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All Journal Journal of Mathematics and Scientific Computing With Applications (JRSIKOM) Jurnal Riset Sistem Informasi dan Aplikasi Komputer
Janurianty, Intan
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Mathematics

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ANALYSIS OF THE BEST FERTILIZER SELECTION FOR CORN PLANTS USING THE MULTI-ATTRIBUTE UTILITY THEORY METHOD Sahrudin; Andini, Dwi; Malakiano Ritonga, Sandrina; Janurianty, Intan; Widya, Ade
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 1 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v5i1.980

Abstract

Corn (Zea mays L.) is one of the most important food crops in the world, serving as a staple food for many countries, as well as a primary source of livestock feed and an industrial raw material. This research aims to determine the best fertilizer for corn plants (Zea mays L.) by applying the Multi-Attribute Utility Theory (MAUT) method, a systematic approach to multi-criteria decision making. This research was conducted in Rundeng District, Subulussalam City, Aceh, with three alternative fertilizers analyzed, namely NPK Mutiara, Urea, and Phonska. The criteria used include price, nutritional efficiency, and availability of fertilizer on the market, with weights for each criterion of 0.3, 0.5, and 0.2. The research results show that Phonska fertilizer has the highest global utility value of 0.895, making it the best choice based on the specified criteria. Phonska stands out for its optimal balance between affordable price, high nutritional efficiency and good availability on the market. Urea is in second place with a global utility value of 0.856, superior in terms of cheaper price, but lower efficiency than Phonska. Mutiara NPK, despite having the highest nutritional efficiency, only obtained a global utility value of 0.807 due to its higher price and lower availability. This research provides data-based guidance for farmers to choose the most suitable fertilizer, which is expected to increase corn crop productivity, reduce production costs, and support agricultural sustainability. By using the MAUT method, this research proves that a data-based approach can help make more rational decisions in the agricultural sector.
ANALYSIS OF THE BEST FERTILIZER SELECTION FOR CORN PLANTS USING THE MULTI-ATTRIBUTE UTILITY THEORY METHOD Sahrudin; Andini, Dwi; Malakiano Ritonga, Sandrina; Janurianty, Intan; Widya, Ade
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 1 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v5i1.980

Abstract

Corn (Zea mays L.) is one of the most important food crops in the world, serving as a staple food for many countries, as well as a primary source of livestock feed and an industrial raw material. This research aims to determine the best fertilizer for corn plants (Zea mays L.) by applying the Multi-Attribute Utility Theory (MAUT) method, a systematic approach to multi-criteria decision making. This research was conducted in Rundeng District, Subulussalam City, Aceh, with three alternative fertilizers analyzed, namely NPK Mutiara, Urea, and Phonska. The criteria used include price, nutritional efficiency, and availability of fertilizer on the market, with weights for each criterion of 0.3, 0.5, and 0.2. The research results show that Phonska fertilizer has the highest global utility value of 0.895, making it the best choice based on the specified criteria. Phonska stands out for its optimal balance between affordable price, high nutritional efficiency and good availability on the market. Urea is in second place with a global utility value of 0.856, superior in terms of cheaper price, but lower efficiency than Phonska. Mutiara NPK, despite having the highest nutritional efficiency, only obtained a global utility value of 0.807 due to its higher price and lower availability. This research provides data-based guidance for farmers to choose the most suitable fertilizer, which is expected to increase corn crop productivity, reduce production costs, and support agricultural sustainability. By using the MAUT method, this research proves that a data-based approach can help make more rational decisions in the agricultural sector.
Implementasi Optical Character Recognition Dalam Pengolahan Data Statistik Survei Penduduk Di Badan Pusat Statistik Kota Tebing Tinggi Andini, Dwi; Ritonga, Sandrina Malakiano; Janurianty, Intan; Nst, Rini Halila
(JRSIKOM) Jurnal Riset Sistem Informasi dan Aplikasi Komputer Vol. 1 No. 4 (2025): Volume 1 Nomor 4 Tahun 2025
Publisher : PT Siantar Codes Academy Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.180997/jrsikom.v1i4.94

Abstract

Implementasi teknologi Optical Character Recognition (OCR) di lingkungan Badan Pusat Statistik (BPS) Kota Tebing Tinggi bertujuan untuk meningkatkan efisiensi dan akurasi dalam pengolahan data survei penduduk. Teknologi ini memungkinkan konversi dokumen fisik menjadi data digital secara otomatis, sehingga mampu mengurangi beban kerja manual dan meminimalkan kesalahan input data. Penelitian ini menggunakan pendekatan studi kasus deskriptif kualitatif melalui observasi langsung dan wawancara dengan staf BPS. Hasil penelitian menunjukkan bahwa penggunaan aplikasi OCR seperti CamScanner mampu mempercepat proses input data dan mendukung transformasi digital di institusi pemerintah. Keberhasilan implementasi dipengaruhi oleh kesiapan infrastruktur, pemilihan perangkat yang sesuai, pelatihan operator, serta integrasi sistem OCR ke dalam sistem kerja yang ada. Kata kunci: Optical Character Recognition, BPS, CamScanner
Co-Authors Andini, Dwi Malakiano Ritonga, Sandrina Nst, Rini Halila Ritonga, Sandrina Malakiano Sahrudin widya, Ade