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INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 13 Documents
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Search results for , issue "Vol. 8 No. 3 (2025)" : 13 Documents clear
IoT-Based Package Drop Box System Using Arduino Uno and ESP32-CAM Simatupang, Frengki; Sigiro, Marojahan M.T; Sinambela, Eka Stephani; Manalu, Istas Pratomo
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.581

Abstract

The rapid growth of e-commerce activity in Indonesia has led to a surge in package delivery volumes, resulting in increased workloads for couriers and a higher risk of delivery failures when recipients are not present at the delivery location. This issue demands an innovative solution that can address logistical challenges in an automated, secure, and efficient manner. This study aims to design and implement an IoT-Based Package Drop Box system that integrates Arduino Uno, Wemos D1 Mini, ESP32-CAM, and the GM66 barcode scanner, along with a web interface and WhatsApp notification service. The methodology follows a prototype engineering approach consisting of need analysis, system design, implementation, and hardware-software testing phases. The test results demonstrate that the system can successfully verify package tracking numbers via barcode scanning at an optimal distance of 10–19 cm, automatically unlock the box, capture images inside the drop box using ESP32-CAM, and send real-time delivery notifications to users via WhatsApp. The system is also capable of storing data locally when the internet connection is lost and synchronizing it once the connection is restored. The findings conclude that the integrated system provides a practical and reliable solution to common delivery issues and has the potential to be further developed for smart home environments or broader Internet of Things-based logistics systems.
Use of Cosine Similarity, Manhattan Distance, and Jaccard Similarity Methods to Improve the Accuracy of Manual Payment Evidence Validation in ERP Applications Muslim, Muslim; Amira, Sheilla; Usino, Wendi
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.594

Abstract

Manual validation of payment receipts in Enterprise Resource Planning (ERP) applications often faces challenges in terms of Accuracy, especially when payment data must be matched with existing transactions. Data mismatches can lead to recording errors and increase the burden of manual verification. This study aims to improve the Accuracy of payment receipt validation by comparing three Similarity methods: Cosine Similarity, Jaccard Similarity, and Manhattan Distance. In this research, Optical Character Recognition (OCR) is utilized to validate scanned images of payment receipts. By using OCR, data from receipt images can be automatically extracted into text format for further processing. The experimental results show that Cosine Similarity delivers the best performance, with a Precision of 100%, Recall of 90%, and Accuracy of 90%. On the other hand, Jaccard Similarity failed to identify any valid data, resulting in 0% across all evaluation metrics. Meanwhile, Manhattan Distance achieved high Precision (100%) but performed poorly in Recall and Accuracy, both at 10%. Based on these findings, Cosine Similarity is recommended as the most effective method for enhancing OCR-based payment validation in ERP systems. This study also opens the opportunity to develop hybrid approaches, combining Cosine Similarity and Manhattan Distance methods to further improve overall system performance.
Predicting Student Final Grades Using Random Forest Algorithms and Linear Regression Mahyudi, Mahyudi; Endaryono; Ristiawan, Rifki
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.618

Abstract

The increasing adoption of intelligent systems in higher education has encouraged the use of data-driven approaches to predict students’ academic performance. Accurate prediction models are essential to support early intervention and informed academic decision-making. This study aims to conduct a comparative analysis between Random Forest and Linear Regression algorithms in predicting students’ final academic scores. The dataset consists of assessment components, including quiz scores, assignment scores, and midterm examination (UTS) scores, which are used as predictor variables. The data were divided into training and testing sets with a ratio of 80:20. Model performance was evaluated using accuracy, error metrics, and feature importance analysis. The experimental results indicate that Random Forest outperforms Linear Regression in terms of predictive accuracy and robustness. Furthermore, both models consistently identify midterm examination scores as the most influential factor affecting students’ final performance. These findings demonstrate that ensemble-based learning methods are more suitable for academic performance prediction and can serve as a reliable foundation for intelligent academic support systems in higher education.

Page 2 of 2 | Total Record : 13

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