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All Journal International Journal of Health, Economics, and Social Sciences (IJHESS) Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Bangunan Konstruksi
Tanwir
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Public Health Social Sciences Transportation

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Implementation of IoT in Smart City Management to Improve Energy Efficiency Muhammad Bitrayoga; Evan Haviana; Amat Suroso; Tanwir; Sri Widiastuti
International Journal of Health, Economics, and Social Sciences (IJHESS) Vol. 7 No. 1: January 2025
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/ijhess.v7i1.6927

Abstract

The rapid growth of urbanization in Palembang, South Sumatra Province, poses challenges in the management of energy resources and the environment. This study aims to evaluate the application of Internet of Things (IoT) in the E-Cleanness City Information and Management System program as a solution in improving energy efficiency and smart city operations. Mixed research methods were used, combining quantitative and qualitative analysis to obtain a comprehensive picture of the impact of IoT implementation. The results showed a 10% decrease in energy consumption and a 15% increase in operational efficiency after IoT implementation. The main factors contributing to the efficiency improvement include optimization of waste collection routes, reduction in the number of operational vehicles, and improved response times. In addition, user and system manager satisfaction surveys indicated high levels of satisfaction, indicating positive acceptance of the technology. The social and economic benefits include reduced operational costs, improved quality of life, creation of new jobs, and increased environmental awareness. However, challenges such as limited technological infrastructure, the need for further training, complex system integration, and data security and privacy issues need to be addressed to optimize IoT implementation. This research recommends infrastructure development, intensive training programs, and implementation of strong data security protocols as strategic steps to support the sustainability of smart city management in Palembang. The findings are expected to serve as a reference for the development of similar policies in other cities in Indonesia in an effort to achieve energy efficiency and environmental sustainability through IoT technology.
Problem Sosial dalam Perencanaan AMDAL Terkait Rencana Pembangunan Tanggul Sungai Tallo Tanwir; Marbath Assagaf, Muhammad Shahibul; Arsyad; Suwahyo, Chaidir
Jurnal Bangunan Konstruksi Vol 2 No 2 (2024): Barakka - Jurnal Bangunan Konstruksi
Publisher : PSTS FT UIM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63877/jbk.v2i2.103

Abstract

Perencanaan merupakan penyusunan secara sistematis tentang tindakan yang akan dilakukan. Setiap kegiatan yang akan dilakukan mesti diawali dengan perencanaann agar kegiatan tersebut berjalan sesuai tahapan yang benar demi mencapai tujuan yang akan dicapai. Begitu juga ketika akan dilakukan pembangunan infrastruktur guna menopang aktivitas masyarakat dan juga untuk memenuhi kebutuhan masyarakat. Penelitian ini bertujuan untuk mengindetifikasi dan menganalisis problem sosial yang akan ditmbulkan dari rencana pembangunan infrastruktur saat penyusunan AMDAL. Metode penelitian yang digunakan adalah observasi lapangan, analisis data sekunder, dan wawancara dengan pihak terkait. sikap responden terkait dengan kepemilikan baik bangunan maupun sarana usaha pada areal tapak rencana kegiatan mengisyaratkan perlunya pendekatan yang lebih persuasif dari pemrakarsa kegiatan kepada masyarakat agar konflik sosial bisa dihindari. Untuk mengatasi masalah sosial dalam pembangunan infrastruktur, beberapa langkah mitigasi dapat dilakukan: Partisipasi Masyarakat: Melibatkan masyarakat dalam perencanaan, pelaksanaan, dan pemantauan proyek. Kompensasi yang Adil: Memberikan kompensasi yang layak dan transparan kepada masyarakat yang terkena dampak. Program Rehabilitasi dan Resettlement: Menyediakan program rehabilitasi dan resettlement yang komprehensif bagi masyarakat yang direlokasi. Penguatan Kelembagaan: Memperkuat kelembagaan masyarakat untuk melindungi hak-hak mereka. Evaluasi Dampak Sosial: Melakukan evaluasi dampak sosial secara berkala untuk mengidentifikasi masalah dan mencari solusi. Dengan menerapkan langkah-langkah mitigasi yang tepat, diharapkan pembangunan infrastruktur dapat memberikan manfaat yang lebih besar bagi masyarakat dan meminimalkan dampak negatifnya.
COMPARATIVE ANALYSIS OF RANDOM FOREST AND SUPPORT VECTOR MACHINE FOR FOOD CALORIE LEVEL CLASSIFICATION Oktaviadi Resmiranta, Dading; Tanwir; I Gede Yogi Pratama; Naufal Hanif; Azral Satriani; Khairan Marzuki
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.450

Abstract

The rapid escalation of global metabolic health concerns emphasizes the critical urgency for advanced technological solutions that facilitate precise and automated monitoring of daily caloric intake. This research conducts a rigorous comparative analysis to evaluate the predictive performance and computational efficiency of Random Forest (RF) and Support Vector Machine (SVM) algorithms in classifying food calorie levels. The methodology commenced with a comprehensive data preprocessing phase involving multi-strategy missing value imputation and the discretization of caloric values into ordinal categories. Feature selection was meticulously executed using linear regression coefficients to identify high-impact nutritional variables. To ensure a robust evaluation, the dataset was partitioned using an 80:20 ratio for training and testing, complemented by cross-validation to minimize bias and variance. Experimental results indicated that the Random Forest (RF) demonstrated superior classification capabilities, achieving a peak accuracy of 94.8% alongside balanced precision and recall scores. Statistical evaluation via confusion matrices further revealed that Random Forest exhibited enhanced generalization across high-dimensional nutritional features compared to the geometric approach of Support Vector Machine (SVM). Furthermore, the analysis of computational overhead provided critical insights into the real-time deployment feasibility of each model. Ultimately, the findings suggest that the Random Forest serves as a robust engine for personalized dietary management systems, offering a reliable framework for future developments in preventive digital healthcare. By successfully bridging machine learning with nutritional science, this study establishes a benchmark for high-accuracy food classification essential for modern health-centric mobile applications.
Co-Authors Amat Suroso Arsyad Azral Satriani Evan Haviana I Gede Yogi Pratama Judijanto, Loso Khairan marzuki Marbath Assagaf, Muhammad Shahibul Muhammad Bitrayoga Naufal Hanif Oktaviadi Resmiranta, Dading Rinovian Rais Sri Widiastuti Suwahyo, Chaidir