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All Journal JSAI (Journal Scientific and Applied Informatics) Oikonomia : Journal of Management Economics and Accounting
Wishnu Aribowo Probonegoro
Institut Sains dan Bisnis Atma Luhur
Computer Science & IT Economics, Econometrics & Finance

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The Role of Early Warning Systems in Reducing Disaster Impacts: An Analysis of Digital Infrastructure Readiness Parlia Romadiana; Wishnu Aribowo Probonegoro; Lili Indah Sari
Oikonomia : Journal of Management Economics and Accounting Vol. 3 No. 1 (2025): Oikonomia-December
Publisher : PT. Hafasy Dwi Nawasena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61942/oikonomia.v3i1.514

Abstract

The increasing intensity and frequency of natural disasters require disaster risk reduction systems that are not solely technology-driven but are also supported by adequate digital infrastructure readiness and governance. Early Warning Systems (EWS) are widely recognized as strategic instruments for reducing disaster impacts; however, their effectiveness remains inconsistent across regions. This study aims to analyze the role of Early Warning Systems in reducing disaster impacts by emphasizing digital infrastructure readiness as a key determinant of system effectiveness. The study employs a qualitative descriptive-analytical method with a policy and system analysis approach, using secondary data from reputable scientific literature, national and international policy documents, and disaster management reports. The findings indicate that EWS often function only as information delivery mechanisms due to limitations in digital infrastructure, fragmented governance, and low levels of community digital literacy. The readiness of communication networks, data system integration, energy reliability, and institutional coordination are identified as critical prerequisites for transforming EWS into effective collective mitigation mechanisms. This study concludes that strengthening EWS requires a systemic approach that integrates technology, digital infrastructure, and disaster governance to achieve meaningful disaster impact reduction.
Analisis Pengaruh Penggunaan QRIS Konvensional dan QRIS ESB terhadap Efisiensi Pembayaran Digital pada Rumah Makan BJ di Era Digitalisasi Wishnu Aribowo Probonegoro; Lili Indah Sari; Ardiana
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9718

Abstract

The development of digitalization has encouraged the adoption of cashless payment systems through the Quick Response Code Indonesian Standard (QRIS) in the culinary sector, including Rumah Makan BJ. This study aims to analyze the effect of Conventional QRIS and ESB QRIS on digital payment efficiency. This research employs a quantitative approach with a sample of 122 respondents. Data were collected through questionnaires and analyzed using SPSS version 17. The partial test results indicate that Conventional QRIS has a positive and significant effect on digital payment efficiency, with a t-value of 2.802 and a significance level of 0.006 (<0.05). ESB QRIS shows a stronger influence, with a t-value of 6.029 and a significance level of 0.000 (<0.05). Simultaneously, the ANOVA test shows an F-value of 57.155 with a significance of 0.000, indicating that both variables jointly have a significant effect on digital payment efficiency. The coefficient of determination (R²) of 0.405 indicates that 40.5% of the variation in digital payment efficiency is explained by QRIS usage. These findings are consistent with previous studies on integrated digital payment systems.
Komparasi Algoritma Klasifikasi Performa Akademik Mahasiswa Bisnis Digital: SVM, Random Forest, XGBoost, dan LightGBM dengan Penanganan Class Imbalance Menggunakan SMOTE Lili Indah Sari; Burham Isnanto; Wishnu Aribowo Probonegoro
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 2 (2026): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i2.10535

Abstract

This study aims to compare the performance of classification algorithms, namely Support Vector Machine (SVM), Random Forest, XGBoost, and LightGBM, in predicting the academic performance of Digital Business students at ISB Atma Luhur by handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The dataset consisted of 326 student records with 55 questionnaire-based Likert-scale features, GPA, and semester data classified into two academic performance classes. The research stages included data preprocessing, normalization, SMOTE implementation, feature selection using feature importance, model training, and evaluation using accuracy, precision, recall, F1-score, F1 Macro, AUC-ROC, and training time metrics. The results showed that the XGBoost algorithm achieved the best performance with an accuracy of 0.8621, an F1 Macro score of 0.85, and an AUC value of 0.91. LightGBM produced performance close to XGBoost while providing faster training time. The implementation of SMOTE successfully improved minority class classification performance across all algorithms, particularly in terms of F1-score. The findings indicate that the combination of boosting algorithms and class imbalance handling techniques is effective for machine learning-based academic performance prediction systems.
Co-Authors Ardiana Burham Isnanto Lili Indah Sari Lili Indah Sari Parlia Romadiana