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Agus, Nursikuwagus, and Baswara Agis. "A Mamdani Fuzzy Model to Choose Eligible Student Entry." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 365–72. https://doi.org/10.12928/TELKOMNIKA.v15i1.4893.
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This paper presented about study that have been created a new student choosing system by using fuzzy mamdani inference systems method. Fuzzy mamdani is used because it has characteristics such as human perceptions on choosing of students with some specified criteria. The choosing students who want entry to the school have been difficult if it is manually process. With the fuzzy mamdani, the process can be possible completed execute and can be reduced the time of choose. To accomplish the process, the fuzzy variable is created by the national final exam scores, report grade, general competency test, physical test, interview and psychological test. Based on testing 270 data, the fuzzy mamdani has been reached 75.63% accuracy.
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Agus, Nursikuwagus, and Baswara Agis. "A Mamdani Fuzzy Model to Choose Eligible Student Entry." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 365–72. https://doi.org/10.12928/TELKOMNIKA.v15i1.5099.
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This paper presented about study that have been created a new student choosing system by using fuzzy mamdani inference systems method. Fuzzy mamdani is used because it has characteristics such as human perceptions on choosing of students with some specified criteria. The choosing students who want entry to the school have been difficult if it is manually process. With the fuzzy mamdani, the process can be possible completed execute and can be reduced the time of choose. To accomplish the process, the fuzzy variable is created by the national final exam scores, report grade, general competency test, physical test, interview and psychological test. Based on testing 270 data, the fuzzy mamdani has been reached 75.63% accuracy.
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Harliana, Putri, Mardiana Mardiana, and Yuris Agustira Nainggolan. "Analisa Perbandingan Tingkat Akurasi dalam Memprediksi Laju Inflasi Kota Medan Menggunakan Model Fuzzy Inference System Sugeno dan Mamdani." Hello World Jurnal Ilmu Komputer 1, no. 3 (2022): 145–52. http://dx.doi.org/10.56211/helloworld.v1i3.130.
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Logika fuzzy merupakan perluasan dari penalaran tradisional, di mana x adalah salah satu anggota dari himpunan A atau tidak, atau sebuah x dapat menjadi anggota himpunan A dengan derajat keanggotaan (μ) tertentu. Kemampuan model fuzzy dalam memetakan nilai kabur menjadi alasan penggunaan model inferensi fuzzy dalam berbagai kasus yang menggunakan nilai kabur untuk menghasilkan suatu output yang jelas atau pasti. Dalam penelitian ini akan dilakukan analis tingkat akurasi yang dihasilkan model inferensi fuzzy Sugeno dan Mamdani dalam memprediksi laju inflasi di Kota Medan, hasil prediksi akan dianalisis tingkat akurasinya dengan membandingkan hasil yang diperoleh model fuzzy inferensi Sugeno dan Mamdani dengan nilai aktualnya. Hasil dari analisis yang dilakukan untuk model fuzzy Sugeno tingkat akurasi dipengaruhi nilai regresi linier berganda sedangkan tingkat akurasi dari model fuzzy inferensi Mamdani dipengaruhi oleh ketepatan nilai masukannya. Hasil akhirnya model fuzzy Mamdani lebih akurat dibandingkan dengan model fuzzy inferensi Sugeno dalam uji kasus laju inflasi di Kota Medan.
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Surorejo, Sarif, Ahadan Fauzan Mutaqin, Rifki Dwi Kurniawan, and Gunawan Gunawan. "Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency." Journal of Intelligent Decision Support System (IDSS) 7, no. 2 (2024): 137–45. http://dx.doi.org/10.35335/idss.v7i2.231.
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This study investigates the application of Fuzzy Mamdani's method in predicting the price of cayenne pepper in Tegal Regency, one of the important agricultural commodities that has significant economic implications. This study aims to develop an accurate and reliable cayenne pepper price prediction model in Tegal Regency using the fuzzy Mamdani method. Research methods include collecting historical data on cayenne pepper prices, cayenne pepper production, and rainfall, as well as the implementation of the Mamdani fuzzy method consisting of fuzzification, inference, and defuzzification using Python programming language computing. The results showed that the fuzzy Mamdani method can predict the price of cayenne pepper with a good level of accuracy, with an average prediction error of 16.653285% and a prediction correctness rate of 83.346715%. This finding has implications for improving production planning capabilities and marketing strategies for cayenne pepper farmers in Tegal District, as well as contributing to the scientific literature in the application of fuzzy methods in agriculture
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Liutkevičius, R. "Fuzzy Hammerstein Model of Nonlinear Plant." Nonlinear Analysis: Modelling and Control 13, no. 2 (2008): 201–12. http://dx.doi.org/10.15388/na.2008.13.2.14580.
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This paper presents the synthesis and analysis of the enhanced predictive fuzzy Hammerstein model of the water tank system. Fuzzy Hammerstein model was compared with three other fuzzy models: the first was synthesized using Mamdani type rule base, the second – Takagi-Sugeno type rule base and the third – composed of Mamdani and Takagi-Sugeno rule bases. The synthesized model is invertible so it can be used in the model based control. The fuzzy Hammerstein model was synthesized to eliminate disadvantages of the other fuzzy models. The advantage of the fuzzy Hammerstein model was experimentally proved and presented in this paper.
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Ardi, Yuan, Syahril Effendi, and Erna Budhiarti Nababan. "Mamdani and Sugeno Fuzzy Performance Analysis on Rainfall Prediction." Randwick International of Social Science Journal 2, no. 2 (2021): 176–92. http://dx.doi.org/10.47175/rissj.v2i2.240.
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Fuzzy logic is an extension of traditional reasoning, where x is a member of set A or not, or an x can be a member of set A with a certain degree of membership . The ability of fuzzy models to map fuzzy values is the reason for using fuzzy inference models in various cases that use fuzzy values to produce a clear or definite output. In this research, an analysis of the level of accuracy generated by the Sugeno and Mamdani inference model will be carried out in predicting rainfall at Polonia Station, Medan, North Sumatra. Prediction results will be analyzed for accuracy by comparing the results obtained by Sugeno fuzzy inference models and Mamdani using Mean Absolute Percent Error (MAPE). When compared to the results of the Mean Absolute Percent Error (MAPE) Sugeno fuzzy inference model of 1.33% and mamdani fuzzy inference model of 1.45%, then the accuracy rate for the Sugeno inference model is 100%-1.33% = 98.67% while the Mamdani fuzzy inference model is 100%-1.45 = 98.55%. The end result is that the membership function model used in the Sugeno fuzzy model is more accurate than the Mamdani fuzzy inference model in the test case of rainfall prediction at Polonia station in Medan. North Sumatra. The results of the analysis carried out for the Sugeno and Mamdani fuzzy models are influenced by the accuracy of the input values. Rainfall prediction is an important thing to study, weather conditions in certain areas can be predicted so that it can help people's daily activities, can determine a series of community social activities. For example, information on rainfall and its classification is widely used as a guide for agriculture, tourism and transportation, for example: Cropping Patterns, Harvest Predictions, Shipping and flight schedules
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Putra, Dwipa Junika, Nofriadi Nofriadi, and Erlinda Erlinda. "Implementation of Fuzzy Logic Using Mamdani Method to Determine The Quantity of Bag Production (Case Study In Roman Indah Padang Bag Factory)." JURNAL TEKNOLOGI DAN OPEN SOURCE 5, no. 1 (2022): 1–7. http://dx.doi.org/10.36378/jtos.v5i1.2220.
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Roman Indah bag Factory is a bag manufacturing company that receives orders from customers every month. Fuzzy Logic is used to define the production known so far, so that the preferred result can be used as a basis for manager's decision. In the calculation process, Fuzzy Logic Mamdani requires maximum and minimum production data, maximum and minimum demand data, and maximum and minimum inventory data. Fuzzy logic is able to map an input into an output without factor factors. Fuzzy logic is used to create a model of a system that is able to determine the quantity of production. the factors that affect the quantity of production. Fuzzy logic is called the old new logic because the science of modern fuzzy logic and methodological was discovered only a few years ago, in fact the concept of fuzzy logic itself has been in us for a long time. Mamdani method is the most common method when it comes to fuzzy methodology. Mamdani method uses a set of IF-THEN rules derived from experienced operators/experts. The Mamdani model is often known as the Max-Min model. By using Mamdani method in Roman Indah handbag factory can assist in the efficiency of time and labour, because using Mamdani method can calculate the amount of production in the next month, so from the results can be derived consideration material decision by the manager, whether in determining raw materials, promotion, bag model, consumer, HR, etc. so that more company profits.
