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

Penelitian ini menerangkan penerapan decision tree J48 dan REPTree dengan menggunakan metode fuzzy Tsukamoto dengan objek yang digunakan adalah penentuan jumlah produksi minyak kelapa sawit di perusahaan PT Tapiana Nadenggan dengan tujuan untuk mengetahui decision tree 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 decision tree J48 dan REPTree yaitu untuk mempercepat dalam pembuatan rule yang digunakan tanpa harus berkonsultasi dengan para pakar dalam menentukan rule 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 decision tree 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.Abstract<div>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.</div>

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Perbandingan Decision Tree J48, REPTREE, dan Random Tree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit Menggunakan Fuzzy Tsukamoto

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021833108 ◽

2021 ◽

Vol 8 (3) ◽

pp. 473

Author(s):

Tundo Tundo ◽

Shofwatul 'Uyun

Keyword(s):

Decision Tree ◽

Decision Analysis ◽

Decision Trees ◽

Palm Oil ◽

Error Rate ◽

Oil Production ◽

Random Tree ◽

Actual Data ◽

Fuzzy Method ◽

Forecasting Error

<h2 align="center"> </h2>Penelitian ini menerangkan analisis decision tree J48, REPTree dan Random Tree dengan menggunakan metode fuzzy Tsukamoto dalam penentuan jumlah produksi minyak kelapa sawit di perusahaan PT Tapiana Nadenggan dengan tujuan untuk mengetahui decision tree mana yang hasilnya mendekati dari data sesungguhnya. Digunakannya decision tree J48, REPTree, dan Random Tree yaitu untuk mempercepat dalam pembuatan rule yang digunakan tanpa harus berkonsultasi dengan para pakar dalam menentukan rule yang digunakan. Berdasarkan data yang digunakan akurasi pembentukan rule dari decision tree J48 adalah 95,2381%, REPTree adalah 90,4762%, dan Random Tree adalah 95,2381%. Hasil dari penelitian yang telah dihitung bahwa metode fuzzy Tsukamoto dengan menggunakan REPTree mempunyai error Average Forecasting Error Rate (AFER) yang lebih kecil sebesar 23,17 % dibandingkan dengan menggunakan J48 sebesar 24,96 % dan Random Tree sebesar 36,51 % pada prediksi jumlah produksi minyak kelapa sawit. Oleh sebab itu ditemukan sebuah gagasan bahwa akurasi pohon keputusan yang terbentuk menggunakan tools WEKA tidak menjamin akurasi yang terbesar adalah yang terbaik, buktinya dari kasus ini REPTree memiliki akurasi rule paling kecil, akan tetapi hasil prediksi memiliki tingkat error paling kecil, dibandingkan dengan J48 dan Random Tree. Abstract<div>This study explains the J48, REPTree and Tree Random tree decision analysis using Tsukamoto's fuzzy method in determining the amount of palm oil production in PT Tapiana Nadenggan's company with the aim of finding out which decision tree results are close to the actual data. The decision tree J48, REPTree, and Random Tree is used to accelerate the making of rules that are used without having to consult with experts in determining the rules used. Based on the data used the accuracy of the rule formation of the J48 decision tree is 95.2381%, REPTree is 90.4762%, and the Random Tree is 95.2381%. The results of the study have calculated that the Tsukamoto fuzzy method using REPTree has a smaller Average Forecasting Error Rate (AFER) rate of 23.17% compared to using J48 of 24.96% and Tree Random of 36.51% in the prediction of the amount of palm oil production. Therefore an idea was found that the accuracy of decision trees formed using WEKA tools does not guarantee the greatest accuracy is the best, the proof of this case REPTree has the smallest rule accuracy, but the predicted results have the smallest error rate, compared to J48 and Tree Random.</div>

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Perspective on Crude Palm Oil Production: The Effect of Raw Palm Oil and Biofuel Prices

Jurnal Ekobistek ◽

10.35134/ekobistek.v10i1.60 ◽

2021 ◽

pp. 15-25

Author(s):

Afriany ◽

Rubianto Pitoyo

Keyword(s):

