Suchergebnisse

Kajian tentang peran pengawasan terhadap Organisasi Kemasyarakatan (Ormas) bertujuan untuk mendeskripsikan gambaran realitas permasalahan ormas dan mendeskripsikan pelaksanaan peran pengawasan Badan Kesbangpol terhadap ormas di Kabupaten Bandung dan memberikan rekomendasi mengenai hal-hal yang perlu diatur dalam rancangan peraturan daerah tentang pengawasan ormas. Kajian ini menggunakan pendekatan kualitatif, dengan tujuan agar dapat diperoleh informasi yang mendalam dan lengkap tentang berbagai hal terkait dengan keberadaan ormas dan dinamikanya di Kabupaten Bandung. Hasil studi dapat dikemukakan: bahwa harus diakui keberadaan ormas memberikan kontribusi yang tidak kecil dalam pembangunan, namun kenyataan lainnya masih banyaknya organisasi masyarakat yang bertindak anarkis dan menganggu dalam lingkungan masyarakat; untuk itu seluruh ketentuan operasional dalam rangka implementasi UU Ormas harus didukung oleh peraturan daerah yang lebih spesifik, terutama yang berkaitan dengan fungsi pengawasan dan pemberdayaan yang harus dilakukan dengan lebih efektif; hal-hal substansial yang perlu diatur dalam pengawasan ormas oleh Pemerintah, dalam hal ini adalah Badan Kesatuan Bangsa dan Politik Kabupaten Bandung.Keywords: authir guidelines; journal; template, manuscript (12pt italic)

The inevitable transformations brought about by the rapidly changing Internet of Things (IoT) impact all aspects of life today, including management and businesses. Specifically, areas of businesses depending mainly on internal supply chain capacity are experiencing a paradigm shift to ensure effective company performance regarding purchases, production, company sales, and product distribution. This shift means that challenges faced by the internal chain supply unit can be solved by adopting and adapting IoT as a new way to minimize work delays and save time. Moreover, IoT automatically leads to performance and productivity increases. Therefore, the present paper aims to justify adopting and adapting IoT applications in Indonesian companies, including retail businesses. Most companies' internal supply chain units face several difficulties during and after the devastating peak of COVID-19, which has led to a total global lockdown. These problems' complexity is exponential and requires innovative ways to solve their prevailing challenges. This study used observation, interview, and documentary research methods through a large-scale survey. The survey obtained the necessary information regarding how companies utilize IoT to improve their performance and productivity without hindering their internal supply chain and production units. The study concluded that the adoption of IoT, if well implemented, leads to a sustainable company and uninterrupted supply chain performance, indicating the proper performance of the organization. Doi: 10.28991/esj-2021-SP1-017 Full Text: PDF

Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution among classes. The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known data pre-processing methods. Problems that arise when oversampling with SMOTE are the phenomenon of noise, small disjunct samples, and overfitting due to a high imbalance ratio in a dataset. A high level of imbalance ratio and low variance conditions cause the results of synthetic data generation to be collected in narrow areas and conflicting regions among classes and make them susceptible to overfitting during the learning process by machine learning methods. Therefore, this research proposes a combination between Radius-SMOTE and Bagging Algorithm called the IRS-BAG Model. For each sub-sample generated by bootstrapping, oversampling was done using Radius SMOTE. Oversampling on the sub-sample was likely to overcome overfitting problems that might occur. Experiments were carried out by comparing the performance of the IRS-BAG model with various previous oversampling methods using the imbalanced public dataset. The experiment results using three different classifiers proved that all classifiers had gained a notable improvement when combined with the proposed IRS-BAG model compared with the previous state-of-the-art oversampling methods. Doi: 10.28991/ESJ-2023-07-05-04 Full Text: PDF