APLIKASI PELINDUNG SOURCE CODE PHP DARI PELANGGARAN HAK CIPTA MENGGUNAKAN ALGORITMA RC4 DAN BASE64 SERTA PERLINDUNGAN SATU ARAH ENKRIPSI TANPA DEKRIPSI

Kriptografi adalah suatu ilmu yang mempelajari teknik maupun cara untuk melindungi suatu data agar tetap aman saat digunakan dan tidak dapat dibaca oleh orang yang tidak berkepentingan. Bahasa PHP merupakan bahasa pemrograman berbasis web yang sangat populer saat ini. Namun bahasa tersebut rentan untuk dilakukan pembajakan source code maupun ide dari pembuat asli suatu program tersebut dikarenakan tidak tersedianya software packaging. Keamanan source code masih mengalami banyak masalah dengan banyak penyalahguna’an yang dilakukan terhadap source code asli dari pembuat program. Penelitian ini dimaksudkan untuk membuat suatu sistem keamanan dengan menggunakan algoritma RC4 dan Base64. Algoritma tersebut berfungsi sebagai pengubah data plaintext menjadi chipertext. Aplikasi dari penggunaan algoritma enkripsi tersebut dapat digunakan sebagai pelindung source code untuk para pembuat program yang takut kodenya dicuri atau disalahgunakan. Penerapan dari algoritma ini diharapkan dapat menjaga hak cipta dan privasi kode program dari penyalahgunaan dan pelanggaan hak cipta

HAK ATAS RUANG DALAM DISKURSUS AWE-AWE

Awe-awe is a term for those who have the job of being a road guide for vehicles passing through the Gumitir route, Banyuwangi Regency. The steep road conditions in the Gumitir route area make awe-awe seem to be very much needed. Where this makes awe-awe activities widely practiced by the surrounding community. Initially, the awe-awe was tasked with helping and giving signals to vehicles that were about to pass the bend. However, awe-awe developed into a medium for begging and became a tradition that was passed down to the next generation. The complexity of the awe-awe phenomenon occurs when there is a privatization of space by the power elite which causes a reduction in freedom to do awe-awe. This research uses the perspective of Space theory proposed by Henri Lefebvre. According to Lefebvre, spatial space in modern capitalist society is a battleground that will never end. The purpose of this research is to find out the complexity of the lives of awe-awe actors in the contemporary era which has been colored by capitalist domains and privatization of space so that it has an impact on the lack of fulfillment of space rights by awe-awe actors. The type of research is qualitative research with an ethnographic approach. The results showed that there has been the privatization of space in the Gumitir path area due to the process of land capitalization by the community and local government. This privatization of space also has an impact on the non-fulfillment of the space for awe-awe actors, which over time the meaning of awe-awe has shifted by adjusting the situation and conditions of the existing reality

Training on Making Assessments and Media Bassed on Technological Pedagogical Content Knowledge (TPACK) in Learning

The implementation of technology in learning activities, especially evaluation and learning media, has not been implemented optimally. The fact that educators do not assess all of these aspects and are still fixated on conventional assessments and innovative media and assessment designs as demanded by the 21st century, which is rarely done. For this reason, to improve the skills of educators, especially science educators who are members of the Mempawah Regency MGMP, training in making assessments and TPACK-based learning media is needed. The purpose of this activity is to improve the ability of educators to make TPACK-based assessments and media in learning and to describe educators' responses to the socialization activities carried out. This training uses a qualitative descriptive method and the material exposure through practice, discussion, and presentation. The results of the training are known to be very helpful for educators to develop assessments and learning media that are interesting, innovative, and effective for students by utilizing technology that is easily accessible to anyone and increasing the knowledge and creativity of educators regarding the implementation of technology in preparing assessments and learning media specifically for science subjects

An Empirical Study of Segmented Linear Regression Search in LevelDB

The purpose of this paper is proposing a novel search mechanism, called SLR (Segmented Linear Regression) search, based on the concept of learned index. It is motivated by our observation that a lot of big data, collected and used by previous studies, have a linearity property, meaning that keys and their stored locations show a strong linear correlation. This observation leads us to design SLR search where we apply segmentation into the well-known machine learning algorithm, linear regression, for identifying a location from a given key. We devise two segmentation techniques, equal-size and error-aware, with the consideration of both prediction accuracy and segmentation overhead. We implement our proposal in LevelDB, Google’s key-value store, and verify that it can improve search performance by up to 12.7%. In addition, we find that the equal-size technique provides efficiency in training while the error-aware one is tolerable to noisy data

An Empirical Study of Segmented Linear Regression Search in LevelDB

The purpose of this paper is proposing a novel search mechanism, called SLR (Segmented Linear Regression) search, based on the concept of learned index. It is motivated by our observation that a lot of big data, collected and used by previous studies, have a linearity property, meaning that keys and their stored locations show a strong linear correlation. This observation leads us to design SLR search where we apply segmentation into the well-known machine learning algorithm, linear regression, for identifying a location from a given key. We devise two segmentation techniques, equal-size and error-aware, with the consideration of both prediction accuracy and segmentation overhead. We implement our proposal in LevelDB, Google’s key-value store, and verify that it can improve search performance by up to 12.7%. In addition, we find that the equal-size technique provides efficiency in training while the error-aware one is tolerable to noisy data