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Journal : Journal Computer Science and Informatic Systems : J-Cosys

Analisis Tren Harga Komoditas Jagung Menggunakan Python Meilantika, Dian; Salamudin, Salamudin; Hartati, Sri
Journal Computer Science and Information Systems : J-Cosys Vol 4, No 2 (2024): J-Cosys - September
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53514/jco.v4i2.568

Abstract

Penelitian ini bertujuan untuk menganalisis tren harga jagung menggunakan data historis dari Yahoo Finance, dengan fokus pada identifikasi pola musiman dan volatilitas harga menggunakan Python. Data harga jagung dari 2015 hingga 2024 dianalisis dengan pustaka yfinance untuk mengunduh data, statsmodels untuk dekomposisi deret waktu, dan matplotlib serta seaborn untuk visualisasi. Dekomposisi deret waktu memisahkan harga jagung menjadi komponen tren, musiman, dan residual, sementara log returns digunakan untuk mengukur volatilitas harga harian. Hasil penelitian menunjukkan bahwa harga jagung stabil pada 2015–2020, mengalami lonjakan pada 2021–2022, dan penurunan pada 2023–2024. Pola musiman menunjukkan harga lebih rendah pada bulan Januari hingga Maret dan lebih tinggi pada bulan Juni hingga Oktober. Penelitian ini memberikan wawasan penting bagi petani, pedagang, dan pembuat kebijakan dalam merencanakan strategi pasar dan kebijakan stabilisasi harga jagung.
Penerapan Algoritma K-Means dalam Klasterisasi Wilayah Prioritas Penanganan BBLR dan Risiko Stunting Salamudin, Salamudin; Saputro, Haris; Rusidi, Rusidi; Sulfani, Ari
Journal Computer Science and Information Systems : J-Cosys Vol 5, No 2 (2025): September
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53514/jco.v5i2.709

Abstract

This study aims to classify districts and municipalities in South Sumatra Province based on the risk level of Low Birth Weight (LBW) and severe malnutrition using the K‑Means Clustering algorithm as a basis for mapping priority areas for stunting prevention. The research utilizes secondary data from 2024 consisting of total live births, LBW cases, and severe malnutrition cases across 17 regions. Both risk indicators were transformed into rate-based measurements to ensure proportional comparisons between regions and subsequently normalized using the Min–Max method to equalize variable scales for Euclidean distance computation within the clustering process. The optimal number of clusters was determined through the elbow method combined with the Davies–Bouldin Index (DBI), which indicated that k = 3 provides the most suitable cluster structure for the dataset. The clustering results formed three distinct groups representing low-risk, medium-risk, and high-risk areas. Regions classified into the high‑risk cluster exhibited the highest LBW and malnutrition rates, thus becoming the primary targets for intervention. The findings demonstrate that the K‑Means algorithm is effective for health‑risk mapping using numerical epidemiological data and can serve as a reliable analytical tool to support evidence‑based decision‑making in stunting reduction programs