A problem requires a solution to solve it. One of them is by using Prediction (Forcasting). Prediction is used to assess the prediction of conditions in the future. at AMIK Royal Kisaran, when it comes to making lecture schedules often hampered because there is no estimated number of students. The data used in this study is the data history of the last 15 Academic Years, from 2003/2004 to 2017/2018. Then the data is processed with the Single Exponential Smoothing Method. Alpha value 0 <α <1. Single Exponential Smoothing makes a comparison with the alpha value until alpha is found which has the minimum error. To find the value of the error, the MSE (Mean Square Error) method is used. The results of the testing of this method are in the academic year 2018/2019 prediction of the number of students for the Informatics Management Study Program as many as 89 people and for Students for the Computer Engineering Study Program as many as 30 people. The Single Exponential Smoothing method can predict the number of students in the next period. Keywords: Prediction; Number of Students; Single Exponential Smoothing; Alpha Value; MSE Abstrak: Suatu masalah memerlukan sebuah solusi untuk menyelesaikannya. Salah satunya dengan menggunakan Prediksi (Forcasting). Prediksi digunakan untuk menilai prakiraan keadaan dimasa. di AMIK Royal Kisaran, ketika akan membuat jadwal kuliah sering terhambat karena tidak adanya perkiraan jumlah mahasiswa. Data yang digunakan pada penelitian ini adalah histori data 15 Tahun Akademik terakhir, mulai 2003/2004 sampai dengan 2017/2018. Kemudian data diolah dengan Metode Single Exponential Smoothing. Nilai alpha 0<α<1. Single Exponential Smoothing melakukan perbandingan dengan nilai alpha tersebut sampai ditemukan alpha yang memiliki error paling minimum. Untuk mencari nilai Error digunakan Metode MSE (Mean Square Error). Hasil dari pengujian terhadap metode ini adalah pada Tahun akademik 2018/2019 prediksi jumlah Mahasiswa untuk Program Studi Manajemen Informatika sebanyak 89 orang  dan untuk Mahasiswa untuk Program Studi Teknik Komputer sebanyak 30 orang. Metode Single Exponential Smoothing dapat membantu prediksi jumlah mahasiwa pada satu periode kedepan Kata kunci: Prediksi; Jumlah Mahasiswa; Single Exponential Smoothing; Nilai Alpha; MSE

Nowadays, Teaching and learning activities in the world of education must always follow the development of technology. The use of these technologies will make these activities more effective and efficient. Gamification is part of innovation in education. In this research, gamification is used as a tool for studying activities in project management information systems subject. The method used is technology-based applied research. The Assessment Tool in this study uses Quizizz. Quizizz is used on midterms. This exam was attended by 29 students of the information systems department STMIK Royal. The questionnaire was made using a Mentimeter. The use of Quizizz has a positive impact. The level of student answers questions correctly is 51%. Then 66% prefer Quizizz as assessment tool compared to paper and google forms.

