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All Journal Multitek Indonesia : Jurnal Ilmiah Jurnal Ilmu Multidisplin
Renaldy, Aldi
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Peran Sistem Informasi dan Teknologi Informasi Terhadap Peningkatan Keamanan Informasi Perusahaan Renaldy, Aldi; Fauzi, Achmad; Shabrina, An Nisaa; Novita Ramadhan, Hanny; Novita Ramadhani, Muthia; Aprilia Hikayatuni’mah, Putri; Iskandar, Octo
Jurnal Ilmu Multidisiplin Vol. 2 No. 1 (2023): Jurnal Ilmu Multidisplin (April-Juni 2023)
Publisher : Green Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jim.v2i1.212

Abstract

Teknologi adalah bagian krusial dalam kehidupan pada era sistem informasi yg berkembang pesat. Penggunaan teknologi berita dan komunikasi pada sistem berita sangat krusial dalam bidang manajemen, pendidikan, dan usaha. Keamanan informasi melibatkan pengamanan data terhadap potensi serangan dalam upaya mempertahankan operasi perusahaan, menurunkan eksposur risiko, serta mempercepat proses pengambilan keputusan buat investasi dan prospek komersial. Artikel ini mereview “Peran sistem informasi dan teknologi informasi terhadap peningkatan keamanan perusahaan”. Penelitian ini menggunakan motode kualitatif. Artikel ini bertujuan untuk memberikan informasi bagi penelitian selanjutnya untuk mengembangkan hipotesis yang menghubungkan satu variabel dengan variabel lainnya. Penelitian sitem dan teknologi informasi dimaksudkan untuk menghentikan ancaman terhadap sistem, serta untuk memperkecil kemungkinan terjadinya kebocoran data pada saat sistem informasi mengubah dimensi atau indikator keamanan sistem informasi, diperlukan pengamanan data bagi pengguna sistem informasi.
PENGARUH CONVOLUTIONAL NEURAL NETWORK UNTUK PROSES DETEKSI PENYAKIT PADA DAUN TOMAT Renaldy, Aldi; Litanianda, Yovi; Zulkarnain, Ismail Abdurrazzaq
MULTITEK INDONESIA Vol 18, No 2 (2024): Desember
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v18i2.11816

Abstract

Tomato plants are one of the plants that are often planted by farmers and are the main food requirement insociety. Tomato cultivation is often faced with disease problems that can attack the leaves, stems and fruit.However, many farmers often face difficulties in overcoming this problem. To solve this problem, researcherswill use a web-based system that is able to classify images of tomato leaves. The system will process the imagefirst before training the CNN model. The resulting model will be used to classify images entered through thewebsite. Apart from that, this design also has several useful benefits. The results of the analysis of the modelshow that there are challenges in distinguishing the characteristics of diseases in tomato plants, so that thedevelopment of the CNN model experiences difficulties. Despite these difficulties, the CNN algorithm providesan accuracy score of 0.9091. This number reflects the model's level of accuracy in classifying images into thecorrect categories. From these results, it can be concluded that disease detection in tomato plants using theCNN algorithm requires special effort and attention, especially in collecting representative datasets andmodeling optimal CNN architecture. A deeper understanding of the characteristics of diseases in tomato plantsalso needs to be considered to increase the accuracy of model predictions. Although there is still room forimprovement, these results provide a basis for continuing to develop and improve disease detection models intomato plants using CNN approaches.
PENGARUH CONVOLUTIONAL NEURAL NETWORK UNTUK PROSES DETEKSI PENYAKIT PADA DAUN TOMAT Renaldy, Aldi; Litanianda, Yovi; Zulkarnain, Ismail Abdurrazzaq
MULTITEK INDONESIA Vol 18 No 2 (2024): Desember
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v18i2.11816

Abstract

Tomato plants are one of the plants that are often planted by farmers and are the main food requirement insociety. Tomato cultivation is often faced with disease problems that can attack the leaves, stems and fruit.However, many farmers often face difficulties in overcoming this problem. To solve this problem, researcherswill use a web-based system that is able to classify images of tomato leaves. The system will process the imagefirst before training the CNN model. The resulting model will be used to classify images entered through thewebsite. Apart from that, this design also has several useful benefits. The results of the analysis of the modelshow that there are challenges in distinguishing the characteristics of diseases in tomato plants, so that thedevelopment of the CNN model experiences difficulties. Despite these difficulties, the CNN algorithm providesan accuracy score of 0.9091. This number reflects the model's level of accuracy in classifying images into thecorrect categories. From these results, it can be concluded that disease detection in tomato plants using theCNN algorithm requires special effort and attention, especially in collecting representative datasets andmodeling optimal CNN architecture. A deeper understanding of the characteristics of diseases in tomato plantsalso needs to be considered to increase the accuracy of model predictions. Although there is still room forimprovement, these results provide a basis for continuing to develop and improve disease detection models intomato plants using CNN approaches.
Co-Authors Achmad Fauzi Aprilia Hikayatuni’mah, Putri Iskandar, Octo Novita Ramadhan, Hanny Novita Ramadhani, Muthia Shabrina, An Nisaa Yovi Litanianda, Yovi Zulkarnain, Ismail Abdurrazzaq