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Amrul Hinung Prihamayu. "Prediction Of Closing Price Combined Stock Index (Ihsg) Using The Fuzzy Mamdani Method." SOUTHEAST ASIA JOURNAL oF GRADUATE OF ISLAMIC BUSINESS AND ECONOMICS 1, no. 2 (2022): 74–79. http://dx.doi.org/10.37567/sajgibe.v1i2.1862.
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This study proposes a method for predicting the closing IHSG stock price using the Mamdani fuzzy approach. This model uses historical closing stock price data as input, and generates closing stock price predictions using the Mamdani fuzzy rule. However, experimental results show that this model may not be suitable for predicting stock prices accurately and reliably. Therefore, this study does not recommend the use of the Mamdani fuzzy method for the purpose of predicting closing stock prices.
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Dian Eko Hari Purnomo. "PERANCANGAN MODEL ARTIFICIAL INTELLIGENCE (AI) UNTUK MEMBANTU MENENTUKAN PERSEDIAAN BAHAN BAKU KAYU PADA INDUSTRI FURNITUR DENGAN PENDEKATAN METODE FUZZY MAMDANI." Industry Xplore 9, no. 1 (2024): 323–30. https://doi.org/10.36805/teknikindustri.v9i1.6054.
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Perencanaan persedian dalam suatu industri manufaktur tentunya menjadi bagian yang cukup penting. Hal ini dikarenakan jika bahan baku tidak ada maka artinya proses produksi pada industri manufaktur akan terhambat atau mengalami kendala. Sedangkan jika jumlah ketersediaan bahan baku terlalu berlebihan maka akan menimbulkan biaya tambahan. Sehingga untuk dapat melakukan perencanaan persediaan yang sesuai kebutuhan khususnya pada industri manufaktur yang berupa industri furnitur yang bahan baku utamanya berupa kayu. Penelitian ini berusahan melakukan perencanaan persediaan yang optimal dengan menggunakan pendekatan Logika Fuzzy. Pendekatan Fuzzy yang diadopsi atau yang dimplementasikan dalam penelitian ini adalah berupa Metode Fuzzy Mamdani. Alasan dalam penelitian ini menggunakan Metode Fuzzy Mamdani untuk melakukan proses prediksi persediaan kayu pada industri furnitur karena memiliki struktur yang sederhana dan fleksibel. Dalam mekanisme proses perhitungan Metode Fuzzy Mamdani pada umumnya menggunakan konsep operasi yang berupa mekanismer perhitungan min-max ataupun dapat menggunakan mekanisme max-product dengan menggunakan serangkaian aturan yang telah ditentukan berdasarkan variabel yang digunakan. Data yang digunakan pada penelitian ini adalah tahun 2019, 2020, 2021, dan 2022. Dalam proses penyelesaian masalah yang berupa penentuan jumlah persediaan yang optimal adalah dengan menggunakan proses penyelesaian Metode Fuzzy Mamdani. Dari hasil perhitungan menggunakan metode Fuzzy Mamdani. Dengan menggunakan data pada bulan Januari 2022 yang berupa data permintaan yang mempunyai nilai 1711 kubik dan data produksi yang mempunyai nilai 1469 kubik, maka didapatkan hasil pengolahan data dengan Metode Fuzzy Mamdani untuk jumlah persediaan pada periode bulan Januari 2022 adalah senilai 208 kubik.
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Meng, Lei, Shoulin Yin, and Xinyuan Hu. "An improved Mamdani Fuzzy Neural Networks Based on PSO Algorithm and New Parameter Optimization." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (2016): 201. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp201-206.
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As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.
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Roki Syah Al Zarkasi, Dhebys Suryani Hormansyah, and Dimas Wahyu Wibowo. "IMPLEMENTASI METODE FUZZY MAMDANI DAN LINEAR CONGRUENTIAL GENERATOR (LCG) PADA GAME HIDDEN OBJECT." Jurnal Informatika Polinema 6, no. 4 (2020): 23–30. http://dx.doi.org/10.33795/jip.v6i4.317.
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Game dewasa ini telah menjadi populer di kalangan masyarakat, sebagai suatu bentuk hiburan, mendukung interaksi sosial antara mereka. Game atau permainan sejatinya dibuat untuk digunakan sebagai sarana menghibur. Game hidden object merupakan salah satu konsep game yang ada pada genre game puzzle. Game puzzle merupakan salah satu permainan yang dapat mengasah otak dan sangat menantang. Metode Linear Congruential Genetator (LCG) digunakan untuk membangkitkan bilangan acak dengan distribusi uniform. Random number atau bilangan acak adalah sebuah bilangan yang dihasilkan dari sebuah proses, yang keluarannya tidak dapat diprediksi. Logika Fuzzy adalah peningkatan dari logika Boolean yang mengenalkan konsep kebenaran sebagian. Fuzzy Mamdani merupakan salah satu model dalam metode fuzzy yang sudah umum digunakan. Fuzzy mamdani sering juga dikenal dengan nama Metode Max-Min. Selain itu metode fuzzy mamdani terkenal sederhana dan tidak banyak proses komputasi yang dilakukan dimana proses komputasi dapat membuat sistem akan berjalan lebih lama. Penelitian ini berfokus pada implementasi metode LCG untuk mengacak list object yang ada pada game secara random, dan metode Fuzzy mamdani untuk menghitung skor dan sebagai penentuan level. Fokus Dari penelitian adalah untuk mencai tahu apakah metode LCG dan juga metode fuzzy mamdani dapat diimplemntasikan dengan baik pada game dengan konsep hidden object. Dari hasil implementasi fuzzy mamdani dan LCG didapati bahwa fuzzy mamdani memiliki tingkat keakuratan perhitungan sebesar 98,5% sedangkan LCG memiliki keakuratan pengacakan list objek sebesar 100%. Sehingga dapat ditarik kesimpulan bahwa metode fuzzy mamdani dan LCG dapat di implementasikan dengan baik pada game dengan konsep hidden object.
 Kata kunci: Game, Hidden object, LCG, Fuzzy Mamdani
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Patmawati, Tini, and Slamet Risnanto. "SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN CALON PENERIMA PROGRAM KELUARGA HARAPAN (PKH) MENGGUNAKAN METODE FUZZY MAMDANI." Prosiding Seminar Sosial Politik, Bisnis, Akuntansi dan Teknik 5 (December 9, 2023): 304. http://dx.doi.org/10.32897/sobat.2023.5.0.3109.
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Decision Support Systems (DSS) have become essential tools in complex and data-driven decision-making processes. In the context of identifying beneficiaries for the Program Keluarga Harapan (PKH), the fuzzy Mamdani method has proven effective in addressing uncertainty and complexity by evaluating the eligibility of recipients. The fuzzy Mamdani method employs fuzzy logic principles to handle uncertainty by establishing connections between input variables (e.g., household income, number of dependents, education level) and output variables (PKH recipient status). Fuzzification and defuzzification processes enable the mapping of vague input values to comprehensible output values. This study discusses the implementation of the fuzzy Mamdani method within a DSS for determining PKH recipients. Real-world data collected from potential PKH recipient households is utilized to develop the fuzzy Mamdani model. Model development steps encompass defining input variables, membership functions, fuzzy rules, and the defuzzification mechanism. Consequently, the utilization of the fuzzy Mamdani method within a DSS for PKH accuracy 87%, recall 91% and precision 90%. However, further research and the development of more intricate models may be necessary to optimize the performance of this method in broader and diverse scenarios.
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Mykola, Kosterev, Litvinov Volodymyr, and Kilova Kateryna. "CONSTRUCTION OF MODELS FOR ESTIMATING THE TECHNICAL CONDITION OF A HYDROGENERATOR USING FUZZY DATA ON THE STATE OF ITS LOCAL NODES." Eastern-European Journal of Enterprise Technologies 5, no. 8 (101) (2019): 45–52. https://doi.org/10.15587/1729-4061.2019.180211.
