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PENGELOLAAN SISTEM DRAINASE OLEH DINAS PEKERJAAN UMUM DAN PENATAAN RUANG KOTA PEKANBARU TAHUN 2018 ( STUDI KASUS DRAINASE JL SALEH ABBAS PASAR BAWAH ) Wulandari, Nova; M, Erman
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Vol. 7: Edisi II Juli - Desember 2020
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik Universitas Riau

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Abstract

         The drainage system is a way of removing excess unwanted water in an area. With the drainage system, excess water can be channeled into the drainage channel. When the rainy season arrives, there will be puddles or flooding on the roads and also inundate the market area below. One of the factors in the occurrence of inundation is high rainfall, clogged drainage channels and cannot function properly. From this problem, it is necessary to manage damaged / problematic drainage channels so that the water flow can flow properly.This research was conducted with the aim of describing the management of the drainage system in Pasar Bawah Jl Saleh Abbas in 2018. This research is a case study research with a qualitative approach. The location of this research is the Public Works and Spatial Planning Office of Pekanbaru City. The data collection techniques used were interviews and documentation. The data analysis technique used is qualitative data analysis.The results of the study concluded that the Drainage System Management has not been running optimally, this is due to the unclear field responsible for handling this drainage problem and the absence of a special activity program so that the process in the activities carried out cannot be completed. Keywords: Management, drainage system
PENGARUH PENGALAMAN, PENGETAHUAN, AUDIT TENURE DAN PEER REVIEW TERHADAP KUALITAS AUDIT (Studi Empiris Pada Kantor Akuntan Publik Di Pekanbaru, Batam, Padang dan Medan) Nova Wulandari; Rasuli '; Volta Diyanto
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi Vol 1, No 2 (2014): wisuda oktober 2014
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi

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Abstract

Audit is a process to reduce the information contained in consistencies between managers and share holders. So the company should be more critical in choosing Public Accounting Firm to the financial statements audited by a public accountants a report that is free from material misstatement, be credible. There fore, we need a professional services independent and objective public accountants to assess the fairness of the financial statements presented by management. This study aimed to examine the effect of experience, knowledge, audit tenure and peer review of audit quality in the public accounting firm in Pekanbaru, Batam, Padang and Medan. The sampling technique using purposive sampling. Data collection methods conducted by using a questionnaire. The data collected will be analyzed using multiple linear regression. The results showed that the experience, knowledge, peer reviewed significant effect on audit quality. While no effect exhibited significantly audit tenure on audit quality produced by a public accountant. The amount of donations influence the experience, knowledge, audit tenure and peer review of audit quality by 59%.Keywords: Experience, Knowledge, Audit Tenure, Peer Review and Quality Audit
Sentiment Analysis on the Relocation of the National Capital (IKN) on Social Media X Using Naive Bayes and K-Nearest Neighbor (KNN) Methods Wulandari, Nova; Cahyana, Yana; Rahmat, Rahmat; Hikmayanti, Hanny
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9552

Abstract

This study investigates public sentiment toward the relocation of Indonesia’s capital from Jakarta to East Kalimantan, focusing on reactions from social media platforms such as X (formerly Twitter). Understanding these sentiments is crucial for the government to gauge support for this significant policy shift. The study compares the performance of two classification algorithms, Naïve Bayes and K-Nearest Neighbor (K-NN), in sentiment analysis. A total of 1.277 comments were collected using the tweet-harvest library through a crawling process. The data underwent preprocessing, including cleaning, case folding, normalization, stopword removal, tokenization, and stemming. Sentiment labels were assigned through both manual and automated methods, while feature extraction was performed using the TF-IDF technique. The algorithms' performance was assessed using accuracy, precision, recall, and F1-score metrics. The results revealed that Naïve Bayes outperformed K-NN, with an accuracy of 70%, precision of 72%, recall of 70%, and an F1-score of 69%. In contrast, K-NN achieved an accuracy of 60%, precision of 62%, recall of 60%, and an F1-score of 59%. These results suggest that Naïve Bayes is more effective in classifying sentiment related to the capital relocation. The findings offer valuable insights for policymakers and highlight the potential of automated sentiment analysis as a tool for monitoring public opinion on major governmental policies.