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All Journal IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH
Ayyub, Muhammad Azwar Al
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Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Analysis of Fertilizer Requirements in Red Chili Cultivation Using an Artificial Neural Network Approach Marpaung, Mairani; Apdillah, Dicky; Ayyub, Muhammad Azwar Al
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.387

Abstract

Red chili farmers on the East Coast of North Sumatra still rely on manual calculations to determine the use of NPK Biru 16 Mutiara fertilizer, often leading to inaccurate and inefficient fertilizer application. This study proposes the Backpropagation method within Artificial Neural Networks (ANN) as a solution to analyze fertilizer needs more precisely. The method enables the system to learn from historical data and plant growth patterns, providing accurate recommendations for the type and amount of fertilizer required. The implementation of ANN in this context not only enhances agricultural efficiency but also supports environmental sustainability by minimizing excessive fertilizer usage.
KLASIFIKASI TIPE KACA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Ayyub, Muhammad Azwar Al; Simangunsong, Weny Nur Afdilla; Farhatun, Dini; Dea, Emi; Agustin, Selfina; Rahman, Zulfa Ar; Ridho, Muhammad
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5745

Abstract

Abstract: Glass is a material that is widely used in various fields, such as construction, the automotive industry, and household appliances. Each type of glass has different characteristics based on its chemical composition and production process. Problems arise when the process of identifying glass types is still done manually, which is time-consuming, costly, and prone to error. This study aims to apply the K-Nearest Neighbor (K-NN) method in classifying glass types based on their chemical content attributes. The data in this study was sourced from Kaggle, namely the Glass Identification Dataset. The data used consisted of several chemical features, such as Na, Mg, Al, Si, K, Ca, Ba, and Fe, with seven categories of glass classes. The results showed that the K-NN method was able to classify glass types well and could be an effective solution to assist in the automatic glass identification process. Keyword: Classification, K-Nearest Neighbor, Data Mining, Types of Glass. Abstrak: Kaca merupakan material yang banyak digunakan dalam berbagai bidang, seperti konstruksi, industri otomotif, dan peralatan rumah tangga. Setiap jenis kaca memiliki karakteristik yang berbeda berdasarkan komposisi kimia dan proses produksinya. Permasalahan muncul ketika proses identifikasi jenis kaca masih dilakukan secara manual, sehingga membutuhkan waktu, biaya, dan berpotensi menimbulkan kesalahan. Penelitian ini bertujuan untuk menerapkan metode K-Nearest Neighbor (K-NN) dalam mengklasifikasikan jenis kaca berdasarkan atribut kandungan kimianya. Data dalam penelitian ini bersumber dari Kaggle, yaitu Glass Identification Dataset. Data yang digunakan terdiri dari beberapa fitur kimia, seperti Na, Mg, Al, Si, K, Ca, Ba, dan Fe, dengan tujuh kategori kelas kaca. Hasil penelitian menunjukkan bahwa metode KNN mampu mengklasifikasikan jenis kaca dengan baik dan dapat menjadi solusi yang efektif untuk membantu proses identifikasi kaca secara otomatis. Kata kunci: Klasifikasi, K-Nearest Neighbor, Data Mining, Jenis Kaca.
Co-Authors Agustin, Selfina Apdillah, Dicky Dea, Emi Farhatun, Dini Marpaung, Mairani Muhammad Ridho Rahman, Zulfa Ar Simangunsong, Weny Nur Afdilla