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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Angkasa: Jurnal Ilmiah Bidang Teknologi Jurnal Ilmiah Inovasi G-Tech : Jurnal Teknologi Terapan International Journal of Industrial Optimization (IJIO) Jurnal Sistem Informasi dan Sains Teknologi Jurnal Media Informatika Co-Value : Jurnal Ekonomi, Koperasi & Kewirausahaan Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) SWARNA Journal of Information Technology and Cyber Security KOMPUTASI
Setiawan, Akas Bagus
Politeknik Negeri Jember, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Other

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Studi Efektifitas Lampu Perangkap Hama Padi Tenaga Surya dalam Konteks Ekologi Pertanian Dedes, Khen; Fatimatuzzahra, Fatimatuzzahra; Juli, David; Setiawan, Akas Bagus; Mujiono, Mujiono
SWARNA: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 12 (2025): SWARNA : Jurnal Pengabdian Kepada Masyarakat, Desember 2025
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/swarna.v4i12.1830

Abstract

The increasing intensity of pest attacks on rice plants is one of the major factors contributing to the decline in productivity and quality of yields in Indonesia’s agricultural sector. Farmers’ excessive dependence on chemical pesticides not only leads to pest resistance but also poses negative impacts on human health, the environment, and the ecological balance of paddy fields. Therefore, there is an urgent need for innovative, environmentally friendly, efficient, and sustainable pest control technologies. This study proposes the implementation of a solar-powered pest trap lamp integrated with an Internet of Things (IoT)-based system as an alternative solution within an integrated pest management (IPM) framework. The device is designed to utilize solar energy as its primary power source to support energy-efficient operations while reducing dependence on conventional electricity. The IoT connectivity enables real-time data transmission to a cloud-based server for remote monitoring, data analysis, and evidence-based decision-making. Through this approach, pest control can be carried out more precisely, efficiently, and adaptively according to field conditions. Moreover, the application of this technology has the potential to minimize the use of chemical pesticides, enhance agroecosystem health, and promote the principles of sustainable agriculture and national food security. This study emphasizes that the integration of solar energy and IoT technology in pest trap systems can serve as a significant innovation in the digital modernization of agriculture in Indonesia. Further research recommendations include large-scale field trials, quantitative analysis of pest population reduction effectiveness, and evaluation of the economic, social, and adoption aspects among farmers. Hence, this study is expected to provide a substantial contribution to the development of intelligent, efficient, and sustainable pest management systems.
Decision Support System for Selecting the Best Rental House with Weight Product in Sidokare District, Sidoarjo Regency Setiawan, Akas Bagus; Yuniar, Eka; Hermansyah, Mas'ud; Mujiono, Mujiono; Ariyadi, David Juli
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8865

Abstract

The demand for rental housing in high-mobility areas such as Sidokare District, Sidoarjo Regency, continues to increase along with the region's economic development. However, prospective tenants often face difficulties in selecting the right rental house due to unstructured information and subjective decision-making processes. This study aims to develop a web-based Decision Support System (DSS) capable of providing objective and measurable rental housing recommendations. The method used is the Weighted Product (WP), a multicriteria decision-making technique that normalizes weights through multiplication operations. This system evaluates five rental housing alternatives based on eight main criteria, including price, location, facilities, security, and comfort. The results show that Sukun Rental House is the best alternative with the highest preference value of 0.0769. The practical implication of this study is the availability of an efficient digital tool for residents and students in Sidokare to compare various housing options transparently and quickly. This system successfully minimizes subjectivity in housing selection and helps users find the housing that best suits their financial priorities and functional needs.
An End-to-End Machine Learning Pipeline for Online Purchase Intention Prediction Using Random Forest and MLOps Practices Setiawan, Akas Bagus; Riskiawan, Hendra Yufit; Putranto, Hermawan Arief; Rizaldi, Taufiq; Atmoko, Rachmad Andri
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 18, No 1 (2026): Februari
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/angkasa.v18i1.3841

