Weed Control Decision Support System Based on Precision Agriculture Approach

Sampurno, Rizky Mulya; Seminar, Kudang Boro; Suharnoto, Yuli

doi:10.12928/telkomnika.v12i2.1982

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…The decision-maker controls the data in a man-machine conversation which progresses the choice [72]. The upcoming upgrade will create a single application interface for acquiring data from such sensing devices, enabling the easy and rapid acquisition of required data sets from various remote sensing device sources [73].…”

Section: ) Critics Of the Dss For Fisheriesmentioning

confidence: 99%

Decision Support System in Fisheries Industry: Current State and Future Agenda

Panudju¹,

Rahardja²,

Nurilmala³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Decision Support Systems (DSS) are systems that assist decision-makers and aim to synthesize domain and technical knowledge and package it so non-scientists can use and comprehend it. This study aims to compile initial empirical studies that can objectively reject or confirm the central hypothesis. The materials were retrieved after applying the filtered query across all sources. All search engine providers use five query strings. In each example, five findings were collected, sorted, and compared to one another, and 152 papers were generated. Seventy-six documents were discovered after applying the inclusion and exclusion criteria. Each of the 70 papers was independently examined and analyzed. The method of study followed a specific procedure explicitly developed to minimize the risk of researcher bias. It is primarily concerned with whether fisheries have decision-making systems and what empirical outcomes these systems produce, particularly in real-time analysis. The result shows a dearth of research on intelligent DSS, which accounts for less than 3% of all DSS types discussed in the article. This study offers academics and professionals an overview of the implementation of DSS. These new contributions imply the form of several different new contributions to further research. Furthermore, the possibility of identifying research gaps increases once merged with geoinformation technology or spatial information. We introduced a new model that combines spatial logistics techniques with GIS-based tracing and tracking. The envisioned logistics ensure spatial and logistical traceability in the process of fish products.

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Section: ) Critics Of the Dss For Fisheriesmentioning

confidence: 99%

Decision Support System in Fisheries Industry: Current State and Future Agenda

Panudju¹,

Rahardja²,

Nurilmala³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The use of decision support system in various fields has helped people to make various decisions, make decisions in agriculture for the diagnosis of rice plants [1], computer maintenance, [2] and spraying weeds on plants [3]. Decision support systems can also be applied to a university related to scholarship provision using TOPSIS and Weighted Product methods [4], increasing lecturer satisfaction in terms of preferences and ratings [5], and can improve library performance efficiency in a collage in providing book recommendations alternative to visitors with collaborative filtering methods [6].…”

Section: Introductionmentioning

confidence: 99%

Decision Support System of Lecturer Selection Recommendation with Collaborative Filtering Method

Honggowibowo¹,

Wintolo²,

Indrianingsih³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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The use of decision support systems for the selection of the best lecturers at Adisutjipto Institute of Technology (STTA) is not yet at the level of application. The average tertiary institution in selecting the best lecturers uses certain criteria, for example the criteria in teaching, research and community service. Of the three indicators of lecturer performance in Indonesia, we only use two indicators, namely research and community service. From these two indicators, we made five criteria, namely the number of presenters at the conference, the amount of community service, the number of unpublished research, the number of published research, and the number of citations of scientific articles. The selection was made by users, namely STTA Director, Vice Director for Academic Affairs, Vice Director for Financial Affairs and Community Service Research Center to 62 lecturers who were active in research, community service and publications indexed on Google Scholar. This user restriction is adjusted to the organizational structure at our university, where the five users have authority in assessing the performance of lecturers and giving awards to lecturers who are declared as the best lecturers. After Collaborative Filtering method is done to predict the final result using the rating given by the user. Based on the results of the system testing, it was concluded that the system that was built could be a solution to help select lecturers who were eligible to be given awards as the best of lecturer in the field of research and community service.

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“…In order to reduce noise which caused by clouds and other aerosol problem, discrete wavelet transform (DWT) is used to denoising and extracting the agriculture vegetation information from the terrestrial surface [9], [17][18]. Decision support system for weed control based on a precision agricultural approach that has been done [19] also used MODIS time-series data as the main information of vegetation stage.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Satelite Imagery

et al. 2018

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Spatiotemporal analysis of MODIS Vegetation Index Imagery widely used for vegetation seasonal mapping b oth on forest and agricultural site. In order to provide a long -terms of vegetation characteristic maps, a wide time-series images analysis is needed which require high -performance computer and also consumes a lot of energy resources. Meanwhile, for agriculture monitoring purpose in Indonesia, that analysis has to b e employed gradually an d endlessly to provide the latest condition of paddy field vegetation information. This research is aimed to develop a method to produce the optimized solution in classifying vegetation of paddy fields that diverse b oth spatial and temporal characteristics . The time-series EVI data from MODIS have b een filtered using wavelet transform to reduce noise that caused b y cloud. Sequential K-means and Parallel K-means unsupervised classification method were used in b oth CPU and GPU to find the efficient and the rob ust result. The developed method has b een tested and implemented using the sample case of paddy fields in Java Island. The b est system which can accommodate of the extend-ab ility, affordab ility, redundancy, energy-saving, maintainab ility indicators are ARM-b ased processor (Raspb erry Pi), with the highest speed up of 8 and the efficiency of 60% .

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Weed Control Decision Support System Based on Precision Agriculture Approach

Abstract: Abstract

Cited by 3 publications

References 11 publications

Decision Support System in Fisheries Industry: Current State and Future Agenda

Decision Support System in Fisheries Industry: Current State and Future Agenda

Decision Support System of Lecturer Selection Recommendation with Collaborative Filtering Method

Optimization of Parallel K-means for Java Paddy Mapping Using Time-series Satelite Imagery

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