Legal Protection For Labor Contract In PT. Nawakara Perkasa Nusantara

The problems of this study are: the outsourcing arrangement based on the law applicable and legal protection for contract workers at PT. Nawakara Perkasa Nusantara. Researchers used the method is legal normative juridical approach and specification in this study were included descriptive analysis. Even though sources and types of data in this study are primary data obtained from field studies by interviewing officials and workers in PT. Nawakara Perkasa Nusantara. And secondary data obtained from the study of literature relating to the theory of legal protection and enforcement.Based on the results of research that restrictions or even a rejection of the application of the statutory provisions regarding outsourced workers cannot be done despite how strong workers and unions of the federation units to fight. It is caused by the development of outsourcing itself stating that the areas of specialization, especially in terms of product development expertise of goods and services is growing development. Therefore impact the outsourced workers to work opportunities more widely. Legal protection for contract workers at PT. Nawakara Perkasa Nusantara basically in implementation has not gone as stipulated in the law. The lack of protection regarding the duration of the employment agreement as to which of Article 59 paragraph (2) and (4) which states that PKWT cannot be made to work that is fixed and the period of more than three (3) years, with each year once carried out a contract extension. Interpretation in paragraph (7) stated that the violation of Article 59 paragraph (2) and (4) This will result in the void PKWT turned into PKWTT. In practice, this agreement occurred during the three (3) years with a contract extension once a year. Supposedly workers / laborers in this company have been a permanent employee, when seen from the labor law.Keywords : Protection Law; Labor Contract

The Dispute Settlement over the Ownership of a Double Certificates in Cirebon District Land Office

Birth of the Law of the Republic of Indonesia Number 5 of 1960 on Basic Regulation of Agrarian (BAL) has brought about significant changes in the world Indonesian land. BAL and a set of implementation regulations are expected to provide legal guarantees for the rights holders on the ground. But in fact the land can not be separated from problems, one of which occurred in Cirebon is the emergence of multiple certificate which led to the dispute. Here will be explained the factors that led to the emergence of a double and a certificate of completion method of dispute. To the authors do research with sociological juridical approach that combines literary and legal material facts obtained in the field through interviews. From these studies obtained answers that the emergence of double certificates can occur due to external factors and internal factors. For that matter, BPN trying to find a way out with the mediation. But if it does not receive the meeting point, the parties can file a lawsuit in court.Keywords: Dispute; Double certificates

PENGUJIAN CHARGER MODUL SIMULASI SOLAR CELL UNTUK MENYUPLAI WARNING LIGHT

Simulation Module Testing Solar Cell Charger For Supplies the Warning Light Simulation module has been tested solar cell charger to supply the battery warning light on two different, where the purpose of the author can test solar cell circuit simulation for supply warning light, to save / power supply / energy resources considering the energy resources in our country on the wane, the government is planning to wear solar energy. Thus the authors tried to make a series of simulation testing of the solar cell to supply warning light And there was its function is to determine its capacity tool / charger module simulation of solar cells and battery / batteries are different still feasible or optimal used to supply warning light. The lamp warning light works on voltage DC (Direct Current) which absorbed sunlight the solar cell module is converted into DC voltage. Module testing solar cells as an energy source electricity were, for the storage of electrical energy in the battery as a source of energy that will turn on the lights for warning light for 24 hours. Simulation modules use solar cells to DC LED warning light using very precisely applied is by using solar power, so we can save on electricity costs due to the LED DC can not dole directly from PLN net. In this paper the authors perform Simulation Testing of Solar Cell Charger Module to supply warning light and analysis for the selection of solar cell capacity, and the capacity of the battery / batteries are different

ANALISA SENTIMEN MAHASISWA TERHADAP LAYANAN STMIK PRIMAKARA MENGGUNAKAN ALGORITMA NAIVE BAYES DAN K-NEAREST NEIGHBOR

STMIK Primakara setiap tahunnya selalu melakukan analisa kepuasan mahasiswa terhadap layanan yang disediakan  dengan menyebarkan kuesioner. Namun dalam menganalisa kuesioner tersebut, Pusat Penjaminan Mutu masih melakukan secara manual, dimana  proses ini akan memerlukan waktu yang  lebih lama. Berdasarkan permasalahan tersebut  peneliti merasa perlu dilakukan penelitian analisa sentimen terhadap komentar mahasiswa dengan menggunakan algoritma Naive Bayes dan KNN. Hasil yang diperoleh berdasarkan data uji sebanyak 261 data uji diperoleh jumlah prediksi sentimen positif sebanyak 67 data, netral sebanyak 55 data, dan negatif sebanyak 144, hasil tersebut menandakan bahwa beberapa layanan dinilai masih kurang maksimal seperti wifi, parkir, ac, komputer dan lain-lain. Berdasarkan perhitungan confution matrix KNN unggul di tiga percobaan split data dengan tingkat accuracy   sebesar 79.03%, 78.93%, dan 85.06%. Sedangkan algoritma naive bayes hanya memperoleh tinggkat accuracy   sebesar 68.67%, 65.33%, dan 64.37%. Hal ini menadakan KNN memiliki kinerja yang cukup baik dalam melakukan analisa sentimen pada komentar mahasiswa.STMIK Primakara always analyzes student satisfaction with the services provided by distributing questionnaires every year. However, in analyzing the questionnaires, the Quality Assurance Center still does it manually, where this process will take longer. Based on these problems, researchers feel the need to conduct sentiment analysis research on student comments using the Naive Bayes and KNN algorithms. The results obtained based on test data from 261 test data obtained the number of positive sentiment predictions as many as 67 data, neutral as many as 55 data, and negative as many as 144, these results indicate that some services are considered not optimal such as wifi, parking, air conditioning, computers and others. Based on the calculation of the confusion matrix, KNN excels in three separate data trials with accuracy rates of 79.03%, 78.93% and 85.06%. While the naive Bayes algorithm only obtains an accuracy rate of 68.67%, 65.33% and 64.37%. This shows that KNN has done quite well in conducting sentiment analysis of student comments