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All Journal Indonesian Journal of Conservation Recursive Journal of Informatics
Cahyani, Rima Ayu
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Computer Science & IT Languange, Linguistic, Communication & Media

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Konsep Bangunan Rumah Tinggal sebagai Penerapan Arsitektur Hijau pada Perumahan Sumber Indah Kudus dengan Material Daur Ulang Cahyani, Rima Ayu
Indonesian Journal of Conservation Vol 9, No 2 (2020): December 2020
Publisher : Badan Pengembang Konservasi UNNES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ijc.v9i2.27387

Abstract

Kudus merupakan salah satu kabupaten kecil yang terletak di provinsi Jawa Tengah, jalur Pantai Utara. Dengan luas 425,2 km2 dan jumlah penduduk mencapai 871.311 pada tahun 2019, Kabupaten Kudus memiliki sejumlah perumahan yang di bangun di atasnya. Salah satunya adalah Perumahan Sumber Indah. Permasalahan lingkungan dalan konteks arsitektural dewasa ini adalah mengenai bangunan rumah yang ramah lingkungan. Sebagian besar bangunan pada Perumahan Sumber Indah masih belum menerapkan konsep arsitektur hijau. Rumah dengan konsep arsitektur hijau ini menggunakan bahan dasar bangunan maupun desain eksterior dan interior dengan material daur ulang. Metode yang digunakan dalam penelitian ini adalah dengan menggunakan pendekatan analisis deskriptif kualitatif dalam membandingkan material daur ulang pada rumah. Hasil penelitian ini menjunjukkan bahwa dengan menggunakan material daur ulang dalam mendesain rumah dapat mengurangi biaya dan ramah lingkungan karena dapat mengurangi barang yang harusnya dibuang, seperti botol, kayu bekas, bamboo bekas, dan lainnya, menjadi bermanfaat sebagai desain arsitektural yang estetik dan unik.
Neural Network Optimization Using Hybrid Adaptive Mutation Particle Swarm Optimization and Levenberg-Marquardt in Cases of Cardiovascular Disease Cahyani, Rima Ayu; Purwinarko, Aji
Recursive Journal of Informatics Vol 2 No 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v2i2.78550

Abstract

Abstract. Cardiovascular disease is a condition generally characterized by the narrowing or blockage of blood vessels, which can lead to heart attacks, chest pain, or strokes. It is the leading cause of death worldwide, accounting for approximately 31% or 17.9 million deaths each year globally. Deaths caused by cardiovascular disease are projected to continue increasing until 2030, with the number of patients reaching 23.3 million. As cases of death due to cardiovascular disease become more prevalent, early detection is crucial to reduce mortality rates. Purpose: Many previous researchers have conducted studies on predicting cardiovascular disease using neural network methods. This study extends these methods by incorporating feature selection and optimization with Hybrid AMPSO-LMA. The research is designed to explore the implementation and predictive outcomes of Hybrid AMPSO-LMA in optimizing MLP for cases of cardiovascular disease. Methods/Study design/approach: The first step in conducting this research is to download the Heart Disease Dataset from Kaggle.com. The dataset is processed through preprocessing by removing duplicates and transforming the data. Then, data mining processes are carried out using the MLP algorithm optimized with Hybrid AMPSO-LMA to obtain results and conclusions. This system is designed using the Python programming language and utilizes Flask for website access in HTML. Result/Findings: The research results demonstrate that the method employed by the author successfully improves the accuracy of predicting cardiovascular disease. Predicting cardiovascular disease using the MLP algorithm yields an accuracy of 86.1%, and after optimization with Hybrid AMPSO-LMA, the accuracy increases to 86.88%. Novelty/Originality/Value: This effort will contribute to the development of a more reliable and effective cardiovascular disease prediction system, with the goal of early identification of individuals exhibiting symptoms of cardiovascular disease.
Co-Authors Aji Purwinarko, Aji