Pemanfaatan Algortima K-Means Clustering Sebagai Pengamanan Pencurian Buah Kelapa Sawit Se-Distrik Tandun PT. Perkebunan Nusantara V

Oil palm fruit theft at PT. Perkebunan Nusantara V still occurs frequently, especially in one district, Tandun District which has 6 oil palm and rubber plantations. The high rate of theft of oil palm fruit causes a decrease in the production of oil palm fruit and palm kernel. This study discusses the use of the K-Means Clustering algorithm as one of the data mining algorithms in grouping data on palm oil theft in the oil palm plantation area of Tandun District. With a number of theft data, this study enables the discovery of the potential for theft of oil palm fruit in the location of oil palm plantations at low, medium and high levels. K-Means Clusering algorithm calculation uses 3 supporting parameters, namely the area of each afdeling, the number of fruits that have been saved, and the number of fruits that have been stolen. After knowing the locations that have the potential for theft of oil palm fruit, the company can implement a strategy to safeguard those potential locations.

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Design and Development of an Integrated Small-Scale Oil Palm Fruit Processing Mill

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.367.739 ◽

2011 ◽

Vol 367 ◽

pp. 739-743

Author(s):

C.O. Ilechie ◽

A.O. Akii Ibhadode ◽

B.O. Abikoye

Keyword(s):

Vegetable Oil ◽

Palm Oil ◽

Oil Palm ◽

Palm Kernel ◽

Poor Quality ◽

Small Scale ◽

Palm Fruit ◽

Fully Integrated ◽

Oil Crops ◽

Fruit Processing

The oil palm (elaeis guneensis) is a very important economic crop in West Africa where it is native. The fruit bunch contains 23 to 30% oil and is the highest yielding of all vegetable oil crops. Palm oil is the second most important vegetable oil in world consumption and the first to be commercialized internationally. Africa and indeed Nigerian was the world’s highest producer of palm oil prior to 1961. Today, Nigeria is the fourth largest producer after Indonesia, Malaysia and Thailand. One of the main reasons given for this fall is lack of efficient mechanized processing equipment for the small-scale producers who produce over 80% of the country’s palm oil. Their methods of production are labour intensive, batch, tedious, inefficient, and produce poor quality oil, have low throughput, unable to extract palm kernel alongside palm oil and so productivity is low and products (palm oil and palm kernel) lack competitiveness. This work has developed a mechanized oil palm fruit processing mill with six fully integrated systems for extracting good quality palm oil and palm kernel, while utilizing process wastes as the main source of heat energy. Each system/unit is expected to operate at the best quoted system efficiency. Tests are ongoing to determine and confirm these efficiencies.

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Dbscan Assisted by Hybrid Genetic K Means Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8061.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 1973-1979

Keyword(s):

Data Mining ◽

Time Complexity ◽

Clustering Algorithm ◽

Hybrid Approach ◽

Research Direction ◽

Computational Time ◽

Main Concern ◽

Complex Data ◽

Data Mining Algorithms ◽

Mining Algorithms

The data mining algorithms functioning is main concern, when the data becomes to a greater extent. Clustering analysis is a active and dispute research direction in the region of data mining for complex data samples. DBSCAN is a density-based clustering algorithm with several advantages in numerous applications. However, DBSCAN has quadratic time complexity i.e. making it complicated for realistic applications particularly with huge complex data samples. Therefore, this paper recommended a hybrid approach to reduce the time complexity by exploring the core properties of the DBSCAN in the initial stage using genetic based K-means partition algorithm. The technological experiments showed that the proposed hybrid approach obtains competitive results when compared with the usual approach and drastically improves the computational time.

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Design and development of a manually operated oil palm crusher

Progressive Agriculture ◽

10.3329/pa.v32i1.55716 ◽

2021 ◽

Vol 32 (1) ◽

pp. 60-70

Author(s):

MR Islam ◽

MA Awal ◽

CK Mistry

Keyword(s):

Stainless Steel ◽

Palm Oil ◽

Oil Recovery ◽

Oil Palm ◽

Palm Kernel ◽

Moisture Loss ◽

Design Parameters ◽

Crude Palm Oil ◽

Palm Fruit ◽

Palm Fruits

Palm growers in Bangladesh are currently facing problems to extract crude palm oil from FFB. The traditional method of palm oil processing is time-consuming, laborious, hazardous, and inefficient resulting in the production of low-quality oil. Though mechanical processing is costly but produces good quality Crude Palm Oil (CPO) and the oil recovery rate is high. A low-cost mechanical (manually operated) oil palm crusher was designed and febricated in the workshop of the Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh. The crusher was designed by using Auto-Cad software. It was fabricated according to design parameters. It is fabricated by stainless steel (SS) shaft, stainless steel cylinder, mild steel (MS) hopper, (MS) hollow bars, MS pressure case cap & Bearing. It requires a very small rotating force. The cost of the crusher is only BDT 12000, which is within the buying capacity of the farmers of Bangladesh. Crusher was mounted on the table or bench. At first 500 gm fresh palm fruits were taken for experimentation. About 300 gm mesocarp and 200 gm oil palm kernel (nuts) were found from 500 gm oil palm fruits. Heating 300 gm mesocarp about 15 minutes and was weighted 280 gm mesocarp due to moisture loss. About 62 gm crude palm oil, 124 gm oil cake, 88 gm skum were collected from 280 gm mesocarp. The average m.c (wb) of fresh palm fruit was found 20.73%. The crushing capacity of the mesocarp of the crusher was o.84 kg/hr. The rotating speed of the screw was 26 rpm. Crude oil percentage was found 22.14%. The total time to extract 62 gm crude palm oil from 500 gm of fresh fruit was 1hr and 15 minutes. The weight of the crusher is 11 kg which is easy to operate by one man. The overall performance of the crusher was found quite satisfactory and could be useful for smallholder palm growers in Bangladesh. Progressive Agriculture 32 (1): 60-70, 2021

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Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data

International Journal of Data Warehousing and Mining ◽

10.4018/jdwm.2011010103 ◽

2011 ◽

Vol 7 (1) ◽

pp. 46-66 ◽

Cited By ~ 10

Author(s):

Can Brochmann Yildizli ◽

Thomas Pedersen ◽

Yucel Saygin ◽

Erkay Savas ◽

Albert Levi

Keyword(s):

Data Mining ◽

Real World ◽

Clustering Algorithm ◽

Privacy Preserving ◽

Secure Multiparty Computation ◽

Multiparty Computation ◽

Privacy Preserving Data Mining ◽

Computational Costs ◽

Data Mining Algorithms ◽

Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

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Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

Current Drug Safety ◽

10.2174/1574886313666181026100000 ◽

2019 ◽

Vol 14 (1) ◽

pp. 21-26 ◽

Cited By ~ 2

Author(s):

Viswam Subeesh ◽

Eswaran Maheswari ◽

Hemendra Singh ◽

Thomas Elsa Beulah ◽

Ann Mary Swaroop

Keyword(s):

Data Mining ◽

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Disproportionality Analysis ◽

Positive Signal ◽

Data Mining Algorithms ◽

Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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