In this study discusses the application of fuzzy logic in solving production problems using the Tsukamoto method and the Sugeno method. The problem that is solved is how to determine the production of woven fabric when using three variables as input data, namely: stock, demand and inventory of production costs. The first step is to solve the problem of woven fabric production using the Tsukamoto method which is to determine the input variables and output variables which are firm sets, the second step is to change the input variable into a fuzzy set with the fuzzification process, then the third step is processing the fuzzy set data with the maximum method. And the last or fourth step is to change the output into a firm set with the defuzzification process with a weighted average method, so that the desired results will be obtained in the output variable. The solution to the production problem using the Sugeno method is almost the same as using the Tsukamoto method, it's just that the system output is not a fuzzy set, but rather a constant or a linear equation. The difference between the Tsukamoto Method and the Sugeno Method is in consequence. The Sugeno method uses constants or mathematical functions of the input variables.
This study explains the implementation using the Weighted Aggregated Sum Product Assessment method in determining the best rice to be used for making Serabi cakes, the case was taken from a Serabi cake seller in Tegal City, Central Java with the aim of providing knowledge to Serabi cake traders to be more detailed in determining the rice that is used. suitable for use in making Serabi not just rice is cheap, but it is necessary to see the shape and characteristics of the whole rice. The steps taken to determine the best rice which will then be used as the basis for making Serabi cakes using the Weighted Aggregated Sum Product Assessment method are: (1) Prepare a matrix in which is the value of each set of criteria, (2) Normalize matrix data x becomes normalized data, (3) Calculates alternative values using Weighted Aggregated Sum Product Assessment formula so that the ranking value is found. After these steps are carried out, in this study the best rice that is right to be used as a material for making Serabi is Pelita rice with a yield of 7.12 by occupying the first rank.
<p>Penelitian ini menerangkan penerapan <em>decision tree</em> J48 dan REPTree dengan menggunakan metode <em>fuzzy Tsukamoto</em> dengan objek yang digunakan adalah penentuan jumlah produksi minyak kelapa sawit di perusahaan PT Tapiana Nadenggan dengan tujuan untuk mengetahui <em>decision tree</em> mana yang hasilnya mendekati dari data sesungguhnya sehingga dapat digunakan untuk membantu memprediksi jumlah produksi minyak kelapa sawit di PT Tapiana Nadenggan ketika proses produksi belum diproses. Digunakannya <em>decision tree</em> J48 dan REPTree yaitu untuk mempercepat dalam pembuatan <em>rule </em>yang digunakan tanpa harus berkonsultasi dengan para pakar dalam menentukan <em>rule</em> yang digunakan. Dari data yang digunakan akurasi dari decision tree J48 adalah 95.2381%, sedangkan akurasi REPTree adalah 90.4762%, akan tetapi dalam kasus ini <em>decision tree</em> REPTree yang lebih tepat digunakan dalam proses prediksi produksi minyak kelapa sawit, karena di uji dengan data sesungguhnya pada bulan Maret tahun 2019 menggunakan REPTree diperoleh 16355835 liter, sedangkan menggunakan J48 diperoleh 11844763 liter, dimana data produksi sesungguhnya sebesar 17920000 liter. Sehingga dapat ditemukan suatu kesimpulan bahwa untuk kasus ini data produksi yang mendekati dengan data sesungguhnya adalah REPTree, meskipun akurasi yang diperoleh lebih kecil dibandingkan dengan J48.</p><p><em><strong>Abstract</strong></em></p><div><p><em>This study explains the application of the J48 and REPTree decision tree using the fuzzy Tsukamoto method with the object used is the determination of the amount of palm oil production in the company PT Tapiana Nadenggan with the aim of knowing which decision tree the results are close to the actual data so that it can be used to help predict the amount palm oil production at PT Tapiana Nadenggan when the production process has not been processed. The use of the J48 and REPTree decision tree is to speed up the rule making that is used without having to consult with experts in determining the rules used. From the data used the accuracy of the J48 decision tree is 95.2381%, while the REPTree accuracy is 90.4762%, but in this case the REPTree decision tree is more appropriate to be used in the prediction process of palm oil production, because it is tested with actual data in March 2019 uses REPTree obtained 16355835 liters, while using J48 obtained 11844763 liters, where the actual production data is 179,20000 liters. So that it can be found a conclusion that for this case the production data approaching the actual data is REPTree, even though the accuracy obtained is smaller compared to J48.</em></p></div><p><em><strong><br /></strong></em></p>
