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Annisa Raihanah Maimun
IPB University
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Social Sciences

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Fuzzy Logic System Application for Banana Shelf Life Prediction with Sensor-Based Microclimate Aryo Sumaryanto; Roma Juliana Arios; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Zalfa Cantika; Tiara Arelya Priyanto; tangkas Patiasmara; Nashwa Zahrania Adiputri; Khaerun Nisa; Kezia Joy Audreyna; Fatia Syafana; Wulidah Tamsil Barokah
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 1 (2026): January 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/8dap4r21

Abstract

Bananas are climacteric fruits with high postharvest loss rates due to rapid ripening and spoilage under uncontrolled microclimate conditions. This study aims to develop a fuzzy logic-based system for predicting banana shelf life using real-time sensor data and a web dashboard interface. The research employed a quantitative descriptive method using the Mamdani fuzzy inference system in MATLAB, with input variables including temperature (°C), relative humidity (%), and ethylene concentration (µL/L). The output variable was shelf life, categorized as Fresh, Starting to Spoil, or Spoiled. Simulation results showed that optimal conditions (temperature 18°C, humidity 85%, ethylene 1 µL/L) yielded a defuzzification value of 0.853, indicating high freshness. Conversely, suboptimal conditions (temperature 20°C, humidity 70%, ethylene 0.1 µL/L) produced a value of 0.493, reflecting moderate freshness. The fuzzy logic system effectively modeled nonlinear relationships and uncertainty in sensor data, enabling adaptive shelf life prediction. These findings confirm that integrating fuzzy logic with microclimate sensors and dashboard visualization enhances decision-making in fruit storage management
Application of Fuzzy Logic to Determine the Condition of Candy Packaging Seals Based on Temperature, Pressure, and Heat-Sealing Duration Christabel Jayastu Swadana Suhardi; Roma Juliana Arios; Annisa Raihanah Maimun; Mrr. Lukie Trianawati; Sistya Yurizka Zein; Shadylla Al-Mahra Granada; Resti Herwiyati; Muhammad Ihsan Sulaiman; Fauzan Hilmi; Annisa Triananda Dias Putri; Adit Napaulana; Wuliddah Tamsil Barokah
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 2 (2026): March 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/pb5qfz87

Abstract

The sealing process in candy packaging is crucial for maintaining product safety, quality, and stability. However, variations in temperature, pressure, and sealing time often cause inconsistencies in seal strength and defects when using conventional systems. This study applies the Mamdani fuzzy logic method to optimize temperature, pressure, and sealing duration for consistent seal quality. The input variables are Temperature, Pressure, and Duration, while the output variable, Seal_Conditions, includes “Less tight,” “Optimal,” and “Melt.” The fuzzy inference system consists of fuzzification, rule evaluation, and defuzzification using the centroid method. Nine fuzzy rules were developed to model the sealing behavior based on parameter interactions. The defuzzification result yielded a crisp value of 4.73, indicating a sufficiently tight seal without melting the packaging material. These findings demonstrate that the Mamdani fuzzy logic method effectively manages nonlinear variations in the sealing process, ensuring consistent product quality and minimizing defects and material waste. Therefore, fuzzy logic offers an adaptive and reliable control approach for optimizing the heat-sealing process in candy packaging.
Optimizing Fresh Orange Juice Production at XYZ MSME Bogor Using Forecasting and Sugeno Fuzzy Logic Yusup Cahyadi; Wuliddah Tamsil Barokah; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Najla Athaya; Khaula Amanda; Farhana Adinda Zuhro; Dinar Lukmanudin; Devi Suryani; Christine Amara Tirza; Azhara Paramita; Roma Juliana Arios
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 5 (2025): November 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/ed1av543

Abstract

Determining the optimal daily production quantity is a major challenge for XYZ MSME, a small-scale fresh orange juice producer in Bogor, due to fluctuating demand and the perishable nature of its products. The purpose of this research is to forecast daily production using the Sugeno fuzzy logic method to minimize overproduction and underproduction. The study employs a Sugeno fuzzy inference system (FIS) with input variables including previous day sales and available raw materials. A set of fuzzy rules maps these inputs to recommended production quantities. The results show that the Sugeno FIS produces accurate and adaptive forecasts that closely match actual demand patterns, enabling XYZ MSME to adjust production efficiently. This method helps reduce raw material waste, optimize resource use, and improve overall production efficiency. The study concludes that applying the Sugeno fuzzy logic approach is effective for short-term production planning in MSMEs, particularly for perishable food products.
Application of Multi-Parameter Fuzzy Logic as a Decision Support System for Monitoring the Safety and Quality Stability of Postharvest Coffee Beans in the Agro-Industrial Storage Chain Nuraeni Latifathul Khasanah; Wuliddah Tamsil Barokah; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Anindya Intan Putri; Sherin Emania Putri; Nasyaqilah Andrianita; Azzahra Kamilia Salam; Amanda Dhiya Ardhiningrum; Sania Ramadhani; Daffa Firdaus Putra; Roma Juliana Arios
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 1 (2026): January 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/pdfpje31