Abstract

Predicting online shoppers' purchase intention is a key issue in e-commerce because it directly affects conversion and marketing effectiveness. The main focus of this article is a Random Forest purchase-intention model accompanied by an end-to-end MLOps implementation to ensure production readiness. The dataset used is Online Shoppers Intention with 12,330 samples and 18 features representing administrative, informational, and product-related characteristics, along with behavioral metrics. Preprocessing includes missing-value imputation, numerical feature standardization, categorical feature encoding, and outlier removal using the z-score method. The model is optimized with GridSearchCV and 3-fold cross-validation. Test results show 91.38% accuracy with 73.60% precision, 56.64% recall, and 64.02% F1-score for the positive class. MLOps implementation uses MLflow for experiment tracking, Prometheus-Grafana for monitoring, and a GitHub Actions-based CI/CD pipeline for deployment automation. Overall, the Random Forest model delivers strong predictive performance on e-commerce data and is supported by an MLOps pipeline that improves reproducibility, deployment, and production monitoring
Optimization of real-time forest monitoring system using yolo v9 object detection and 2.4 ghz wireless network: resource allocation, energy efficiency, and industrial deployment strategies Atmoko, Rachmad Andri; Hidayatullah, Rifqi Rahmat; Na’im, Septian Ghuslal Nur; Setiawan, Akas Bagus
International Journal of Industrial Optimization Vol. 7 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/ijio.v7i1.11899

Abstract

Large forest areas are increasingly exposed to illegal activities and environmental threats, while conventional monitoring systems suffer from limited coverage, high energy consumption, and delayed response. To address these challenges, this study proposes an optimized real-time forest monitoring system designed for industrial-scale deployment in remote environments. The primary objective is to enhance surveillance efficiency by integrating AI-based object detection, long-range wireless communication, and resource-efficient system design. The proposed system employs ESP32-CAM sensor nodes integrated with 2.4 GHz CPE wireless links and a gateway-based YOLOv9 object detection framework. Bandwidth utilization is optimized through selective transmission of processed detection metadata instead of raw images, while deployment parameters are optimized using simulation-based planning. A web-based monitoring platform with an optimized REST API supports real-time visualization and alert generation. Experimental results show that the system achieves reliable communication up to 500 m with packet loss below 5% and latency under 50 ms at distances up to 300 m. Human detection accuracy reaches 98.5% under optimal conditions, with performance degradation observed in dense vegetation and low-light environments. Energy evaluation confirms sustainable operation, with ESP32 nodes consuming 160 mA and the gateway operating at 3.7 W. Comparative analysis indicates reductions of 37% in deployment cost, 24% in energy consumption, and 51% in latency compared to similar systems. This study concludes that the proposed architecture effectively balances accuracy, scalability, cost, and energy efficiency. The novelty lies in the integrated optimization of edge-based AI detection, selective data transmission, and simulation-driven deployment for industrial forest monitoring.
Co-Authors Atmoko, Rachmad David Juli Ariyadi Dedes, Khen Fatimatuzzahra Fatimatuzzahra Fatimatuzzahra Harvyanti , Annisa Fitri Maghfiroh Hendra Yufit Riskiawan Hermansyah, Mas'ud Hermansyah, Masud Hermansyah, Mas’ud Hidayatullah, Rifqi Rahmat Izzun Ni`am, Muhammad Juli, David Khen Dedes M Faiz Firdausi Maulana Firmansyah, Akbar Mujiono Mujiono Nasyatha Adlin, Dzakiyyan Na’im, Septian Ghuslal Nur Nur Na’im, Septian Ghuslal Pradana, Reza Putra Putranto, Hermawan Arief Rachmad Andri Atmoko, Rachmad Andri Rahmat Hidayatullah, Rifqi Resya , Fachmi Setyo Wibowo, Nugroho Taufiq Rizaldi Vindarosita, Babylina Yudhistira, Prillinaya Yuniar, Eka