<p>Penelitian ini menerangkan penerapan metode <em>Weighted Aggregated Sum Product Assesment</em> dalam menentukan beras terbaik yang akan digunakan untuk pembuatan kue serabi, kasus diambil dari pedagang kue serabi di Kota Tegal Jawa Tengah dengan tujuan memberikan pengetahuan kepada para pedagang kue serabi agar lebih detail dalam menentukan beras yang layak untuk digunakan dalam pembuatan kue serabi bukan hanya sekedar beras tersebut murah, akan tetapi perluh dilihat bentuk dan ciri keseluruhan beras. Langkah-langkah yang dilakukan untuk menentukan beras terbaik yang kemudian akan digunakan sebagai bahan dasar pembuatan kue serabi dengan menggunakan metode <em>Weighted Aggregated Sum Product Assesment </em>yaitu: (1) Mempersiapkan sebuah matriks yang didalamnya merupakan nilai dari masing masing himpunan dari kriteria, (2) Menormalisasikan data matriks x menjadi data ternormalisasi, (3) Menghitung nilai alternatif dengan menggunakan rumus <em>Weighted Aggregated Sum Product Assesment</em> sehingga ditemukan nilai perangkingan. Setelah langkah-langkah tersebut dilakukan, dalam penelitian ini beras terbaik yang tepat untuk digunakan sebagai bahan pembuatan kue serabi adalah beras pelita dengan hasil 7,12 dengan menduduki <em>rangking</em> pertama.</p><p> </p><p><em><strong>Abstract</strong></em></p><div><p><em>This study explains the application of the Weighted Aggregated Sum Product Assessment method in determining the best rice to be used for making pancake cakes. The steps taken to determine the best rice using the Weighted Aggregated Sum Product Assessment method are: (1) Prepare all rice data to be calculated, (2) Make rice data in the form of matrix x and normalize the data matrix x into normalized data, ( 3) Calculate the alternative value for the best rice by using the formula Weighted Aggregated Sum Product Assessment so that the ranking value is found. After these steps are carried out, the best rice that is right to be used as a pancake cake ingredient is pelita rice with a yield of 7.12 by occupying the first rank. Proving the results of the Weighted Aggregated Sum Product Assessment method, a questionnaire was conducted directly to pancake cake traders, especially those in Tegal, which produced a percentage of 80% from 100, which said that pelita rice was rice worthy of being used as a material for pancake cakes because the pancake produced is more fragrant and fresher and the price is relatively cheap.</em></p></div>
<p>Kain tenun merupakan salah satu produk yang diminati oleh banyak orang. Hal ini menjadi pemicu produsen untuk meningkatkan pengelolahannya. Salah satu usaha yang dilakukan adalah memprediksi produksi yang dapat dilakukan untuk mendapatkan jumlah optimal yang diperoleh, sehingga mendapatkan keuntungan yang besar. Dalam penelitian ini, untuk mendapatkan prediksi jumlah produksi kain tenun dilakukan dengan perhitungan komputerisasi menggunakan metode logika <em>fuzzy </em>Mamdani. Metode ini menggunakan konsep pohon keputusan <em>random tree </em>dalam membentuk <em>rule.</em> <em>Rule </em>yang dibuat berdasarkan pada kriteria dalam penentuan jumlah produksi kain tenun, diantaranya yaitu biaya produksi, permintaan, dan stok.<em> </em>Konsep<em> </em>pohon keputusan <em>random tree</em> dalam penelitian ini digunakan untuk membuat <em>rule</em> secara otomatis berdasarkan data yang tersedia. Pembentukan <em>rule</em> ini berdasarkan data-data kain tenun dan diimplementasikan dalam <em>random tree</em>, sehingga tidak perlu menggunakan pakar. Penelitian ini membuktikan bahwa prediksi yang dilakukan dapat membangun <em>rule</em> dengan nilai akurasi sebesar 100%. Hasil perbandingan prediksi dengan produksi sesungguhnya memiliki persentase <em>error</em> sebesar 3% dengan nilai kebenaran sebesar 97% (berdasarkan perhitungan <em>Average Forecasting Error Rate </em>(AFER))<strong>.</strong> Oleh karena itu ketika diimplementasikan dalam <em>fuz</em><em>z</em><em>y</em> Mamdani dapat menghasilkan prediksi produksi kain tenun yang optimal.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Woven fabric is a product that is in demand by many people. It triggers producers to improve their management. One of the efforts made is to predict the production that can be done to get the optimal amount obtained, to get a significant profit. In this study, to obtain a prediction of the amount of woven fabric production is done by computerized calculations using the Mamdani fuzzy logic method. This method uses the concept of a random tree decision tree in forming rules. The rules are made based on the criteria in determining the amount of woven fabric production, including production costs, demand, and stock. The concept of a random tree decision tree in this study automatically generates rules based on available data. This rule's formation is based on woven fabric data and is implemented in a random tree, so there is no need to use experts. This study shows that the predictions made can build rules with an accuracy value of 100%. The comparison of predictions with actual production has an error percentage of 3% with a truth value of 97% (based on the calculation of the Average Forecasting Error Rate (AFER)). When implemented in Fuzzy Mamdani, it can produce optimal woven fabric production predictions with predicted results less than the actual production.</em></p><p><em><strong><br /></strong></em></p>
