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All Journal Physical Sciences, Life Science and Engineering Journal of Technology and System Information
Putra, I
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Humanities Computer Science & IT Engineering Physics Social Sciences

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Advancing Sustainable Agriculture through Smart Farm Tagging and AI-Driven IoT Dashboards Setiawan, Feri; Kumara, I; Amertha, I; Pioni, Ni; Putra, I; Farel, I
Journal of Technology and System Information Vol. 2 No. 3 (2025): July
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v2i3.4859

Abstract

Traditional livestock management often suffers from inefficient tracking, limited real-time data, and minimal automation, leading to reduced productivity and sustainability issues. This paper introduces Smart Farm Tagging with Basic, Pro, and Advanced versions, a smart livestock monitoring system that integrates Artificial Intelligence (AI), Internet of Things (IoT), Near Field Communication (NFC), barcode technologies, and Global Positioning System (GPS). The system enables real-time tracking and monitoring of key parameters such as species type, gender, health status, body weight, and production output. Initial field data include cattle profiles labeled by health status (“Healthy”), gender (“Female” or “Male”), and weight, with birth date validation ongoing. Furthermore, the AI-powered dashboard integrates operational logs with external weather inputs such as temperature, humidity, and light rain conditions recorded in Sayan, Bali, to predict livestock health trends and recommend timely interventions. Statistical models analyze historical and real-time data to detect diseases, optimize breeding schedules, and enhance resource allocation. By integrating AI, IoT, NFC, and barcode technologies, Smart Farm Tagging presents a scalable, cost-effective, and efficient solution for modern smart farming systems.
The Application of Lean Construction uses the Borda Method and the Root Cause Analysis Method (Case Study: Sunrise Mall 2 Construction) Rahadian, Reza; Putra, I
Physical Sciences, Life Science and Engineering Vol. 2 No. 4 (2025): September
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/pslse.v2i4.521

Abstract

The construction industry often faces challenges such as delays, cost overruns, and waste. This research aims to improve the efficiency of mall construction projects using lean construction principles. The methods applied include borda method for prioritization, pareto method for identification of the dominant cause of the problem, root cause analysis (RCA) method to analyze the root of the problem and fishbone diagram to visualize the root cause of the problem. The case study was conducted on an ongoing mall construction project. Data were collected through field observations, interviews with relevant parties, and analysis of project documents. The results show that the application of the borda method helps identify the factors that have the most influence on project efficiency, the pareto method focuses attention on 20% of the main causes of problems that contribute 80% to inefficiency, and the RCA method uncovers the underlying root causes. Based on the calculation results, it was found that the most common waste parameter and their sub-parameter were overprocessing with the sub-parameter “Repair”; overproduction with the sub-parameter “Design change”; motion with the sub-parameter “No special storage”; defects with the sub-parameter “Suitability/adequacy of heavy equipment used” waiting with the sub-parameter “Delay in material arrival at project site”.
Co-Authors Amertha, I Farel, I Kumara, I Pioni, Ni Rahadian, Reza Setiawan, Feri