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All Journal International Journal of Electrical and Computer Engineering Sinergi PEDAGOGIA Indonesian Journal of Artificial Intelligence and Data Mining Indonesian Journal of Electrical Engineering and Computer Science
Fahrizal, Diki
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Revitalizing IoT-based air quality monitoring system for major cities in Indonesia Kustija, Jaja; Fahrizal, Diki; Nasir, Muhamad
SINERGI Vol 28, No 3 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.3.016

Abstract

An IoT-based air quality monitoring system is a technology that integrates with the internet to monitor and measure numerous air quality metrics in real-time. CO levels, dust particle levels, temperature, and humidity are the air quality characteristics that must be monitored. The air quality monitoring system in its current state requires further development, such as challenges to acquiring accurate and real-time data and difficulty in accessing reliable information. Poor air quality causes various health problems, respiratory, vision, heart disease, and even cancer. The development of air pollution producers continues to increase along with the number of oil-fueled vehicles, industries operated using petroleum-fueled engines, power plants that use energy from coal, gas and petroleum. This study presents an IoT-based air quality parameter monitoring system solution that is connected with the Blynk platform and can be accessible in real time, in an effort to assist the SDGs program, which is mandated by the global community through the UN. The research technique employed is Analysis, Design, Development, Implementation, and Evaluation. The study successfully presented an IoT-based air quality monitoring system connected with the Blynk platform, which showed great accuracy in measurement 94.34% (CO), 81.15% (dust), 99.14% (temperature), and 96.84% (humidity). These results advance urban air quality monitoring and inform sustainable technology development, contributing to environmental and health-related SDGs.
Design and development of coastal marine water quality monitoring based on IoT in achieving implementation of SDGs Kustija, Jaja; Fahrizal, Diki; Nasir, Muhamad; Setiawan, Deny; Surya, Irgi
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1470-1484

Abstract

Indonesia, an archipelagic nation with about 70% ocean territory, relies on oceanographic data for efficient marine environment monitoring and natural resource sustainability. Current data collection is limited by tools measuring only single parameters and lengthy data collection times. This study proposes a marine coastal water quality monitoring tool based on the internet of things (IoT), capable of simultaneously measuring temperature, electrical conductivity, pH, and dissolved oxygen. Utilizing an Atmega328 and a battery lasting up to 119 hours, this system offers a cost-effective solution for real-time oceanographic data collection. Employing the ADDIE methodology, the results demonstrate high measurement accuracy compared to traditional methods, with accuracy of 90.5% for temperature, 93.50% for electrical conductivity, 93.67% for pH, and 96.82% for dissolved oxygen. The development of this tool aims to reduce costs and labor in capturing oceanographic data integrated with IoT, facilitate access and monitoring of water data, and make a significant contribution to achieving SDGs targets. The main focus on the goals of addressing climate change and life underwater, especially in the aspects of water resources management and protection of marine ecosystems in Indonesian.
Solar irradiation intensity forecasting for solar panel power output analyze Sucita, Tasma; Hakim, Dadang Lukman; Hidayahtulloh, Rizky Heryanto; Fahrizal, Diki
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp74-85

Abstract

Accurate forecasting of global horizontal irradiance (GHI) is critical for optimizing solar power plant (SPP) output, particularly in tropical locales where solar potential is high yet underutilized due to forecasting challenges. This research aims to enhance GHI prediction in one of the major cities of Indonesia, where existing models struggle with the area’s natural climate unpredictability. Our analysis harnesses a decade of data 2011-2020, including GHI, temperature, and the Sky Insolation Clearness Index, to calibrate and compare these methodologies. We evaluate and contrast the exponential smoothing method versus the more complicated artificial neural network (ANN). Our findings reveal that the ANN method, notably its fourth iteration model with 12 input and hidden layers, substantially outperforms exponential smoothing with a low error rate of 1.12%. The use of these methodologies forecasts an average energy output of 252,405 Watt for a solar panel with specification 15.3% efficiency and 1.31 m2 surface area throughout the 2021 to 2025 timeframe. The work offers the ANN method as a strong prediction tool for SPP development and urges a further exploration into more advanced forecasting methodologies to better harness solar energy.
A Bibliometric Analysis Ethical Perspectives on SDGs Based Education Goals Using VOSViewer Sumadisastro, Witzir; Robandi, Babang; Fahrizal, Diki
PEDAGOGIA Vol 22, No 2 (2024)
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/pdgia.v22i2.75800