Abstract

Coffee is a key agricultural commodity that plays a strategic role in Indonesia’s economy, yet maintaining the safety and quality of post-harvest coffee beans remains a major challenge due to environmental fluctuations during storage. This study aims to apply a multi-parameter fuzzy logic system as a decision support tool for monitoring the temperature, humidity, and CO₂/VOC gas concentration in the agro-industrial coffee storage chain. A qualitative descriptive approach was employed using literature analysis and fuzzy logic simulation with the Mamdani inference method. The simulation results demonstrated that the fuzzy system can effectively classify storage risk levels into safe, moderate, and high-risk categories based on environmental variations. At optimal conditions of 20–25°C temperature and 60–70% relative humidity, the system maintained coffee bean quality in accordance with SNI 01-2907-2008 standards. Conversely, conditions exceeding 30°C and 75% humidity resulted in a high-risk index due to increased moisture, microbial growth, and oxidation. The fuzzy-based monitoring system offers a more adaptive and precise assessment compared to conventional threshold methods, enabling early detection of quality degradation. This research provides a practical reference for developing intelligent control systems to ensure sustainable post-harvest coffee quality management.
Web Application Using Fuzzy Logic to Assess Recommended Intake Frequency of Extruded Chiki Snacks Bayu Praditya; Wuliddah Tamsil Barokah; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Rinunti Najwa Putri Minanti; Nazwa Salsabila; Tulus Fernando Sihombing; Fairuz Aliyya Khansa; Aurellia Avisa Putri Alya; Rubina Alleysha Putri Aulia; Hanifah Khusnul Syahida; Roma Juliana Arios
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 5 (2025): November 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/nt94t493

Abstract

Excessive consumption of extruded snacks, particularly Chiki products high in sugar, salt, and fat, increases the risk of obesity, hypertension, and metabolic disorders. This study aims to develop a web-based fuzzy logic application using the Mamdani inference system to determine the recommended frequency of consumption based on nutrient composition. The research employed a quantitative descriptive approach integrating three main input variables: sugar, salt, and fat, each represented by fuzzy sets of low, medium, and high levels. The output variable, consumption frequency, was classified into three linguistic terms: daily safe, moderate, and limited. A total of 27 fuzzy rules were constructed and simulated using MATLAB. The results showed that the model effectively translated quantitative nutritional data into qualitative recommendations, with higher nutrient concentrations corresponding to lower consumption frequency. The fuzzy Mamdani model provided smooth decision boundaries, demonstrating high interpretability and potential as a nutritional decision support system for consumer health guidance.
Analysis of the Effect of Protein Content and Preheating Temperature on the Hardness of SPC Biscuits Using the Fuzzy Logic Method Muhammad Alpiansyah; Wuliddah Tamsil Barokah; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Syifa Fauziyah; Nida Qotrunnada; Naura Alissa Davellitha; Meylani Awaliyah; Izza Afkarina Umar; Davina Baiza Radityaputri; Akmal Kayru Raid; Rome Juliana Arians
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 1 (2026): January 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/6h6fq558

