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All Journal JOIV : International Journal on Informatics Visualization Generation Journal Jurnal Teknik Informatika (JUTIF) International Journal of Applied Information Systems and Informatics
Nur Shofa, Rahmi
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Computer Science & IT

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Classification of Guava Fruit Types Using Principal Component Analysis and K-Nearest Neighbor Algorithms Andrean Nugraha, Rezky; Wahyu Hidayat, Eka; Nur Shofa, Rahmi; Eka Wahyu Hidayat, S.T., M.T.; Rahmi Nur Shofa, S.T., M.T.
Generation Journal Vol 7 No 1 (2023): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v7i1.17900

Abstract

The maturity level of guava fruit can be determined by looking at various factors. Shape is one of the factors that play a role in identifying certain objects. The classification of guava fruit can be seen from the shape, texture and color. The shape of the guava fruit is quite diverse ranging from round (Round shape) to oval (Pear shape). So a Matlab application was built to determine the type of guava based on its color, shape and texture. K-Nearest Neighbor can classify objects based on learning data that is closest to the object so that the results can be more accurate. Principal Component Analysis (PCA) is a statistical technique for simplifying many-dimensional data sets into lower dimensions (extration features). The combination of K-Nearest Neighbor with Principal Component Analysis produces a fairly high accuracy for determining the type of guava using a total of 45 images and divided into two data including training data with a total of 36 guava data and test data with a total of 9 guava data.
K-Nearest Neighbor and Weight K-Nearest Neighbor Classification of Cork Fish Using Gray-Level-Co-Occurrence Matrix Algorithm Approach Fitriani Dewi, Euis Nur; Rachman, Andi Nur; Nur Shofa, Rahmi; Tarempa, Genta Nazwar
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.2745

Abstract

Ornamental cork fish is a type of fish that is in great demand among the public as an ornamental fish. Ornamental cork fish have various types and colors; each variation has its own name and is a selling point among ornamental cork fish lovers. With a good motif, ornamental cork fish will have an expensive market value. However, for the most part, there are still many who do not know for sure what type of ornamental cork fish is included in the variation type classification because the colors are varied and seem similar. Because of this, this research created a system that can classify types of ornamental cork fish automatically based on data while still paying attention to the level of accuracy of the classification. The algorithm used for the initial classification process is KNN, which is chosen for its accuracy comparison level value. This algorithm does not consider the weight of each data point to be classified. The data processing process carried out only looks at the highest number of classes, which becomes the benchmark for labels from the classification results. In the classification process method using the KNN algorithm, there are still shortcomings in the classification process, so this research carried out a process of comparing classification accuracy using the Weight-KNN algorithm to increase the classification accuracy value. The process of the Weight-KNN algorithm stages is to carry out classification based on nearest neighbors first but still paying attention to the weight of each data. So that the classification process of determining the type of ornamental cork fish variation will be more accurate. Based on the results of experiments conducted, this research will focus on comparing the classification results between the KNN and Weight-KNN algorithms on ornamental cork fish. The results obtained state that the Weight-KNN algorithm has a higher level of accuracy with a weight of 83.6%, whereas using the KNN algorithm, it is only 80.6%.
GRAPHICAL COMPUTING FOR BATIK PATTERN DESIGN BASED ON L-SYSTEM Hidayat, Eka Wahyu; Anshary, Muhammad Adi Khairul; Nur Shofa, Rahmi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1595

Abstract

The challenge faced by the batik industry in the industrial era 5.0 is the adaptation to technology in the production process. One way to overcome this challenge is to start from the basics in the batik industry, namely the creative process of designing batik patterns. It is important to pay special attention to this process to enhance digital transformation in the batik industry. The purpose of this paper is to present the design and creation of batik patterns using the L-System-based fractal approach. Previous research has shown that the L-System can be used to model plant growth in 2D and 3D contexts. In a similar way, the L-System is used in this study to create batik patterns. Experiments were conducted through three stages, namely Data Acquisition, Data Identification, and Modeling. The experiment results in a dataset of batik motifs that can be used as parameters to replace line segments in the L-System. The design and creation of batik patterns using the L-System only needs to be done once, so that from one pattern, a variety of different motifs can be produced easily by simply changing the parameters. This shows that the design and creation of batik patterns using L-System is more efficient and practical. In addition, the fractal dimension calculation is used to understand and describe the fractal properties of the resulting objects. In this study, it was found that there are four motifs without ornaments that have higher fractal dimension values than motifs with equivalent ornaments.
Development of Warehouse Management System to Manage Warehouse Operations Kusuma Dewi, Irena; Nur Shofa, Rahmi
Journal of Applied Information System and Informatic (JAISI) Vol 1, No 1 (2023): November 2023
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v1i1.8991

Abstract

The Warehouse Management System (WMS) is software designed to assist in managing and monitoring warehouse processes. The purpose of this research is to improve warehouse operational management by developing a WMS using the Extreme Programming method and implementing a monitoring system to display real-time temperature and humidity measurements. The research addresses issues observed in the warehouse operations of PT. Shippindo Teknologi Logistik (Shipper), a warehouse rental services company. The problems identified in Shipper's warehouse operations largely stem from human errors. Hence, this research aims to provide a viable solution to reduce human errors by implementing various management processes, including inbound and outbound management and tracking, as well as visualizing product placement within the racks using rack maps. Additionally, the integration of a temperature and humidity monitoring system in the warehouse helps monitor the warehouse's condition in real-time. Testing using the black-box method for WMS in this research was successful, demonstrating that the system can execute all functions and display temperature and humidity data as per the designed specifications (inbound, mapping, storage, temperature and humidity monitoring, outbound). The average error in temperature and humidity measurements is relatively low, with 0.9% for temperature and 1.3% for humidity. However, further development is still required to enhance the system for better performance. This includes creating a more robust model for product detection labeling on the storage page to improve label accuracy and developing control systems for advanced temperature and humidity monitoring.
Co-Authors Andrean Nugraha, Rezky Anshary, Muhammad Adi Khairul Eka Wahyu Hidayat, S.T., M.T. Fitriani Dewi, Euis Nur Genta Nazwar Tarempa Hidayat, Eka Wahyu Kusuma Dewi, Irena Rachman, Andi Nur Rahmi Nur Shofa, S.T., M.T. Wahyu Hidayat, Eka