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All Journal Bioeksperimen: Jurnal Penelitian Biologi BAREKENG: Jurnal Ilmu Matematika dan Terapan
Aji, Alfian Chrisna
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Fitoremediasi Logam Kromium di Tanah Sawah dengan Rami (Boehmeria nivea) dan Environmental Health Agriculture System (EHAS) Aji, Alfian Chrisna; Masykuri, Mohammad; Rosariastuti, Retno
Bioeksperimen: Jurnal Penelitian Biologi Vol 5, No 2: September 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/bioeksperimen.v5i2.9232

Abstract

Chromium metal is one of the heavy metal wastes from various industries and is persistent for the agricultural environment, especially in rice fields. Chromium metal can change biodiversity and ecosystem function in paddy soil. Chromium metal phytoremediation that pollutes paddy soils with hemp (Boehmeria nivea) is important because paddy soils play a role as a living medium for food crops, especially rice (Oryza sativa). One indicator of the success of phytoremediation is the reduction of chromium metal content in the soil, so it requires a policy system to maintain a healthy environmentally friendly agriculture. This study aimed to determine the ability of Boehmeria nivea to reduce levels of chromium metal in the soil and provide policy solutions to keep environmentally healthy agriculture. This study used a complete randomized block design, random sampling of chromium metal data. The results showed the initial concentration of chromium metal in the soil was 2.36 ppm, after treatment with the interaction between Agrobacterium sp. I3 with Boehmeria nivea (P0B1T1) and interaction of organic matter (compost) with Boehmeria nivea (P0B2T1) obtained Cr 1.37 ppm metal content with a decrease of 42.01%. The resulting policy solution is the Environment Health Agriculture System (EHAS). The conclusion of this study was phytoremediation of chromium metal using Boehmeria nivea combined with the Environment Health Agriculture System can create a healthy environmentally friendly agricultural system.
ANALYSIS OF WEATHER CHANGES FOR ESTIMATION OF SHALLOT CROPS FLUCTUATION USING HIDDEN MARKOV Pradana, Yan Aditya; Azka, Dea Alvionita; Aji, Alfian Chrisna; Fauzi, Irfan Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (762.482 KB) | DOI: 10.30598/barekengvol16iss1pp331-340

Abstract

Climate change has an impact on increasing the temperature of the earth's surface or what is known as global warming. The impact of global warming will affect the pattern of precipitation, evaporation, water run-off, soil moisture and climate variations which are very volatile can threaten the success of horticultural production, especially shallots. Shallots are a strategic commodity but are strongly influenced by fluctuations in production. The development of shallots is one of them constrained by the weather/climate which affects the production of shallots. From these constraints, shallots are also a commodity that contribute significantly to inflation. Hidden Markov Models (HMM) is one of the stochastic processes when the future only depends on condition now, in markov chain all of the element observable, and the probability move to another probability. Prediction and estimation of shallot crops with rainfall input, temperature, and humidity is done with data starting in 2016 until 2020. Estimated shallot crops follows the optimum movement pattern of prediction shallot in each of each variable. The planting months that are usually carried out in the two districts are around February, May, June and September the lowest shallot crops in April or May because transition of rainy to dry season. And the highest shallot crops in October or November. The best accuracy of estimation is rainfall factor with MAPE 5,89% with high accuracy category while 5,84% in MAPE temperature and in 5,55% in humidity factor in category high.
Co-Authors Azka, Dea Alvionita Irfan Miftahul Fauzi Mohammad Masykuri Retno Rosariastuti YAN ADITYA PRADANA