Production Process ◽

Palm Oil ◽

Time Series Data ◽

Oil Production ◽

Series Data ◽

Production Data ◽

Crude Palm Oil ◽

Specialized Production ◽

Production Factors ◽

The Relationship

Efficiency is the important things in production process. Some production factors as labor, materials, and machinery must be calculate accurately. The purpose of this research is for analyzing the influence of raw palm oil and Biofuel prices against crude palm oil production. Sample in this research is time series data that specialized production data, The technique analysis is using analysis of multiple linier regression. The results from analysis show correlation between raw palm oil and biofuel prices with crude palm oil production is 57.1 %, The relationship between raw palm oil against crude palm oil production have significant effect and biofuel prices against crude palm oil production have no significant effect. Based on the results of F test there are the significant influence between raw palm oil and biofues prices against crude palm oil production. finding in this research is PT. Wilmar using 3 type of fuel for production process, petroleum, biofuel and waste of raw palm oil production which makes biofuel prices have no effect on crude palm oil production

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A Model to Manage Crude Palm Oil Production System

MATEC Web of Conferences ◽

10.1051/matecconf/201925502001 ◽

2019 ◽

Vol 255 ◽

pp. 02001 ◽

Cited By ~ 1

Author(s):

Inyang John ◽

Andrew-Munot Magdalene ◽

Syed Shazali Syed Tarmizi ◽

Johnathan Tanjong Shirley

Keyword(s):

Simulation Model ◽

Production Process ◽

Conceptual Model ◽

Palm Oil ◽

Production System ◽

Oil Production ◽

Future Research ◽

Crude Palm Oil ◽

Model Based ◽

The Future

This paper reviews key production process for crude palm oil and highlights factors that highly influence the production of crude palm oil. This paper proposes a generic conceptual model for crude palm production process considering these factors. The conceptual model could be modified to consider other factors not included in this paper. The future research would be to construct a simulation model based on the conceptual model proposed in this paper and analyse the effect of these factors on the performance of crude palm oil production system.

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Research and Implementation of the MES System in the Cold-Rolled Plant Based on ERP

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.225-226.573 ◽

2011 ◽

Vol 225-226 ◽

pp. 573-576

Author(s):

Kai Bi Zhang ◽

Xiao Jin Ren

Keyword(s):

Information Systems ◽

Process Control ◽

Data Acquisition ◽

Production Process ◽

Information Management ◽

Production Performance ◽

Production Data ◽

Actual Production ◽

Cold Rolled ◽

Mes System

In the article, functional requirements need to be analyzed for Chongqing four manufacturing execution systems in cold-rolled steel production line. Combined with the idea of design of the system it can be proposed overall design of information systems and network topologies. Meanwhile, it can also be designed technology and production performance modules based the ERP system platform. Focus on design the production performance of functional modules and interface combined with 1450 actual production process. Production data to be collected need to be establishing the simulation model of the node .Do achieve the simulation of the actual production process. Meantime, the simulation data will be collected to the production performance module through the interface with the real-time database. Chongqing four steel companies as state Enterprises, it should be attached great importance to enterprise information. It has not basically realized the bottom of the production process control system, but also the initial establishment of the upper information management system. However, the underlying automation system and upper management information systems are not integrated. This situation seriously affects the application of effective information systems. The method can only deal with the current manual operations, which leads to the following series of questions: the basis of the data delay line failure; Based data acquisition product line transmission efficiency is low and error rate is high; Production site data scattered in many different processes link manually using the original production data acquisition and transmission are difficult to track products from production through to finished off the assembly line of the whole process; Top manufacturing information system based on production data can not directly access to hard to complete implementation of its functions. In response to these problems, Chongqing four steel companies build MES system, marketing integration, the information management systems and process control systems integration as a whole, and thus the implementation of ERP systems and decision support systems, and further enhance their core competitiveness.[1,7]

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Pollutant in palm oil production process

Journal of the Air & Waste Management Association ◽

10.1080/10962247.2013.873092 ◽

2013 ◽

Vol 65 (7) ◽

pp. 773-781 ◽

Cited By ~ 30

Author(s):

