The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character. The opinions expressed by the public can take form support, criticism and input. Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments. This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media. Data used in this study was obtained by crawling on social media Twitter. This study uses a classification algorithm, Naïve Bayes and Support Vector Machine. Comparison two algorithms produces predictions obtained that the Naïve Bayes algorithm with SMOTE gets accuracy value 70.32%, and AUC value 0.954. While Support Vector Machine with SMOTE gets accuracy value 66.92% and AUC value 0.832. From these results can be concluded that Naïve Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naïve Bayes and the vector machine support is 3.4%. Naïve Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum.

The development of educational technology is growing rapidly, this is happening with the growing trend in the trend of online learning technology, mobile & multimedia. In the course of logic and program algorithm, students are introduced about the introduction of computer technology, in order to create a technology-based educational environment then made learning media introduction computer, made possible by the learning media, computer introduction, can help the learning process in the course of logic & programming algorithm.

Intisari— Dalam Penilaian Karyawan Terbaik pada PT. Berkah Jaya Motor, ada beberapa faktor yang menjadi penilaian dan berdasarkan penilaian kinerja karyawan diperusahaan. Penilaian karyawan di PT. Berkah Jaya Motor masih mengalami kendala karena masih menggunakan sistem Penilaian dengan cara Perundingan. Demi efisiensi kerja maka pengambilan keputusan yang tepat sangat diperlukan. Dengan tujuan untuk membangun dan memberikan alternatif. Untuk Penilaian karyawan terbaik dengan menggunakan metode Simple Additive Weighting (SAW). dimana ada beberapa kriteria yang masing-masing memiliki bobot penilaian sehingga memberikan hasil penilaian karyawan yang akurat terhadap setiap kinerja karyawan terbaik. Hasil akhir diperoleh dari proses perhitungan, yaitu penjumlahan dari matriks ternormalisasi dengan bobot per kriteria yang menunjukan rangking pemilihan karyawan terbaik dari pertama hingga yang terakhir dari kriteria. Dari penilaian tersebutlah menjadi alternatif yang kemudian mendapat Karyawan Terbaik. Kata Kunci— Sistem Pendukung Keputusan, Karyawan Terbaik, Simple Additive Weighting Referensi : [1]  A. G. Anto, H. Mustafidah, and A. Suyadi, “Sistem Pendukung Keputusan Penilaian Kinerja Karyawan Menggunakan Metode SAW (Simple Additive Weighting) di Universitas Muhammadiyah Purwokerto,” JUITA, vol. 4, pp. 193–200, 2015, Accessed: Jun. 18, 2021.[Online]. Available: http://www.jurnalnasional.ump.ac.id/index.php/JUITA/article/view/876 [2]  A. T. Widiyanto and Y. Erliani, “Sistem Pendukung Keputusan Dalam Menentukan Karyawan Terbaik Pada PTt. Tembaga Mulia Semanan Dengan Metode Topsis,” 2016. [3]  I. Pratama, Sistem Informasi dan Implementasinya. 2019. [4]  D. I. Sabanayo, “Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik Menggunakan Metode SAW Pada PT. Berkah Cahaya Muria Kudus,” 2015. [5]  T. Syahputra, M. Yetri, and S. D. Armaya, “Sistem Pengambilan Keputusan Dalam Menentukan Kualitas Pemasukan Pangan Segar Metode Smart,” JURTEKSI, vol. 04, no. 01, 2017, Accessed: Jun. 18, 2021. [Online]. Available: https://jurnal.stmikroyal.ac.id/index.php/jurteksi/article/view/19/18 [6]  I. Fahmi, Teori dan Teknik Pengambilan Keputusan Kualitatif dan Kuantitatif . 2016. [7]  S. Mallu, “Sistem Pendukung Keputusan Penentuan Karyawan Kontrak Menjadi Karyawan Tetap Menggunakan Metode TOPSIS,” JITTER, vol. 01, no. 02, 2015, Accessed: Jun. 18, 2021. [Online]. Available: http://journal.widyatama.ac.id/index.php/jitter/article/view/53 . 2021 [8] K. Safitri, F. Tinus Waruwu, and Mesran, “Sistem Pendukung Keputusan Pemilihan Karyawan Berprestasi Dengan Menggunakan Metode Analytical Hieararchy Process Process Studi Case PT.Capella Dinamik Nusantara Takengon vol. 1, no. 1, pp. 12–16, 2017, Accessed: Jun. 18,  ,” vol. 1, no. 1, pp. 12–16, 2017, Accessed: Jun. 18, 2021. [Online]. Available: https://ejurnal.stmik budidarma.ac.id/index.php/mib/article/view/317/268 [9]  G. Taufiq, “Implementasi Logika Fuzzy Tahani Untuk Model Sistem Pendukung Keputusan Evaluasi Kinerja Karyawan,” Jurnal Pilar Nusa Mandiri, vol. XII, no. 1, 2016, Accessed: Jun. 18, 2021. [Online]. Available: http://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/254/224 [10] D. Nofriansyah, Konsep Data Mining Vs Sistem Pendukung Keputusan, I. Yogyakarta: Deepublish, 2014. [11] D. Fatihudin, Metode Penelitian untuk Ekonomi, Manajemen dan Akuntansi. 2015. [12]  J. Hartono, Analisis & Desain Sistem Informasi Pendekatan Terstruktur Teori Dan Praktik Aplikasi Bisnis. 2014. [13]  I. G. B. Subawa, I. M. A. Wirawan, and I. M. G. Sunarya, “Pengembangan Sistem Pendukung Keputusan Pemilihan Pegawai Terbaik Menggunakan Metode Simple Additive Weighting (SAW) Di PT Tirta Jaya Abadi Singaraja,” Karmapati, vol. 4, no. 5, 2015, Accessed: Jun. 18, 2021. [Online]. Available: https://ejournal.undiksha.ac.id/index.php/KP/article/view/6623/4511

