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All Journal Jurnal ULTIMA InfoSys METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Edward Rajagukguk
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Computer Science & IT

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PENRAPAN FUZZY TIME SERIES UNTUK PREDIKSI PRODUKSI IKAN TAMBAK DI SUMATERA UTARA Samuel Bakkara; Sri Agustina Rumapea; Edward Rajagukguk
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 1 (2025): Volume 11 Nomor 1 Tahun 2025
Publisher : Universitas Methodist Indonesia

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Abstract

Fish is one of the most potential animal food products in Indonesia. Over the years, the majority of animal food consumption in Indonesia has been contributed by fish products. The increasing growth rate in North Sumatra is balanced by the food potential that can support the region's food security. Based on data from the fisheries sector potential in North Sumatra, pond fish farming in the province of North Sumatra covers an area of 20,000 hectares, spread across various districts/cities. The pond fish cultivated include catfish, catfish, tilapia, carp, snapper, milkfish, grouper. Given the potential of the pond aquaculture sector and the increasing awareness of the population in North Sumatra to consume fish as a source of nutrition, this research aims to predict pond fish production in North Sumatra. The prediction of pond fish production in North Sumatra is conducted to address the challenges of fluctuating pond fish production, uncertainty in supply and demand using the fuzzy time series method. The results of the study using the Fuzzy Time Series method in forecasting pond fish production in North Sumatra obtained forecast accuracy for catfish of 11.702%, for catfish of 10.359%, for tilapia of 21.636%, for carp of 15.788%, for snapper of 11.487%, for milkfish of 12.87%, for grouper of 12.263%, and for shrimp of 14.814%.).
PENRAPAN FUZZY TIME SERIES UNTUK PREDIKSI PRODUKSI IKAN TAMBAK DI SUMATERA UTARA Samuel Bakkara; Sri Agustina Rumapea; Edward Rajagukguk
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 1 (2025): Volume 11 Nomor 1 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fish is one of the most potential animal food products in Indonesia. Over the years, the majority of animal food consumption in Indonesia has been contributed by fish products. The increasing growth rate in North Sumatra is balanced by the food potential that can support the region's food security. Based on data from the fisheries sector potential in North Sumatra, pond fish farming in the province of North Sumatra covers an area of 20,000 hectares, spread across various districts/cities. The pond fish cultivated include catfish, catfish, tilapia, carp, snapper, milkfish, grouper. Given the potential of the pond aquaculture sector and the increasing awareness of the population in North Sumatra to consume fish as a source of nutrition, this research aims to predict pond fish production in North Sumatra. The prediction of pond fish production in North Sumatra is conducted to address the challenges of fluctuating pond fish production, uncertainty in supply and demand using the fuzzy time series method. The results of the study using the Fuzzy Time Series method in forecasting pond fish production in North Sumatra obtained forecast accuracy for catfish of 11.702%, for catfish of 10.359%, for tilapia of 21.636%, for carp of 15.788%, for snapper of 11.487%, for milkfish of 12.87%, for grouper of 12.263%, and for shrimp of 14.814%.).
Applied Machine learning for Pediatric Nutrition A K-Means Clustering Application Based on WHO Z-Scores Julius Sepadan Maruli Tua Manurung; Sri Agustina Rumapea; Edward Rajagukguk; Fernando Rumapea
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4495

Abstract

Abstract—Nutritional status in early childhood is a key indicator of public health and serves as a basis for targeted nutritional interventions. This study applies the K-Means Clustering algorithm to anthropometric data—including weight, height, and head circumference—to classify children's nutritional status into five categories: severely undernourished (−3 SD), moderately undernourished (−2 SD), normal, mildly overnourished (+1 SD), and moderately overnourished (+2 SD). The clustering process is based on Z-scores derived from WHO standards, namely Weight-for-Age (WAZ), Height-for-Age (HAZ), and Head Circumference-for-Age (HCAZ), which serve as input features for the clustering algorithm. The number of clusters (k = 5) is aligned with national nutritional classification guidelines. The clustering results are visualized to illustrate the data distribution across the three indicators, and evaluated using Euclidean distance to assess the proximity of each data point to its assigned cluster centroid. The results demonstrate that K-Means Clustering can effectively classify children's nutritional status in a manner consistent with manual classification based on WHO thresholds. This study highlights the potential of data mining approaches in supporting health information systems for the early and automated detection of child malnutrition. Index Terms—Nutritional Status; Anthropometry; Z-Score; K-Means Clustering
Co-Authors Fernando Rumapea Julius Sepadan Maruli Tua Manurung Samuel Bakkara Sri Agustina Rumapea Sri Agustina Rumapea