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Muhammad Faiz Assariy
College of Vocational Studies, IPB University
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Social Sciences

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⁠Implementation of Fuzzy Logic in Management Decision Making Supply of Raw Materials for Pie Production in the Food Industry Alif Permata Gusti; Muhammad Faiz Assariy; Daffa Zulqisthi; Lukie Trianawati; Tyara Restiani; Rinriani Hanifah; Nasya Alivia Cahyaning Putri; Muhammad Naufal Denasfi; Dinda Anissa Rahmah; Alief Riza Candra Dewi Afivah; Chika Hayya Sabillah
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 4 (2025): September 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/xnxxdt92

Abstract

Raw material inventory management is a critical factor in the food industry, influencing production efficiency and product quality. Unstable inventory levels can lead to significant challenges, including material spoilage, stock shortages, and quality degradation, ultimately impacting the ability to meet market demand. To address the complexities and uncertainties inherent in inventory management, this study explores the application of Fuzzy Sugeno inference systems. This method allows for the flexible processing of imprecise inventory data, generating accurate numerical outputs that can directly inform operational decision-making. By analyzing production data for pie crusts from April 2023 to May 2024, the study identified significant fluctuations in initial stock, production, and incoming stock levels. To capture the inherent uncertainty in these parameters, Fuzzy Sugeno was employed to categorize them into fuzzy sets. The implementation of the model in MATLAB yielded precise outputs that align with the specific needs of inventory management in the food industry. The results demonstrate that the proposed Fuzzy Sugeno-based approach can significantly enhance inventory prediction accuracy and reduce the risk of stockouts or excess inventory. By adapting to changing market demands and operational conditions, this method contributes to improved production efficiency, cost reduction, and overall business sustainability in the food industry.
The Effect of Temperature, Weight and Roasting Time on the Roasted Level of Coffee Beans Using Fuzzy Logic Fannan Cakti Wibisono; Chika Hayya Sabillah; Muhammad Faiz Assariy; Daffa Zulqisthi; Amanda Catur Sukmarani; Putri Alya Faisal; Muhammad; Cut Mesya Meuthia; Farida Fadhilatun Nisa; Nuriah Nurani Muhsin; Lukie Trianawati
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 3 (2025): June 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/crtn5j19

Abstract

This study examines the effect of temperature, roasting time, and coffee bean weight on the maturity level of roasted coffee beans, utilizing fuzzy logic to model and predict optimal roast outcomes. Coffee bean roasting level, a critical factor in determining flavor, is assessed using Mamdani-type fuzzy logic, which takes three input parameters—temperature, time, and weight—divided into ranges for specific roast outcomes. Case study parameters, including a coffee bean weight of 450 grams, roasting time of 39 minutes, and temperature of 130°C, were analyzed to classify the roast maturity. The fuzzy logic output indicates that this specific case falls within the medium roast level by the similarity of centroid between moment calculation and matlab calculation is 10,7, demonstrating the model's capability to provide precise roast classifications. This structured approach to evaluating coffee roasting parameters contributes to enhancing consistency and quality in the coffee industry, highlighting the potential of fuzzy logic for robust decision support in coffee quality control.
Implementation of Fuzzy Logic to Control Temperature and Incubation Time for the pH of Yoghurt ratna aryanti; Chika Hayya Sabillah; Muhammad Faiz Assariy; Daffa Zulqihisti; Ronnauli Lamria Sirait; Rizki Laksana Putra; Maharshi Kaloka Parahita; Fauzan Maula Abdullah; Dhiya Ananta Pranadita; Delia Noor S; Afrida Rif'atul Hanifa; Lukie Trianawati
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 4 (2025): September 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/9r33bp20

Abstract

This research develops a fuzzy logic-based yoghurt production model by considering incubation temperature and incubation time as the main variables that influence the final pH of yoghurt. In this study, 49 yoghurt samples were produced and tested in two replications to obtain pH data which was then processed through fuzzy modeling. This fuzzy model is for determining fuzzy rules and membership functions. By using two fuzzy sets for each parameter and the Mamdani method, a total of 49 fuzzy logic rules were built to describe the relationship between incubation variables and final pH. Matlab 2015a software was used to process data and assess model accuracy. The results showed that this model was effective for predicting the final pH of yoghurt, which is important for maintaining product quality. In the future, it is hoped that this model can be applied to automated production lines, thereby supporting yoghurt production on an industrial scale with greater consistency and efficiency, especially ini the food sector.
Co-Authors Afrida Rif'atul Hanifa Alief Riza Candra Dewi Afivah Alif Permata Gusti Amanda Catur Sukmarani Chika Hayya Sabillah Cut Mesya Meuthia Daffa Zulqihisti Daffa Zulqisthi Delia Noor S Dhiya Ananta Pranadita Dinda Anissa Rahmah Fannan Cakti Wibisono Farida Fadhilatun Nisa Fauzan Maula Abdullah Lukie Trianawati Maharshi Kaloka Parahita Muhammad Muhammad Naufal Denasfi Nasya Alivia Cahyaning Putri Nuriah Nurani Muhsin Putri Alya Faisal ratna aryanti Rinriani Hanifah Rizki Laksana Putra Ronnauli Lamria Sirait Tyara Restiani