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All Journal INFO ARTHA Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Pratama, Rizki Adhi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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ANTI-AVOIDANCE AND PROFIT SHIFTING IN ASEAN MULTINATIONAL ENTERPRISES: IS IT EFFECTIVE? Pratama, Rizki Adhi
JURNAL INFO ARTHA Vol 4, No 1 (2020): JULY EDITION
Publisher : Polytechnic of State Finance STAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (607.818 KB) | DOI: 10.31092/jia.v4i1.641

Abstract

Using ORBIS company micro-level data, this paper discussed the effectiveness of anti-avoidance regulation in tackling outbound profit shifting in ASEAN. Using fixed effect panel data for the period from 2009 - 2018,  the thesis found that the elasticity of outbound profit shifting to positive tax rate differential is roughly 1.56%, where anti-avoidance effect brings back profit by 1.06%, which resulted in net impact of 0.5% of outbound profit shifting. While negative tax rate differential brings inbound profit shifting by 0.75%. Also, this paper conclude too strict anti-avoidance regulation will result in the decreasing effect. Dengan menggunakan data mikro yang disediakan oleh ORBIS, penelitian ini ini mencoba mengukur tingkat efektivitas  peraturan anti penghindaran pajak di ASEAN dalam mencegah pergeseran laba keluar negeri. Dengan menggunakan metode efek tetap data panel yang mencakup periode 2009 – 2018, thesis ini menemukan bahwa tingkat elastisitas atas outbound profit shifting terhadap perbedaan tarif pajak positif adalah 1,56%, dimana efek aturan penghindaran pajak dapat mencegah pergeseran profit sebesar 1,06%, yang menghasilkan dampak bersih pergeseran laba keluar negeri yang tidak bisa dicegah sebesar sebesar 0,5%.Selain itu, thesis ini juga menyimpulkan bahwa peraturan anti penghindaran pajak yang terlalu ketat akan menurunkan efektivitas peraturan anti penghindaran pajak. 
Implementation Of Machine Learning To Identify Types Of Waste Using CNN Algorithm Haqqi, Matsnan; Rochmah, Lailatur; Safitri, Arisanti Dwi; Pratama, Rizki Adhi; Tarwoto
JURNAL FASILKOM Vol. 14 No. 3 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i3.8116

Abstract

Waste management remains a significant challenge globally, particularly in Indonesia, where the annual waste generation reached 24.67 million tonnes in 2021, with only 50.43% properly managed. To address the issue of mixed organic and inorganic waste and the lack of public awareness regarding waste separation, this study applied machine learning, specifically the Convolutional Neural Network (CNN) algorithm, to classify waste types. The research aimed to develop an effective automated waste classification model to improve waste management processes. The research involved collecting a dataset of 2,848 images representing six waste categories: glass, cardboard, paper, metal, organic, and plastic. Preprocessing techniques such as cropping, noise reduction with Gaussian filters, and data augmentation were applied to enhance data quality. The dataset was divided into training, validation, and testing subsets in a 70:20:10 ratio. The CNN model employed feature extraction through convolution, activation, and pooling layers, followed by classification using a fully connected layer and a softmax function. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved an overall accuracy of 95%, with an average precision, recall, and F1-score of 0.95 across all classes. These results demonstrate the CNN model’s ability to reliably classify waste types. Compared to previous studies, this research achieved higher accuracy through the use of enhanced preprocessing and CNN optimization. This study highlights the potential of CNN-based models for automated waste classification, contributing to sustainable waste management practices and fostering environmental awareness in the future research.
Co-Authors Haqqi, Matsnan Rochmah, Lailatur Safitri, Arisanti Dwi Tarwoto