Journal of Software Engineering and Technology

Portable Solar Panel System Design for IoT-Based Remote Areas

Fatih Humam Ramadhan — 2025-08-04

Inequality of energy access in remote areas is still a major challenge in sustainable development, especially in developing countries such as Indonesia. This research aims to design and develop a portable solar panel system based on the Internet of Things (IoT) that can provide clean and efficient energy solutions for communities in areas without a power grid. The research method used is Research and Development (R&D) with stages: needs analysis, design, prototyping, field trials, and evaluation. The results of the five-day system test showed that the device was able to produce an average power of 43.2 watts per day with a working efficiency of 92.6%. The output voltage is stable at the range of 12V, sufficient for basic needs such as lighting and charging of electronic devices. IoT features enable real-time energy monitoring through mobile apps, which is proven to improve system control and reliability. In addition, the portable system design makes it easy for users to carry and operate devices independently. This research shows that the integration of portable solar panels with IoT technology can be a practical and sustainable solution in expanding energy access in remote areas.

Development of Biofiltration-Based Wastewater Treatment Technology and Smart Sensors

Tantra Agun Wiguna — 2025-07-18

The problem of wastewater pollution due to domestic activities and small industries is still a serious challenge in various regions, especially in developing countries such as Indonesia. Existing wastewater treatment technologies tend to be less efficient, expensive, and unresponsive to real-time changes in water quality. This research aims to develop a biofiltration-based wastewater treatment system integrated with smart sensors based on the Internet of Things (IoT). The research method used is Research and Development (R&D), with stages of prototype design, laboratory tests, sensor validation, and limited field tests. The developed prototype consists of a biological filtration medium (gravel, activated charcoal, coconut fiber) and a digital sensor module to measure water quality parameters such as pH, TDS, BOD, COD, DO, and heavy metals. The test results showed that the system was able to reduce BOD levels by up to 80%, COD by up to 77%, as well as increase DO and neutralize pH. The sensor system provides real-time monitoring with high accuracy. These results show that the technology developed is effective, cost-effective, and applicable for small-scale wastewater treatment. This research also supports the implementation of green technology and sustainable water management.

Development of Cyber Threat Early Detection System Using Distributed Machine Learning Algorithms

Dwi Febri Syawaludin — 2025-07-15

This research aims to develop a distributed machine learning-based system for the early detection of cyber threats. With the rise of cyberattacks targeting critical sectors such as education, government, and healthcare, traditional intrusion detection systems have become less effective at identifying novel threats. To address this, the study introduces a federated learning approach, which allows machine learning models to be trained across distributed nodes while maintaining data privacy. The system architecture integrates various nodes in a collaborative manner, enabling real-time detection of cyber threats with improved efficiency and data security. The study evaluates the system's performance using real-world datasets and compares the federated approach with centralized models, achieving competitive accuracy and privacy benefits. The findings highlight the importance of system speed, education level, and the role of federated learning in improving cybersecurity. This research contributes to the development of more adaptive, scalable, and privacy-preserving security systems in the context of modern network infrastructures

Integration of Zero Trust Architecture Model in Cloud-Based Academic Information System Design

Wahyu Eko Saputro — 2025-08-07

This study examines the application of Zero Trust Architecture (ZTA) in designing cloud-based academic information systems in educational institutions. With the increasing reliance on cloud-based systems, threats to sensitive data are increasingly a major concern. Zero Trust, which considers that every access is a potential threat, offers a stricter approach to data security. This study uses a qualitative approach with a case study design, involving in-depth interviews with IT teams, system administrators, and system users (lecturers and students) at three universities that have implemented ZTA. The results show that ZTA's implementation has improved access control and reduced the risk of data leakage, but the main challenge is the user's adaptation to more stringent authentication procedures. Although data security is improved, user convenience is slightly affected by the more complicated verification process. This study suggests the need for more intensive training and socialization to improve user understanding of ZTA, as well as the development of systems that balance security and convenience. These findings are expected to provide guidance for educational institutions in adopting ZTA to improve the security of cloud-based academic information systems.

Big Data Processing System Optimization for Digital Healthcare Based on Hadoop and Spark Architecture

Ahmad Bahar — 2025-07-18

The rapid growth of data in digital healthcare demands efficient, fast, and scalable processing systems. Data from Electronic Health Records (EHRs), Internet of Medical Things (IoMT) devices, and telemedicine services generate massive and complex volumes of information. This research aims to optimize big data processing by utilizing Hadoop and Spark integrated architectures in hospital information systems. The method used is a qualitative approach with in-depth interview techniques, observations, and questionnaires to the information technology and hospital management teams. The research was conducted in two private hospitals that have implemented a comprehensive digital system. The results show that the integration of Hadoop as a distributed storage system with Spark as an in-memory processing engine can increase operational efficiency by up to 47%, reduce execution time by up to 60%, and provide more stable performance than conventional methods such as MapReduce. Data visualization supports this claim with a significant comparison of runtime and resource usage. These findings imply that the Hadoop–Spark architecture is a strategic solution for real-time and batch processing of health data. This research also offers an application model that can be replicated in other health institutions in Indonesia.