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The task on estimating the technical condition of a hydrogenerator under conditions of fuzzy information has been resolved. To this end, a series of models have been constructed for the integrated estimation of the technical condition of a hydrogenerator based on data about the states of its local nodes. The technical states of local nodes are determined based on the earlier devised fuzzy models of the Mamdani type and represent the fuzzy values, which was taken into consideration in the model for estimating technical condition of a hydrogenerator. The fuzzy methods by Mamdani, Sugeno, Zadeh, as well as the simplified fuzzy inference, were used to build the models. The fuzzy model by Mamdani has a qualitative base of rules only, which simplifies its construction by an expert. The models based on the fuzzy algorithm by Sugeno imply a rule base with weight coefficients, determined by the Saati method. The simplified method and the method by Zadeh require minimal expert participation when constructing a fuzzy model. Examples of estimating the technical condition of a hydrogenerator have been considered based on five devised fuzzy models; the sensitivity of models to the quality and reliability of input information has been tested. It has been determined that the most reliable result from estimating the state of a hydrogenerator with an error of 1.5–2 % is produced by models built according to Zadeh method and the simplified fuzzy inference, since they have the least dependence on the uncertainty of input data on the states of local nodes, which themselves were obtained based on fuzzy models. High accuracy of these models and low dependence on the quality of incoming information are explained by the minimal participation of an expert during its configuration. The fuzzy models built using the algorithms by Mamdani and Sugeno yield a greater error of 3–4 %. Oure findings could be used to assess the remaining or spent resource of hydrogenerators, the probability of their failure over a time interval, and to execute the risk-oriented control over an electricity energy system and its subsystems
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Rafiqa, Dewi. "EVALUASI MEMBANGKITKAN FUNGSI KEANGGOTAAN PADA FUZZY MODEL MAMDANI." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 1, no. 1 (2016): 41–45. https://doi.org/10.5281/zenodo.546760.
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This paper discusses the comparison of two forms in raising function membership. Determining the membership functions through an isosceles triangle will scores declined in the triangle. While the right-angled triangle, after the peak of the triangle there is no possibility of value decrease. So that the value obtained an ascending grades and dropped out after the peak of the triangle. calculated by Mamdani models, the output value calculated by the isosceles triangle has a value lower compared with those produced by a right-angled triangle. This matter due to a right triangle does not have the side that decline after peak triangles, so it does not have an approach to variable thereafter.
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Dewi, Siti utami, and Rr Tutik Sri Haryati. "Breast Cancer Risk Analysis Using Fuzzy Inference System with the Mamdani Model: Literature Review." JIKO (Jurnal Ilmiah Keperawatan Orthopedi) 5, no. 2 (2022): 40–47. http://dx.doi.org/10.46749/jiko.v5i2.70.
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Breast cancer is one of the most common cancer causes of death in sufferers. Breast cancer is the second leading cause of death for Indonesian women. For this reason, a device that can identify the risk of breast cancer is needed. The fuzzy method with the Mamdani model is one method that has been widely used in software development to determine the level of breast cancer risk. Purpose: To know the software development of the Fuzzy Inference System Method with the Mamdani model to identify the level of breast cancer risk. Methods: This research is a literature study with the data collection process through the PubMed, Science Direct, ProQuest, and Google Scholar databases. The criteria for the articles used are 2010-2021 publications. Results: Based on a review of 10 journals using the Fuzzy Inference System (FIS) Mamdani model can provide effective results to identify risks for people who have the possibility of developing breast cancer. Conclusion: The fuzzy expert system using the Mamdani method can be applied to the problem domain of breast cancer diagnosis with a fairly good level of accuracy to be able to overcome the inequality of mammography results.
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Martin, Martin, and Lala Nilawati. "Model Fuzzy Mamdani Untuk Penilaian Tingkat Kepuasan Pelayanan Pengaduan Masyarakat." Jurnal Informatika 5, no. 2 (2018): 237–47. http://dx.doi.org/10.31311/ji.v5i2.4170.
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AbstrakKualitas pelayanan adalah salah satu keunggulan kompetitif, karena pelayanan yang baik adalah salah satu faktor dasar yang mampu mempengaruhi tingkat kenyamanan penerima layanan. Pelayanan publik oleh aparatur pemerintah dewasa ini masih banyak dijumpai kelemahan, sehingga belum dapat memenuhi kualitas yang diharapkan masyarakat. Penelitian ini ditujukan untuk melihat seberapa besar kepuasan pelayanan, dan pengaruh tingkat pelayanan terhadap tingkat kepuasan berdasarkan Logika Fuzzy Inference System Model Mamdani. Ada empat variabel input yang digunakan yaitu kejelasan informasi, kejelasan persyaratan, kemampuan petugas dan ketersediaan sarana dan prasarana untuk menghasilkan output kepuasan pelayanan. Berdasarkan tahapan-tahapan menggunakan Logika Fuzzy Inference System Model Mamdani mulai dari pembentukan himpunan fuzzy, aplikasi fungsi impilkasi, komposisi aturan sampai proses penegasan (defuzzyfikasi), dapat dibuktikan adanya korelasi antara variabel-variabel input sehingga dapat menentukan output hasil kepuasan pelayanan. Hasil penelitian ini diharapkan dapat digunakan oleh pihak instansi, sebagai pendukung sistem keputusan terhadap hasil penilaian yang diberikan oleh masyarakat untuk pelayanan yang dirasakan. Pengembangan penelitian ini kedepan nya akan diuji coba kembali dengan menambahkan lebih banyak variabel dan akan dibuat sebuah interface untuk memudahkan pemprosesan hasil penilaian kualitas pelayanan pengaduan masyarakat. Kata Kunci: Pelayanan, Fuzzy Mamdani, Logika Fuzzy.AbstractService quality is one of the competitive advantages, because good service is one of the basic factors that can affect the comfort level of service recipients. Public services by the government apparatus today are still often found to be weak, so that they cannot meet the quality expected by the community. This study is intended to see how much service satisfaction is, and the effect of service levels on satisfaction levels based on Mamdani Model Fuzzy Inference System Logic. There are four input variables used namely clarity of information, clarity of requirements, ability of officers and availability of facilities and infrastructure to produce service satisfaction output. Based on the stages using Mamdani Model Fuzzy Inference System Logic starting from the formation of fuzzy sets, application of the implementation function, composition of the rules until the confirmation process (defuzzyfication), it can be proved the correlation between input variables so that it can determine the output of service satisfaction. The results of this study are expected to be used by the agency, as a support system for the decision on the results of the assessment given by the community for perceived services. The future development of this research will be re-tested by adding more variables and an interface will be created to facilitate the processing of the results of the quality assessment of public complaints services. Keywords: Service, Fuzzy Mamdani, Fuzzy Logic.
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Martin, Martin, and Lala Nilawati. "Model Fuzzy Mamdani Untuk Penilaian Tingkat Kepuasan Pelayanan Pengaduan Masyarakat." Jurnal Informatika 5, no. 2 (2018): 237–47. http://dx.doi.org/10.31294/ji.v5i2.4170.
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AbstrakKualitas pelayanan adalah salah satu keunggulan kompetitif, karena pelayanan yang baik adalah salah satu faktor dasar yang mampu mempengaruhi tingkat kenyamanan penerima layanan. Pelayanan publik oleh aparatur pemerintah dewasa ini masih banyak dijumpai kelemahan, sehingga belum dapat memenuhi kualitas yang diharapkan masyarakat. Penelitian ini ditujukan untuk melihat seberapa besar kepuasan pelayanan, dan pengaruh tingkat pelayanan terhadap tingkat kepuasan berdasarkan Logika Fuzzy Inference System Model Mamdani. Ada empat variabel input yang digunakan yaitu kejelasan informasi, kejelasan persyaratan, kemampuan petugas dan ketersediaan sarana dan prasarana untuk menghasilkan output kepuasan pelayanan. Berdasarkan tahapan-tahapan menggunakan Logika Fuzzy Inference System Model Mamdani mulai dari pembentukan himpunan fuzzy, aplikasi fungsi impilkasi, komposisi aturan sampai proses penegasan (defuzzyfikasi), dapat dibuktikan adanya korelasi antara variabel-variabel input sehingga dapat menentukan output hasil kepuasan pelayanan. Hasil penelitian ini diharapkan dapat digunakan oleh pihak instansi, sebagai pendukung sistem keputusan terhadap hasil penilaian yang diberikan oleh masyarakat untuk pelayanan yang dirasakan. Pengembangan penelitian ini kedepan nya akan diuji coba kembali dengan menambahkan lebih banyak variabel dan akan dibuat sebuah interface untuk memudahkan pemprosesan hasil penilaian kualitas pelayanan pengaduan masyarakat. Kata Kunci: Pelayanan, Fuzzy Mamdani, Logika Fuzzy.AbstractService quality is one of the competitive advantages, because good service is one of the basic factors that can affect the comfort level of service recipients. Public services by the government apparatus today are still often found to be weak, so that they cannot meet the quality expected by the community. This study is intended to see how much service satisfaction is, and the effect of service levels on satisfaction levels based on Mamdani Model Fuzzy Inference System Logic. There are four input variables used namely clarity of information, clarity of requirements, ability of officers and availability of facilities and infrastructure to produce service satisfaction output. Based on the stages using Mamdani Model Fuzzy Inference System Logic starting from the formation of fuzzy sets, application of the implementation function, composition of the rules until the confirmation process (defuzzyfication), it can be proved the correlation between input variables so that it can determine the output of service satisfaction. The results of this study are expected to be used by the agency, as a support system for the decision on the results of the assessment given by the community for perceived services. The future development of this research will be re-tested by adding more variables and an interface will be created to facilitate the processing of the results of the quality assessment of public complaints services. Keywords: Service, Fuzzy Mamdani, Fuzzy Logic.
