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Mrr Lukie Trianawati
IPB University
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Social Sciences

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Mango Quality Measurement System Based on Ripeness, Size, and Spot Area Using Fuzzy Inference System Aziz Rabih Rabbani; Muhammad Faiz Assariy; Daffa Zulqisthi; Chika Hayya Sabillah; Shalinadira Koswara; Lola Amelia; Iofema Malika; Hilda Fitria Hidayah; Fildza Khairunida; Annisa Pertiwi Hakim; Atikah Fahni; Mrr Lukie Trianawati
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 2 (2025): March 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/2d4bfb38

Abstract

Mango (Mangifera indica) quality assessment poses challenges in traditional grading methods, primarily relying on visual inspection, which can be subjective. This research aims to develop a Fuzzy Inference System (FIS) for evaluating mango quality based on parameters such as color, size, and spots. A qualitative data collection approach was employed through literature review and expert opinions, followed by the application of fuzzy logic using MATLAB to analyze the parameters affecting quality. The results demonstrate that mangoes classified as "Good" are characterized by larger size, a high or medium distribution of red color, and small spots. Defuzzification was performed using the Centroid Method to derive a crisp output, which indicated a quality leaning towards the "Well" category with a value of approximately 0.5584. This study highlights the efficacy of fuzzy logic in transforming qualitative assessments into quantitative measures, enhancing the reliability of agricultural evaluations.
Implementation of Fuzzy Logic in Yeast Concentration and Fermentation Time for Tempeh Quality Almaida Pramesti; Aura Fadhilah; Belvana Fazwa Athallah; Grace Octo Suma Panjaitan; Nastiti Ayu Utami; Prima Ananta Nugraha; Rohidi; Mrr Lukie Trianawati; Daffa Zulqisthi; Muhammad Faiz Assariy; Chika Hayya Asabillah
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: 10.62535/1am4xb61

Abstract

This study investigates the application of fuzzy logic to optimize yeast concentration and fermentation time in tempeh production. Tempeh, a traditional Indonesian food made by fermenting soybeans, requires precise conditions to ensure high quality, including optimal yeast concentration and incubation duration. Fuzzy logic provides a flexible approach by allowing variable inputs within ranges, rather than fixed values, making it ideal for controlling uncertain factors in fermentation. The study used MATLAB software for fuzzy logic modeling, incorporating yeast concentration and fermentation time as input variables and tempeh quality as the output. Key parameters for each input were defined, and fuzzy rules were applied to predict tempeh quality under varying conditions. Results indicate that a yeast concentration of 30% and a fermentation time of 53 hours yield a quality rating of 6.9, indicating satisfactory tempeh. The fuzzy model proved accurate, with close alignment between predicted and actual results, underscoring fuzzy logic's effectiveness in refining fermentation processes. 
Design of Temperature and Humidity Control System for Cocoa Bean Dryer Using Fuzzy Mamdani Method Taufan Taufan Putra ananta; Chika Hayya Sabillah; Muhammad Faiz Assariy; Daffa Zulqisthi; Mariati; Putri Nurhaliza Candra; Audrey Anastasia Silaen; Febrina Disa Cinta Prasetyo; Windy Fadhilah Aprilia; Alifvia Nabila Cesarina Subekti; Mrr Lukie Trianawati
Journal of Applied Science, Technology & Humanities | JASTH Vol. 2 No. 1 (2025): January 2025
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/qfzg3m58

Abstract

This research aims to develop a fuzzy logic control system for optimizing temperature and humidity during cocoa bean fermentation. The study employs a literature review method and data analysis using MATLAB. A Mamdani-type fuzzy inference system is designed to control the temperature and humidity of a cocoa bean dryer. Input variables include temperature (0-65°C) and humidity (0-100%), while output variables are heater and fan settings. The system's performance is evaluated using a case study, demonstrating its ability to adjust heater and fan operations based on environmental conditions. Results show that for an input of 65°C temperature and 50% humidity, the system recommends turning off the heater (membership value 79.8) and running the fan at high speed (membership value 84.7). This fuzzy logic approach offers a promising solution for maintaining optimal conditions during cocoa bean fermentation, potentially improving the quality and consistency of the final product. Keywords: cocoa fermentation, fuzzy logic control, temperature regulation
Implementation of Fuzzy Logic in Stabilizing Temperature and Humidity in Freeze Dryers for Dried Apple Ghassan Rafananda Raja Harahap; Shafira Fayza Dewani Sudiro; Rayna Suci Alifah; Salsabila Salsabila; Ade Trisna Iswanda; Xaviera Yasmin Azahra; Ghina Khairunnisa Putri; Syifa Az Zahra; Hidayat Faiz Sanjaya; Izharul Haq Ar Rafi; Aisyah Sakha Damarjati; Enjela Rahmawati; Nanda Octavia; Ester Angeline; Muhammad Danang Mukti Darmawan; Fiqri Nurfadillah; Mrr Lukie Trianawati
Journal of Applied Science, Technology & Humanities | JASTH Vol. 1 No. 4 (2024): September 2024
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/3bjxym42

