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All Journal Habitat AGRARIS: Journal of Agribusiness and Rural Development Research JMM (Jurnal Masyarakat Mandiri)
Destyana Ellingga Pratiwi
Department Of Agricultural Socio-Economics, Faculty Of Agriculture, Universitas Brawijaya, Jl. Veteran Kota Malang 65145, Indonesia
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Social Sciences Other

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Risk Mitigation Strategies in Semi-Organic Rice Supply Chains: Lesson Learned from the Involved Actors Agustina Shinta Hartati Wahyuningtyas; Novi Haryati; Destyana Ellingga Pratiwi; Luisa Maliny Situmeang
AGRARIS: Journal of Agribusiness and Rural Development Research Vol 7, No 2: July-December 2021
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (456.263 KB) | DOI: 10.18196/agraris.v7i2.10126

Abstract

Rice is the main consumption food for Indonesians. The demand for food increased from 114.6 kg per capita in 2016 to 124.89 kg in 2017. However, rice farmers and supply chain actors in rice agribusiness have experienced high challenges, such as production, transportation, price, product quality, and the environment. This research aimed to understand actors involved in the supply chain, their perception of occurring risks, and evaluation and risk mitigation in the supply chain. This was a quantitative descriptive study done purposively in Watugede Village, Singosari Sub-District, Malang Regency. Non-probability sampling was taken to gather primary data. The respondent of this research was 16 involved actors, from on-farm actors to consumers. The data were analyzed using the Fuzzy analytical hierarchy process (FAHP) to provide descriptive risk mitigation strategies. The results show that six involved actors are suppliers, farmers, grinders, traders, and buyers. Each actor faces different risks, and thus, the recommended mitigation strategies are adjusted to their risks. Sharing information, optimizing the level of supply availability, measuring supply chain performance, and building more coordination with the government are the best strategies to mitigate risks.
PEMBERDAYAAN PEREMPUAN SEBAGAI AGEN PENGGERAK DALAM PENGOLAHAN SAMPAH DAPUR MENJADI ECO ENZYME Agustina Shinta Hartati Wahyuningtyas; Anisa Aprilia; Tri Ardyati; Kiki Fibrianto; Fahriyah Fahriyah; Riyanti Isaskar; Arie Srihardyastutie; Destyana Ellingga Pratiwi; Vi’in Ayu Pertiwi; Yusri Fajar
JMM (Jurnal Masyarakat Mandiri) Vol 7, No 1 (2023): Februari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v7i1.12000

Abstract

Abstrak: Peningkatan jumlah penduduk mengakibatkan adanya peningkatan produksi sampah, utamanya limbah organik rumah tangga yang dapat menimbulkan dampak buruk bagi lingkungan maupun kesehatan masyarakat jika tidak diimbangi dengan pengelolaan sampah yang tepat. Tujuan kegiatan pengabdian ini adalah untuk mengembangkan pengetahuan, kesadaran, dan kebiasaan baik terutama bagi kaum perempuan agar mampu mengolah sampah atau limbah rumah tangga menjadi eco enzyme. Metode pengabdian meliputi persiapan, sosialisasi, dan pelatihan. Mitra dari kegiatan ini adalah kelompok ibu-ibu PKK RW VII Kelurahan Lesanpuro, Kecamatan Kedungkandang, Kota Malang dengan jumlah peserta yang terlibat sebanyak 86 20 orang. Hasil dari kegiatan tersebut, yaitu peserta dapat membuat dan melestarikan eco enzyme yang didukung dengan hasil evaluasi di mana peserta memiliki peningkatan pemahaman terkait pengolahan sampah dari aspek pengetahuan, kebiasaan, dan kepedulian.Abstract: The growth of population causes the increase of waste production, especially household waste which can cause a negative impact for environment and health if it isn't used the right waste’s management. The community service aims to increase the knowledge, awareness, and good habits to manage household waste into eco enzymes. The first method of this service is preparation, and then followed by socialization. And also training. The partners are the women of RW VII, who are the member of PKK group in the Kelurahan Lesanpuro of Kecamatan Kedungkandang, Kota Malang. There are 86 women involved. The result of the activity shows that participants can make and preserve eco enzymes, which is supported by the evaluation result. It shows that the participants understanding about organic waste management have increased based on the aspects of knowledges, habits, and care. 
Comparative Study of SVR, Regression and ANN Water Surface Forecasting for Smart Agriculture Arief Andy Soebroto; Imam Cholissodin; Destyana Ellingga Pratiwi; Guruh Prayogi Willis Putra
HABITAT Vol. 33 No. 1 (2022): April
Publisher : Department of Social Economy, Faculty of Agriculture , University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.habitat.2022.033.1.9

Abstract

In the smart agriculture system based on green-based technology of artificial intelligence (AI), flooding can be predicted early by forecasting the water surface and good agricultural irrigation. The process of rising and falling of the water surface in a water basin area can be explained theoretically, but since there are many related variables and the complexity of dependencies between variables, the mathematical model is difficult to construct. Forecasting water surface in the field of irrigation needs too many variable parameters, such as cross-sectional area, depth, volume of rivers and so on. Based on patterns in each period, forecasting can be done using a statistical method and AI. This study uses the support vector regression (SVR) method, regression, multiple linear regression, and algorithm backpropagation, all compared to one another. The results of tests carried out between SVR and multiple linear regression show that SVR is superior. This can be seen from the result of the mean square error (MSE) obtained for each method. SVR 0.03 and for multiple linear regression, 0.05. The result is also supported by the best MSE result in the regression method, which is 0.338, and the best MSE value in artificial neural network (ANN), which is 0.428.
Co-Authors Agustina Shinta Hartati Wahyuningtyas Anisa Aprilia Arie Srihardyastutie Arief Andy Soebroto Fahriyah Fahriyah Guruh Prayogi Willis Putra Imam Cholissodin KIKI FIBRIANTO Luisa Maliny Situmeang Novi Haryati Riyanti Isaskar Tri Ardyati Vi’in Ayu Pertiwi Yusri Fajar