PJU (Public Street Lighting), is regulated in the regulation of the Minister of Transportation of the Republic of Indonesia Number 27 of 2018, concerning street lighting. In line with the statement of the village minister, development of underdeveloped areas and transmigration (Mendes PDTT), eko putro Sandjojo, said that village funds could be used to make street lamps for villages that do not have street lights. The area of the northern ring road, sub-district of datuk bandar timur in the direction of the port of Teluk Nibung, part of the road has no street lighting at night, plus the road conditions are still classified as red soil. Based on the results of field observations, the team researched making cheap street lamps, by utilizing the basic work system of the joule tief circuit, where the input is 7.4 VDC (in 3.7VDC arranged in series-parallel) with a lamp load of 12 watts multiplied by 4 lamps, with a total load. 48 watts, get the frequency measurement results of 12.30 Khz and Iout 0.14A, and Vpk-pk 82 V. Charging input is 7.4VDC, the current 19800mAh in the battery is divided by the maximum current of the solar module 1 watt, which is 160mA, so the charging time the battery is in the range of 2.1 hours. For the design of the lampposts that were made, using a paralon pipe with a length of 5m, with a circle diameter of 9cm, and a second pole connecting 1.5 m long with a circle diameter of 5cm. Keywords: Cheap street lights; joule tief series; 1 watt solar panel Abstrak : PJU (Penerangan Jalan Umum), diatur dalam peraturan mentri perhubungan republik indonesia nomor 27 tahun 2018, tentang alat penerangan jalan. Sejalan dengan pernyataan mentri desa, pembangunan daerah tertinggal dan transmigrasi (Mendes PDTT) eko putro Sandjojo, mengatakan bahwa dana desa bisa digunakan untuk membuat lampu jalan bagi desa yang tidak memiliki lampu jalan. Wilayah jalan lingkar utara, kecamatan datuk bandar timur searah menuju pelabuhan teluk nibung, sebahagian dari jalan tersebut tidak memiliki penerangan jalan dimalam hari, ditambah dengan kondisi jalan yang masih tergolong tanah merah. Berdasarkan hasil pengamatan dilapangan maka tim meneliti membuat lampu jalan murah, dengan memanfaatkan dasar sistem kerja rangkaian joule tief, dimana untuk input 7,4 VDC (in 3,7VDC disusun seri-paralel) dengan beban lampu 12 watt dikalikan 4 lampu, dengan total beban 48 watt, mendapati hasil pengukuran frequensi 12,30 Khz dan Iout 0,14A, dan Vpk-pk 82 V. Pengisian input 7,4VDC didapati, arus 19800mAh pada baterai dibagi dengan arus maksimum modul surya 1 watt, yaitu  160mA, sehingga lama pengisian baterai berada pada rentang waktu 2,1 jam. Untuk rancangan tiang lampu yang dibuat, menggunakan pipa paralon dengan panjang 5m, dengan diameter lingkaran 9cm, dan penyambung tiang kedua sepanjang 1,5 m dengan diameter lingkaran 5cm. Kata Kunci : Lampu jalan murah; rangkaian joule tief; panel surya 1 watt

The Family Hope Program (PKH) is a government program in the form of cash for Very Poor Households (RTSM) whose qualifications are met related to efforts to improve human quality, not in the field of education but also health. In the short term, the PKH program is calculated to reduce the expenditure of poor families and reduce poverty in the long term. To receive the PKH Program) the government has set several criteria, including income, house ownership status, house size, floor type, roof, wall, and type of water source. As for the way to do the settlement of the criteria that have been set, namely by utilizing the Data Mining technique through the Naïve Bayes method. The dataset in this study is the data of recipients of the 2020 Family Hope Program as many as 82 samples. The results of the classification modeling with the Naïve Bayes Algorithm produce precision values for the positive class 100%, for the negative class 77%, the recall value for the positive class 80%, for the negative class 100%, the f1-score value for the positive class 89%, for the negative class 87%, and 88% accuracy value. The purpose of this research is to help the Social Service in classifying the recipients of the Family Hope Program (PKH). Keywords: The Family Hope Program; Data Mining; Naïve Bayes  Abstrak: Program Keluarga Harapan (PKH) merupakan program pemerintah dalam bentuk tunai untuk Rumah Tangga Sangat Miskin (RTSM) yang kualifikasinya terpenuhi terkait dengan upaya peningkatan kualitas manusia tersebut bukan dalam bidang pendidikan tetapi juga kesehatan. dalam jangka pendek Program PKH diperhitungkan bisa mengurangi biaya pengeluaran keluarga miskin serta mengurangi kemiskinan dalam jangka panjang. Untuk menerima Program PKH) pemerintah sudah menetapkan beberapa kriteria, diantaranya Penghasilan, Status Kepemilikan Rumah, Ukuran Rumah, tipe Lantai, Atap, Dinding, serta jenis sumber air. Adapun cara untuk melakukan penyelesaian terhadap kriteria yang sudah ditetapkan yaitu dengan memanfaatkan teknik Data Mining melalui Metode Naïve Bayes. Dataset dalam penelitian ini adalah data penerima Program Keluarga Harapan tahun 2020 sebanyak 82 sampel. Hasil pemodelan klasifikasi dengan Algoritma Naïve Bayes menghasilkan besaran nilai precision untuk kelas positif 100%, untuk kelas negatif 77%, nilai recall untuk kelas positif 80%, untuk kelas negatif 100%, nilai f1-score untuk kelas positif 89%, untuk kelas negatif 87%, dan nilai akurasi 88%. Tujuan dilaksanakannya penelitian ini adalah untuk membantu Dinas Sosial mengklasifikasikan penerima Program Keluarga Harapan (PKH). Kata kunci: Program Keluarga Harapan; Data Mining; Naïve Bayes