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All Journal Bulletin of Electrical Engineering and Informatics Building of Informatics, Technology and Science Bulletin of Artificial Intelligence
Ahmad Ari Aldino
Monash University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Classification of Fruits High in Vitamin C Using Self-Organizing Map and the K-Means Clustering Algorithm Nuke L Chusna; Nurhasan Nugroho; Umbar Riyanto; Ahmad Ari Aldino
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4104

Abstract

Vitamin C-rich fruits not only taste fresh and delicious but also have the potential to increase the body's resistance to various diseases and maintain a proper nutritional balance. Information about fruits high in vitamin C is very important in order to increase public knowledge about which fruits contain high levels of vitamin C. However, to classify fruits high in vitamin C based on their image, a model is needed that is able to analyze the characteristics present in the image of the fruit. The purpose of this study is to build a classification model for high-vitamin C fruits with a combination of the Self-Organizing Map (SOM) artificial neural network algorithm and K-Means Clustering. Prior to classification, an image segmentation process is carried out using the K-Means Clustering algorithm, which will separate the image into parts that have similar visual characteristics. After the segmented image, the features of the object are extracted based on shape and texture. After the features of the image have been obtained, proceed with classifying images using the SOM algorithm by mapping multidimensional data into a lower-dimensional spatial representation to obtain the appropriate group or class. The accuracy test results for the built model produce an accuracy value of 93.33% and are included in the good category
Multiple Attribute Decision Making Menggunakan Metode TOPSIS Dalam Penentuan Staff Marketing Terbaik Setiawansyah Setiawansyah; Very Hendra Saputra; Sanriomi Sintaro; Ahmad Ari Aldino
Bulletin of Artificial Intelligence Vol 2 No 2 (2023): October 2023
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Multiple Attribute Decision Making (MADM) is an approach used in decision making to select the best alternative from a number of given criteria or attributes. This research aims to apply the TOPSIS method in selecting the best marketing staff so that it can become a reference and benchmark for companies in selecting the best marketing staff using a decision support system model. The results of the ranking of the selection of the best marketing staff who got rank 1 with a value of 0.644, namely Ahmad, rank 2 with a value of 0.539, namely Hermawan, rank 3 with a value of 0.529, namely Santoso, rank 4 with a value of 0.443, namely Jayanti, rank 5 with a value of 0.399, namely Heru.
Employing PIPRECIA-S weighting with MABAC: a strategy for identifying organizational leadership elections Setiawansyah Setiawansyah; Sitna Hajar Hadad; Ahmad Ari Aldino; Pritasari Palupiningsih; Gibtha Fitri Laxmi; Dyah Ayu Megawaty
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7713

Abstract

The election of organizational leaders, especially in organizations whose members have diverse backgrounds and interests, can cause various problems. Problems in the selection of school organization leaders include the absence of an objective selection of organizational leadership candidates because they are selected based on comparisons between candidates without considering the criteria in the selection of organizational leadership candidates. Research related to the multi-attributive border approximation area comparison (MABAC) and simplified pivot pairwise relative criteria importance assessment (PIPRECIA-S) methods has never been conducted so far, so it is a reference in conducting this research using the MABAC and PIPRECIA-S methods. This study aims to select the head of the school organization using the MABAC method and PIPRECIA-S weighting can increase the objectivity of the criteria assessment results by relying on calculations from the PIPRECIA-S weighting method. Based on the selection results using the MABAC method and PIPRECIA-S weighting, candidate 1 was recommended as the leader of the school organization because it achieved rank 1 with a total score of 0.293. The contribution of this research is to help in the selection of the head of the organization using the PIPRECIA-S and MABAC methods as a decision-making solution.
Co-Authors Chusna, Nuke L Dyah Ayu Megawaty Gibtha Fitri Laxmi Nurhasan Nugroho Palupiningsih, Pritasari Sanriomi Sintaro Setiawansyah Setiawansyah Sitna Hajar Hadad Umbar Riyanto Very Hendra Saputra