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Journal : Jurnal Pilar Nusa Mandiri

PENERAPAN METODE ITERATIVE DICHOTOMIZER 3 (ID 3) UNTUK MENENTUKAN BEASISWA BERPRESTASI PADA SMP PGRI CARINGIN SUKABUMI Saputra, Rizal Amegia; Ramdhani, Lis Saumi; Supriatman, Supriatman
Jurnal Pilar Nusa Mandiri Vol 15 No 1 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Maret 2
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1207.643 KB) | DOI: 10.33480/pilar.v15i1.29

Abstract

Scholarships are assistance from the government to students / students who are less able or have the ability in the academic and non-academic fields that are given individually to reduce the burden in terms of material. Frequently stalling time in selection, the number of students who apply for scholarships, the number of students whose homes are far from school, the number of students who race to come early as one of the criteria eligible to receive scholarships as well as the most scholarship applicants feel disadvantaged by unfavorable decisions. Iterative Dichotomizer 3 (ID3) algorithm is the most basic decision tree learning algorithm (decision tree learning algorithm). This algorithm conducts a thorough search on all possible decisions. In this research, it will be analyzed the application of the iterative dichotomizer 3 method in the case of determining achievement scholarships. In order to make decisions quickly and accurately. From 708 scholarship candidates including 28 eligible and 680 scholarship recipients, 136 scholarship recipients were obtained from ID3 algorithm with 3 eligible and 133 who had not, and obtained an accuracy rate of 97.75% so that it could be concluded that good and can help the school.
APPLICATION OF EXPERT SYSTEM FOR ANDROID-BASED FOOD LAND SUITABILITY AND HOLTICULTURE Ramdhani, Lis Saumi; Susilawati, Desi; Saputra, Rizal Amegia
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1461

Abstract

Plant land suitability is a way of evaluating the characteristics of planted land-based on certain criteria to determine which types of plants are most suitable for planting in that land, land suitability has not been utilized properly by farmers due to limited knowledge about the varieties of plant types that can be planted in their land, selection The types of plants are still based on traditions and elements of the surrounding agricultural environment which are only limited to a few types of plants without taking into account the suitability of the plants planted to their land characteristics. For this reason, an expert system application was created to help farmers determine the suitability of land for food crops and horticulture on an Android basis because on an Android basis it can make it easier for users, especially farmers to determine the suitability of their land without the need to find a plant land expert and can easily accessible to anyone, anywhere. To produce a good expert system, the research method will be used, namely the certainty factor method. The results of testing expert system applications with certainty factor methods are proven to be able to provide accurate land suitability information
ABILITY CONVOLUTIONAL FEATURE EXTRACTION FOR CHILI LEAF DISEASE USING SUPPORT VECTOR MACHINE CLASSIFICATION Saputra, Rizal Amegia; Haryanto, Toto; Wasyianti, Sri
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.4961

Abstract

Chili plants are among the most commonly used food ingredients in various dishes in Indonesia. Leaves on chili plants are often affected by disease; if the disease is not treated immediately, it can damage the plant and cause crop failure. Early detection of chili plant diseases is important to reduce the risk of crop failure. The development of technology and the application of machine-learning algorithms can automatically monitor chili plants using a computer system. Using this algorithm, the system analyzes and identifies diseases that a camera can observe and record. In this study, the proposed method for feature extraction uses a convolutional neural network (CNN) algorithm with transfer learning using VGG19. For classification using SVM for training data, accuracy generated 95%, precision 95%, recall 95%, and F1-Score 95%, and testing data accuracy generated 90%, precision 89%, recall 90%, and F1-Score 89%, proving that the convolutional process with architecture VGG19 and SVM algorithm is acceptable for classification. In future research, other architectures or extraction fusions can be used to maximize the results.