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All Journal EIGEN MATHEMATICS JOURNAL Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Nusantara Hasana Journal
Hidayat, Muhammad Irfan
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Agriculture, Biological Sciences & Forestry Humanities Economics, Econometrics & Finance Education Mathematics Nursing

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TRANSFORMASI DIGITAL DAN MODEL KERJA HYBRID: IMPLIKASI UNTUK MANAJEMEN SUMBER DAYA MANUSIA Hidayat, Muhammad Irfan; Yaksibun, Yaksibun; Muhammad Arifin; Yudhinanto CN
Nusantara Hasana Journal Vol. 5 No. 7 (2025): Nusantara Hasana Journal, December 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i7.1804

Abstract

Digital transformation and the implementation of hybrid work models have brought significant changes to Human Resource Management (HRM) practices. This article aims to analyze the impact of digital transformation and hybrid work on HRM functions, particularly in recruitment, employee development, performance management, and the strategic role of HRM within organizations. This study employs a literature review method by examining national and international academic journals published within the last five years (2021–2025). The findings indicate that digital transformation and hybrid work can enhance operational efficiency, work flexibility, employee satisfaction, and organizational performance when supported by adaptive and data-driven HRM policies. Nevertheless, these practices also present challenges related to employee engagement, organizational culture, and performance evaluation systems. Therefore, HRM is required to assume a more strategic role in managing change to ensure sustainable organizational success.
Analisis Peramalan Jumlah Kunjungan Wisatawan Domestik Menggunakan Model Long Short-Term Memory (LSTM) di Kota Pangkal Pinang Hidayat, Muhammad Irfan; Kumala, Mei Dita
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 9 No. 1 (2026): Volume 9 Nomor 1 Tahun 2026
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v9i1.7478

Abstract

Sektor pariwisata berperan penting sebagai pendorong utama pertumbuhan ekonomi suatu daerah, dengan jumlah wisatawan menjadi indikator yang patut diperhatikan. Peningkatan kunjungan wisatawan berimplikasi pada meningkatnya potensi risiko, sehingga diperlukan upaya peramalan yang tepat. Dalam penelitian ini, metode Long Short-Term Memory (LSTM) diterapkan untuk memprediksi jumlah perjalanan wisatawan domestik di Kota Pangkal Pinang dan dibandingkan dengan metode ARMA serta SARIMA. Hasil perbandingan menunjukkan bahwa metode LSTM menghasilkan nilai RMSE sebesar 32.431,219, yang lebih rendah dibandingkan ARMA (41.273,347) dan SARIMA (101.884,554). Hasil studi menunjukkan bahwa LSTM memiliki performa prediksi yang lebih efektif, sehingga metode ini lebih direkomendasikan.
Peramalan Jumlah Perjalanan Wisatawan di Kota Pangkal Pinang: ARIMA vs. LSTM vs. Prophet” Hidayat, Muhammad Irfan; Kumala, Mei Dita
Eigen Mathematics Journal Vol 9 No 1 (2026): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v9i1.345

Abstract

Accurate forecasting of tourism demand is crucial for regional economic planning, yet existing studies often rely on univariate time-series models or single forecasting techniques without considering spatial interdependence among regions. This study proposes a correlation-based comparative forecasting framework to evaluate the performance of ARIMA, Long Short-Term Memory (LSTM), and Prophet in predicting domestic tourist trips in Pangkal Pinang City, Indonesia. Monthly data from January 2019 to July 2025 were obtained from Statistics Indonesia, and highly correlated neighboring regions were systematically selected using a heatmap correlation analysis to enhance model input relevance. After applying Min–Max normalization and an 80:20 train–test split, all models were evaluated using Root Mean Squared Error (RMSE) under identical experimental settings. The results indicate that Prophet consistently achieves the lowest RMSE, demonstrating superior capability in capturing non-linear dynamics, seasonal variability, and abrupt structural changes in tourism demand compared to ARIMA and LSTM. These findings provide empirical evidence that decomposable time-series models with automatic trend and seasonality handling offer distinct advantages over both classical statistical and deep learning approaches in medium-term tourism forecasting. The proposed framework contributes a concise, data-efficient, and replicable methodology that supports evidence-based tourism planning and strategic decision-making.
Co-Authors Kumala, Mei Dita MUHAMMAD ARIFIN Yaksibun, Yaksibun Yudhinanto CN