Abstract

Freeze drying is a process to remove water from a material by sublimation, in the form of ice under low pressure. This process is used to stabilize food, and pharmaceutical products. During the process, the dried product maintains its quality, including biological, nutritional, and sensory properties, because freezing the water within the material before lyophilization prevents chemical, biochemical, and microbiological reactions. The aim of this research is to determine the optimal temperature and time to produce good products in the freeze drying process. The method used for this article is the literature study and observation method. This research uses manual calculations which are then implemented in a control system using a fuzzy logic algorithm in the Matlab application. In conclusion, both the manual calculation results and the matlab calculations that have been conducted are the same, which confirms the accuracy and validity of the preceding calculations.
Implementation of Fuzzy Logic in Determining the Acceptance Status of Fresh Milk Based on pH and Density Ahmad Daffa Naufal Ziddani; Fiqri Nurfadillah; Muhammad Danang Mukti Darmawan; Ester Angeline; Nanda Octavia; Syahla Aqilah; Salsabila Arina Pramudita; Wianda Aghnia Salsabila; Febby Aurellya; Novi Giotta Hutapea; Asochia Naomi Sitohang; Assadel Zhafif Alwaini; Ayu Shakira Nur Riawan; Aqila Asysyakur; Difa Mukmilatul Kautsar; Raisa Fasya; Luluah Sabrina Putri; Mrr Lukie Trianawati
Journal of Applied Science, Technology & Humanities | JASTH Vol. 1 No. 3 (2024): June 2024
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/xvycaa24

Abstract

Fuzzy logic, known as fuzzy set theory, has become widely used to deal with uncertainty in research data processing. Fuzzy logic methods are known for their ease of implementation in machine language environments and their effectiveness in combining machine language representations with humanlanguage with an emphasis on meaning or importance. Fuzzy logic maps input space to output space, and this concept is closely related to dealing with uncertainty in data. In applying fuzzy logic in this study, the variables pH and density of milk are considered inputs whose value ranges are divided into low, medium, and high categories. The result of the fuzzy system is the acceptability state of fresh milk. By applying this method using MATLAB software, the simulation results show that at a milk pH of 6.2 and a specific gravity of 1.0320, the acceptability state of fresh milk is 30. After the defuzzification process and manual calculation, the final result is 29.30~30. From these results, fuzzy logic provides high accuracy to support progressive decision-making. This allows the system to consider the complexity of milk quality criteria that cannot always be measured in a binary way (e.g., good or bad), resulting in more precise and accurate decisions.
Application of Fuzzy Logic in Tempe Production Using the Tsukamoto Algorithm Daffa Zulqisthi; Muhammad Faiz Assariy; Mrr Lukie Trianawati; Syafira Aristawidya; Chandradewi Ekaputri; Hafizhah Nur Humairah; Gita Aisy Affatunnisa; Aliya Fidella; Muhammad Ghifari Fazan Putra; 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/aygmd359

Abstract

Fuzzy logic is widely used in various fields to formalize human behavior by considering inaccurate or partial reasoning. This study implements the Tsukamoto fuzzy logic method to analyze tempe production based on demand and inventory data. The research utilizes library research methods and data analysis using MATLAB (Matrix Laboratory). The Tsukamoto method was chosen for its high tolerance for data and flexibility. The system uses two input variables: demand and inventory, with output being production quantity. The fuzzy sets for demand are categorized as "Decrease" and "Increase" with a domain of [1000; 2000], inventory as "Few" and "Many" with a domain of [100; 600], and production as "Decrease" and "Increase" with a domain of [1520; 2100]. Using four fuzzy rules and the defuzzification process, the system can predict optimal production quantities. For example, with a demand of 1500 and inventory of 400, the system calculates a production quantity of 1823 units. This implementation demonstrates the effectiveness of the Tsukamoto method in automating production decisions based on demand and inventory variables.
Implementation of Fuzzy Logic in Temperature Control Systems for Baking Ovens Andhika Rizky Maulana; Chika Hayya Sabillah; Muhammad Faiz Assariy; Daffa Zulqhisti; Rahadian Ilyasa Muhammad; Mutiara Ade Rosseta; Fairuz Kamilah Ihsan; Melati Afdizha; Cindy Novitasari; Azhar Nasywa Saputra; Mrr 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: 10.62535/8y8g8f28

