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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Heru Sukoco
IPB University
Computer Science & IT Engineering

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Prediksi Waktu Tanam Cabai Rawit Berdasarkan Kondisi Lingkungan Berbasis Internet of Things (IoT) Menggunakan Metode Neural Network Yan Mitha Djaksana; Agus Buono; Sri Wahjuni; Heru Sukoco
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5199

Abstract

In Indonesian cuisine, the red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation of this chili variety is not without its challenges, primarily due to the volatile nature of the chili prices. Farmers often struggle with the critical decision of when to plant Tabasco peppers to optimize their yields and income. Understanding the complexities of this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advances in Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study focuses on the development of an IoT-based solution aimed at helping farmers in precisely determining the optimal planting time for Tabasco pepper. It uses five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight (hours) and groundwater usage data (m3) to make data-driven planting decisions. The urgent need for such a system becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Using historical data from 2019, obtained from the Jakarta Provincial Government Open Data DKI, and climate data from the Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The result is a reliable prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.
Sistem Pemantauan dan Pengendalian Logistik Buah Mangga Berbasiskan Machine Learning Buyung Hardyansyah; Heru Sukoco; Sony Hartono Wijaya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5226

Abstract

Fruits are highly perishable goods, which means they have a short shelf life and can pose significant challenges in trade. A long supply chain can trigger the process of fruit spoilage. The logistics environment, both internal and external, can also affect the decrease in quality of goods. One common issue facing producers is the variability in consumer demand for fruit quality. To address this problem, a machine learning-based logistics monitoring and recommendation system can be developed, utilizing the Long Short-Term Memory (LSTM) and Decision Tree algorithms. Using machine learning algorithms, the system can analyze data from devices equipped with the Internet of Things (IoT), such as temperature and humidity sensors, to identify potential issues in the supply chain and provide recommendations to optimize logistics operations. In this study, a machine learning-based monitoring system is developed to monitor the shelf life of perishable goods, with a specific focus on mango fruit. The system utilizes LSTM to predict mango ripeness and decision tree algorithms to recommend fruit ripeness. The objective is to provide producers with recommendations that optimize the logistics process for high-quality mangoes and meet the consumer demands for quality fruit. The implementation of a machine learning-based logistics monitoring and recommendation system can provide significant benefits to mango producers. Using advanced technologies, such as LSTM and Decision Tree algorithms, producers can optimize their logistics operations, improve fruit quality, reduce waste, and improve customer satisfaction.
Co-Authors Agus Buono Buyung Hardyansyah Sony Hartono Wijaya Yan Mitha Djaksana