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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Moneter : Jurnal Keuangan dan Perbankan
Pulung Nurtantio Andono
Dian Nuswantoro University
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Orchid types classification using supervised learning algorithm based on feature and color extraction Pulung Nurtantio Andono; Eko Hari Rachmawanto; Nanna Suryana Herman; Kunio Kondo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3118

Abstract

Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.
The evaluation of convolutional neural network and genetic algorithm performance based on the number of hyperparameters for English handwritten recognition Muhammad Munsarif; Edi Noersasongko; Pulung Nurtantio Andono; Moch Arief Soeleman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1250-1259

Abstract

Convolutional neural network (CNN) has been widely applied to image recognition, especially handwritten English recognition. CNN's performance is good if the hyperparameter values are correct. However, the determination of precise hyperparameters is not a trivial task. This task is made more difficult when combined with a larger number of hyperparameters resulting in a high dimensionality of the search space. Usually, hyperparameter optimization uses a finite number. Previous studies have shown that a large number of hyperparameters can result in optimal CNN performance. However, the studies only apply to text mining datasets. This study offers two novelties. First, it applied 20 hyperparameters and their ranges to handwritten English. Second, this paper conducted seven experiments based on different hyperparameters and the number of hyperparameters. This paper also compares the existing methods, namely random and grid search. The experiment resulted in the proposed model being superior to the existing methods. EX3 is better than other experiments and a larger number of hyperparameters and layer-specific hyperparameter values are unimportant.
DESIGN OF IOT AND ONION AGRICULTURE DATABASE USING BPR LIFE CYCLE Nisrina Salwa Thifaal; Farrikh Alzami; Alvin Steven; Rindra Yusianto; Filmada Ocky Saputra; Mila Sartika; Pulung Nurtantio Andono; Firman Wahyudi
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023)
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (632.824 KB) | DOI: 10.32832/moneter.v11i1.54

Abstract

One of the food commodities produced by the agricultural sector with high economic value is red onion. As the population of Indonesia increases, the need for red oniom has also increased. The level of red onion production from year to year is also increasing. Especially the central Java area as the largest red onion producing center in 2021. Therefore, the amount of red onion production needs to be maintained and increased by monitoring overall land conditions. Such as weather conditions, air, temperature, and humidity. A sensor to detect these factors is already available but there is no database to accommodate the data from the sensor. The purpose of this research is to produce a Business Process Model and Notation (BPMN) of red onion surveillance system on Internet of Things (IoT) based farmland. The stages carried out are by collecting data related to the research and analyzing business processes using the Business Process Reengineering Life Cycle (BPR) method. This method improves business processes to become more efficient and renewable. This research produces a database design to accommodate incoming data from Internet of Things sensors. Things (IoT) on red onion farming.
Implementation Of Extreme Gradient Boosting Algorithm For Predicting The Red Onion Prices Pungky Nabella Saputri; Farrikh Alzami; Filmada Ocky Saputra; Pulung Nurtantio Andono; Rama Aria Megantara; L Budi Handoko; Chaerul Umam; Firman Wahyudi
Moneter: Jurnal Keuangan dan Perbankan Vol. 11 No. 1 (2023)
Publisher : Universitas Ibn Khladun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.456 KB) | DOI: 10.32832/moneter.v11i1.55

Abstract

Red Onion or the Latin name Allium Cepa is included in the group of vegetable plants that are needed by the public for food needs. Red Onions are one of the seasonal crops so their availability can change in the market which causes price instability due to a lack of supply of production by several factors: 1) not yet it's harvest time, 2) crop attacked disease pests and fungi, and 3) weather factor. Therefore, a study is needed to predict red onion prices, so that it can be used as information for the government to stabilize red onion prices. The method used in this study is CRISP-DM and the Extreme Gradient Boosting algorithm to predict the price of red onions by taking data samples from Tegal and Pati Cities. The results of this study are that the Extreme Gradient Boosting algorithm is able to produce Tegal District Root Mean Square Error (RMSE) values of 5107.97% and Mean Absolute Percentage Error (MAPE) values of 0.17%. For prediction results with Pati Regency data samples, it produces a Root Mean Square Error (RMSE) value of 6049.74% and a Mean Absolute Percentage Error (MAPE) of 0.17%.
Co-Authors Alvin Steven Alzami, Farrikh Chaerul Umam Edi Noersasongko Eko Hari Rachmawanto Filmada Ocky Saputra Firman Wahyudi Firman Wahyudi Kunio Kondo L. Budi Handoko Megantara, Rama Aria Mila Sartika Moch Arief Soeleman Muhammad Munsarif Nanna Suryana Herman Nisrina Salwa Thifaal Pungky Nabella Saputri Yusianto Rindra