Abstract

This study aimed to analyze and model the non-linear effect of protein content and preheating temperature on the hardness of Soy Protein Concentrate (SPC)-based biscuits using the Sugeno fuzzy logic method. A descriptive quantitative approach was employed, utilizing the Sugeno-type Fuzzy Inference System (FIS) designed and implemented using MATLAB software. The inputs were protein concentration (7-16%) and preheating temperature (70-90°C), with biscuit hardness (747.5-2176.5 gf) as the output. The system successfully mapped the complex interactions through nine fuzzy if-then rules. The results showed that increasing protein content generally increases hardness, particularly at higher preheating temperatures. However, excessive heating at medium protein content led to a decrease in hardness due to structural degradation. The defuzzification surface indicated that the preheating temperature has a relatively stronger influence on the final hardness than protein content. The developed fuzzy model provides accurate and interpretable predictions (e.g., 11.5% protein and 80°C yields a medium hardness of 1.45 x 10³ gf), proving its effectiveness as an adaptive decision-support tool for optimizing high-protein biscuit production.
Evaluating the Quality Grade of Cookies through Crispness and Baking Temperature Analysis Using a Fuzzy Inference System Raihan Vivardian; Annisa Raihanah Maimun; Wuliddah Tamsil Barokah; Mrr. Lukie Trianawati; Arisya Putri Hanifah; Tausy Fadilla; Salma Anjela; Fransiska Yulia Chandra; Octari Rahmawati; Pelita Sari; Kayana Devin Bazzani Karo Karo; Roma Juliana Arios
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 1 (2026): January 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/p44bqb37

Abstract

Cookies are one of the most popular processed food products due to their distinctive taste, long shelf life, and variety of textures ranging from soft to crisp. Physical properties such as crispiness play an important role in determining consumer preferences and acceptance. These characteristics are influenced by several factors, one of which is the baking temperature that determines the quality of soft cookies. Advances in computing technology have enabled objective food quality analysis through artificial intelligence approaches. This study aims to evaluate the suitability of cookies based on crispiness and baking temperature parameters using the Fuzzy Inference System (FIS) method. The research was conducted using a fuzzy system designed with input variables of temperature and crispiness, and output variables of product quality categorized as poor, moderate, and good. Each variable was represented in the form of a triangular membership function. The results showed that fuzzy logic was able to effectively integrate parameters, making it a smart and objective quality assessment method.
Fuzzy Logic Approach to Determine Apple Maturity for Harvesting Decision Muhammad Rafi Andika Wijaya; Wuliddah Tamsil Barokah; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Syadza Afifah Nuri; Senja Surya Gama; Putri Nurbaiti; Nisrina Fatin Nadra; Fional Alvina Nirmala Putri; Ayu Nur Fadhila; Anindya Salsabila Hamdani Putri; Roma Juliana Arios
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 1 (2026): January 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/2awk4n51

Abstract

This study aims to develop a fuzzy logic–based system for determining the maturity level of Anna apples as a basis for harvest decision-making. Five key parameters were used as input variables: Flesh Firmness (FF), Soluble Solid Content (SSC), Starch Pattern Index (SPI), Total Acidity (TA), and Hue°, with one output variable representing the maturity level (unripe, ripe, and overripe). The research employed a literature review method with a qualitative descriptive approach by collecting data from scientific journals and relevant documents discussing apple maturity and the application of fuzzy logic in agriculture. Data analysis was conducted using MATLAB through processes including fuzzification, formulation of If–Then rules, Mamdani inference, and defuzzification to produce a crisp output value representing apple maturity. The results indicate that the fuzzy logic system effectively models apple maturity parameters and provides accurate, objective, and efficient decisions compared to conventional subjective methods. The application of fuzzy logic offers a non-destructive classification method that assists farmers in determining the optimal harvest time, improving post-harvest quality, and maintaining product consistency.
Evaluation of Cassava Chip Crispness Using a Fuzzy Logic System Based on Temperature and Vacuum Pressure Variables Arya Rafly Maulana Sopyan; Wuliddah Tamsil Barokah; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Riza Safira; Riyatin Tsani Fatikhah; Nisa Akmala Sidik; Luthfitah Sys Febriana; Hikma Hijrianita Putri Lesmana PS; Chiara Kalyla; Aulia Zahwa Gharini; Roma Juliana Arios
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 1 (2026): January 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/cz87d281