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RafiuM., Isiaka, Omidiora Elijah O, Olabiyisi Stephen O, and Okediran Oladotun O. "Mamdani Fuzzy Model for Learning Activities Evaluation." International Journal of Applied Information Systems 7, no. 3 (2014): 1–8. http://dx.doi.org/10.5120/ijais14-451155.
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Huong, Trieu Thu, Chu Thi Hong Hai, and Phan Thanh Duc. "An extension of complex fuzzy inference system for alert earlier credit risk at business banks." Edelweiss Applied Science and Technology 9, no. 4 (2025): 2147–56. https://doi.org/10.55214/25768484.v9i4.6500.
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The purpose of this study is to suggest a novel integrated model for assessing credit risk at commercial banks that is based on a complex fuzzy transfer learning framework. Research Design and Methodology: We used transfer learning on a complex fuzzy inference system, complex fuzzy set theory, and a complex fuzzy inference system to build a credit risk prediction model. Parallel to this, we compared the proposed model with the previously used credit risk prediction method known as the Mamdani CFIS model. Results: The study has validated the complex fuzzy inference model's capacity to accurately predict credit risk. When compared to the Mamdani CFIS model, the suggested model exhibits superior time performance. In particular, the time needed to construct the intricate fuzzy inference system and to carry out inference in the suggested model is much reduced when compared to the Mamdani CFIS. Conclusion: In addition to elucidating the role and possibilities of complex fuzzy inference systems, this work shows that the transfer learning model on complex fuzzy inference systems may significantly accelerate the prediction of credit risk. This is especially important in the context of early warning, which enables commercial banks to implement more efficient risk prevention and management strategies.
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Nisa, Auliah Khoirun, Muhammad Abdy, and Ahmad Zaki. "Penerapan Fuzzy Logic untuk Menentukan Minuman Susu Kemasan Terbaik dalam Pengoptimalan Gizi." Journal of Mathematics, Computations, and Statistics 3, no. 1 (2020): 51. http://dx.doi.org/10.35580/jmathcos.v3i1.19902.
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Penelitian terapan ini bertujuan untuk membangun model Penentuan Minuman Susu Kemasan Terbaik dengan variabel pertimbangan adalah harga dan nutrisi. Langkah-langkah yang digunakan pada penelitian ini yaitu fuzzifikasi, penentuan aturan fuzzy, inferensi fuzzy dengan metode mamdani, dan defuzzifikasi. Data yang digunakan adalah data yang diambil dari survey langsung di lapangan yang dilakukan oleh peneliti di salah satu supermarket di makassar. Hasil dari penelitian ini adalah susu kemasan sampel 16 yang menjadi susu kemasan yang paling cocok untuk direkomendasikan kepada masyarakat karena memiliki nutrisi tinggi dan harga yang terjangkau.Kata Kunci: Fuzzy logic, Mamdani, Susu, Harga, Nutrisi This applied research aims to build a model of determining the best packaged milk with consideration variables are price and nutrition. The steps used in this research are fuzzification, fuzzy rule determination, fuzzy inference with mamdani method, and defuzzification. The data used are data taken from direct field surveys conducted by researchers in one of the supermarkets in Makassar. The results of this study is sample 16 packaged milk which is the most suitable packaged milk to recommended because it has high nutrition and affordable prices.Keywords: Fuzzy logic, Mamdani, Milk, Price, Nutrition
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Sultanova, A., and М. Abdullayeva. "Forecasting of optimal parameters production dielectric fluid for pulse capacitors using fuzzy inference models." Bulletin of Science and Practice 4, no. 12 (2018): 332–37. https://doi.org/10.5281/zenodo.2271372.
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The article is devoted to the determination of the optimal parameters of a dielectric fluid as an impregnant in power capacitors. The optimal parameters for the synthesis of the ester are found on the basis of a not clear Mamdani model. The model providing optimization of the chemical process is offered and on the basis of statistical data, the algorithm of training of the fuzzy model is developed. The goal was solved with fuzzy data and a regression model of the three-stage process was obtained. Optimization was carried out and optimum parameters were found. Based on the statistical data, a fuzzy Mamdani model was compiled.
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Putri, Anggia Dasa. "Penerapan Logika Fuzzy Mamdani untuk Memperkirakan Pembelian Tas Branded Wanita di Batam." Jurnal Desain Dan Analisis Teknologi 4, no. 1 (2025): 30–43. https://doi.org/10.58520/jddat.v4i1.71.
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Penerapan metode Fuzzy Mamdani dalam memprediksi pembelian tas branded wanita di Batam adalah pendekatan inovatif yang menggunakan logika fuzzy untuk menangkap ketidakpastian perilaku konsumen. Penelitian ini bertujuan mengidentifikasi faktor-faktor yang memengaruhi keputusan pembelian dan mengembangkan model prediksi yang akurat. Melalui survei terhadap 200 responden di Batam, variabel yang dianalisis meliputi harga, merek, kualitas, dan preferensi konsumen. Metode Fuzzy Mamdani dipilih karena kemampuannya menangani variabel linguistik yang tidak dapat diukur secara kuantitatif. Setiap faktor diwakili oleh himpunan fuzzy dan diolah dengan aturan fuzzy untuk menghasilkan output prediksi. Proses ini mencakup penentuan derajat keanggotaan dan penerapan operator Max-Min. Hasil penelitian menunjukkan bahwa model Fuzzy Mamdani dapat memprediksi pembelian tas branded wanita dengan akurasi yang signifikan. Faktor harga dan merek memiliki pengaruh terbesar terhadap keputusan pembelian, diikuti oleh kualitas dan preferensi pribadi. Temuan ini memberikan wawasan berharga bagi pemasar dan produsen dalam merancang strategi pemasaran yang efektif. Secara keseluruhan, penelitian ini berkontribusi pada pengembangan metodologi dalam analisis perilaku konsumen dan pengambilan keputusan di era digital
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Bella Khuri Aini Alfari, Tri Hastono, and Wirinda Nur Aziza. "Penentuan Bonus Karyawan Menggunakan Fuzzy Mamdani." Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer 2, no. 2 (2024): 34–44. http://dx.doi.org/10.61132/mars.v2i2.90.
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Determining the provision of bonuses or rewards to employees plays a crucial role in maintaining the quality and motivation of employees. One effort that can be made to establish a bonus policy is by implementing the fuzzy Mamdani method as a systematic approach in determining employee bonuses, particularly at PT. ABC. By utilizing the fuzzy Mamdani method to process subjectivity in evaluations, it generates membership levels that allow for a more contextual employee assessment. Through the analysis of data involving various performance variables and bonus criteria, this research aims to present a fuzzy Mamdani system model that can support accurate and adaptive decision-making in determining employee bonuses. The results of this research and the evaluation of this model are expected to contribute significantly to the development of more effective bonus policies in the workplace of PT.
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Bellini, Francesco, Yas Barzegar, Atrin Barzegar, Stefano Marrone, Laura Verde, and Patrizio Pisani. "Sustainable Water Quality Evaluation Based on Cohesive Mamdani and Sugeno Fuzzy Inference System in Tivoli (Italy)." Sustainability 17, no. 2 (2025): 579. https://doi.org/10.3390/su17020579.
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Clean water is vital for a sustainable environment, human wellness, and welfare, supporting life and contributing to a healthier environment. Fuzzy-logic-based techniques are quite effective at dealing with uncertainty about environmental issues. This study proposes two methodologies for assessing water quality based on Mamdani and Sugeno fuzzy systems, focusing on water’s physiochemical attributes, as these provide essential indicators of water’s chemical composition and potential health impacts. The goal is to evaluate water quality using a single numerical value which indicates total water quality at a specific location and time. This study utilizes data from the Acea Group and employs the Mamdani fuzzy inference system combined with various defuzzification techniques as well as the Sugeno fuzzy system with the weighted average defuzzification technique. The suggested model comprises three fuzzy middle models along with one ultimate fuzzy model. Each model has three input variables and 27 fuzzy rules, using a dataset of nine key factors to rate water quality for drinking purposes. This methodology is a suitable and alternative tool for effective water-management plans. Results show a final water quality score of 85.4% with Mamdani (centroid defuzzification) and 83.5% with Sugeno (weighted average defuzzification), indicating excellent drinking water quality in Tivoli, Italy. Water quality evaluation is vital for sustainability, ensuring clean resources, protecting biodiversity, and promoting long-term environmental health. Intermediate model evaluations for the Mamdani approach with centroid defuzzification showed amounts of 72.4%, 83.4%, and 92.5% for the first, second, and third fuzzy models, respectively. For the Sugeno method, the corresponding amounts were 76.2%, 83.5%, and 92.5%. These results show the precision of both fuzzy systems in capturing nuanced water quality variations. This study aims to develop fuzzy logic methodologies for evaluating drinking water quality using a single numerical index, ensuring a comprehensive and scalable tool for water management.