Abstract

To produce bread of the highest quality, it is essential to use the right type of bread oven, especiallygiven the rapid growth of the bread industry. This study investigates the use of fuzzy logic as an automatedmethod for controlling the baking oven temperature. By using fuzzy logic, it is expected that the oven canmaintain an ideal temperature, resulting in consistent bread quality that meets expected standards. Fuzzy controlsystems differ from conventional control systems, which tend to be rigid.The aim of this research is to develop a more efficient automated control system for regulating oventemperature to ensure consistent bread quality throughout the baking process. It is hoped that, with thisautomated control system, bread quality will not only improve but also enhance the production efficiency of thebaking industry as a whole. Additionally, this study considers the type of bread being baked as a factorinfluencing the quality of the final product. Initial trials show that the fuzzy logic design produces satisfactoryresults, with the ability to adjust rules as needed.
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
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.
Co-Authors Ade Trisna Iswanda Ahmad Daffa Naufal Ziddani Aisyah Sakha Damarjati Akmal Kayru Raid Alifvia Nabila Cesarina Subekti Aliya Fidella Almaida Pramesti Amanda Dhiya Ardhiningrum Andhika Rizky Maulana Anindya Intan Putri Anindya Salsabila Hamdani Putri Anisa Raihanah Annisa Pertiwi Hakim Annisa Raihanah Maimun Aqila Asysyakur Arya Rafly Maulana Sopyan Aryo Sumaryanto Asochia Naomi Sitohang Assadel Zhafif Alwaini Atikah Fahni Audrey Anastasia Silaen Aulia Zahwa Gharini Aura Fadhilah Aurellia Avisa Putri Alya Ayu Nur Fadhila Ayu Shakira Nur Riawan Azhar Nasywa Saputra Azhara Paramita Aziz Rabih Rabbani Azzahra Kamilia Salam Bayu Praditya Belvana Fazwa Athallah Chandradewi Ekaputri Chiara Kalyla Chika Hayya Asabillah Chika Hayya Sabillah Christine Amara Tirza Cindy Novitasari Daffa Firdaus Putra Daffa Zulqhisti Daffa Zulqisthi Davina Baiza Radityaputri Devi Suryani Difa Mukmilatul Kautsar Dinar Lukmanudin Enjela Rahmawati Ester Angeline Fairuz Aliyya Khansa Fairuz Kamilah Ihsan Fajrina Aulia Naifa Farhana Adinda Zuhro Fatia Syafana Febby Aurellya Febrina Disa Cinta Prasetyo Fildza Khairunida Fional Alvina Nirmala Putri Fiqri Nurfadillah Ghassan Rafananda Raja Harahap Ghina Khairunnisa Putri Gita Aisy Affatunnisa Grace Octo Suma Panjaitan Hafizhah Nur Humairah Hanifah Khusnul Syahida Hidayat Faiz Sanjaya Hikma Hijrianita Putri Lesmana PS Hilda Fitria Hidayah Iofema Malika Izharul Haq Ar Rafi Izza Afkarina Umar Kezia Joy Audreyna Khaerun Nisa Khaula Amanda Lola Amelia Luluah Sabrina Putri Luthfitah Sys Febriana Mariati Melati Afdizha Meylani Awaliyah Muhammad Alpiansyah Muhammad Danang Mukti Darmawan Muhammad Faiz Assariy Muhammad Ghifari Fazan Putra Muhammad Jundi Muhammad Rafi Andika Wijaya Mutiara Ade Rosseta Myra Riqqah Widyadhana Najla Athaya Nanda Fadilah Nanda Octavia Nashwa Zahrania Adiputri Nastiti Ayu Utami Nasyaqilah Andrianita Naura Alissa Davellitha Nazwa Salsabila Nida Qotrunnada Nisa Akmala Sidik Nisrina Fatin Nadra Novi Giotta Hutapea Nuraeni Latifathul Khasanah Prima Ananta Nugraha Putri Nurbaiti Putri Nurhaliza Candra Rafa Anargya Putri Rahadian Ilyasa Muhammad Raisa Fasya Rayna Suci Alifah Rinunti Najwa Putri Minanti Riyatin Tsani Fatikhah Riza Safira Rohidi Rohmi Rihhadatul ‘Aisy Roma Juliana Arios Rome Juliana Arians Rubina Alleysha Putri Aulia Salmadillah Dwiswara Salsabila Arina Pramudita Salsabila Salsabila Sania Ramadhani Senja Surya Gama Shafira Fayza Dewani Sudiro Shalinadira Koswara Sherin Emania Putri Syadza Afifah Nuri Syafira Aristawidya Syahla Aqilah Syifa Az Zahra Syifa Fauziyah tangkas Patiasmara Taufan Taufan Putra ananta Tiara Arelya Priyanto Tiara Cahya Iman Tulus Fernando Sihombing Wianda Aghnia Salsabila Windy Fadhilah Aprilia Wulidah Tamsil Barokah Wuliddah Tamsil Barokah Xaviera Yasmin Azahra Yusup Cahyadi Zalfa Cantika