<p>Penelitian ini menerangkan tentang analisis perbandingan <em>fuzzy Tsukamoto dan Sugeno</em> dalam menentukan jumlah produksi kain tenun dengan menggunakan <em>base rule decision tree. </em>Dari hasil analisis penelitian ini, maka ditemukan beberapa perbedaan yang sangat signifikan: (1) Metode <em>fuzzy Tsukamoto</em> dari hasil yang diperoleh lebih mendekati dari data sesungguhnya, dibandingkan dengan <em>fuzzy Sugeno</em>, (2) Selisih yang diperoleh dengan menggunakan <em>fuzzy Tsukamoto</em> dengan data produksi sesungguhnya selalu konsisten yaitu hasil <em>fuzzy Tsukamoto</em> selalu lebih besar, sedangkan untuk <em>fuzzy Sugeno </em>tidak konsisten, (3) Hasil selisih untuk <em>fuzzy Tsukamoto</em> relatif mendekati dari data produksi sesungguhnya, sedangkan untuk <em>fuzzy Sugeno </em>relatif jauh selisih yang dihasilkan. Sehingga dapat disimpulkan bahwa metode yang paling mendekati nilai kebenaran adalah produksi yang mengunakan metode <em>Tsukamoto</em> dengan keakuratan yang diperoleh menggunakan <em>base rule decision tree</em> sebesar 83.3333 %<strong>.</strong></p><p><em><strong><br /></strong></em></p><p><em><strong>Abstract</strong></em></p><p><em><strong><br /></strong></em></p><p><em>This study describes the comparative analysis of fuzzy Tsukamoto and Sugeno determining the amount of woven fabric production using a decision tree base rule. From the results the analysis of this study, we found several very significant differences: (1) The fuzzy Tsukamoto method of the results obtained is closer to the actual, compared to fuzzy Sugeno, (2) The difference obtained by using fuzzy Tsukamoto with actual production data is always consistent is that Tsukamoto fuzzy results are always greater, while for Sugeno's fuzzy inconsistency, (3) The difference results for fuzzy Tsukamoto are relatively close to the actual production data, whereas Sugeno fuzzy is relatively far from the difference produced. So it can be concluded that the method closest to the truth value is production using the Tsukamoto method with the accuracy obtained using the base rule decision tree of 83.3333%.</em></p><p><em><strong><br /></strong></em></p>
<p>Penelitian ini bertujuan untuk membantu pengrajin kayu di Dongkelan, Krapyak, Yogyakarta dalam menentukan kayu terbaik untuk dijadikan sebagai bahan gitar, karena sering terjadi keluhan dari para pembeli bahwa bahan yang dijadikan bahan gitar cepat lapuk dan kusam dari segi warnah. Berdasarkan permasalahan tersebut, dicari suatu solusi dengan menggunakan metode <em>Decision Support System</em> <em>Multi Objective Optimization on the basic of Ratio Analysis</em><em> </em>(MOORA) serta dibantu oleh pakar dalam menentukan kriteria yang tepat berkaitan penentuan kayu terbaik yang digunakan dalam pembuatan bahan gitar, setelah berdiskusi panjang ditemukan hasil kriteria yang tepat berdasarkan permasalahan, berupa kriteria kekuatan kayu, serat kayu, tekstur, dan berat kayu. Semua kriteria tersebut, kemudian diproses dengan menggunakan metode MOORA, dengan data yang digunakan sebanyak 29 jenis data kayu, yang diperoleh dari pengrajin yang ada di wilayah tersebut. Setelah diproses, diperoleh hasil 3 kayu terbaik yang layak untuk digunakan sebagai bahan pembuatan gitar secara berurutan dalah kayu Bubinga dengan nilai 18,36785, kayu Bocote dengan nilai 17,33385, dan kayu Eboni dengan nilai 17,33385 dari beberapa pilihan alternatif kayu yang ada. Membuktikan hasil dari metode MOORA<em>, </em>maka dilakukan responden secara langsung dengan memberikan hasil metode kepada pakar pembuat gitar. Dari 15 pakar pembuat gitar, 13 mengatakan setuju dengan peringkat 3 terbesar, dan 2 mengatakan kurang setuju. Sehingga ditemukan tingkat akurasi berdasarkan penilaian pakar sebesar 86,67 %.</p><p> </p><p><strong>Abstract</strong></p><div><p><em>This study aims to assist wood craftsmen in Dongkelan, Krapyak, Yogyakarta in determining the best wood to be used as guitar material, because there are frequent complaints from buyers that the material used for guitar is rotten quickly and is dull in terms of color. Based on these problems, a solution was sought using the Multi Objective Optimization on the basic of Ratio Analysis (MOORA) Decision Support System method and assisted by experts in determining the right criteria related to determining the best wood used in making guitar materials, after a long discussion found the results. the right criteria based on the problem, in the form of wood strength criteria, wood grain, texture, and wood weight. All of these criteria are then processed using the MOORA method, with the data used as much as 29 types of wood data, which are obtained from craftsmen in the area. After processing, the 3 best woods that are suitable for use as a guitar-making material are Bubinga wood with a value of 18.36785, Bocote wood with a value of 17.333385, and Eboni wood with a value of 17.333385 from several alternative wood choices. . Proving the results of the MOORA method, the respondents directly gave the results of the method to guitar-making experts. Of the 15 expert guitar makers, 13 said they agreed with the third largest ranking, and 2 said they disagreed. So that it found the level of accuracy based on expert judgment of 86.67%. </em></p></div><p><strong><br /></strong></p>
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