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All Journal MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering Social Sciences Insights Journal
Saputra, Muhammad Renaldy
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Social Sciences

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Monitoring Of Household Electricity Usage Based On The Internet Of Things Fatin, Muhammad Hanif; Nasron, Nasron; Sarjana, Sarjana; Saputra, Muhammad Renaldy
MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering Vol 7 No 2 (2025): Motivection : Journal of Mechanical, Electrical and Industrial Engineering
Publisher : Indonesian Mechanical Electrical and Industrial Research Society (IMEIRS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46574/motivection.v7i2.460

Abstract

The increasing demand for energy efficiency in the digital era has accelerated the adoption of Internet of Things (IoT)-based technologies in household electricity management. This study presents the design and implementation of an IoT-based real-time electricity monitoring system using the ESP32 microcontroller and PZEM-004T sensor, integrated with the Blynk application for remote access. The system measures voltage, current, power, energy consumption, and cost, displaying data on both an LCD and a mobile interface. Experimental testing involved household appliances such as fans and rice cookers under individual and combined usage, with measurements taken at 15-minute intervals. The results demonstrated strong agreement between theoretical calculations and real-time data, with the measured values slightly higher due to the dynamic nature of electrical loads. The system achieved a low average error rate of 0.17%, with a maximum error of 0.30%. These findings confirm the accuracy and reliability of the system, supporting its potential for enhancing user awareness, improving billing precision, and contributing to sustainable energy use in smart home applications.
Integration of machine learning in e-commerce: A systematic literature review on consumer behavior prediction and product recommendation Syamsuri, Abd. Rasyid; Arohman, Rifki; Saputra, Muhammad Renaldy; Ikhlash, Muhammad; Damanik, Sri Karyani
Social Sciences Insights Journal Vol. 3 No. 3 (2025): Social Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/sg7wnx04

Abstract

This systematic literature review examines the integration of machine learning (ML) in e-commerce, focusing on consumer behavior prediction and product recommendation systems. Following PRISMA guidelines, we searched Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, identifying 1,247 records. After screening, 48 peer-reviewed articles (2019-2024) were included. This review makes three novel contributions: (1) a taxonomy of ML algorithms categorizing approaches by function (prediction vs. recommendation) and technique (supervised, unsupervised, deep learning); (2) a comparative analysis of algorithm performance across different e-commerce contexts; and (3) identification of specific research gaps requiring investigation. Findings reveal that hybrid recommendation systems combining collaborative filtering with deep learning achieve superior accuracy (mean improvement of 15-23% over single-method approaches), while gradient boosting methods (XGBoost, LightGBM) demonstrate the highest predictive performance for purchase behavior. Critical challenges include cold-start problems, data sparsity, algorithmic bias, and privacy concerns. We propose an integrative framework mapping ML technique to specific e-commerce applications and identify five priority areas for future research. Limitations include English-language restrictions and potential publication bias toward positive results.
The impact of online shopping features on consumer buying behavior: A systematic literature review Syamsuri, Abd. Rasyid; Arohman, Rifki; Saputra, Muhammad Renaldy; Halim, Abd.; Surbakti, Afridayanti
Social Sciences Insights Journal Vol. 3 No. 3 (2025): Social Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/x4zvq336

Abstract

This study examines the impact of online shopping features on consumer buying behavior by synthesizing findings from recent scholarly works through a systematic literature review. The research aims to identify how various digital features, including personalization, security mechanisms, interactive technologies, and social commerce elements, influence consumer trust, decision-making, and purchase intentions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured selection process was applied to peer-reviewed journal articles, academic reports, and books published within the last five years, resulting in 25 studies included in the final review. A descriptive synthesis approach was used to categorize themes and compare patterns across different contexts. The findings indicate that functional aspects such as website design and usability strongly affect satisfaction and trust, while interactive and experiential features, including augmented reality, chatbots, and live streaming commerce, enhance engagement and drive purchasing outcomes. Additionally, the review highlights challenges related to privacy concerns, consumer fatigue, and ethical issues, suggesting that sustainable e-commerce strategies require balancing technological innovation with consumer-centric design. The study implies that platforms integrating trust, convenience, and personalization are more likely to achieve long-term consumer loyalty and competitive advantage in digital markets.
Co-Authors A Halim Abd. Rasyid Syamsuri Arohman, Rifki Damanik, Sri Karyani Fatin, Muhammad Hanif Muhammad Ikhlash Nasron Nasron Sarjana Sarjana Surbakti, Afridayanti