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All Journal Jurnal Riset Informatika Jurnal Manajemen Informatika dan Sistem Informasi
Yusuf Arif Setiawan
Universitas Nusa Mandiri
Computer Science & IT

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RANGKING INDEKS BERITA LARANGAN MUDIK PADA PORTAL MEDIA ONLINEDENGAN METODE TF-IDF DAN COSINE SIMILARITY MENGGUNAKAN MACHINE LEARNING Muhammad Syahrani; Kusnadi - Kusnadi; Bambang Joko Triwibowo; Yusuf Arif Setiawan; Fariszal Nova Arviantino; Didi Rosiyadi
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 5 No. 1 (2022): MISI Januari 2022
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v5i1.500

Abstract

Usaha pemerintah Indonesia dalam pencegahan penyebaran virus Covid 19 dengan dikeluarkannya peraturan yang diterapkan sampai tingkat daerah. Dan tradisi tahunan masyarakat Indonesia mudik lebaran 2021 telah dilarang. Opini berita tentang pelarangan mudik lebaran baik dimedia cetak maupun media online dan dimedia sosialpun ramai diperbincangkan, tentu masyarakat yang akan mudik merasakan kebingungan dengan pemberitaan tersebut dan belum mengetahui kapan dan sampai kapan diberlakukan. Hal ini peneliti bereksperimen mengumpulkan berita-berita yang ada di portal media online. Kumpulan berita tersebut dijadikan dataset, selanjutnya dilakukan preprocessing meliputi tahapan tokenizing, filtering dan stemming. Pencarian informasi berita yang akurasi dapat menggunakan algoritma vector space model dengan menghitung TF IDF dan cosine similarity pada setiap judul berita (dokumen) dan pada paper ini peneliti dengan menggunakan machine learning. Dataset yang digunakan 5 judul berita yang masing-masing diberi label D1, D2, D3, D4, dan D5. Hasil penelitian menunjukan bahwa rangking indek berita larangan mudik yang paling tinggi terdapat pada dokumen 5(D5) dengan skor 0,612. Hasil tersebut menguatkan akan tujuan penelitian yaitu untuk mengetahui keyword yang cocok digunakan agar dapat memperoleh berita yang relevan dan sesuai keinginan dengan menghitung dan merangking hasil nilai cosine similarity.
Classification for Papaya Fruit Maturity Level with Convolutional Neural Network Nurmalasari Nurmalasari; Yusuf Arif Setiawan; Widi Astuti; M Rangga Ramadhan Saelan; Siti Masturoh; Tuti Haryanti
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.541

Abstract

Papaya California (Carica papaya L) is one of the agricultural commodities in the tropics and has a very big opportunity to develop in Indonesia as an agribusiness venture with quite promising prospects. So the quality of papaya fruit is determined by the level of maturity of the fruit, the hardness of the fruit, and its appearance. Papaya fruit undergoes a marked change in color during the ripening process, which indicates chemical changes in the fruit. The change in papaya color from green to yellow is due to the loss of chlorophyll. During storage, the papaya fruit is initially green, then turns slightly yellow. The longer the storage color, the changes to mature the yellow. The process of classifying papaya fruit's ripeness level is usually done manually by business actors, that is, by simply looking at the color of the papaya with the normal eye. Based on the problems that exist in classifying the ripeness level of papaya fruit, in this research, we create a system that can be used to classify papaya fruit skin color using a digital image processing approach. The method used to classify the maturity level of papaya fruit is the Convolutional Neural Network (CNN) Architecture to classify the texture and color of the fruit. This study uses eight transfer learning architectures with 216 simulations with parameter constraints such as optimizer, learning rate, batch size, number of layers, epoch, and dense and can classify the ripeness level of the papaya fruit with a fairly high accuracy of 97%. Farmers use the results of the research in classifying papaya fruit to be harvested by differentiating the maturity level of the fruit more accurately and maintaining the quality of the papaya fruit.
Classification for Papaya Fruit Maturity Level With Convolutional Neural Network Nurmalasari Nurmalasari; Yusuf Arif Setiawan; Widi Astuti; M. Rangga Ramadhan Saelan; Siti Masturoh; Tuti Haryanti
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1169.294 KB) | DOI: 10.34288/jri.v5i3.225

Abstract

Papaya California (Carica papaya L) is one of the agricultural commodities in the tropics and has a very big opportunity to develop in Indonesia as an agribusiness venture with quite promising prospects. So the quality of papaya fruit is determined by the level of maturity of the fruit, the hardness of the fruit, and its appearance. Papaya fruit undergoes a marked change in color during the ripening process, which indicates chemical changes in the fruit. The change in papaya color from green to yellow is due to the loss of chlorophyll. The papaya fruit is initially green during storage, then turns slightly yellow. The longer the storage color, the changes to mature the yellow. The process of classifying papaya fruit's ripeness level is usually done manually by business actors, that is, by simply looking at the color of the papaya with the normal eye. Based on the problems that exist in classifying the ripeness level of papaya fruit, in this research, we create a system that can be used to classify papaya fruit skin color using a digital image processing approach. The method used to classify the maturity level of papaya fruit is the Convolutional Neural Network (CNN) Architecture to classify the texture and color of the fruit. This study uses eight transfer learning architectures with 216 simulations with parameter constraints such as optimizer, learning rate, batch size, number of layers, epoch, and dense and can classify the ripeness level of the papaya fruit with a fairly high accuracy of 97%. Farmers use the results of the research in classifying papaya fruit to be harvested by differentiating the maturity level of the fruit more accurately and maintaining the quality of the papaya fruit.
Co-Authors Bambang Joko Triwibowo Didi Rosiyadi Fariszal Nova Arviantino Kusnadi - Kusnadi M Rangga Ramadhan Saelan M. Rangga Ramadhan Saelan Muhammad Syahrani Nurmalasari Nurmalasari Siti Masturoh Tuti Haryanti Widi Astuti