Seyed Ehsan Hosseini ◽

Mazlan Abdul Wahid

Keyword(s):

Production Process ◽

Palm Oil ◽

Oil Production

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

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020701751 ◽

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

Penelitian ini menerangkan tentang analisis perbandingan fuzzy Tsukamoto dan Sugeno dalam menentukan jumlah produksi kain tenun dengan menggunakan base rule decision tree. Dari hasil analisis penelitian ini, maka ditemukan beberapa perbedaan yang sangat signifikan: (1) Metode fuzzy Tsukamoto dari hasil yang diperoleh lebih mendekati dari data sesungguhnya, dibandingkan dengan fuzzy Sugeno, (2) Selisih yang diperoleh dengan menggunakan fuzzy Tsukamoto dengan data produksi sesungguhnya selalu konsisten yaitu hasil fuzzy Tsukamoto selalu lebih besar, sedangkan untuk fuzzy Sugeno tidak konsisten, (3) Hasil selisih untuk fuzzy Tsukamoto relatif mendekati dari data produksi sesungguhnya, sedangkan untuk fuzzy Sugeno relatif jauh selisih yang dihasilkan. Sehingga dapat disimpulkan bahwa metode yang paling mendekati nilai kebenaran adalah produksi yang mengunakan metode Tsukamoto dengan keakuratan yang diperoleh menggunakan base rule decision tree sebesar 83.3333 %. Abstract 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%.

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Penerapan Decision Tree Data Mining Pada Produksi Kelapa Sawit PT Hindoli Di Sungai Lilin Kabupaten Musi Banyuasin

Jurnal Pengembangan Sistem Informasi dan Informatika ◽

10.47747/jpsii.v1i2.168 ◽

2020 ◽

Vol 1 (2) ◽

pp. 84-99

Author(s):

Atika Kurnia ◽

Ahmad Haidar Mirza ◽

Andri Andri

Keyword(s):

Data Mining ◽

Decision Tree ◽

Palm Oil ◽

Oil Palm ◽

Production Data ◽

Data Mining Algorithm ◽

Oil Processing ◽

Tree Data ◽

Decision Tree Method ◽

High Quality Seed

Data mining is an interesting pattern extraction of large amounts of data. PT Hindoli itself has a decision support information system that applies the c4.5 data mining algorithm. Given the large amount of data available, data mining estimates that palm oil production for a month is from production data. As one of the companies engaged in processing palm oil and producing palm oil, palm oil, and high-quality seed oil, which are grown by farmers into materials that can be sold and will be distributed to production data. The method used is the decision tree method to explore data, find hidden relationships between a number of prospective variables, among others, the number of producing oil palm based on the year, production, competition, and price, resulting in data accumulation or data grouping every month. Input with the target variable is expected to help PT Hindoli in monitoring palm oil processing.

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Optimal Control Problem of Converter Steelmaking Production Process Based on Operation Optimization Method

Discrete Dynamics in Nature and Society ◽

10.1155/2015/483674 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 3

Author(s):

Jun Zhang

Keyword(s):

Optimal Control ◽

Optimal Control Problem ◽

Control Problem ◽

Carbon Content ◽

Production Process ◽

Production Data ◽

Converter Steelmaking ◽

Operation Optimization ◽

Dynamic Operation ◽

Actual Production

Dynamic operation optimization has been utilized to realize optimal control problem for converter. The optimal control indicator is determined via current state of converter smelting production process, and the set values of operation variable would control converter production. Relationship between various operating variables, current temperature, and carbon content is constructed through operation analysis of a great deal of actual production data; then, the dynamic optimal control indicator is derived from historical excellent smelting data; finally, the dynamic operation optimization model is built by taking the minimum deviation between the current data—molten steel temperature and carbon content—and optimal data which are determined by the optimal control indicator as objective function. DE (differential evolution) with improved strategy is used to solve the proposed model for obtaining the set values of each operating variable, which is beneficial for further control. Simulation of actual production data shows the feasibility and efficiency of the proposed method. That proved that the proposed method solves the optimal control problem of converter steelmaking process as well.

