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All Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding SeNTIK STI&K
Adhitio Satyo Bayangkari Karno
STMIK Jakarta STI&K
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media

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Metode Pembelajaran Mesin untuk Memprediksi Emisi Manure Management Widi Hastomo; Nur Aini; Adhitio Satyo Bayangkari Karno; L.M. Rasdi Rere
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1778.055 KB) | DOI: 10.22146/jnteti.v11i2.2586

Abstract

Indonesia is committed to reducing greenhouse gas (GHG) emissions through a nationally determined contribution (NDC) scheme. The target to reduce GHG emissions is 29% through the business as usual (BAU) scheme or 41% with international aid. These ambitious targets require transformations in energy, food, and land-use systems, which need to cope with the potential trade-offs among many targets, such as food security, energy security, avoided deforestation, biodiversity conservation, land use competition, and freshwater use. Mitigation and adaptation have complementary roles in responding to climate change at both temporal and spatial scales. This paper aims to perform simulations and predictions on manure management emissions producing CO2eq using machine learning methods of long short-term memory (LSTM) and gated recurrent unit (GRU). The hidden layer architecture used was six combinations, while the dataset was obtained from the fao.org repository. The optimizer used in this paper was RMSprop, with a graphical user interface using the Streamlit dashboard. The results of this study are (a) cattle with fifteen epochs using hidden layer four combinations (LSTM, GRU, LSTM, GRU) yielded RMSE 450,601; (b) non-dairy cattle with fifteen epochs and one hidden layer (GRU, GRU, GRU, GRU) yielding RMSE 361.421; (c) poultry birds with twelve epoch values and three hidden layers (GRU, GRU, LSTM, LSTM) resulted in an RMSE value of 341.429. The challenges faced were the determination of epochs, the combination of hidden layers, and the characteristics of the relatively small number of datasets. The results of this study are expected to provide added value for developing better decision support tools and models to assess emission trends in the livestock sector and develop CO2eq emission mitigation strategies that lead to sustainable fertilizer management practices.
DETEKSI COVID-19 IMAGE CHEST X-RAY DENGAN CONVOLUTION NEURAL NETWORK EFFICIENT NET-B7 Adhitio Satyo Bayangkari Karno; Dodi Arif; Indra Sari Kusuma Wardhana
Prosiding Seminar SeNTIK Vol. 5 No. 1 (2021): Prosiding SeNTIK 2021
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Di era pandemi keberadaan para medis dan rumah sakit terhadap jumlah pasien covid sangat tidak seimbang, lembaga kesehatan memerlukan alat bantu untuk tetap dapat memberikan pelayanan kesehatan. Kecerdasan buatan mampu memprediksi data image chest x-ray terhadap pasien penderita covid dan penyakit lainnya. Penelitian ini bermaksud untuk dapat mendeteksi covid-19 daridata image chest x-raymenggunakan Convolution Neural Network (CNN). Operasi yang ringandengan kualitas akurasi sangat baik dari arsitekturEfficienNet-B7 dapat dipergunakan oleh komputer performa tanpa Graphics Processing Unit (GPU).Dataset yang dipergunakan berbentukimage chest x-ray berjumlah 4.000 image, terdiri dari 4 klasifikasi yaitu covid, normal, lung opacity dan viral pneumonia masing-masing dengan jumlah data 1.000 image.Hasil penelitian dengan trainning 50 epoch diperoleh nilai akurasi trainning95,5% , akurasi validasi 91,8% dan akurasi testing 96%. Untuk tiap kelas hasil testing covid (96%) , normal (95%), lung opacity (93%) dan viral pneumonia (98%)
Co-Authors Dodi Arif Indra Sari Kusuma Wardhana LM Rasdi Rere LM Rasdi Rere Nur Aini Widi Hastomo