Duties and Responsibilities of Notary on The Act on His Own Viewed From Act No. 2 of 2014 on The Department of Notary

Notary is a public official who has the authority to make authentic documents and any other authority as referred to in Article 1 paragraph (1) of Act no. 2 of 2014 on the amendment of Act No. 30 of 2004 on the department of notary. The aim of this study was to: 1) To determine the duties and responsibilities of the Deed made by it in carrying out its duties and his position based on Act no. 2 of 2014 concerning Notary. 2) To find out the weaknesses of the duties and responsibilities of the Deed made by Act no. 2 of 2014 concerning Notary. 3) To find a solution weakness of the duties and responsibilities Against the Act made. The data used in this study are primary data, secondary and tertiary then analyzed by juridical empirical method that is reviewing the law relating to the issues discussed.Based on the data analysis concluded that: 1) The duties and responsibilities of a notary deed prepared to very low and many things that are broken. 2) the provisions set forth in Act no. 2 of 2014 concerning Notary less able to protect the client. 3) the provisions set forth in Act no. 2 of 2014 concerning on Department of Notary must include also the protection of the client as a result of the negligence of the notary who is aggrieved clients.Keywords: Notary Duties; Responsibilities Notary; Deed Of Notary

Penerapan Algoritma Consultant-Guided Search Dalam Masalah Penjadwalan Job Shop Untuk Meminimasi Makespan

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop schedulingproblems minimizing makespan. CGS is a metaheuristics inspired by people making decisionsbased on consultant's recommendations. A number of cases from literatures is developed to evaluatethe optimality of this algorithm. CGS is also tested against other metaheuristics, namely GeneticAlgorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluationsare conducted using the best makespan obtained by these algorithms. From computational results,it is shown that CGS is able to find 3 optimal solutions out of 10 cases. Overall, CGS performs bettercompared to the other algorithms where its solution lies within 0 - 6,77% from the optimal solution,averaging only 2,15%. Futhermore, CGS outperforms GA in 7 cases and performs equally well inthe other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases

Penerapan Algoritma Consultant-Guided Search dalam Masalah Penjadwalan Job Shop untuk Meminimasi Makespan

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop scheduling problems minimizing makespan. CGS is a metaheuristics inspired by people making decisions based on consultant’s recommendations. A number of cases from literatures is developed to evaluate the optimality of this algorithm. CGS is also tested against other metaheuristics, namely Genetic Algorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluations are conducted using the best makespan obtained by these algorithms. From computational results, it is shown that CGS is able to find 3 optimal solutions out of 10 cases. Overall, CGS performs better compared to the other algorithms where its solution lies within 0 - 6,77% from the optimal solution, averaging only 2,15%. Futhermore, CGS outperforms GA in 7 cases and performs equally well in the other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases

Penerapan Algoritma Consultant-Guided Search dalam Masalah Penjadwalan Job Shop untuk Meminimasi Makespan

This research uses the Consultant-Guided Search (CGS) algorithm to solve job shop schedulingproblems minimizing makespan. CGS is a metaheuristics inspired by people making decisionsbased on consultant’s recommendations. A number of cases from literatures is developed to evaluatethe optimality of this algorithm. CGS is also tested against other metaheuristics, namely GeneticAlgorithms (GA) and Artificial Immune Systems (AIS) for the same cases. Performance evaluationsare conducted using the best makespan obtained by these algorithms. From computational results,it is shown that CGS is able to find 3 optimal solutions out of 10 cases. Overall, CGS performs bettercompared to the other algorithms where its solution lies within 0 - 6,77% from the optimal solution,averaging only 2,15%. Futhermore, CGS outperforms GA in 7 cases and performs equally well inthe other 3 cases. CGS is also better than AIS in 8 cases and is equally well in only 2 cases