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All Journal Jurnal Tata Kota dan Daerah Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) JATI (Jurnal Mahasiswa Teknik Informatika)
Fatmawati, Aisyah
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Social Sciences Transportation

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IMPLEMENTASI NAÏVE BAYES UNTUK KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SOSIAL Fatmawati, Aisyah; Irma Purnamasari, Ade; Ali, Irfan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 1 (2024): JATI Vol. 8 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i1.8714

Abstract

Pemberian bantuan sosial artinya suatu upaya untuk membantu masyarakat yang membutuhkan. Tetapi, pada mendistribusikan bantuan sosial, krusial buat memastikan bahwa bantuan tersebut diberikan pada mereka yang benar-benar membutuhkannya. Penelitian ini bertujuan untuk berbagi suatu model prediksi kelayakan penerima bantuan sosial menggunakan metode Naive Bayes. Metode Naive Bayes dipilih karena kemampuannya pada mengatasi masalah pembagian terstruktur mengenai menggunakan dataset yang kompleks. Model ini memanfaatkan perkiraan independensi antar-fitur yang mempermudah perhitungan probabilitas kelas. Pada konteks ini, kelas yg diprediksi merupakan "kelayakan" atau "tidak kelayakan" menjadi penerima bantuan sosial. Penelitian ini dibutuhkan dapat menyampaikan kontribusi dalam menaikkan efisiensi serta keadilan pada pendistribusian bantuan sosial dengan memanfaatkan metode Naive Bayes, diperlukan dapat menghasilkan model yang dapat dengan seksama memprediksi kelayakan penerima bantuan sosial sesuai informasi yang tersedia. Implementasi model ini di lapangan diharapkan bisa membantu lembaga penyedia bantuan sosial untuk mengoptimalkan sumber daya mereka dan memastikan bantuan disalurkan pada yang benar-benar membutuhkan. Dari perhitungan tersebut, nilai akurasi hasil pengujian klasifikasi menggunakan algoritma Naive Bayes adalah sebesar 86.36%.
Mapping Soil Fertility Status of Alluvial Formations Using the SFI Method and Kriging Interpolation Geographic Information Systems Basuki, Basuki; Fatmawati, Aisyah; Rohman, Fahmi Arief
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 1 (2025): February 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v14i1.1-9

Abstract

Land degradation can be characterized by a decrease in soil productivity. Jember Regency has the potential to develop food crop commodities. A decrease in soil productivity can be caused by a decrease in soil fertility. The study aims to look at the index and distribution of soil fertility in rice fields in southern Jember. The SFI (soil fertility index) technique was utilized in this study to calculate the soil fertility index. SFI is broken down into multiple parts, including calculating the Minimum Soil Fertility Index (MSFI), weighting, and scoring, which are then incorporated into the SFI calculation. The determination of MSFI is done using principal component analysis (PCA). The results of the MSFI analysis involved spatial mapping using kringing analysis to determine the area distribution of each class. The soil fertility index of the research location ranged from 1.72 to 2.28, with a low-class area of 9,224.19 ha (99.522%) and a very low-class area of 44,266 ha (0.478%). Parameters that influence soil fertility levels include cation exchange capacity, total soil nitrogen, and soil organic carbon, with a cumulative value of 84.8%. Keywords: Kriging interpolation, MSFI, paddy field, soil fertility index, soil mapping.
FACTORS AFFECTING THE CITY’S RESILIENCE TO FLOOD DISASTER IN EAST BANJARMASIN DISTRICT Fatmawati, Aisyah; Christia, Meidiana; Surjono
Tata Kota dan Daerah Vol. 15 No. 2 (2023): Jurnal Tata Kota dan Daerah
Publisher : Department of Urban and Regional Planning, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.takoda.2023.015.02.10

Abstract

Resilience is a system that can fight and adapt to certain disturbances and recover its normal function or state of balance. Improving city resilience is essential for urban communities, especially for areas at risk of being hit by disasters. Based on data from the Banjarmasin City Government, the floods that hit almost the entire city of Banjarmasin had an inundation height of between 0.25 meters to more than 0.5 meters, and East Banjarmasin Subdistrict was the sub-district that had been submerged by flood inundation for the longest time and had the highest inundation height among other districts. The study aims to identify the factors that influence community resilience to floods and formulate a model to increase this resilience. The approach method used is to use a quantitative method with SEM-PLS analysis to determine the influencing factors and formulate a model for increasing the city’s resilience. The study results show that the factors influencing city resilience to floods are green open spaces, building shapes, income, insurance ownership, level of education, employment, beliefs, and norms.
Co-Authors Basuki , Christia, Meidiana Irfan Ali, Irfan Irma Purnamasari, Ade Rohman, Fahmi Arief Surjono