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Barraza, Juan, Patricia Melin, Fevrier Valdez, and Claudia I. Gonzalez. "Modeling of Fuzzy Systems Based on the Competitive Neural Network." Applied Sciences 13, no. 24 (2023): 13091. http://dx.doi.org/10.3390/app132413091.
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This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the parameters of the membership functions are adapted according to the values of the input data. In the approach, two fuzzy inference systems, Sugeno and Mamdani, are proposed. Additionally, variations of these models are presented using three types of membership functions, including Trapezoidal, Triangular, and Gaussian functions. The proposed models are applied to three classification datasets: Wine, Iris, and Wisconsin Breast Cancer (WDBC). The simulations and results present higher classification accuracy when implementing the Sugeno fuzzy inference system compared to the Mamdani system, and in both models (Mamdani and Sugeno), better results are obtained when the Gaussian membership function is used.
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Zaky Noer Jati, Tri Hastono, and Ferdi Andrian. "Prediksi Produksi Bawang Merah di Kota Yogyakarta menggunakan Metode Fuzzy Mamdani." Jurnal Publikasi Ilmu Komputer dan Multimedia 3, no. 1 (2024): 129–37. http://dx.doi.org/10.55606/jupikom.v3i1.2635.
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This study discusses the prediction of shallot production in Yogyakarta City using the Fuzzy Mamdani method. The research is important due to the impact of shallot production fluctuations on the national economy. The Fuzzy Mamdani method was chosen for its high level of flexibility and tolerance for existing data. Data was collected from Bappeda DIY and shallot farmers to build the prediction model. The prediction implementation was carried out using Matlab R2015a with Fuzzy Logic Matlab Toolbox. The prediction results were evaluated using the RMSE value. The study concludes that the Fuzzy Mamdani method can be used for predicting shallot production with an error rate of 14.11%. However, the study suggests a review of the data used in the prediction to ensure its accuracy. Several references used in the study are also included.
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Dewi, Rafiqa. "EVALUASI MEMBANGKITKAN FUNGSI KEANGGOTAAN PADA FUZZY MODEL MAMDANI." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 1, no. 1 (2017): 41. http://dx.doi.org/10.30645/jurasik.v1i1.7.
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Paper ini membahas tentang perbandingan dua bentuk dalam membangkitkan fungsi keanggotaan. Menentukan fungsi keanggotaan melalui segitiga sama kaki akan mendapatkan nilai menurun pada segitiga tersebut. Sedangkan pada segitiga siku-siku, setelah puncak segitiga tidak ada kemungkinan nilai menurun. Sehingga nilai yang diperoleh merupakan nilai menaik dan putus setelah puncak segitiga. Dihitung dengan model Mamdani, nilai output yang dihitung dengan segitiga sama kaki memiliki nilai yang lebih rendah dibandingkan dengan yang dihasilkan oleh segitiga siku-siku. Hal ini disebabkan segitiga siku-siku tidak memiliki bagian sisi yang menurun setelah puncak segitiganya, sehingga tidak memiliki pendekatan kepada variabel setelahnya.
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Kothamasu, Ranganath, and Samuel H. Huang. "Adaptive Mamdani fuzzy model for condition-based maintenance." Fuzzy Sets and Systems 158, no. 24 (2007): 2715–33. http://dx.doi.org/10.1016/j.fss.2007.07.004.
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Єпік М. О. "МЕХАНІЗМ НЕЧІТКОГО ВИВЕДЕННЯ В ІНТЕЛЕКТУАЛЬНІЙ СИСТЕМІ ДІАГНОСТИКИ ЗАХВОРЮВАНЬ". Science Review, № 2(19) (28 лютого 2019): 3–9. http://dx.doi.org/10.31435/rsglobal_sr/28022019/6363.
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This article is devoted consideration of mechanism of fuzzy conclusion in the intellectual system of diagnostics of diseases. The process of realization of inference method is described. The general structure of model of diagnostics of diseases is presented. The example of model fragment is considered. Description of base of fuzzy rules of the intellectual system is presented. The examples of external and internal representation of rules are resulted. The stages of algorithm of fuzzy conclusion of Mamdani are considered. Description of application of algorithm is presented for Mamdani for the intellectual system of diagnostics of diseases.
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Wisnu Setiawan, Tri Hastono, Riyan Fahmi Gunawan, and Rama Sona. "AVOCADO STOCK PREDICTION IN BANTUL CITY USING MAMDANI FUZZY LOGIC." Jurnal Publikasi Ilmu Komputer dan Multimedia 3, no. 1 (2023): 35–46. http://dx.doi.org/10.55606/jupikom.v3i1.2524.
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This journal discusses the development of an avocado stock prediction model in Bantul City using the Mamdani fuzzy logic approach. In this context, fuzzy logic is used to address the uncertainty and complexity associated with factors affecting avocado supplies, such as stock variability, demand and price variables. The Mamdani approach is applied to formulate fuzzy rules based on a combination of expert knowledge and historical avocado stock data. This method aims to produce avocado stock predictions that are more accurate and adaptive to market dynamics. Through a series of experiments, the results show that the Mamdani fuzzy logic model has a significant level of accuracy, outperforming traditional stock prediction methods. The results obtained show the potential of this model in improving the efficiency of avocado inventory management at the local level. This research makes an important contribution especially in the context of agribusiness, providing a foundation for a more sophisticated and adaptive prediction approach to avocado fruit stock management. The implications are widely applicable in the agribusiness sector and provide a basis for the development of similar prediction systems for other agricultural commodities.
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Kumar, Neerendra, and Zoltán Vámossy. "Robot navigation with obstacle avoidance in unknown environment." International Journal of Engineering & Technology 7, no. 4 (2018): 2410. http://dx.doi.org/10.14419/ijet.v7i4.14767.
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In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.
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Jarti, Nanda, and Samsul Arifin. "FUZZY LOGIC DALAM PENENTUAN KARYAWAN TERBAIK PADA POSISI STOREKEEPER MENGGUNAKAN METODE MAMDANI DIKOTA BATAM." JR : Jurnal Responsive Teknik Informatika 2, no. 02 (2021): 62–72. http://dx.doi.org/10.36352/jr.v2i02.235.
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Penelitian ini bertujuan untuk mengimplementasi dalam penentuan karyawan terbaik pada posisi storekeeper dengan logika fuzzy metode Mamdani. Proses yang dilakukan adalah menentukan empat variabel input yaitu variabel sikap pribadi dengan himpunan fuzzy sangat baik, baik, cukup baik, tidak baik dan variabel pengetahuan pekerjaan dengan himpunan fuzzy sangat faham, faham, cukup faham, tidak faham dan variabel kerjasama dengan himpunan fuzzy sangat baik, baik, cukup baik, tidak baik dan variabel kemampuan kepemimpinan dengan himpunan fuzzy sangat bagus, bagus, cukup bagus, tidak bagus. dan variabel output atau keputusan dengan himpunan fuzzy terbaik dan tidak baik. Untuk mendapatkan output diperlukan empat tahapan yaitu pembentukan himpunan fuzzy, aplikasi fungsi implikasi, komposisi aturan, dan penegasan (defuzzy). Hasil defuzzifikasi merupakan nilai untuk menentukan karyawan pada posisi storekeeper terbaik atau tidak baik. Model fuzzy yang telah dibuat akan dilakukan pengujian model dengan cara menentukan tingkat keakuratan dan error dari model tersebut. Dengan hasil perhitungan dengan metode mamdani 61, 1875 dan perhitungan MATLAB 61,4.
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Alieva, E., and Z. Maharramov. "FUZZY MULTIPLE APPROACH TO ANALYZING QoS INDICATORS OF MULTI-SERVICE NETWORKS." Sciences of Europe, no. 117 (May 23, 2023): 92–97. https://doi.org/10.5281/zenodo.7961136.
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To study the QoS quality of service parameters, the Saaty hierarchy analysis method was applied and, on its basis, the membership function was determined. A system of linguistic rules has been compiled for fuzzy analysis. The fuzzy-set approach is implemented by the Mamdani fuzzy model in the Matlab package.
