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All Journal Kubik KUBIK: Jurnal Publikasi Ilmiah Matematika FARABI: Jurnal Matematika dan Pendidikan Matematika
Aslam, Fazri
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics

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Analisis Penghitungan Anggaran Belanja Daerah Badan Perencanaan Pembangunan Daerah (BAPPEDA) Kota Medan Menggunakan Metode Activity Based Costing (ABC) Aslam, Fazri; Rangkuti, Abriadi; Arini, Arini; Ramadhani, Rika; Rakhmawati, Fibri
FARABI: Jurnal Matematika dan Pendidikan Matematika Vol 7 No 1 (2024): FARABI
Publisher : Program Studi Pendidikan Matematika FKIP UNIVA Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47662/farabi.v7i1.722

Abstract

The Regional Revenue and Expenditure Budget (APBD), is the annual financial plan of regional governments in Indonesia which is approved by the Regional People's Representative Council. The APBD is determined by Regional Regulations and the APBD Fiscal Year covers a period of one year, starting from January 1 to December 31. This research aims to determine the grouping of activities from highest to lowest using the Activity Based Costing (ABC) method calculation. The research results show two analyzes, namely analysis in the sub-activities section and analysis in the operations expenditure section. The data used is Quantitative Data, which is data in the form of numbers or nominal data, data in the form of the Medan City BAPPEDA Budget Realization Report . Qualitative data is data that is not in the form of numbers or nominal, data in the form of direct observations from BAPPEDA Medan City regarding the implementation of the APBD. Data obtained from PERWAL APBD 2022 BAPPEDA Section is the total budget amount of Rp. 27,307,689,269 with 3 general activities, 12 sub-activities and 48 total operational expenditures.
Risk Analysis of Oyster Mushroom Cultivation Success through Artificial Neural Network with Backpropagation Algorithm Aslam, Fazri; Lubis, Riri Syafitri
KUBIK Vol 10 No 1 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

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Abstract

Consumption mushroom cultivation is still rare in most parts of Indonesia, although the demand for this agricultural product continues to increase. Mushroom business opportunities are actually quite promising. This research aims to analyze the prediction of the risk level of oyster mushroom cultivation success using the artificial neural network method with the Backpropagation algorithm. This research combines qualitative and quantitative approaches, with data analysis methods in the form of Backpropagation algorithm training implemented through MATLAB software. Based on the results of testing or training conducted using the 5-3-1 Artificial Neural Network (JST) architecture and Epoch 1, the minimum error is 0.6 or 4 kg of yield (IDR 80,000), while the maximum error is 0.7 or 5 kg of yield (IDR 100,000). with a training MSE of 0.0964 with This means that artificial neural networks can create patterns to predict the yield of oyster mushroom cultivation.
Risk Analysis of Oyster Mushroom Cultivation Success through Artificial Neural Network with Backpropagation Algorithm Aslam, Fazri; Lubis, Riri Syafitri
KUBIK Vol 10 No 1 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Consumption mushroom cultivation is still rare in most parts of Indonesia, although the demand for this agricultural product continues to increase. Mushroom business opportunities are actually quite promising. This research aims to analyze the prediction of the risk level of oyster mushroom cultivation success using the artificial neural network method with the Backpropagation algorithm. This research combines qualitative and quantitative approaches, with data analysis methods in the form of Backpropagation algorithm training implemented through MATLAB software. Based on the results of testing or training conducted using the 5-3-1 Artificial Neural Network (JST) architecture and Epoch 1, the minimum error is 0.6 or 4 kg of yield (IDR 80,000), while the maximum error is 0.7 or 5 kg of yield (IDR 100,000). with a training MSE of 0.0964 with This means that artificial neural networks can create patterns to predict the yield of oyster mushroom cultivation.
Co-Authors Arini Arini Rakhmawati, Fibri Ramadhani, Rika Rangkuti, Abriadi Riri Syafitri Lubis