Abstract

The study evaluates cassava chip crispness using a fuzzy logic system based on temperature and vacuum pressure variables. Fuzzy logic system is applied to objectively assess crispness, modeling the relationship between frying variables and chip texture. The application of fuzzy systems as a quality control tool can help optimize the production process so that chips meet standards. This study employs a literature review combined with an expert-based approach to design a fuzzy logic system for evaluating the crispiness of cassava chips based on temperature and pressure variables. For input and output variables, we use temperature and vacuum pressure. The temperature range used is 140-200°C and for pressure variable, we use -65, -68, -72 CmHg. Sensory values for the three crispness categories show that the undercooked category received an average score of 3.76 ± 0.52, the crisp (optimal) category received 3.72 ± 0.88, and the overcooked category received 3.85 ± 0.11. Combination of temperature 170°C and vacuum pressure -68.5 CmHg yields the best crispness result, showing that the chips reach the desired texture, not too hard and not too soft. This demonstrates that the centroid method provides a representative defuzzified value that closely reflects actual frying conditions, ensuring consistent product quality.
Application of Fuzzy Logic For Feasibility Evaluation of Pasteurized Milk Consumption Based On Processing Temperature and pH Nauval Artyasta; Wuliddah Tamsil Barokah; Annisa Raihanah Maimun; Mrr. Lukie Triniawati; Phasya Laila Safitri; Indah Puji Lestari; Fawziya Nusa Hrishita; Priskila Margaretha Sibagariang; Fila Adida Pranata; Fathan Ahmad Munawwar; Roma Juliana Arios
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 1 (2026): January 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/fkxacz02

Abstract

This study utilizes a fuzzy logic approach to analyze the consumption feasibility of pasteurized milk, focusing on the interplay between temperature and pH as key quality indicators. Given milk's perishable nature and the inherent imprecision of conventional monitoring methods, fuzzy logic provides a more adaptive and realistic assessment system. The Mamdani fuzzy system employed involves fuzzification, inference, and defuzzification to convert temperature and pH data into a quantifiable crisp output. Results, validated by the Fuzzy Control Surface and Centroid calculation (Sample 1: Temp 63, pH 5.4), demonstrate that the highest risk of damage occurs when low temperature combines with acidic (low) pH, leading to an "Not Acceptable" classification. Conversely, maintaining a neutral or high pH significantly mitigates the risk, even under cold conditions. In conclusion, the fuzzy logic approach proves effective for automated quality monitoring, accurately identifying high-risk conditions based on the simultaneous relationship between temperature and pH.
Co-Authors Adit Napaulana Akmal Kayru Raid Amanda Dhiya Ardhiningrum Anindya Intan Putri Anindya Salsabila Hamdani Putri Annisa Triananda Dias Putri Arisya Putri Hanifah Arya Rafly Maulana Sopyan Aryo Sumaryanto Aulia Zahwa Gharini Aurellia Avisa Putri Alya Ayu Nur Fadhila Azhara Paramita Azzahra Kamilia Salam Bayu Praditya Chiara Kalyla Christabel Jayastu Swadana Suhardi Christine Amara Tirza Daffa Firdaus Putra Davina Baiza Radityaputri Devi Suryani Dinar Lukmanudin Fairuz Aliyya Khansa Farhana Adinda Zuhro Fathan Ahmad Munawwar Fatia Syafana Fauzan Hilmi Fawziya Nusa Hrishita Fila Adida Pranata Fional Alvina Nirmala Putri Fransiska Yulia Chandra Hanifah Khusnul Syahida Hikma Hijrianita Putri Lesmana PS Indah Puji Lestari Izza Afkarina Umar Kayana Devin Bazzani Karo Karo Kezia Joy Audreyna Khaerun Nisa Khaula Amanda Luthfitah Sys Febriana Meylani Awaliyah Mrr Lukie Trianawati Mrr. Lukie Trianawati Mrr. Lukie Triniawati Muhammad Alpiansyah Muhammad Ihsan Sulaiman Muhammad Rafi Andika Wijaya Najla Athaya Nashwa Zahrania Adiputri Nasyaqilah Andrianita Naura Alissa Davellitha Nauval Artyasta Nazwa Salsabila Nida Qotrunnada Nisa Akmala Sidik Nisrina Fatin Nadra Nuraeni Latifathul Khasanah Octari Rahmawati Pelita Sari Phasya Laila Safitri Priskila Margaretha Sibagariang Putri Nurbaiti Raihan Vivardian Resti Herwiyati Rinunti Najwa Putri Minanti Riyatin Tsani Fatikhah Riza Safira Roma Juliana Arios Rome Juliana Arians Rubina Alleysha Putri Aulia Salma Anjela Sania Ramadhani Senja Surya Gama Shadylla Al-Mahra Granada Sherin Emania Putri Sistya Yurizka Zein Syadza Afifah Nuri Syifa Fauziyah tangkas Patiasmara Tausy Fadilla Tiara Arelya Priyanto Tulus Fernando Sihombing Wulidah Tamsil Barokah Wuliddah Tamsil Barokah Yusup Cahyadi Zalfa Cantika