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Sriyanti, Agus, Marsono Marsono, and Milfa Yetri. "Sistem Pendukung Keputusan Menentukan Stock BBM Berdasarkan Hasil Penjualan Menggunakan Metode Fuzzy Mamdani." Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) 3, no. 1 (2024): 46–53. http://dx.doi.org/10.53513/jursi.v3i1.5658.
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Stok adalah suatu kegiatan perusahaan untuk menghasilkan barang atau jasa dari bahan-bahan atau sumber-sumber faktor Stok dengan tujuan untuk dijual lagi. Stok sangat berperan penting bagi orang yang ingin membangun suatu usaha.  Pada saat ini, persaingan dibidang  usaha sangat kompetitif, ini dikarenakan tuntutan zaman yang semakin serba cepat. Apabila suatu usaha tidak dapat memenuhi kebutuhan permintaan konsumen karena kekurangan persediaan maka para konsumen tersebut bisa saja beralih ke perusahaan lain karena kecewa. Hal ini tentu dapat minimbulkan kerugian bagi perusahaan dan mengurangi laba perusahaan. Untuk menghindari hal tersebut maka beberapa hal yang perlu diperhatikan yaitu permintaan dan persedian barang sehingga dapat mendukung jalannya suatu usaha dengan baik dan tidak menimbulkan kerugian. Oleh karena itu salah satu cara yang bisa digunakan untuk menyeselesaikan masalah tersebut dengan mengembangkan sebuah aplikasi dalam sistem pedukung keputusan. Sistem Pendukung Keputusan (SPK) yaitu aplikasi interaktif berbasis komputer yang membuat sosiasi data maupun model matematis guna membantu proses pengambilan keputusan dalam mengatasi permasalahan. Sistem pendukung keputusan dapat dikembangkan dalam upaya penentuan yang akan digunakan  dengan  metode Fuzzy Mamdani. Implementasi Metode Fuzzy Mamdani merupakan salah  satu pendekatan yang menggunakan beberapa tahapan tertentu. Beberapa bentuk dalam fuzzy logic banyak yang diterapkan untuk menyelesaikan berbagai suatu permasalahan salah satunya yaitu fuzzy Mamdan. Sistem pendukung keputusan menggunakan logika fuzzy telah dilakukan oleh beberapa peneliti. Logika fuzzy dapat dikategorikan sebagai sistem pendukung keputusan ketika solusi atau hasil yang diperoleh dari perhitungan logika fuzzy dapat dijadikan suatu ketupusan.
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Rahma, Farah Ningrum, Ruli A. Siregar Riki, and Rusjdi Darma. "Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 298–305. https://doi.org/10.11591/ijai.v10.i2.pp298-305.
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The research objective of supervisory control and data acquisition (SCADA), with fuzzy Mamdani logic simulation on the loading section of distribution transformer substations. Data acquisition is available when saving SAIFI SAIDI data and storing the results of monitoring equipment. The method used is Mamdani fuzzy logic, there are two input variables, namely current and voltage devices. The membership function in Mamdani fuzzy logic has been created based on the input current and voltage variables. Currently: parameter {0, 600} low is created {0, 350, 450, 600}, normal {400-650} parameter is created {400, 500, 550, 650}, parameter high {≥600} is created {600, 650, 750, 1000}, when determining the voltage: low {≤10.5} parameters {0 4 7 10.5}, normal {9-14} parameters {9, 10, 13, 14} and high {≥13} - parameters {13, 14, 15, 16}. Based on the results of the Mamdani logic rule test on the output current containing a transformer and a voltage sensor, the results obtained are IF (normal current; (630) AND voltage (high); (13.2) (high load transformer). The components in the simulation tool include miniature substations made with the 1A travel substation model, 3A substation as the main substation, the relay as distribution substation as the monitoring application. Telestatus and Telecontrol use a microcontroller. Initial scenario. After substation is resumed, data is stored after downtime, service life, duration, and data period. Initial scenario After substation is resumed, data is stored after downtime, service life, duration, and data period.
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Nanda Aiken El Islamy, Tri Hastono, and Adhitya Chandra Wibowo. "Weekly Tofu Stock Prediction with Mamdani Fuzzy Method." Jurnal Publikasi Ilmu Komputer dan Multimedia 3, no. 1 (2024): 60–74. http://dx.doi.org/10.55606/jupikom.v3i1.2584.
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This research aims to predict weekly tofu stock using the Mamdani Fuzzy Method. This method is used to overcome the uncertainty and complexity in estimating tofu stock, which is influenced by various factors such as inventory, ingredients, and ingredient prices. By applying fuzzy logic to describe the uncertainty in the data, this model can provide more accurate stock predictions. This research involves collecting weekly tofu stock data from a specific time period and developing a fuzzy inference system based on relevant input variables. Experimental results and model validation show that the Mamdani Fuzzy Method can be an effective approach to predict weekly tofu stocks with satisfactory accuracy. The practical implications of this research can assist the tofu industry in optimizing inventory management and responding more appropriately to fluctuations in market demand.
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V.Mawale, Maya, and Vinay Chavan. "Implementation & Simulation of Fuzzy Logic Controllers for Productivity and Fertility of Soil and Performance Evaluation of Triangular Membership Function." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 09 (2014): 1098–102. https://doi.org/10.5281/zenodo.14752192.
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As soil is complex system and soil fertility represents crop productivity and soil productivity hence MIMO system is a necessity of fuzzy logic controller model design using simulation technique. Same model gives prediction of lots of problem and save development time. Modelling and simulation tools made a dynamic evolution in the design and control of prediction system. The basic requirements of prediction system are accuracy in result. The objective of this paper is to investigate the effect of triangular membership functions in the developed Simulink model of Mamdani model based fuzzy control for prediction of soil productivity. The built in membership functions of Matlab is selected for evaluation. The evaluation is done using the developed 207 fuzzy rules through the implementation in Matlab/Simulink model. The results of all soil parameter are analysed. The performances of triangular membership functions on mamdani model based fuzzy control starting currents are concerned for the developed model.
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Ningrum, Rahma Farah, Riki Ruli A. Siregar, and Darma Rusjdi. "Fuzzy mamdani logic inference model in the loading of distribution substation transformer SCADA system." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 298. http://dx.doi.org/10.11591/ijai.v10.i2.pp298-305.
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<span id="docs-internal-guid-152e332e-7fff-7073-a541-1d420decb47b"><span>The research objective of supervisory control and data acquisition (SCADA), with fuzzy Mamdani logic simulation on the loading section of distribution transformer substations. Data acquisition is available when saving SAIFI SAIDI data and storing the results of monitoring equipment. The method used is Mamdani fuzzy logic, there are two input variables, namely current and voltage devices. The membership function in Mamdani fuzzy logic has been created based on the input current and voltage variables. Currently: parameter {0, 600} low is created {0, 350, 450, 600}, normal {400-650} parameter is created {400, 500, 550, 650}, parameter high {≥600} is created {600, 650, 750, 1000}, when determining the voltage: low {≤10.5} parameters {0 4 7 10.5}, normal {9-14} parameters {9, 10, 13, 14} and high {≥13} - parameters {13, 14, 15, 16}. Based on the results of the Mamdani logic rule test on the output current containing a transformer and a voltage sensor, the results obtained are IF (normal current; (630) AND voltage (high); (13.2) (high load transformer). The components in the simulation tool include miniature substations made with the 1A travel substation model, 3A substation as the main substation, the relay as distribution substation as the monitoring application. Telestatus and Telecontrol use a microcontroller. Initial scenario. After substation is resumed, data is stored after downtime, service life, duration, and data period. Initial scenario After substation is resumed, data is stored after downtime, service life, duration, and data period.</span></span>
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Fayaz, Muhammad, Israr Ullah, and DoHyeun Kim. "An Optimized Fuzzy Logic Control Model Based on a Strategy for the Learning of Membership Functions in an Indoor Environment." Electronics 8, no. 2 (2019): 132. http://dx.doi.org/10.3390/electronics8020132.
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The Mamdani fuzzy inference method is one of the most important fuzzy logic control (FLC) techniques and has several applications in different fields. Despite its applications, the Mamdani fuzzy inference method has some core issues which still require solutions. The most critical issue is the selection of accurate shape and boundaries of membership functions (MFs) in the universe of discourse. In this work, we introduced a methodology called learning to control (LtC) to resolve the problem. The proposed methodology consisted of two main modules, namely, a control algorithm (CA) module and a learning algorithm (LA) module. In the CA module, the Mamdani FLC method has been used, whereas, in the LA module, we have used the artificial neural network (ANN) algorithm. Inputs into the ANN were the error difference between environmental temperature and the required temperature. The output of the ANN was the MF set to the FLC. Inputs into the fuzzy logic controller (FLC) were the error difference between environmental temperature and required temperature (D), and the output was the required power for the fan actuator. The purpose of the ANN was to tune the MFs of the FLC to improve its efficiency. The proposed learning-to-control method along with the conventional fuzzy logic controller method was applied to the data to evaluate the model’s performance. The results indicate that the proposed model’s performance is far better than that of conventional fuzzy logic techniques.
