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All Journal JAM Elektron Jurnal Ilmiah JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Research in Counseling and Education Teknomekanik JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Andalas Journal of Electrical and Electronic Engineering Technology Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat
Surfa Yondri
Jurusan Teknik Elektro Politeknik Negeri Padang
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences

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The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer Roza Susanti; Zas Ressy Aidha; Surfa Yondri; Sir Anderson; Tri Oktaviandra
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 2 (2022): November 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v2i2.27

Abstract

This study uses a backpropagation neural network to determine the evenness of the chayote chip dough. The Tcs3200 Color Sensor mounted on the stirrer alt is used as a sensor to determine the color of the chayote emping dough. A regression score of 1 indicates that the input and target data match in the test results of the artificial neural network, which has an objective error (MSE) value of 0.0096306 achieved in the 313th epoch. Changes in RGB color readings on the TCS sensor from min values ??<40 and max values>52 in mixing dough are influenced by distance and light intensity which will be converted in the form of frequency.
Comparative analysis of the least squares method and double moving average technique for forecasting product inventory Yondri, Surfa; Meidelfi, Dwiny; Lestari, Tri; Sukma, Fanni; Mutia, I.S
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 - Suryadi A fadly Ahmad Bahri Joni Malyan Ambiyar, Ambiyar Asmar Yulastri Dedi Kurniadi Dwiny Meidelfi Efendi, Raimon Ganefri . Hendra Hidayat Hengki Purnama Sari Junaldi Junaldi Milda Yuliza Mutia, I.S Randy Heriyanto Revalin Herdianto Rina Pratiwi Pudja I. A Roni Putra Roza Susanti Sir Anderson Sukma, Fanni Tri Lestari Tri Oktaviandra Waskito Witrionanda Witrionanda Yuyun Irawati Zas Ressy Aidha