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All Journal The IJICS (International Journal of Informatics and Computer Science) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer)
Amallia, Dwi Novia
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perancangan Smartgarden Berbasis Internet Of Things Untuk Monitoring dan Kontrol Nutrisi Tanaman sinaga, Dedi Candro Parulian; Marpaung, Endra Ary Prasasty; Hasugian, Penda Sudarto; Amallia, Dwi Novia; Setiawan, Chandra
Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 24 No 1 (2025): Februari 2025
Publisher : PRPM STMIK TRIGUNA DHARMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jis.v24i1.10629

Abstract

Perancangan Smartgarden berbasis Internet of Things (IoT) untuk monitoring dan kontrol nutrisi tanaman bertujuan untuk meningkatkan efisiensi dan produktivitas pertanian dengan memanfaatkan teknologi terkini. Sistem ini mengintegrasikan sensor-sensor untuk memantau parameter lingkungan seperti kelembapan tanah, suhu udara, intensitas cahaya, dan kadar nutrisi di dalam media tanam secara real-time. Data yang terkumpul akan dikirimkan ke platform cloud melalui koneksi internet, memungkinkan pemantauan jarak jauh menggunakan aplikasi mobile atau web. Sistem ini dilengkapi dengan mekanisme kontrol otomatis untuk mengatur irigasi, pemberian nutrisi, dan pencahayaan berdasarkan kebutuhan tanaman yang terdeteksi. Dengan menggunakan algoritma berbasis data sensor, sistem dapat mengoptimalkan pemberian air dan nutrisi, serta menjaga kondisi lingkungan agar tetap ideal bagi pertumbuhan tanaman. Hasil yang diharapkan adalah peningkatan kualitas dan kuantitas tanaman, serta efisiensi penggunaan sumber daya seperti air dan pupuk. Selain itu, sistem ini memberikan kemudahan bagi petani untuk memantau dan mengontrol kebun mereka secara lebih efektif dan praktis, bahkan tanpa kehadiran fisik di lokasi.
Implementation of K-Nearest Neighbor Algorithm for Scientific Determination of Aid Recipients at STM Agape Sinaga, Dedi Candro Parulian; Siahaan, R. Fanry; Tarigan, Nera Mayana Br; Lubis, Rodiah Hannum; Amallia, Dwi Novia
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 3 (2025): November
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i3.9484

Abstract

Providing assistance to underprivileged families is an important social effort to enhance community welfare; however, the selection of aid recipients often encounters problems such as subjectivity, unstructured data, and time inefficiency when conducted manually. This study aims to develop and evaluate a decision support system for determining aid recipients at STM Agape using the K-Nearest Neighbor (KNN) algorithm to improve accuracy and objectivity in the selection process. The research methodology employed a quantitative classification approach, where data were collected from families based on predefined criteria, including family income, number of dependents, housing conditions, and the occupation of the head of the household. The dataset was divided into training and testing data, and all attributes were normalized prior to processing. The KNN algorithm was applied using Euclidean distance to measure similarity between data instances, classifying each family into “eligible” or “ineligible” categories. The results indicate that the proposed system achieved higher classification accuracy and more consistent decision outcomes compared to manual selection methods. Additionally, the implementation of KNN reduced processing time and minimized subjective bias in determining aid recipients. These findings demonstrate that the KNN-based system is effective as a decision support tool, enabling STM Agape to distribute social assistance in a more targeted, objective, transparent, and efficient manner.
Co-Authors Chandra Setiawan, Chandra Hasugian, Penda Sudarto Lubis, Rodiah Hannum Marpaung, Endra Ary Prasasty Siahaan, R. Fanry Sinaga, Dedi Candro Parulian Tarigan, Nera Mayana Br