Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Education Technology Information Social Sciences and Health Asian Journal of Social and Humanities
Harry Pratomo Bagaskoro
Universitas Pertahanan Republik Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Neuroscience Nursing Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Data Mining Applications to Prediction Stock Prices Using Decision Trees and Neural Networks Dadan Shavkat Riswantoro; Harry Pratomo Bagaskoro; Bambang Suharjo; Danang Rimbawa
Asian Journal of Social and Humanities Vol. 4 No. 10 (2026): Asian Journal of Social and Humanities
Publisher : Pelopor Publikasi Akademika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59888/ajosh.v4i10.749

Abstract

Stock price prediction remains a significant challenge in financial markets due to the high volatility and complexity of influencing factors. This study explores the application of hybrid models combining Decision Tree (DT) and Neural Network (NN) methodologies to enhance stock price prediction accuracy. The research utilizes extensive historical market data as the foundational input for training both models individually. The Decision Tree model is employed for its interpretability and ability to handle non-linear relationships, while the Neural Network model capitalizes on its capacity to learn complex patterns through its layered architecture. After training and evaluating each model separately, a hybrid approach is introduced, which averages the predictions from both the DT and NN models. Performance is quantitatively assessed using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the hybrid model consistently outperforms individual models, achieving an MAE of 5.6 and RMSE of 7.94, with an overall accuracy of 91.5%. This fusion of methodologies demonstrates improved accuracy and significantly reduces error margins, showcasing the complementary strengths of both algorithms. The findings suggest that leveraging hybrid models can effectively mitigate risks associated with market fluctuations and enhance investment strategies. This research contributes to the field of financial forecasting by providing investors with more robust tools for making informed decisions, and offers recommendations for future research directions in integrating machine learning techniques for financial prediction.
Dinamika Kematangan Digitalisasi Pemerintahan dan Fenomena Konvergensi SPBE di Indonesia: Pendekatan Ekonometrika Data Panel 2021–2024 Harry Pratomo Bagaskoro; Aulia Khamas Heikhmakhtiar; Rudy Agus Gemilang Gultom
Journal of Education Technology Information Social Sciences and Health Vol. 5 No. 2 (2026): September 2026
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jetish.v5i2.8988

Abstract

Akselerasi transformasi digital melalui Sistem Pemerintahan Berbasis Elektronik (SPBE) berisiko memperlebar kesenjangan digital (digital divide) jika peningkatan skor tidak diimbangi oleh konvergensi kapasitas antarwilayah. Belum ada studi empiris longitudinal berbasis ekonometrika data panel yang menguji dinamika kematangan e-government secara nasional serta membuktikan keberadaan fenomena β-konvergensi pada era transisi GovTech di Indonesia. Penelitian ini bertujuan untuk menganalisis pemodelan longitudinal kinerja SPBE, menguji disparitas spasial (Jawa vs Luar Jawa), serta membuktikan ada/tidaknya pola β-konvergensi pada tingkat Pemerintah Daerah. Penelitian kuantitatif ini menerapkan pemodelan data panel seimbang (balanced panel dataset) pada N = 369 Pemerintah Daerah selama kurun waktu 2021–2024 (T = 4, total 1.476 observasi panel) dari Keputusan Menteri PANRB. Pemodelan ekonometrika data panel dan uji konvergensi beta dipilih karena mampu mengontrol heterogenitas yang tak terobservasi (unobserved heterogeneity) antar instansi serta mengukur kelajuan efek catch-up secara empiris. Hasil statistik menunjukkan kenaikan rata-rata indeks dari 2,24 (2021) menjadi 3,17 (2024). Namun, uji spasial mengonfirmasi adanya kesenjangan struktural di mana skor Pemda Jawa (rata-rata = 3,85) signifikan lebih tinggi dibandingkan Luar Jawa (rata-rata = 2,84) dengan t = 14,85 (p < 0,001). Uji beda laju pertumbuhan (2021–2024) juga membuktikan bahwa Pemda Jawa mengalami akselerasi yang signifikan lebih cepat (t = 3,69, p < 0,001). Terjadi divergensi spasial alih-alih konvergensi, menunjukkan efek Matthew Effect ('the rich get richer') dalam TIK pemerintah. Transformasi digital e-government Indonesia mengalami akselerasi kuantitatif namun secara bersamaan menderita ketimpangan spasial yang semakin melebar. Direkomendasikan adanya skema alokasi dana perimbangan TIK afirmatif dan redistribusi kapasitas SDM siber ke daerah Luar Jawa.
Co-Authors Aulia Khamas Heikhmakhtiar Bambang Suharjo Dadan Shavkat Riswantoro Gultom, Rudy Agus Gemilang H. A. Danang Rimbawa