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All Journal Journal of Intelligent Decision Support System (IDSS) Journal of Artificial Intelligence and Digital Business
Ubaidillah, Muhamad Rizal
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Penerapan K-Means Clustering untuk Klasifikasi Jenis Beras Berdasarkan Tren Harga di Pasar Induk Beras Cipinang (PIBC) Ubaidillah, Muhamad Rizal; Santoso, Nugroho Adhi; Utami, Erni Ungguk Sedya
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2594

Abstract

Harga beras yang fluktuatif di Pasar Induk Beras Cipinang (PIBC) memerlukan analisis berbasis data untuk memahami pola pergerakan harga dan mendukung kebijakan distribusi yang tepat. Penelitian ini bertujuan untuk mengelompokkan jenis-jenis beras berdasarkan kesamaan tren harga tahunan selama periode 2020–2024. Metode yang digunakan adalah algoritma K-Means Clustering dengan tahapan pra-pemrosesan data menggunakan Min-Max Scaling, penentuan jumlah klaster optimal melalui metode Elbow, dan evaluasi hasil menggunakan Silhouette Score. Hasil penelitian menunjukkan bahwa 14 jenis beras berhasil dikelompokkan ke dalam tiga klaster utama, yaitu klaster harga rendah (seperti IR-64 I, IR-64 II, dan Muncul III), klaster harga menengah (seperti Cianjur Kepala, Setra Ramos, dan Saigon), serta klaster harga tinggi (Ketan Hitam dan Ketan Putih Paris), dengan nilai Silhouette Score sebesar 0,507 yang menunjukkan kualitas pengelompokan cukup baik. Implikasi penelitian ini adalah metode data mining dapat digunakan untuk memetakan segmentasi pasar beras secara efektif, sehingga pihak terkait seperti pedagang, distributor, dan pembuat kebijakan dapat lebih mudah merumuskan strategi distribusi dan intervensi harga yang tepat sasaran.
Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction Surorejo, Sarif; Ubaidillah, Muhamad Rizal; Syefudin, Syefudin; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i2.245

Abstract

The chicken farming industry is an important sector in the Indonesian economy, but there are food security issues with the presence of tiren chicken. This research aims to develop a more accurate and efficient method of detection of tiren chickens using Naive Bayes Classifier with Gaussian and Bernoulli kernels and GLCM feature extraction. Data is collected from various internet sources, then pre-processing and feature extraction is carried out. The Naive Bayes Classifier algorithm is implemented with two kernels and evaluated using accuracy, precision, recall, and f1-score metrics. The Gaussian kernel showed an accuracy of 0.75, higher than Bernoulli's kernel which was only 0.50. Models with Gaussian kernels have high performance in detecting tiren chickens and normal chicken precision. The combination of Gaussian and Bernoulli kernels and GLCM feature extraction is effective in improving the detection accuracy of tiren chickens, contributing significantly to food safety and consumer confidence
Co-Authors Gunawan Gunawan Santoso, Nugroho Adhi Sarif Surorejo Syefudin, Syefudin Utami, Erni Ungguk Sedya