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Irza Sukmana
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Faculty of Engineering, Universitas Lampung. Jl. Soemantri Brojonegoro No.1 Bandar Lampung – Indonesia. http://eng.unila.ac.id
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Journal of Engineering and Scientific Research (JESR)
Published by Universitas Lampung
ISSN : 26850338     EISSN : 26851695     DOI : https://doi.org/10.23960/jesr.v4i1.78
Core Subject : Science, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering
The focus and scopes of JESR is on but not limited to Mechanical Engineering and Material Sciences, Chemical and Environmental, Industrial and Manufacturing Engineering, Computer and Information Technology, Electrical and Telecommunication, Civil and Geodetic Engineering, Architecture and Urban Planning, Geophysical Science and Engineering, and other multidisciplinary research. The main criteria for publication are including the originality, scientific quality and interest to the aim and focus. JESR publishes twice a year for June and December editions. We welcome for publication collaborations with organizer of International Seminars, Conferences and Symposiums around the world. We are encouraging authors to submit their manuscript through our online system.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 153 Documents
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Three-Phase Load Balancing Optimization using Mixed Integer-Linear Programming Model: A Case Study at Electrical Engineering Building, University of Lampung Fahrur Riza Priyana; Lukmanul Hakim; Ageng Sadnowo Replianto; Sumadi; Zulmiftah Huda
Journal of Engineering and Scientific Research Vol. 8 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Lampung Jl. Soemantri Brojonegoro No.1 Bandar Lampung, Indonesia 35141

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jesr.v8i1.263

Abstract

Load imbalance in a three-phase low voltage distribution system is a significant operational challenge and can result in voltage drops and reduced efficiency. Based on the daily three-phase load profile of the electrical engineering academic building at the University of Lampung, particularly during operating hours, the R-phase overloaded (+13.64% deviation). Meanwhile, the S-phase underloaded (-1.70% deviation), and the T-phase underloaded (-11.94% deviation). This study implements an optimization model to balance loads by relocating load units between phases. This model was developed using Mixed-Integer Linear Programming (MILP) framework to produce practical and implementable recommendations. An innovative approach to dynamic load identification is introduced, where the model intelligently determines which loads are active at each time interval based on aggregate power data from the power meter data acquisition system. The optimal solution involved relocating six air conditioning units from the overloaded Phase R (three to Phase S and three to Phase T). This implementation successfully achieved a near-perfectly balanced system, validated by an 11.83% reduction in the aggregate load imbalance metric.
Scalable Automated Proctoring: Integrating Browser-Native Artificial Intelligence and Continuous Trust Evaluation Somnath Ghorpade; Prathamesh Bomble; Omkar Gargote; Vrushabh Chavan
Journal of Engineering and Scientific Research Vol. 8 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Lampung Jl. Soemantri Brojonegoro No.1 Bandar Lampung, Indonesia 35141

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jesr.v8i1.281

Abstract

Abstract—The ability of most insitutions to make an agreementswiftly to remote education highlighted numerous integrity issues,thus established the need for an application-wise authenticproctoring strategy. Nonetheless, present automated solution forproctoring is either based upon sophisticated deep convolutionalnetwork (such a deep YOLO network) time-consuming requiresgreat deal of computer power or constantly intrusive onlinedesktop application to integrate the knowledge from students notbeing located together with test location. Many of these approachdisqualify the students utilizing common computer parts theywere in all probability to use to even go on the web with, compromise the citizens of those pupils, two the ability to meddle withevidence acquired on a far more powerful proctoring solution.We propose a stream-lined, wholly virtual multi-modal AI basedProctoring system integrated to a Django platform (PostgreSQLserver for data storage) to get over those limitations. In thisproject, we demonstrated a system of make sure the proctoringsoftware unwilling run while offline onto a pupil computer. Thisproject we used the enormous computer-processing and memorylearning pipeline is similar to repacement by existing proctoringsystems with a lean angular multiplier modality spatially-tracersystem using Face–based facial recognitions of pupil to do GazeTraccing (em the result straight away) for objects detectionduring image minutely track for obiquitous objects on pupilview(s). Fourth, using all of those background sound analysisplus ongoing browser app up to a modern ”Trust Value”. Thetrust value will evaluate the behavior of student through out thewhole duration exam. This propose to features a way to designa proctoring system that utilizes minimal amount of bandwidthand computational resource, can be expanded to accomodatemany students taking a test simulteneously and comport toaccommodate with the student wellbeing- rather than likely makeany of these metrics deteriorate.Index Terms—Remote Proctoring, Uncertainty Estimation,Multi-Modal Fusion, YOLOv11, LSTM, Edge Computing.
CareMatrix: An Integrated Smart Healthcare System Jaydeep Kumbhar; Omkar Burungale; Praveen Barapatre; Satish Khalse
Journal of Engineering and Scientific Research Vol. 8 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Lampung Jl. Soemantri Brojonegoro No.1 Bandar Lampung, Indonesia 35141

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jesr.v8i1.283

Abstract

The increasing demand for real-time, patient-centrichealthcare has accelerated the integration of Internet of Things(IoT) technologies with intelligent analytics. Traditional healthcare models rely on episodic patient visits and are inadequate forcontinuous monitoring and early detection of critical conditions.This paper presents CareMatrix, an integrated smart healthcaresystem combining IoT-based Remote Patient Monitoring (RPM)with machine learning for real-time anomaly detection.The system utilizes an ESP32 microcontroller integrated witha MAX30102 sensor to acquire physiological parameters suchas heart rate and oxygen saturation (SpO2). The data arepreprocessed at the edge and transmitted via WiFi to theThingSpeak cloud using REST APIs for real-time storage andvisualization.A Random Forest classifier trained on 10,000 patient recordsachieves an accuracy of 92.4%, precision of 91.2%, recall of90.8%, and F1-score of 91.0%. The system generates alerts within2–3 seconds upon detecting abnormal conditions, enabling timelyintervention.The proposed framework provides a scalable and efficientsolution for continuous healthcare monitoring and supportsproactive, data-driven decision-making.

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