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All Journal Jurnal Informatika Journal Of Management Science (JMAS)
Tarjono
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Trust, Perceived Quality, and Value for Money as Determinants of Customer Loyalty: Insights from the inDrive App Putra, Wira Pramana; Supriyanto, Wawan; Noraga, Gilang Bhirawa; Tarjono
Junal Ilmu Manajemen Vol 8 No 1 (2025): January: Management Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jmas.v8i1.653

Abstract

One of the industries affected by the growth in the number of internet users and advances in digital technology in Indonesia is online transportation. However, the inDrive company showed the smallest performance, which only reached 4.03 million downloads, this merchant was very far behind its competitors, namely Gojek, Grab and Maxim. InDrive has implemented a unique concept, namely the bidding feature in order to achieve fairness and transparency and maximize services with the SHIELD security feature. Therefore, this study aims to identify the effect of value for money, perceived quality on trust, mediate trust in value for money and perceived quality on customer loyalty, and trust in customer loyalty. This research is a quantitative study, with data sources obtained from online questionnaires distributed to 130 respondents who have used the inDrive application in Indonesia who were analyzed using the Structural Equation Model on SmartPLS. The findings of this study are that perceived quality and value for money has a positive and significant effect on trust, trust mediates perceived quality and value for money on customer loyalty and trust has a positive and significant effect on customer loyalty. Trust is the most significant variable affecting customer loyalty, so inDrive Indonesia management must focus on maintaining consistency and increasing consumer trust
Evaluasi Kinerja Model Deep Learning dalam Memprediksi Kejadian Hujan Di Wilayah Panjang Bandar Lampung Tarjono; Triloka, Joko; Mutiara, Suci
Jurnal Informatika Vol 25 No 1 (2025): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/jurnalinformatika.v25i1

Abstract

Global warming and climate change have increased the frequency and intensity of extreme weather events, significantly impacting human life and the environment. Urban areas such as Kecamatan Panjang in Bandar Lampung City frequently experience flooding due to extreme rainfall and poor drainage systems. This study compares the effectiveness of three deep learning model architectures- Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformers — in predicting rainfall events in Kecamatan Panjang. The data used includes key meteorological variables such as air temperature, dew point, humidity, and air pressure, collected from the Maritime Meteorology Station in Panjang (BMKG) over the past three years. The models were trained using this historical data, with the data divided into training and testing sets. The study results indicate that the Transformer model performs best with the highest accuracy compared to CNN and RNN. The Transformer model efficiently captures long-term dependencies in sequential data, providing more accurate and timely predictions. Model performance evaluation was conducted using accuracy, F1 score, precision, recall, ROC AUC, RMSE, and MAE metrics. The use of deep learning models in rainfall prediction is expected to assist in flood risk mitigation and planning for adaptation to increasingly frequent extreme weather due to climate change. This research significantly advances more accurate and efficient weather prediction systems for urban areas prone to hydrological disasters.
Co-Authors Gilang Bhirawa Noraga Mutiara, Suci Putra, Wira Pramana Supriyanto, Wawan Triloka, Joko