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All Journal Zero : Jurnal Sains, Matematika, dan Terapan Formosa Journal of Computer and Information Science
Zulhamsyah Fachrurrazi Nasution
Fakultas Vokasi, Universitas Sumatera Utara, Medan, 20155, Indonesia
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Mathematics Physics

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Application of Linear Programming Based Transportation Models to Optimize Natural Disaster Relief Distribution Saddam Husein; Zulhamsyah Fachrurrazi Nasution; Ema Sri Rezeki; Afdhal Ahkrizal
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28440

Abstract

Disaster relief distribution is a complex logistical problem and requires optimal planning for targeted and efficient distribution. This study aims to apply a linear programming-based transportation model to optimize aid distribution from several warehouses to affected shelters, with clearly defined constraints based on field conditions. The method used is a quantitative approach through simulation supported by empirical data representing post-disaster conditions. The model is formulated in an objective function to minimize total distribution costs with warehouse capacity and shelter requirements constraints. The process solution model is carried out using LINDO (Linear, Interactive, Discrete Optimizer) optimization software to ensure calculation accuracy. The optimization results show a cost reduction from 1,550 to 650 units, or a savings of 58.06% while still satisfying all supply and demand constraints. These findings indicate that the linear programming-based transportation model is effective in increasing aid distribution efficiency and supporting more targeted logistics decision-making.
IoT-Based Multi-Sensor Fusion for Goat Behavioral Pattern Recognition Using K-Means Clustering in a Smart Farming Environment Yudhistira Pratama; Normalina Napitupulu; Zulhamsyah Fachrurrazi Nasution; Adli Abdillah Nababan
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16599

Abstract

Monitoring goat behavior in commercial farms typically relies on direct observation, which does not scale and misses conditions that develop gradually. This study deployed an eight-sensor IoT network across two zones of a slatted-floor goat pen in North Sumatra, Indonesia, and applied K-Means clustering to 49 days of sensor data. After a systematic data cleaning step that removed sensor dropouts, ADC saturation events, and an isolated methane spike, 213,704 records were retained (98.6% of raw data). K-Means with K=8 on the cleaned dataset yielded a Silhouette Score of 0.297 and Davies-Bouldin Index of 1.177, identifying eight behavioral and environmental states without a dedicated anomaly cluster. Results include two heat stress levels (THI means 90.7 and 92.1), three nocturnal resting states differentiated by waste pit gas concentration, a daytime active-vocal state, and an evening post-feeding fermentation peak.
Co-Authors Adli Abdillah Nababan Afdhal Ahkrizal Ema Sri Rezeki Normalina Napitupulu Saddam Husein Yudhistira Pratama