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Mishra, Sunny, Amit K. Shukla, and Pranab K. Muhuri. "Explainable Fuzzy AI Challenge 2022: Winner’s Approach to a Computationally Efficient and Explainable Solution." Axioms 11, no. 10 (2022): 489. http://dx.doi.org/10.3390/axioms11100489.
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An explainable artificial intelligence (XAI) agent is an autonomous agent that uses a fundamental XAI model at its core to perceive its environment and suggests actions to be performed. One of the significant challenges for these XAI agents is performing their operation efficiently, which is governed by the underlying inference and optimization system. Along similar lines, an Explainable Fuzzy AI Challenge (XFC 2022) competition was launched, whose principal objective was to develop a fully autonomous and optimized XAI algorithm that could play the Python arcade game “Asteroid Smasher”. This research first investigates inference models to implement an efficient (XAI) agent using rule-based fuzzy systems. We also discuss the proposed approach (which won the competition) to attain efficiency in the XAI algorithm. We have explored the potential of the widely used Mamdani- and TSK-based fuzzy inference systems and investigated which model might have a more optimized implementation. Even though the TSK-based model outperforms Mamdani in several applications, no empirical evidence suggests this will also be applicable in implementing an XAI agent. The experimentations are then performed to find a better-performing inference system in a fast-paced environment. The thorough analysis recommends more robust and efficient TSK-based XAI agents than Mamdani-based fuzzy inference systems.
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Nursikuwagus, Agus, and Agis Baswara. "A Mamdani Fuzzy Model to Choose Eligible Student Entry." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 1 (2017): 365. http://dx.doi.org/10.12928/telkomnika.v15i1.4893.
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Adi Darma, Wawang. "PENILAIAN KINERJA DOSEN DENGAN METODE FUZZY MAMDANI DI AMIK CBI SUKABUMI." JURNAL BUANA INFORMATIKA CBI 4, no. 1 (2016): 19–38. http://dx.doi.org/10.53918/bi.v4i1.6.
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Penilaian kinerja dosen adalah (1) proses penilaian bagaimana seseorang telah bekerja dibandingkan dengan target yang telah ditentukan. (2) proses pendokumentasian pengukuran, pengetahuan, keterampilan, sikap, dan keyakinan peserta didik secara individu atau kelompok orang, lembaga, atau sistem pendidikan secara keseluruhan. Model GAP Analysis dan Fuzzy Logic Mamdani merupakan bentuk analisis dan metode yang digunakan dalam menilai kinerja dosen di AMIK Citra Buana Indonesia (CBI) Sukabumi. Analisis yang dilakukan adalah analisis perbedaan (gap) antara penilaian kinerja mahasiswa dan dosen dengan standar penilaian lembaga dari beberapa indikator penilaian yaitu produktivitas, kualitas kerja, pemecahan masalah serta motivasi dan tanggung jawab. Langkah-langkah yang dilakukan dalam metode fuzzy mamdani adalah (a). Fuzzifikasi. (B). Aplikasi berfungsi implikasi (c). Komposisi dari aturan dengan metode maksimum. (D). Metode defuzzifikasi centroid. Hasil penelitian penilaian kinerja fakultas cukup akurat dengan selisih nilai penilaian 0,6 dengan metode fuzzy mamdani dan penggunaan konvensional yang selama ini dilakukan oleh lembaga tersebut.
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Hasanah, Nur, and Retantyo Wardoyo. "Purwarupa Sistem Pakar dengan Mamdani Product untuk Menentukan Menu Harian Penderita DM." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 7, no. 1 (2013): 45. http://dx.doi.org/10.22146/ijccs.3051.
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AbstrakPada 2025 diperkirakan 12,4 juta orang yang mengidap Diabetes Melitus (DM) di Indonesia. Perencanaan makan merupakan salah satu pilar dalam pengelolaan DM. Sistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Logika fuzzy fleksibel, memiliki kemampuan dalam proses penalaran secara bahasa dan memodelkan fungsi-fungsi matematika yang kompleks. Penelitian ini bertujuan menerapkan metode ketidakpastian logika fuzzy pada purwarupa sistem pakar untuk menentukan menu harian. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Mamdani Product. Pendekatan basis pengetahuan yang digunakan pada sistem pakar ini adalah dengan Rule-Based Reasoning. Proses inferensi pada sistem pakar menggunakan logika fuzzy dengan mesin inferensi Mamdani Product. Fuzzifier yang digunakan adalah Singleton sedangkan defuzzifier yang digunakan adalah Rata-Rata Terpusat. Penggunaan kombinasi Singleton fuzzifier, mesin inferensi Product dan defuzzifier Rata-Rata Terpusat yang digunakan pada sistem pakar dapat diterapkan untuk domain permasalahan yang dibahas. Meskipun demikian, terdapat kemungkinan Singleton fuzzifier tidak dapat memicu beberapa atau semua aturan. Jika semua aturan tidak dapat dipicu maka tidak dapat disimpulkan kebutuhan kalori hariannya. Kata kunci— sistem pakar, logika fuzzy, mamdani product, diabetes, menu AbstractIt is predicted that 12.4 million people will suffer from Diabetes Mellitus (DM) in Indonesia in 2025. Menu planning is one of the important aspects in DM management. Expert system can be used as a consultant that gives suggestion to users as well as an assistant for experts. Fuzzy logic is flexible, has the ability in linguistic reasoning and can model complex mathemathical functions. This research aims to implement fuzzy logic uncertainty method into expert sistem prototype to determine diabetic daily menu. The advantage is to find out the accuracy of Mamdani Product inference engine. The knowledge-based approach in this expert system uses Rule-Based Reasoning. The inference process employs fuzzy logic making use of Mamdani Product inference engine. The fuzzifier used is Singleton while defuzzifier is Center Average. The combination of Singleton fuzzifier, Mamdani Product inference engine and Center Average defuzzifier that is used can be applied in the domain of the problem under discussion. In spite of the case, there is possibility that Singleton fuzzifier can’t trigger some or all of the rules. If all of the rules can’t be triggered then the diabetic daily menu can’t be concluded. Keyword— expert system, fuzzy logic, mamdani product, diabetes, menu
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Yudianingrum, Risa Dista. "PENENTUAN PERSEDIAAN OPTIMAL PACKING MATERIAL MENGGUNAKAN METODE FIS MAMDANI PADA PERUSAHAAN TEKSTIL DI JAWA TENGAH." Jurnal Ilmiah Teknik Industri 8, no. 3 (2020): 194. http://dx.doi.org/10.24912/jitiuntar.v8i3.7653.
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Pengelolaan persediaan merupakan bagian penting dalam perusahaan yang mendukung berlangsungnya proses produksi. Namun dalam pengelolaan persediaan barang, masih ditemukan beberapa persediaan barang yang jumlahnya berlebihan dan ada yang kekurangan. Perlu adanya analisis jumlah pengadaan barang yang dapat menyebabkan jumlah persediaan barang menjadi optimal. Apabila dapat ditentukan jumlah pengadaan barang optimal, maka jumlah persediaan barang juga akan optimal karena jumlah persediaan barang adalah jumlah stok awal barang ditambah jumlah pengadaan barang. Penelitian ini menerapkan Metode FIS (Fuzzy Inference System) Mamdani untuk menentukan jumlah pengadaan barang yang optimal berdasarkan jumlah stok awal barang dan jumlah permintaan barang. Disusun model FIS Mamdani dengan menentukan Himpunan Fuzzy sebagai variabel linguistik pembentuk aturan dalam model persediaan agar dapat menghasilkan jumlah persediaan barang optimal dan memenuhi batas yang diberikan oleh perusahaan. Hasil penelitian pada 4 jenis barang yang dipilih menunjukkan bahwa jumlah pengadaan barang berdasarkan model persediaan FIS Mamdani lebih baik dibandingkan dengan pengadaan real di perusahaan tekstil Jawa Tengah.
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Wahyudi, Adhie Tri, Ida Giyanti, and Bernadhetta Vivi Kritiana. "Studi Penentuan Jumlah Produksi Botol Kemasan Minuman Yang Optimal Dengan Fuzzy Time Series Markov Chain Dan Fuzzy Inference System." JISI: Jurnal Integrasi Sistem Industri 10, no. 2 (2023): 99. http://dx.doi.org/10.24853/jisi.10.2.99-110.
