cluster 1 with 46 customers, Cluster 2 with 76 customers, and Cluster 3 with 62 customers. For product segmentation, four clusters were identified: Cluster 1 with 52 products, Cluster 2 with 12 products, Cluster 3 with 20 products, and Cluster 4 with 53 products. The K-Medoids algorithm demonstrated superior performance compared to K-Means in terms of cluster separation and interpretability, with visualizations that enhance the understanding of customer and product distributions. This research aids the company in enhancing customer satisfaction, optimizing inventory, and increasing profitability.
Co-Authors Alicia Arifin Anggraiwan, Yonathan Barus, Simon Prananta Brigita Tiara Elgityana Melantika Clara Della Evania Dhela Asafiani Agatha Dhela Asafiani Agatha Ferdinand Nathaniel Widjaya Gregoria Illya Gunawan, Naftali Brigitta Januaviani, Trisha Magdalena Adelheid Jeffry Wijaya Jimy, Valensius Jocelyn Irene Gani kalfin Kalfin Kalfin, Kalfin Kefas Ronaldo Immanuel Natalie, Karen Pangruruk, F. Anthon Prya Artha Widjaja Riswandi, Calvin Sawaluddin Nasution Sherly Taurin Siridion Siridion, Sherly Taurin Sukono Sukono Tulus Tulus Valensius Jimy Vanessa Supit Widjaja, Preya Artha Windjaya, Putri Angelina Wiwik Wiyanti Wiwik Wiyanti Yosia Yosia Yosia Yosia, Yosia