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IMPLEMENTASI PERATURAN PEMERINTAH NOMOR 66 TAHUN 2010 TERHADAP PEMBERDAYAAN DEWAN PENDIDIKAN DAN KOMITE SEKOLAH Arif Suryono; Rahmad Santosa; Haryadi Haryadi
Jurnal Dinamika Hukum Vol 13, No 2 (2013)
Publisher : Faculty of Law, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jdh.2013.13.2.208

Abstract

Arief Suryono, Rahmad Santosa, dan HaryadiUniversitas Jenderal Soedirman PurwokertoE-mail:The paradigm of education, has actually brought a major change in educational policy-making at the local level as well as the school, including implementation Goverment Regulations No. 66 Year 2010. This research aims to know the implementation government regulation Goverment Regulations No. 66 Year 2010 of the empowerment board education and the school committee.The study was conducted in 18 provinces. Method of data collection using questionnaires and in-depth interviews, while data analysis is done using a simple statistik (quasi statistics), the cross-tabulation and percentages. The results show on implementation government regulation No 66 year 2010, board of education, department of education, school committee, principals and teachers need more constructive communication, and it’s can happen by clearly partnership, employment patterns and authority. The functions and duties of the school committee only on preparation and adoption of new RAPBS and school committee with the school system didn’t have a clear working partnership with it. Board of education and the school committee also didn’t have a definite budget, this difficult to recruit human resources who have qualified in the field of education, which can improve the best performance of the board of education and school committees.Keywords:   board of education, school committes, school system
TRANSFORMASI SOSIAL DI PEDESAAN: STUDI FENOMENOLOGIS PROSES PENDIDIKAN DAN PEMBERDAYAAN MASYARAKAT Rahmad Santosa
Jurnal Kependidikan Vol. 41, No.1 (2011)
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (160.034 KB) | DOI: 10.21831/jk.v41i1.501

Abstract

The study is aimed at describing the social transformation among members of the village community. The study is qualitative research using phenomenological paradigms for data analyses. The subject consists of poor farmers and pottery makers in the village of Panjangrejo, Bantul Regency. Findings show that the change from being a traditional village of pottery production to a modern village of pottery production begins with innovation by individual pottery makers, is followed by internal dynamics in the community, and finally triggers the birth of the community empowerment and social transformation processes
Classification of flood disaster level news articles using Machine Learning Rahmad Santosa; Arna Fariza; Firman Arifin
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3646

Abstract

Floods have a significant socio-economic impact on Indonesian society. Much of this information is sourced from online news articles and social media. This research investigates whether the Support Vector Machine (SVM) method can be used for flood disaster level classification (low, medium, and high). Our methodology involves preparing data extracted from textual news articles on the National Disaster Management Agency (BNPB) website on the topic of flooding. We then labeled the data according to Regulation No. 02/2012 on general guidelines for disaster assessment and used the Support Vector Machine (SVM) method. Training and testing were conducted using different datasets, followed by accuracy and error evaluation. In addition, we considered the performance comparison of SVM with other classification methods, including Decision Tree, Naive Bayes, Adaboost, Random Forest, and Xgboost. The experimental results show that SVM still does not get good accuracy results for flood disaster level classification. The SVM accuracy level result of (52%) is still low compared to Random Forest (78%), and Xgboost (68%). Further research is expected to increase the accuracy of SVM for flood level classification.