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All Journal JAM : Jurnal Aplikasi Manajemen Jurnal Manajemen Bisnis Eka Prasetya HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi International Journal of Educational Research Excellence (IJERE) Journal of Business Studies and Management Review Qawwam PROCURATIO: Jurnal Manajemen dan Bisnis Jurnal Media Akademik (JMA) The Indonesian Journal of General Medicine Policy and Law Journal
Johannes
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Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences Veterinary Other

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TRANSFORMATIONAL LEADERSHIP: A LITERATURE REVIEW OF CONCEPTS, TRENDS, AND FUTURE RESEARCH DIRECTIONS Firdaus; Aditya Putri, Metha; Johannes
Qawwam : The Leader's Writing Vol. 6 No. 2 (2025): December
Publisher : Fakultas Ushuluddin Adab dan Dakwah Insitut Agama Islam Negeri Kerinci

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32939/qawwam.v6i2.513

Abstract

This research aims to identify and analyze thematic, methodological, and research gaps in the study of transformational leadership over the past two decades. The method used is Systematic Literature Review (SLR) with the PRISMA approach, which selects 20 articles from internationally reputable journals indexed by Scopus. Data are collected and analyzed based on research focuses, methods, and key findings related to the influence of transformational leadership on performance, innovation, organizational culture, and employee engagement. The results of the study show that transformational leadership consistently contributes positively to improving individual and organizational performance through mediation mechanisms such as trust, organizational learning, and knowledge management. However, previous research still shows limitations in contextual aspects, especially in the areas of leadership digitalization, organizational culture differences, and longitudinal research design. This study recommends that future research integrate cross-cultural and multi-method approaches to broaden understanding of transformational leadership dynamics in the digital age. Practically, the results of this research contribute to the development of leadership strategies that are adaptive, innovative, and oriented towards human resource empowerment in the context of modern organizations.
PENGARUH DESTINATION ATTRIBUTES DAN MEMORABLE TOURISM EXPERIENCE TERHADAP REVISIT INTENTION PADA GENERASI Z Anisa Apriliani; Johannes; Ihsan, Moh
Journal of Business Studies and Management Review Vol. 9 No. 1 (2025): JBSMR, Vol 9 No.1 December 2025
Publisher : Management Department, Faculty of Economics and Business, Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jbsmr.v9i1.49023

Abstract

This study aims to explain the influence of destination attributes and memorable tourism experience on revisit intention. The research employs a quantitative approach with data collected through an online questionnaire distributed via Google Form. The population is Generation Z, with samples determined using the purposive sampling technique. A total of 150 respondents who are students of Universitas Maritim Raja Ali Haji (UMRAH) were selected because they had visited Lagoi Bay at least once. The results show that most respondents experienced a clean beach atmosphere, comfortable facilities, and beautiful scenery that created a memorable travel experience. The findings indicate that destination attributes and memorable tourism experience have a positive influence on revisit intention. In other words, the better the destination’s attributes and the more memorable the tourist experience, the greater the visitors’ desire to return to Lagoi Bay.
PENERAPAN ALGORITMA RANDOM FOREST REGRESSION DALAM PREDIKSI HARGA SAHAM BBRI: IMPLEMENTATION OF THE RANDOM FOREST REGRESSION ALGORITHM FOR PREDICTING BBRI STOCK PRICES Winardi, Kevin; Nugroho, Yulianus Febry Tri; Johannes; Herdiatmoko, Hendrik Fery
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p9-18

Abstract

Pergerakan harga saham dipengaruhi oleh berbagai faktor dan bersifat fluktuatif, sehingga diperlukan metode prediksi yang mampu menangkap pola data yang kompleks. Penelitian ini bertujuan untuk memprediksi harga saham menggunakan metode Random Forest Regression. Data yang digunakan dibagi menjadi data pelatihan dan data pengujian untuk mengevaluasi kinerja model. Kinerja model dievaluasi menggunakan beberapa metrik, yaitu Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model Random Forest Regression setelah optimasi menghasilkan nilai MAE sebesar 64,02,  RMSE sebesar 84,51, dan R² sebesar 0,8484. Nilai-nilai tersebut mengindikasikan bahwa model memiliki tingkat kesalahan prediksi yang rendah dan mampu menjelaskan 84,84% variasi pada data harga saham. Berdasarkan hasil tersebut, dapat disimpulkan bahwa Random Forest memiliki kinerja yang baik dan cukup andal dalam memprediksi harga saham.   Stock price movements are influenced by various factors and exhibit high volatility, making accurate prediction a challenging task. This study aims to predict stock prices using the Random Forest Regression method. The dataset is divided into training and testing sets to evaluate the model’s performance. The performance of the model is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that the Random Forest Regression model after optimization achieves an MAE of 64,02, an RMSE of 84,51, and an R² value of 0.8484. These results indicate a low prediction error and demonstrate that the model is able to explain 84.84% of the variance in stock price data. Therefore, it can be concluded that Random Forest is an effective and reliable method for stock price prediction.
Co-Authors Aditya Putri, Metha Andang Fazri Anisa Apriliani Bela Septia Ningsih Dessy Puspita Tjahjadi Edward Farah Amandasari FELICIA Firdaus Fitriani, Enny Fitriaty Harnon Hendrik Fery Herdiyatmoko Huda, Nazara Nurul Johnny Chandra Karolus Nahas Martaida Sinaga Moh Ihsan Muhammadi Naila Putri Azzahra Novianlee Nata NOVITA EKASARI Nugroho, Yulianus Febry Tri Ricky Rini Fadhillah Putri Suci Mardiana, Citra syahmardi yacob Vella Monica Winardi, Kevin