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All Journal FARABI: Jurnal Matematika dan Pendidikan Matematika AKSIOMA : Jurnal Matematika dan Pendidikan Matematika
Abriadi Rangkuti
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Analisis Penghitungan Anggaran Belanja Daerah Badan Perencanaan Pembangunan Daerah (BAPPEDA) Kota Medan Menggunakan Metode Activity Based Costing (ABC) Fazri Aslam; Abriadi Rangkuti; Arini Arini; Rika Ramadhani; Fibri Rakhmawati
FARABI: Jurnal Matematika dan Pendidikan Matematika Vol 7 No 1 (2024): FARABI: Jurnal Matematika dan Pendidikan Matematika
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.
Penerapan metode radial basis function neural network dalam memprediksi curah hujan Abriadi Rangkuti; Rima Aprilia
AKSIOMA: Jurnal Matematika dan Pendidikan Matematika Vol. 16 No. 2 (2025): AKSIOMA: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/w2rkdg62

Abstract

North Tapanuli Regency is highly vulnerable to natural disasters such as floods and landslides due to high rainfall intensity and complex geographical conditions. To anticipate these risks, this study aims to predict rainfall using the Radial Basis Function Neural Network (RBFNN) method. The number of nodes in the input layerwas determined based on significant lags identified in the Partial Autocorrelation Function (PACF) plot. The hidden layerwas constructed using the K-Means clustering method to define cluster centers and standard deviations. Model performance was evaluated using the Root Mean Square Error (RMSE) metric. The results indicate that the optimal network structure is found in the 4-7-1 architecture, yielding an RMSE of 11.006. This model was then used to predict rainfall for the period from September 1 to December 31, 2024. The prediction results show that rainfall intensity tends to remain stable, ranging from 5 to 9 mm, with the highest value of 9.35 mm on September 1 and the lowest value of 5.40 mm on September 22. Based on these findings, the potential for natural disasters due to high rainfall during the forecast period is relatively low. The RBFNN model has proven effective for handling nonlinear meteorological data patterns, making it a valuable tool for a reference for further academic research in rainfall prediction and data clustering.
Co-Authors Arini Arini Fazri Aslam Fibri Rakhmawati Rika Ramadhani Rima Aprilia