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All Journal Indonesian Community Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Enny Itje Sela
Universitas Teknologi Yogyakarta, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Environmental Science Public Health Social Sciences Other

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Klasifikasi Varietas Benih Padi Berdasarkan Morfologi dengan Algoritma Random Forest Muhamad Hafidz Ghifary; Enny Itje Sela
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.371

Abstract

Rice seeds are one of the main elements in agricultural businesses. The choice of type of rice seed planted can influence the quality of the harvest obtained. The large number of varieties of rice seeds with similar shapes makes identifying the type of rice seed an activity that is not easy and requires experts to do. One fairly fast way to identify rice seed varieties is to use machine learning technology. This research will implement machine learning classification algorithms, namely KNN, Naïve Bayes, and Random Forest. Identification of rice seed varieties is carried out based on the morphological features of the seeds. The dataset used is in the form of seed morphological feature values, namely aspect ratio, solidity, circumference, area, area, roundness, circularity and equivalent diameter. Research stages starting from preprocessing, feature extraction, and experimental parameter values were carried out to find the model with the best performance. Feature selection can increase the testing accuracy on KNN and Random Forest models. The test results obtained an accuracy of 78.3% with KNN, 61.7% using Naïve Bayes, and 90% using Random Forest.
Pemanfaatan Teknologi Informasi untuk Inovasi Motif, Diversifikasi Produk, dan Perluasan Jaringan Pasar pada Batik Nitik Kembangsore Rianto Rianto; Enny Itje Sela; Nur Wening
I-Com: Indonesian Community Journal Vol 4 No 4 (2024): I-Com: Indonesian Community Journal (Desember 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/icom.v4i4.5689

Abstract

Batik Nitik Kembangsore operates in the traditional batik industry, with unique and exclusive motifs. However, the current production is limited to jarit cloth with a relatively high price, resulting in low sales turnover due to the high selling price. This Community Partnership Program proposes a solution to overcome these challenges by diversifying products and creating contemporary batik motifs using artificial intelligence. Leonardo.ai's online tools will be used to design new motifs while maintaining the aesthetic value and philosophy of Batik Nitik Kembangsore. In addition, to strengthen the marketing and sales system, the website www.kembangsore.com will be developed as an integrated digital platform. With this strategy, it is hoped that Batik Nitik Kembangsore can expand its market reach, increase competitiveness, and reach broader consumers. This innovation supports the sustainability of the traditional batik industry and strengthens Batik Nitik Kembangsore's position in the modern market.
Co-Authors Muhamad Hafidz Ghifary Nur Wening Rianto Rianto