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Perusahaan perlu berhati-hati saat menentukan jumlah produksi botol minuman kemasan. Jumlah persediaan bahan baku dan permintaan menjadi faktor penting dalam menentukan jumlah produksi. Ketidakpastian jumlah permintaan produk minuman mengakibatkan jumlah produksi botol kemasan minuman kurang selaras dengan jumlah persediaan bahan baku minuman dan jumlah permintaan produksi botol kemasan. Adanya faktor ketidakpastian jumlah permintaan menjadikan pentingnya pemilihan metode peramalan yang tepat untuk memprediksi jumlah permintaan berdasarkan data historis. Penelitian ini bertujuan melakukan studi penentuan jumlah produksi botol kemasan minuman yang optimal dengan mempertimbangkan faktor ketidakpastian. Metode Fuzzy Time Series Markov Chain (FTSMC) adalah satu salah metode peramalan berbasis time series dan dapat diterapkan pada permasalahan ketidakpastian peramalan yang terjadi. Hasil peramalan, setelah melalui uji verifikasi menjadi input metode Fuzzy Inference System (FIS) untuk menentukan jumlah produksi botol minuman agar bersesuaian dengan jumlah permintaan dan jumlah bahan baku. Model FIS yang dibangun pada penelitian ini adalah Fuzzy Sugeno dan Fuzzy Mamdani. Untuk mengetahui metode yang memberikan hasil yang optimal pada permasalahan ini, kedua model yang dibangun tersebut dianalisis dengan membandingkan nilai Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa kedua model, yatu model FTSMC Fuzzy Mamdani dan model FTSMC Fuzzy Sugeno mampu memberikan solusi optimal.
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Wahyudi, Adhie Tri, Ida Giyanti, and Bernadhetta Vivi Kritiana. "Studi Penentuan Jumlah Produksi Botol Kemasan Minuman Yang Optimal Dengan Fuzzy Time Series Markov Chain Dan Fuzzy Inference System." JISI: Jurnal Integrasi Sistem Industri 10, no. 2 (2023): 11. http://dx.doi.org/10.24853/jisi.10.2.11-21.
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Perusahaan perlu berhati-hati saat menentukan jumlah produksi botol minuman kemasan. Jumlah persediaan bahan baku dan permintaan menjadi faktor penting dalam menentukan jumlah produksi. Ketidakpastian jumlah permintaan produk minuman mengakibatkan jumlah produksi botol kemasan minuman kurang selaras dengan jumlah persediaan bahan baku minuman dan jumlah permintaan produksi botol kemasan. Adanya faktor ketidakpastian jumlah permintaan menjadikan pentingnya pemilihan metode peramalan yang tepat untuk memprediksi jumlah permintaan berdasarkan data historis. Penelitian ini bertujuan melakukan studi penentuan jumlah produksi botol kemasan minuman yang optimal dengan mempertimbangkan faktor ketidakpastian. Metode Fuzzy Time Series Markov Chain (FTSMC) adalah satu salah metode peramalan berbasis time series dan dapat diterapkan pada permasalahan ketidakpastian peramalan yang terjadi. Hasil peramalan, setelah melalui uji verifikasi menjadi input metode Fuzzy Inference System (FIS) untuk menentukan jumlah produksi botol minuman agar bersesuaian dengan jumlah permintaan dan jumlah bahan baku. Model FIS yang dibangun pada penelitian ini adalah Fuzzy Sugeno dan Fuzzy Mamdani. Untuk mengetahui metode yang memberikan hasil yang optimal pada permasalahan ini, kedua model yang dibangun tersebut dianalisis dengan membandingkan nilai Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa kedua model, yatu model FTSMC Fuzzy Mamdani dan model FTSMC Fuzzy Sugeno mampu memberikan solusi optimal.
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Wu, Beining, Feiyi Guo, Bo Bian, and Yu Yuan. "Non-subjective Class Trading Strategy Model Based on Apriori Algorithm." Learning & Education 10, no. 7 (2022): 92. http://dx.doi.org/10.18282/l-e.v10i7.2960.
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In order to quantify the transactions in investment,this paper establishes a non-subjective transaction strategy model 
 based on Apriori algorithm.The technical analysis index is blurred through the triangular fuzzy device,designing the fuzzy decision 
 system with a non-subjective class fuzzy transaction rule library,a product inference machine with Mamdani meaning,and a central 
 average average fuzzy device.The structural parameters of the system are estimated using a recursive least squares method with 
 forgetting factors,and a neural network trading strategy optimized based on Apriori and genetic algorithm is proposed.
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Ilham, Muhamad, Aden Aden, and Andi Nur Rahman. "PENGGUNAAN METODE FUZZY DALAM MEMBANGUN SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN PEMBIAYAAN DI KOPERASI SYARIAH BENTENG MIKRO INDONESIA CABANG JASINGA." MathVisioN 3, no. 2 (2021): 51–57. http://dx.doi.org/10.55719/mv.v3i2.296.
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Koperasi simpan pinjam merupakan lembaga yang bergerak dalam jasa keuangan dengan cara menghimpun simpanan dan menyalurkan pembiayaan kembali dengan prosedur yang mudah dan cepat. Penyaluran pembiayaan tahap pertama terhadap anggota koperasi memerlukan pertimbangan yang matang agar dana yang di salurkan tepat dengan kemampuan anggota tersebut. Pengambilan keputusan masih dilakukan secara manual. Namun, pengambilan keputusan secara manual ada kemungkinan terjadi kesalahan, serta dalam jumlah banyak butuh waktu relatif lama dalam mengambil keputusan menentukan besaran pembiayaan. Untuk itu, dibutuhkan sistem pendu-kung keputusan dalam pemberian pembiayaan. Penelitian ini bertujuan untuk menerapkan metode fuzzy Mamdani dalam membangun sistem pendukung keputusan. Semua informasi diproleh dari buku, jurnal, dan lain-lain. Sistem pendukung keputusan ini menggunakan software matlab. Berdasarkan pengujian metode fuzzy Mamdani dan implementasi terhadap software matlab. Penelitian ini telah berhasil melakukan model persamaan fuzzy Mamdani sebagai pendukung keputusan pemberian pembiayaan bagi anggota koperasi dengan ditunjukanya hasil penelitian yang valid lebih dari 70% antara keputusan manual dengan menggunakan software matlab.
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Xiaoguang Hu. "Simulation of a Mathematical Model of Rail Transportation Scheduling Based on Bp Neural Network." Journal of Electrical Systems 20, no. 6s (2024): 1552–64. http://dx.doi.org/10.52783/jes.3074.
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Rail transportation, a cornerstone of modern logistics and passenger transit systems, plays a pivotal role in facilitating the efficient movement of goods and people across vast distances. Operating on a network of interconnected tracks, rail systems offer a reliable and environmentally sustainable mode of transportation, particularly for long-distance travel and freight shipments. The paper presents a comprehensive investigation into the application of advanced computational techniques in the realm of rail transportation management. Specifically, Mamdani fuzzy logic and Backpropagation (BP) Neural Networks are employed to address critical challenges in scheduling and classification within rail networks. The utilization of Mamdani fuzzy logic facilitates nuanced decision-making in scheduling processes, considering uncertainties and complexities inherent in rail operations. Through linguistic rules and fuzzy sets, the scheduling system can effectively adapt to various operational constraints and disruptions, leading to more resilient and efficient scheduling solutions. Additionally, the integration of BP Neural Network enhances classification accuracy and prediction capabilities, enabling precise forecasting of train movements, passenger flows, and other key variables.
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Andrasto, T., Musaropah, Haryono, T. Joko, and Kardoyo. "Simulation and design of smart clothesline using fuzzy for weather forecast." IOP Conference Series: Earth and Environmental Science 969, no. 1 (2022): 012058. http://dx.doi.org/10.1088/1755-1315/969/1/012058.
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Abstract Information about weather is very important for human life. For this reason, a weather prediction system is needed that can read predictions correctly. One of the correct prediction systems is fuzzy systems. Fuzzy systems are used because they can make accurate and accurate weather predictions like human logic. The system used needs to be simulated to obtain the right model. The right software to simulate is Simulink MATLAB. In this study will take the DHT 22 and LDR (Light Dependent Resistor) sensor data from Arduino which will be processed by Simulink MATLAB using the Fuzzy Mamdani system. From the experiments conducted, we managed to make a simulation of predicting the weather using Mamdani fuzzy logic. The defuzzification results from this study can be used to control motors, heaters, etc.