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Evaluating the Sustainability and Inherent Safety of a Crude Palm Oil Production Process in North-Colombia

Applied Sciences ◽

10.3390/app11031046 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1046

Author(s):

Angel Darío González-Delgado ◽

Andrés F. Barajas-Solano ◽

Jeffrey Leon-Pulido

Keyword(s):

Production Process ◽

Palm Oil ◽

Return On Investment ◽

Oil Production ◽

Rapid Expansion ◽

Process Equipment ◽

Inherent Safety ◽

Safety Index ◽

Crude Palm Oil ◽

Complete Evaluation

The African palm is the main source of vegetable oil worldwide, representing about 29.60% of the total oil and fat production around the world. The rapid expansion of this sector has faced several concerns related to environmental and social aspects that have driven the search for sustainable alternatives. In this work, the inherent safety analysis and sustainability evaluation for the crude palm oil production process was performed using the inherent safety index (ISI) method and the sustainable weighted return on investment metric (SWROIM), respectively. The process was designed for a processing capacity of 30 t/h of palm bunches and under North-Colombian conditions. Three technical indicators were considered to evaluate the process sustainability including exergy efficiency, potential environmental impacts output (PEI output), and the total inherent safety index (ITI). The economic factor is directly considered since the SWROIM is an extension of the conventional return on investment (ROI). The resulting ITI at 11 indicated an inherently safe process, and the highest risk was observed for the process equipment safety subindex. The SWROIM reached a higher value (53%) compared to the conventional ROI (49.39%), which suggests positive impacts on sustainability. The novelty of this work lies in detecting the inherent risks and providing a decision making criteria for this project through a complete evaluation that relates economic, energy, environmental, and safety criteria.

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ANALISIS PENGARUH MODAL DAN TENAGA KERJA KERJA TERHADAP PRODUKSI KELAPA SAWIT PADA PT. GRUTI LESTARI PRATAMA MEDAN

Studia Economica : Jurnal Ekonomi Islam ◽

10.30821/se.v1i1.236 ◽

2015 ◽

Vol 1 (1) ◽

pp. 124

Author(s):

Rianto Rianto

Keyword(s):

Production Process ◽

Palm Oil ◽

Significant Influence ◽

Confidence Level ◽

Raw Materials ◽

Oil Production ◽

Work Force ◽

Advanced Technology ◽

Process Factors ◽

North Sumatra

Needs of optimal capital and skilled labor plays an important role in carrying out the production process. Factors palm oil production is crucialn for manufacturers producing cooking oil, butter and other manufacturers mainly use raw materials from palm oil almost of the producers are located in the region of North Sumatra. The use of capital and labor effective and efficient will result in optimal production of palm oil. The use of capital and labor in the production process of palm oil at PT. Gruti Pratama Lestari significant effect on production. Economic indicators that affect the level of production is investment/capital, labor and technology advancement. Capital formation, the material in the form of machines, tools and equipment as well as improving the quality of human resources is seen as a major factor in the economic development that will lead to the full utilization of economic resources available. The use of more advanced technology will create efficiencies in production that ultimately result in increased production. PT. Gruti Pratama Lestari is one of the private plantations producing palm oil fresh fruit bunches (FFB) in North Sumatra. The presence of all these companies was instrumental in improving the region's economic growth, apart from an increase inrevenue (PAD) can also create new jobs for the society in general as well as surrounding communities in particular. To support the palm oil production process PT. Gruti Pratama Lestari in need of capital and labor. The results of the analysis of data with simultaneous test shows that capital and labor are very significant influence on the production of palm oil PT. Gruti Pratama Lestari at 95% confidence level. The results of the analysis with partial test shows that capital very significant influence on the production of palm oil, and labor have a significant effect on the production of palm oil at 95% confidence level. Judging from the use of factors of production to the production of capital turned out to be more dominant than labor. This means that the use of capital has more influence on the increase in palm oil production than labor. Standardized Coeffecients capital of .0915% and the Standardized Coeffecients work force by 0132 against the production of palm oil, a much greater capital than labor means that PT. Gruti Pratama Lestari using capital-intensive technology.

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