scholarly journals Analisis Perbandingan Fuzzy Tsukamoto dan Sugeno dalam Menentukan Jumlah Produksi Kain Tenun Menggunakan Base Rule Decision Tree

2020 ◽  
Vol 7 (1) ◽  
pp. 171
Author(s):  
Tundo Tundo ◽  
Riolandi Akbar ◽  
Enny Itje Sela
Keyword(s):  
Comparative Analysis ◽  
Decision Tree ◽  
Woven Fabric ◽  
Production Data ◽  
Actual Production ◽  
Truth Value ◽  
The Difference ◽  
Fabric Production

<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>

scholarly journals Penerapan Decision Tree J48 dan Reptree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit menggunakan Metode Fuzzy Tsukamoto

2020 ◽  
Vol 7 (3) ◽  
pp. 483
Author(s):  
Tundo Tundo ◽  
Shofwatul 'Uyun
Keyword(s):  
Decision Tree ◽  
Production Process ◽  
Palm Oil ◽  
Oil Production ◽  
Actual Data ◽  
Production Data ◽  
Actual Production ◽  
Speed Up ◽  
Rule Making

<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>

scholarly journals Kinerja Logika Fuzzy Sugeno dalam Menangani Prediksi Kain Tenun dengan Kombinasi Random Tree dalam Membangun Rule

2021 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Tundo Tundo
Keyword(s):  
Fuzzy Logic ◽  
Decision Tree ◽  
Error Rate ◽  
Random Trees ◽  
Random Tree ◽  
Production Costs ◽  
Woven Fabric ◽  
Forecasting Error ◽  
A Value ◽  
Fabric Production

This study describes the performance of Sugeno fuzzy logic in determining the amount of woven fabric production by using a combination of random tree decision trees in forming rules. The criteria used in determining the amount of production, namely, production costs, demand, and stock obtained from woven fabric entrepreneurs in Mlaki Wanarejan Utara Pemalang. The random tree decision tree is used, one of which is to automatically generate rules from the available data without consulting with experts, in addition to introducing random trees in the field of research because there are still few studies using this decision tree. The results of this study, it was found that the accuracy while the prediction results tested obtained an Average Forecasting Error Rate (AFER) of 42% with a value 58% truth after being compared with the actual production data.Keywords : Fuzzy Logic, Fuzzy Sugeno Method, Rule, Random tree, Prediction.

scholarly journals Application of The Fuzzy Inference System Method to Predict The Number of Weaving Fabric Production

2018 ◽  
Vol 7 (1) ◽  
pp. 19 ◽  
Author(s):  
Tundo Tundo ◽  
Enny Itje Sela
Keyword(s):  
Fuzzy Set ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Production Costs ◽  
Woven Fabric ◽  
Mathematical Functions ◽  
Inference System ◽  
The Difference ◽  
Input Variables ◽  
Fabric Production

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.

FINANCIAL CONTROLLING IN THE SYSTEM OF FINANCIAL MANAGEMENT

2018 ◽  
Vol 28 (1) ◽  
pp. 137-141
Author(s):  
Petya Yordanova – Dinova
Keyword(s):  
Comparative Analysis ◽  
Financial Management ◽  
Financial Stability ◽  
Business Environment ◽  
Innovative Approach ◽  
Financial Sustainability ◽  
The Common ◽  
The Difference ◽  
Effective Use ◽  
And Control

This paper explores the comparative analysis of the financial controlling, who is a result from the common controlling concept and the financial management. In the specialized literature, financial controlling is seen as an innovative approach to financial management. It is often presented as the most promising instrument of financial diagnostics. Generally speaking, financial controlling is seen as a process of managing the company`s assets which are valued in monetary measures. The difference between the financial management and the financial controlling is that the second covers all functions of management, analysis and control of finances, aiming at maximizing their effective use and increasing the value of the enterprise. Financial controlling is often seen as a function of the common practice of financial management. Its objective is to preserve the financial stability and financial sustainability of enterprises operating in a highly aggressive business environment.

scholarly journals Comparative Analysis of Kazakhstani and European Design Specifications: Raft Foundation, Pile Foundation, and Piled Raft Foundation

2021 ◽  
Vol 11 (7) ◽  
pp. 3099
Author(s):  
Assel Zhanabayeva ◽  
Nazerke Sagidullina ◽  
Jong Kim ◽  
Alfrendo Satyanaga ◽  
Deuckhang Lee ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
International Standards ◽  
Piled Raft ◽  
Codes Of Practice ◽  
Piled Raft Foundation ◽  
Raft Foundation ◽  
Eurocode 7 ◽  
Elastic Settlement ◽  
Geotechnical Design ◽  
The Difference

The introduction of Eurocode in Kazakhstan allows for the application of modern technological innovations and the elimination of technical barriers for the realization of international projects. It is significant to study the international standards and design requirements provided in Eurocode. This study presents a comparative analysis of Kazakhstani and European approaches for the geotechnical design of foundations and provides the design methods in the considered codes of practice. Three different types of foundations (i.e., raft, pile, and piled raft foundations) were designed following SP RK 5.01-102-2013—Foundations of buildings and structures, SP RK 5.01-103-2013—Pile foundations, and Eurocode 7: Geotechnical design for the Nur-Sultan soil profile. For all three types of foundations, the calculated results of bearing resistance and elastic settlement showed the conservativeness of Eurocode over SNiP-based Kazakhstani building regulations, as the values of bearing resistance and elastic settlement adhering to Kazakhstani code exceeded the Eurocode values. The difference between the obtained results can be explained by the application of higher values of partial safety factors by Eurocode 7. Sensitivity analysis of the bearing resistance on foundation parameters (i.e., raft foundation width and pile length) for the Kazakhstani and European approaches was performed to support the conclusions of the study.