Abstract

This study investigates the integration of ethical perspectives into the frameworks of Education for Sustainable Development (ESD) and Society 5.0, with a focus on their alignment with the Sustainable Development Goals (SDGs). Utilizing a bibliometric analysis, the research examines academic and professional literature published between 2010 and 2024. Data was collected from Google Scholar using keywords such as "Ethical Education," "Sustainable Development Goals and Education," and "Technology in Education." The analysis employed VOSviewer to map and visualize thematic connections and subject linkages within the literature.The findings indicate a notable increase in research on ethical education and the SDGs, with publication peaks in 2012 and 2023, alongside a slight decline in 2021, reflecting fluctuating scholarly attention. These results highlight the growing recognition of ethics as an essential element in educational strategies that advance sustainable development goals. The study emphasizes the importance of integrating ethical considerations into the development of inclusive, equitable, and sustainability-oriented educational objectives. The conclusions advocate for the establishment of ethically grounded educational frameworks that align with the objectives of Society 5.0, which aims to harmonize technological advancements with societal well-being. This research offers actionable insights for educators, policymakers, and researchers, providing strategies to strengthen the role of ethics in achieving educational and sustainable development objectives.
Development Tourism Destination Recommendation Systems using Collaborative and Content-Based Filtering Optimized with Neural Networks Fahrizal, Diki; Kustija, Jaja; Akbar, Muhammad Aqil Haibatul
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.28713

Abstract

Tourism, a vital sector in the global economy, benefits significantly from advancements in infrastructure, accessibility, and information availability. However, the vast volume of information can overwhelm travelers, underscoring the need for efficient recommendation systems. This research aims to develop an advanced tourist destination recommendation system by integrating Collaborative Filtering (CF) and Content-Based Filtering (CBF) models with Neural Networks. This approach seeks to enhance recommendation accuracy by closely aligning with user preferences and addressing the challenge of limited data. The study utilizes data from 523 tourist destinations across West Java, along with user preference assessments, encompassing stages of data collection, labeling, pre-processing, pre-training, neural network-based training, model evaluation, and the presentation of recommendation outcomes. The optimization of the recommendation models through neural networks has notably improved the precision of tourist destination suggestions, achieving Root Mean Square Error (RMSE) values below 0.37 for both CF and CBF approaches. This research significantly contributes to increasing the search efficiency and accuracy for tourist destination information, offering a strategic solution to the prevalent issue of information overload in the tourism industry.
Object detection in printed circuit board quality control: comparing algorithms faster region-based convolutional neural networks and YOLOv8 Kustija, Jaja; Fahrizal, Diki; Nasir, Muhamad; Adriansyah, Andi; Muttaqin, Muhammad Husni
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2796-2808

Abstract

Along with the development of electronic technology, the integration of numerous components on printed circuit board (PCB) boards has resulted in increasingly complex and intricate layouts. Small defects in traces can lead to failures in electronic functions, making the inspection of PCB surface layouts a critical process in quality control. Given the limitations of manual inspection, which struggles to detect such defects due to their size and complexity, there is a growing need for a PCB inspection system that utilizes automated optical inspection (AOI) based on deep learning detection. This research develops and compares two deep learning algorithms, faster region-based convolutional neural networks (R-CNN) and YOLOv8, to identify the most effective algorithm for detecting defects on PCB layouts. The findings of this study indicate that the YOLOv8 algorithm outperforms faster R-CNN, with the YOLOv8x variant emerging as the best model for defect detection. The YOLOv8x model achieved performance scores of 0.962 (mAP@50), 0.503 (mAP@50:95), 0.953 (Precision), 0.945 (Recall), and 0.949 (F1-score). These results provide a strong foundation for further research into the application of AOI for PCB defect detection and other quality control processes in manufacturing, using optimized deep learning models.
Co-Authors Akbar, Muhammad Aqil Haibatul Andi Adriansyah Babang Robandi Dadang Lukman Hakim Deny Setiawan Hidayahtulloh, Rizky Heryanto Jaja Kustija, Jaja Muhamad Nasir, Muhamad Muttaqin, Muhammad Husni Sumadisastro, Witzir Surya, Irgi Tasma Sucita