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All Journal Jurnal Pendidikan Matematika (JUDIKA EDUCATION) Basis : Jurnal Ilmiah Matematika Sains Data Jurnal Studi Matematika dan Teknologi
Syahputra, Mario
Unknown Affiliation
Computer Science & IT Education Electrical & Electronics Engineering Engineering Mathematics Physics

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Penerapan Model Logistik Untuk Optimalisasi Portofolio Investasi Saham Syahputra, Mario; Clara, Nur Cellia; Kinanti, Tri; Dongoran, Raisha Zuhaira
Basis : Jurnal Ilmiah Matematika Vol. 4 No. 1 (2025): BASIS: Jurnal Ilmiah Matematika
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/basis.v4i1.1434

Abstract

Penelitian ini bertujuan untuk mengoptimalkan portofolio investasi saham dengan menggunakan model regresi logistik, dengan mempertimbangkan variabel volume perdagangan, harga historis, dan price to earning ratio (P/E). Data yang digunakan merupakan data sekunder dari 20 emiten yang terdaftar di Bursa Efek Indonesia, diambil pada tanggal 30 Oktober 2024. Pengolahan data dilakukan dengan menerapkan model regresi logistik untuk menganalisis hubungan antara variabel independen dan probabilitas kenaikan harga saham. Model ini dilatih dengan data historis saham untuk mengestimasi kemungkinan kenaikan harga, yang kemudian digunakan sebagai dasar dalam pemilihan saham optimal. Hasil penelitian menunjukkan bahwa dari 20 emiten yang dianalisis, terdapat tiga saham dengan probabilitas kenaikan harga di atas 50%, yaitu BREN (87,71%), BUMI (67,11%), dan EMTK (52,08%). Model ini dapat membantu investor dalam mengoptimalkan portofolio investasi jangka pendek dengan mempertimbangkan toleransi risiko masing-masing investor.
Klasifikasi Cryptocurrency Menggunakan Multi Channel Clustering Syahputra, Mario; Rakhmawati, Fibri
Jurnal Pendidikan Matematika : Judika Education Vol. 8 No. 3 (2025): Jurnal Pendidikan Matematika:Judika Education
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/judika.v8i3.15218

Abstract

This study aims to (1) classify cryptocurrencies based on the integration of market data and on-chain data to provide a more accurate mapping of digital asset characteristics. The method used is Multi-Channel Clustering, which enables the combination of multiple data views (multi-view) in the clustering process. The data includes market capitalization, trading volume, percentage gain, and volatility from the market data channel, as well as coin supply and active addresses from the on-chain channel. All data were normalized using the Min-Max Normalization method to ensure scale uniformity across variables. The clustering process was carried out using a K-Means algorithm adapted for the multi-channel context. The results of the study identified three main clusters: Cluster 1 contains coins with medium to low market and on-chain activity characteristics such as Tron and Cro; Cluster 2 includes coins with high volume and significant on-chain activity but not dominant, such as Ethereum and Solana; and Cluster 3 consists solely of Bitcoin, which has a unique profile in both channels. In conclusion, the Multi-Channel Clustering method proves effective in producing a more comprehensive classification of cryptocurrencies and can serve as a decision-support tool in highly volatile and complex market environments.  Keywords: Cryptocurrancy, Classification, Multi-Channel Clustering.
Penerapan Metode Autoregressive (AR) dalam Memprediksi Tren Pertumbuhan Start Up di Kota Medan Syahputra, Mario; Hutapea, Rahmat; Nuryadi, Agung Hidayat; Panjaitan, Rio Anggara; Sari, Rina Widya
Sains Data Jurnal Studi Matematika dan Teknologi Vol 4, No 2: July-December 2026 (In Progress)
Publisher : Sekolah Tinggi Agama Islam Nurul Islam Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52620/sainsdata.v4i2.345

Abstract

The rapid growth of startups in Medan City highlights the need for data-driven approaches to understanding digital entrepreneurship dynamics. This study aims to apply the Autoregressive (AR) model to predict startup growth trends in Medan using historical data from 2016 to 2024. The method employed is time series analysis with a first-order AR(1) model, utilizing secondary data from the Investment and Licensing Agency of North Sumatra Province. The findings indicate a significant increase in startup numbers since 2022, with projections suggesting continued growth to 127 startups by 2030. The AR model proves to offer a simple yet accurate forecasting framework, making it a valuable tool for strategic planning. These results have important implications for policymakers, investors, and entrepreneurs in designing sustainable startup development strategies in urban areas.
Co-Authors Clara, Nur Cellia Dongoran, Raisha Zuhaira Hutapea, Rahmat Kinanti, Tri Nuryadi, Agung Hidayat Panjaitan, Rio Anggara Rakhmawati, Fibri Sari, Rina Widya