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All Journal Jurnal Statistika dan Aplikasinya MATHunesa: Jurnal Ilmiah Matematika Journal of Mathematics, Computation and Statistics (JMATHCOS)
Ayu Sofia
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Mathematics Social Sciences Other

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CLASSIFICATION ANALYSIS USING MINIMUM SPANNING TREE AND PREDICTIONS USING ARIMA ON THE MOST INFLUENTIAL STOCKS ON THE LQ45 INDEX Theo Markus; Ayu Sofia; Dwi Mahrani
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09113

Abstract

The 2023 recession, largely driven by high inflation, highlights the importance of investing. The stock market, with its regulated framework and potential for significant returns, presents a viable investment option. The LQ45, an index tracking the top 45 Indonesian stocks by market capitalization, provides a benchmark. To identify promising investments, this study employed the Minimum Spanning Tree (MST) method to pinpoint the most influential stocks within the LQ45 network, followed by Auto-Regressive Integrated Moving Average (ARIMA) for price prediction. The MST analysis, utilizing degree centrality, closeness, and betweenness measures, identified BBNI as the most influential, followed by BBTN and BMRI. Price predictions for BBNI and BBTN exhibited close alignment with actual market prices, while BMRI showed a larger deviation. For BBNI shares, the ARIMA(1,0,0) model is used with a MAPE of 1.78%; for BBTN shares, the ARIMA(0,2,2) model is used with a MAPE of 2.65%; and for BMRI shares, the ARIMA(2,2,1) model is used with a MAPE of 1.84%. This research contributes to the field of stock market analysis by demonstrating the effectiveness of combining network analysis techniques, specifically the MST method, with time series forecasting models like ARIMA for stock selection. The findings provide valuable insights for investors seeking to navigate market volatility and make informed investment decisions. The findings of this research can serve as a valuable guide for investors considering BBNI, BBTN, and BMRI shares.
ANALISIS HUBUNGAN ANTARA ANGKA TIDAK SEKOLAH (ATS), TINGKAT KEMISKINAN, DAN ANGKA MELEK HURUF (AMH) DENGAN BIPLOT Linda Rassiyanti; Ayu Sofia; Rizka Pitri
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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Abstract

Tujuan dari penelitian ini adalah untuk memperdalam analisis tentang hubungan antara pendidikan, literasi, dan kemiskinan. Metode penelitian yang digunakan pada penelitian ini adalah metode analisis biplot yang merupakan teknik statistik deskriptif yang memungkinkan penyajian visual simultan dari objek pengamatan dan variabel dalam ruang dua dimensi. Data yang digunakan dalam penelitian ini adalah Rata-Rata Lama Sekolah (RLS), Angka Melek Huruf (AMH), dan Persentase Orang Miskin. Tiap variabel dilakukan analisis desktiptif untuk mengetahui sebaran datanya, lalu selanjutnya dilakukan analisis biplot, dan menarik kesimpulan. Hasil penelitian menunjukkan bahwa RLS dan AMH memiliki hubungan yang kuat dengan tingkat kemiskinan (persentase orang miskin). Tingkat pendidikan yang lebih tinggi secara umum berkorelasi dengan tingkat kemiskinan yang lebih rendah. Provinsi DKI Jakarta dan Kep. Riau memiliki keberhasilan dalam taraf pendidikan yang tinggi dan kemiskinan rendah, sedangkan Provinsi Papua Pegunungan dan Nusa Tenggara Timur masih memerlukan perhatian untuk mengurangi kemiskinan dan meningkatkan taraf pendidikan. Provinsi Papua Pegunungan dan Papua Tengah terlihat jauh dari kelompok lainnya, menunjukkan karakteristik yang unik, seperti kemiskinan sangat tinggi dan pendidikan sangat rendah.
Forecasting International Tourist Arrivals to Indonesia Using LSTM: Post-Pandemic Analysis for 2024-2025 Ayu Sofia; Dien, Zulfanita; Erda, Gustriza
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.7309

Abstract

As Indonesia's main foreign exchange contributor, the tourism sector experienced significant dynamics after the COVID-19 pandemic, characterized by a sharp decline in the number of foreign tourists during the pandemic and consistent recovery in the post-pandemic period. This study aims to predict the number of foreign tourists to Indonesia from September 2024 to August 2025 using the Long Short-Term Memory (LSTM) method. The LSTM model is optimized with an 80:20 data split for training testing and uses optimal parameters, namely Learning Rate 0.005, Batch Size 64, Optimizer Adam, and Epoch 200. The prediction results show an increase in the number of tourists to a peak of 1,390,564 in November 2024, followed by a gradual decline to 987,970 in August 2025, with an accuracy level indicated by a MAPE value of 14.39%
Co-Authors Dien, Zulfanita Dwi Mahrani Gustriza Erda Linda Rassiyanti Rizka Pitri Theo Markus