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All Journal JIKO (Jurnal Informatika dan Komputer) Journal of Intelligent Software Systems
Pereira, Elisabet da Conceição
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Computer Science & IT

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SALES PREDICTION OF VEGETABLE SEED PRODUCTS USING SIMPLE LINEAR REGRESSION Sari, Dini Fakta; Sofian, Muhammad Ali; Nurcahyo, Agung Wilis; Wiharyanto, Kelik; Pereira, Elisabet da Conceição
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2001

Abstract

The growth of the modern agricultural sector drives the need for an accurate sales prediction system, especially for vegetable seed products that are highly dependent on the season and market demand. An imbalance between stock and demand can cause losses, either in the form of overstock or undersupply. This condition requires a data-based planning strategy to ensure stock availability according to actual needs in the field. A historical data-based sales prediction approach is a relevant solution to optimize the distribution and procurement process. This study aims to apply a simple linear regression method in predicting vegetable seed sales based on historical data for one year. The prediction model is built using the time variable (month) as the independent variable and the number of seed requests as the dependent variable. This technique was chosen because of its ability to identify linear relationship patterns between time and sales trends in a simple but effective way. The data used comes from internal records of farmers and distributors, which are then classified into two main categories: leafy vegetable seeds (spinach, kale, mustard greens) and fruit vegetable seeds (tomatoes, chilies, eggplants). The results of the study showed that simple linear regression was able to provide fairly accurate predictive results. This model can be used as a basis for decision making in production planning, supply chain management, and seed inventory management, thus supporting the efficiency of farming businesses and reducing potential losses due to mismatches between demand and supply.
DIABETES PREDICTION USING MACHINE LEARNING Pereira, Elisabet da Conceicao; Andriyani, Widyastuti
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2104

Abstract

Diabetes mellitus merupakan salah satu penyakit metabolik kronis yang prevalensinya terus meningkat secara global. Penyakit ini ditandai oleh tingginya kadar glukosa darah yang dapat menyebabkan. Penelitian ini bertujuan untuk membangun model prediksi diabetes dengan menggunakan algoritma Random Forest berdasarkan data klinis lokal. Metodologi yang digunakan mencakup tahapan pra-pemrosesan data, pelatihan model, dan evaluasi performa menggunakan metrik akurasi, presisi, recall, dan F1-score. Dataset yang digunakan terdiri dari berbagai fitur klinis seperti usia, kadar glukosa, tekanan darah, indeks massa tubuh (BMI), serta riwayat diabetes keluarga. Setelah melalui proses pembersihan data dan normalisasi, model dilatih menggunakan 80% data dan diuji dengan 20% sisanya. Hasil pengujian menunjukkan bahwa algoritma Random Forest memberikan performa yang sangat baik, dengan akurasi mencapai 87%, presisi 84%, recall 82%, dan F1-score sebesar 83%. Hasil ini menunjukkan bahwa algoritma ini mampu memprediksi risiko diabetes dengan ketepatan yang tinggi. Model prediktif ini diharapkan dapat diimplementasikan sebagai sistem pendukung keputusan medis yang membantu tenaga kesehatan dalam melakukan skrining awal terhadap pasien.
Co-Authors Andriyani, Widyastuti Dini Fakta Sari, Dini Fakta Nurcahyo, Agung Wilis Sofian, Muhammad Ali Wiharyanto, Kelik