Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Teknomekanik Journal of Computer Networks, Architecture and High Performance Computing Journal of Soft Computing Exploration
Fanni Sukma
Department of Information Technology, Politeknik Negeri Padang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mechanical Engineering

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Android Based Spark and Gas Leak Detection and Monitoring Dwiny Meidelfi; Hanriyawan Adnan Moodutor; Fanni Sukma; Sandri Adnin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1489

Abstract

LPG cylinder leakage is one of the causes of fires in the community. To prevent fires, a fire and gas leak detection and monitoring device were made using a fire detector sensor and an Android-based MQ-6 to trigger it. Data collection techniques in the manufacture of gas and fire leak detection using a flame detector and the MQ-6 sensor can be obtained from datasheets, journals, books and articles, and several internet sites that support the manufacture of this device. In the manufacture of gas leak detection devices or tools, there are also two parts, namely the first to make hardware (hardware), then software (software). The result of this tool detection is that users can find out the level of LPG due to leaking of LPG cylinders and detect fire using Android notifications in real-time and the data is displayed in detail on the browser page. The conclusion of this study is that users are safer because there is a gas leak, the tool will detect LPG gas, then a message will be displayed on the LCD screen and a notification on Android and the buzzer will automatically turn on. If there is a fire from detecting the gas leak, the fire detector will detect the fire, which will result in a notification sent to Android that there is a fire and the buzzer will turn on
An intelligent academic recommendation system for learning support in higher education Dwiny Meidelfi; Dikky Chandra; Fanni Sukma; Ulya Ilhami Arsyah; Sri Yusnita
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.28

Abstract

Higher education institutions increasingly rely on data-driven approaches to improve student learning outcomes. However, many academic advisory systems still provide general recommendations without considering individual learning patterns and academic performance. This study proposes an intelligent academic recommendation system that utilizes machine learning techniques to support personalized learning in higher education. The proposed system analyzes student academic data including grade point average, attendance, assignment scores, and study habits to predict academic performance. The proposed approach was evaluated using a dataset consisting of 1000 simulated student records representing academic performance indicators in higher education. Based on prediction results, the system generates personalized learning recommendations to assist students in improving their academic outcomes. Several machine learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine, were evaluated to determine the most suitable predictive model. Experimental results show that the Random Forest algorithm achieved the highest prediction accuracy compared with other models. The developed system provides proactive learning recommendations that can assist both students and academic advisors in making better academic decisions.
Comparative analysis of the least squares method and double moving average technique for forecasting product inventory Surfa Yondri; Dwiny Meidelfi; Tri Lestari; Fanni Sukma; I.S Mutia
Teknomekanik Vol. 7 No. 1 (2024): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v7i1.29672

Abstract

The cosmetics industry necessitates efficient inventory management to balance customer demand with stock control. This case study explores how Liza Cosmetics Shop optimized inventory for Lip Cream Implora 01, a popular product, using data-driven forecasting techniques. Traditional trend-based methods often resulted in inaccurate forecasts. This study proposed implementing the SDLC Waterfall Model to apply two forecasting techniques: Least Squares and Double Moving Average. Historical sales data (April 2021 - June 2022) was analyzed to identify demand patterns, seasonality, and trends. The Least Squares method was chosen for its suitability in capturing stable, linear relationships between sales and time, while the Double Moving Average method catered to data exhibiting both long-term trends and short-term fluctuations. Rigorous testing using white-box and black-box methods ensured the accurate functionality and system behavior of the implemented models. The Mean Absolute Percentage Error (MAPE) determined the method best suited for predicting July 2022 demand. This case study contributes insights into data-driven inventory management in cosmetics, highlighting benefits such as optimized stock levels, reduced costs, and enhanced customer satisfaction through improved demand fulfillment. This studys’ limitations including unforeseen marketing campaigns and economic fluctuations impacting forecasts were acknowledged. Despite these challenges, the study emphasizes the potential of data-driven techniques to optimize inventory management and meet customer demands effectively.
Co-Authors Dikky Chandra Dwiny Meidelfi Hanriyawan Adnan Moodutor I.S Mutia Sandri Adnin Sri Yusnita Surfa Yondri Tri Lestari Ulya Ilhami Arsyah