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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Multidisciplinary Sciences and Arts
Kenneth Pinandhito
Politeknik Negeri Semarang, Indonesia
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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From Intuition to Automation: A Comparative Study of Traditional Investment Decisions and Robo-Advisory Adoption Among Retail Investors in Indonesia Kenneth Pinandhito; Prima Ayundyayasti; Rola Nurul Fajria; Toga Aldila Cinderatama; Dina Yeni Martia
International Journal of Multidisciplinary Sciences and Arts Vol. 4 No. 3 (2025): International Journal of Multidisciplinary Sciences and Arts, Article July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v4i3.6463

Abstract

The development of digital technology has transformed the investment decision-making process from being based on human analysis to automated algorithm-based solutions such as robo-advisors. This study aims to compare traditional and technology-based investment decision-making approaches among retail investors in Indonesia, focusing on adoption, perceived trust, and effectiveness of robo-advisor use. Using quantitative descriptive-correlational approach to compare traditional and technology-based investment decision-making methods, this study collected survey data from 120 individual. The results show a significant positive correlation between trust in traditional methods and the use of investment applications, although the adoption rate of robo-advisors is still low. The main barriers faced are low digital literacy and lack of trust in automated systems. These findings emphasize the importance of targeted investor education and increased transparency on robo-advisory platforms. This study contributes to the literature on fintech adoption in emerging markets and offers practical insights for fintech developers and policymakers.
Monitoring of Irrigation Channel Discharge Based on IoT and LoRaWAN Communication with Long Short-Term Memory (LSTM) Predictive Analysis Toga Aldila Cinderatama; Afta Ramadhan Zayn; Rinanza Zulmy Alhamri; Yoppy Yunhasnawa; Kenneth Pinandhito
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16612

Abstract

This study presents the design and implementation of an Internet of Things (IoT)-based irrigation canal monitoring system that integrates Long Range Wide Area Network (LoRaWAN) communication with a Long Short-Term Memory (LSTM) prediction model. The system is designed to provide real-time monitoring and prediction of water discharge in irrigation canals to support efficient water resource management on agricultural land. The proposed system consists of two IoT sensor nodes, each equipped with a YF-B5 flow sensor, an HC-SR04 ultrasonic sensor, and a DS18B20 temperature sensor, all connected to an ESP32 microcontroller. The collected data are transmitted via the LoRaWAN protocol and stored in Firebase Realtime Database, where they are visualized through an Android application. The predictive component employs an LSTM algorithm to forecast future water discharge based on historical time-series data. Experimental results indicate that the LSTM model achieved a Mean Absolute Error (MAE) of 13.1640 and a Root Mean Squared Error (RMSE) of 23.0630, demonstrating high accuracy and stability in predicting water discharge fluctuations. The integration of IoT, LoRaWAN, and LSTM-based prediction enables real-time monitoring and predictive analysis for smart irrigation management.
Co-Authors Afta Ramadhan Zayn Dina Yeni Martia Prima Ayundyayasti Rinanza Zulmy Alhamri Rola Nurul Fajria Toga Aldila Cinderatama Toga Aldila Cinderatama Yoppy Yunhasnawa