scholarly journals Comparative Analysis of Different Local Anesthetic Solutions Available in Market: An In Vitro and Clinical Study

2021 ◽  
Author(s):  
Eisha Imran ◽  
Faisal Moeen ◽  
Beenish Abbas ◽  
Bakhtawar Yaqoob ◽  
Mehreen Wajahat ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Local Anesthetic ◽  
Compositional Analysis ◽  
Bacterial Activity ◽  
Similar Composition ◽  
Ph Values ◽  
Significant Difference ◽  
The Mean ◽  
The Difference

Abstract Objectives The study aimed to evaluate and compare various commercially available local anesthetic solutions. Materials and Methods A total of 150 commercially available local anesthetic cartridges of similar composition (2% lidocaine with epinephrine 1:100,000) were randomly collected and divided into 3 groups. The designations of groups were selected from their product names such that each group consisted of 60 cartridges. Group S (Septodont, France) Group M (Medicaine, Korea) and Group H (HD-Caine, Pakistan). The samples were divided into five sub-groups, each consisting of 10 cartridges from each group to investigate each parameter. Results The acquired data was statistically analyzed and compared (using SPSS version 12). Compositional analysis revealed a non-significant (P>0.05) difference when the three Groups were compared with standard lidocaine and epinephrine solutions. The mean pH values of samples from group S, M and H respectively fell within the range of pH values of commercially available solutions. Non-significant difference in EPT values of Group S and H was found when efficacy was compared (p = 0.3), however a significant difference (p < 0.01) was observed in contrast to Group M. Anti-bacterial activity was observed in all the group and a non-significant difference in cell viability values of Group S and M was found (p = 0.6), while the difference was significant in comparison to Group H. Conclusion Within the limitations of these investigations, it appears that the properties of different manufacturers fall within the recommended ranges as mentioned in literature and do not appear to be statistically different in the variables we have tested.

scholarly journals Data mining of coronavirus: SARS-CoV-2, SARS-CoV and MERS-CoV

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jung Eun Huh ◽  
Seunghee Han ◽  
Taeseon Yoon
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
High Similarity ◽  
Incubation Periods ◽  
Initial Question ◽  
The Difference ◽  
Blast Program

Abstract Objective In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms. Results The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.

Linguistic cues facilitate children’s understanding of belief-reporting sentences

2021 ◽  
pp. 014272372110486
Author(s):  
Xiaowen Zhang ◽  
Peng Zhou
Keyword(s):  
Gold Medal ◽  
Judgment Task ◽  
False Belief ◽  
Value Judgment ◽  
Additional Information ◽  
Linguistic Cues ◽  
Truth Value ◽  
The Difference ◽  
Underlying Mechanisms ◽  
Mental Verbs

It has been well-documented that although children around 4 years start to attribute false beliefs to others in classic false-belief tasks, they are still less able to evaluate the truth-value of propositional belief-reporting sentences, especially when belief conflicts with reality. This article investigates whether linguistic cues, verb factivity in particular, can facilitate children’s understanding of belief-reporting sentences. Two experiments were implemented, one testing children’s knowledge of verb factivity using a gold medal task, and one investigating children’s interpretation of belief-reporting sentences using a truth-value-judgment task. Both experiments took advantage of the contrast between neutral non-factive mental verbs and strong negatively biased mental verbs. What sets the two apart is that the complement clause following a strong negatively biased mental verb is definitely false, whereas the one following a neutral non-factive mental verb remains indeterminate in the absence of additional information. The findings were that, first, 4-year-old children were able to tell the difference between the two types of mental verbs in factivity, and second, children’s performance was significantly improved when a strong negatively biased mental verb than when a neutral non-factive mental verb was used as the main verb of the belief-reporting sentences. The findings suggest that the use of strong negatively biased mental verbs facilitates children’s understanding of belief-reporting sentences. Implications of the findings are discussed in relation to the underlying mechanisms connecting verb factivity and false-belief understanding.

scholarly journals Pemberdayaan Kelompok Tenun Nari-Nari Melalui Pewarnaan Benang Menggunakan Ekstrak Kayu Kuning Dan Kayu Mahoni

2020 ◽  
Vol 5 (2) ◽  
pp. 446-452
Author(s):  
Ruslan R ◽  
Agrippina Wiraningtyas ◽  
Ahmad Sandi ◽  
Muhammad Nasir
Keyword(s):  
Natural Dyes ◽  
Raw Material ◽  
Woven Fabric ◽  
Synthetic Dyes ◽  
Community Group ◽  
Training Stage ◽  
Red Color ◽  
Different Color ◽  
Fabric Production ◽  
The Cost

The "Nari-Nari" Weaving Village in Rabadompu Timur Village, Bima City, is a community group engaged in the weaving industry which has been carried on for generations. During this time, Bima woven fabric products use yarn raw material that has been colored using synthetic dyes. Yarn with synthetic dyes has a more diverse color, the fabric coloring process is easier and the cost is cheap, but synthetic dyes are carcinogenic and harmful to the environment. The solution to the problems faced by using natural dyes obtained from plants. This activity aims to train the Nari-Nari weaving group in yarn coloring using natural dyes. The method used is training through several stages of the activity namely the stage of socialization of activities; the training stage of yarn dyeing and woven fabric production. The dyes used are yellow wood extract and mahogany wood. The results obtained in this activity are the colored yarn has a different color based on the extract of the dye and fixation material. In yellow wood obtained with a maroon red color on alum, black on tunjung and reddish beige on lime. In mahogany wood is obtained beige on alum, black gray on tunjung and beige on lime.  

Sign in / Sign up

Export Citation Format

Share Document