The final project or thesis is the result of research that addresses a problem according to the student's field of science. By increasing the number of graduates, the number of final project documents produced will also be even greater. The large number of scientific papers or final project documents will be difficult to find according to the topic if they are not grouped. A large number of documents will not be effective if classification is done manually. This study makes a scientific paper classification application aimed at classifying the scientific work (final project) of students in the field of Informatics Engineering. This application was built by implementing the Naive Bayes Classifier algorithm based on background parameters and will be classified into 5 categories, namely image processing, data mining, decision making systems, geographic information systems and expert systems. With the research stages, namely data collection, preprocessing, calculation of the Naive Bayes Classifier method, implementation and system testing. This study uses 170 scientific papers, which are divided into 150 data for training and 20 data for testing. The results of this study illustrate that the Naive Bayes Classifier algorithm is a simple algorithm that can be used to classify scientific papers with an average accuracy of 86.68% and the average processing time required in each test is 5.7406 seconds / test.Keywords:scientific work, naive bayes classifier, classification,training, testing ABSTRAKTugas akhir atau skripsi merupakan hasil penelitian yang membahas suatu masalah sesuai bidang ilmu dari mahasiswa. Dengan bertambah jumlah lulusan, maka jumlah dokumen tugas akhir yang dihasilkan juga akan semakin besar. Jumlah dokumen karya ilmiah atau tugas akhir yang besar akan sulit dicari sesuai dengan topik jika tidak dikelompokkan. Jumlah dokumen yang besar akan tidak efektif jika dilakukan klasifikasi secara manual. Penelitian ini membuat aplikasi klasifikasi karya ilmiah bertujuan untuk mengklasifikasikan karya ilmiah (tugas akhir) mahasiswa dalam bidang ilmu Teknik Informatika. Aplikasi ini dibangun dengan mengimplementasikan algoritma Naive Bayes Classifier berdasarkan parameter latar belakang dan akan diklasifikasikan menjadi 5 kategori yaitu pengolahan citra, data mining, sistem pengambilan keputusan, sistem informasi geografis dan sistem pakar. Dengan tahapan penelitian yaitu pengumpulan data, preprocessing, perhitungan metode Naive Bayes Classifier,implementasi dan pengujian sistem.Penelitian ini menggunakan data sebanyak 170 data karya ilmiah, yang dibagi menjadi 150 data untuk pelatihan dan 20 data untuk pengujian. Hasil penelitian ini menggambarkan bahwa algoritma Naive Bayes Classifier merupakan algoritma sederhana yang mampu digunakan untuk melakukan klasifikasi karya ilmiah dengan rata-rata akurasi 86,68% serta rata-rata waktu proses yang dibutuhkan dalam setiap pengujian yaitu 5,7406 detik/pengujian.Kata Kunci:Karya ilmiah, Naive bayes classifier, Klasifikasi, Pelatihan, Pengujian.

This study discusses the implementation of the sexual violence reporting system (SIPORAS) service as the implementation of Permen No. 30 of 2021 at Universitas Malikussaleh. On the basis of the Minister of Education and Culture 30/2021 in providing efforts to create a safe, healthy, and comfortable campus from various forms of gender-based violence, especially sexual violence to produce human resources. Indonesia that is superior, humane and has character. Several previous studies have shown that many students are aware of forms of sexual violence on campus. However, not all students who know information about the prevention of sexual violence cases and do not know about institutions that specifically handle sexual violence cases, while student understanding is important in order to involve students to confirm cases of violence at the college level and create a safe and friendly campus and avoid various kinds of sexual violence cases. Therefore, there is a need for research related to the application and socialization of the use of online-based Sexual Violence Reporting System Services (SIPORAS) in the higher education environment, namely Malikussaleh University which is managed by the University PPKS Task Force Institute and the Center for Gender Studies and Counseling.

Pusong Baro Village  and  Ulee Jalan Village are two villages located in  Banda Sakti district, under the administration of Lhokseumawe City.   It has abundant  natural wealth from the  fishery sector, and  has great potential as a  dry fish producing village in the Lhokseumawe City area. However,  the development of the  community's dry  fish processing business production is still not  good.  Problems in the management of dried fish  are constrained by the lack of  processing of existing  dried fish, products that are packaged not yet on standard packaging, marketing of products  that  are still  conventionally  so that the  sales commodities are small,  as well as less  qualified marketing management.  In overcoming the obstacles of dry fish production in Pusong Baro Village  and  Ulee Jalan  Village, this  community  service activity provides  facilities to the community  villages in the form of business tools that facilitate the production process  such as vacuum sealer machines, fish drying ovens,  oil slicing  machines and making fish drying houses  .  The output of    community service activities in  Pusong Baro Village  and  Ulee Jalan Village is that they can develop better dry fish processing,   product packaging  which has been standardized and interesting, increased ability of  community business management, publication of  service journals, simple IPR  , creation of an introduction catalog website    dried fish products as  well as uploading videos  of service activities on the YouTube platform.  With  this service activity, it is hoped that production activities can  increase to  be even better  than before and can expand the dried fish market  commodity   community production of  Pusong Baro Village  and  Ulee Road Village.