Non-Cash Food Assistance or Bantuan Pangan Non-Tunai (BPNT) is food assistance from the government given to the Beneficiary Family (KPM) every month through an electronic account mechanism that is used only to buy food at the Electronic Shop Mutual Assistance Joint Business Group Hope Family Program (e-Warong KUBE PKH ) or food traders working with Bank Himbara. In its distribution, BPNT still has problems that occur that are experienced by the village apparatus especially the apparatus of Desa Wanasari on making decisions, which ones are worthy of receiving (poor) and not worthy of receiving (not poor). So one way that helps in making decisions can be done through the concept of data mining. In this study, a comparison of 2 algorithms will be carried out namely Naive Bayes Classifier and Decision Tree C.45. The total sample used is as much as 200 head of household data which will then be divided into 2 parts into validation techniques is 90% training data and 10% test data of the total sample used then the proposed model is made in the RapidMiner application and then evaluated using the Confusion Matrix table to find out the highest level of accuracy from 2 of these methods. The results in this classification indicate that the level of accuracy in the Naive Bayes Classifier method is 98.89% and the accuracy level in the Decision Tree C.45 method is 95.00%. Then the conclusion that in this study the algorithm with the highest level of accuracy is the Naive Bayes Classifier algorithm method with a difference in the accuracy rate of 3.89%.

AbstrakKemajuan teknologi diiringi dengan meningkatnya ancaman terhadap keamanan serta kerahasiaan pesan/informasi. Salah satu cara untuk menjaga keamanan dan kerahasiaan pesan/informasi dapat menggunakan teknik steganografi. Steganografi adalah teknik untuk menyembunyikan pesan/informasi pada sebuah media, bisa berupa media gambar, suara ataupun video, sehingga pesan yang disembunyikan sulit dikenali oleh indera manusia. Penelitian ini bertujuan untuk membuat aplikasi steganografi dengan metode least significant bit serta implementasi vigenere cipher untuk meningkatkan keamanan pesan/informasi. Informasi/pesan akan disisipkan pada satu bit paling kanan ke pixel file objek tanpa merubah medianya. Penelitian ini menghasilkan aplikasi yang dapat menyembunyikan informasi/pesan pada media gambar. Untuk meningkatkan sistem pengamanannya, proses deskripsi disertai dengan metode vigenere cipher jika pesan/informasi diakses oleh orang yang tidak berhak atas informasi/pesan tersebut.Kata kunci: least significant bit, steganografi, vigenere cipher Abstract Technological advances are accompanied by increasing threats to the security and confidentiality of message/information. One way to maintain the security and confidentiality of message/information can be using steganography techniques. Steganography is a technique for hiding message/information in a media, it can be in the form of image, sound or video media, so the hidden message is difficult to recognize by the human senses. This study aims to make the application of steganography with the least significant bit method and the implementation of the vigenere cipher to improve message / information security. Information/message will be inserted in the rightmost bit into the pixel file object without changing the media. This research produces an application that can hide information/message on image media. To improve the security system, the description process is accompanied by the vigenere cipher method if the message/information is accessed by people who are not entitled to the information/message.