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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech)
Syahril
Universitas Muhammadiyah Riau
Computer Science & IT Decision Sciences, Operations Research & Management

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SISTEM PENDUKUNG KEPUTUSAN PENENTUAN CALON PENERIMA BEASISWA MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS Arya Hakam; Wide Mulyana; Syahril
JURNAL FASILKOM Vol 11 No 3 (2021): Jurnal Fasilkom
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1493.848 KB) | DOI: 10.37859/jf.v11i3.3292

Abstract

Universitas Muhammadiyah Riau merupakan sebuah intansi yang bergerak di bidang pendidikan yang berjalan sudah 12 tahun. Dalam proses Pendidikan terhadap mahasiswa pihak Univeritas Muhammadiyah Riau juga membantu dalam penyaluran beasiswa dari Pemerintah Provinsi Riau yaitu beasiswa Bidikmisi (Biaya Pendidikan Mahasiswa Miskin Berprestasi). Permasalahan yang sering muncul ketika melakukan penyeleksian calon penerima beasiswa yaitu terjadi kesulitan dalam penyeleksian calon penerima mahasiswa dengan kondisi banyaknya mahasiswa yang mendaftar beasiswa dengan kriteria yang banyak sehingga penyeleksian kurang mendetail dan Ketatnya persaingan antara mahasiswa dan tipisnya perbedaan kondisi mahasiswa sehingga menyulitkan pemberian beasiswa yang lebih tepat sasaran. Untuk mengurangi terjadinya kesalahan dalam pemberian beasiswa yang kurang tepat dan membuat data penyeleksian beasiswa lebih tersistemisasi maka dibutuhkan sebuah sistem pendukung keputusan sebagai alat bantu bagi para pengambil keputusan dengan menambahkan kebijaksanaan manusia menggunakan metode Analytical Hierarcy Proces (AHP) sehingga meringankan pekerjaan tim penyeleksi yang dilakukan setiap program studi. Hasil yang dicapai dalam pengimplementasian sistem pendukung keputusan ini yaitu dapat merekomendasikan calon penerima beasiswa dimana tampilan data mahasiswa calon beasiswa pada sistem diurutkan dari nilai terbesar hingga terkecil dengan status rekomendasi apabila nilai mencapai standar yang telah ditentukan.
Peramalan Kedatangan Wisatawan ke Suatu Negara Menggunakan Metode Support Vector Machine (SVM) Harun Mukhtar; Rahmad Gunawan; Amin Hariyanto; Syahril; Wide Mulyana
Jurnal CoSciTech (Computer Science and Information Technology) Vol 3 No 3 (2022): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v3i3.4211

Abstract

Tourism is one of the most promising ecosystems for economic sectors worldwide. A strong tourism sector directly contributes to the country's national income, fights unemployment, and improves the balance of payments. Tourism development can be seen from the increase in arrivals to a nation; based on data obtained from the UNWTO from 1995-2019, it has increased and decreased. The sudden increase and decrease in tourists will have positive and negative impacts. Forecasting is an activity to predict events that will occur in the future by taking data from the past. So this study will expect tourist arrivals to a country using the Support Vector Machine (SVM) method. SVM has properties about maximizing margins and kernel tricks to map nonlinear data. The results obtained in this study indicate that SVM Confidence is 86.3%, has a MAPE value of 56.00%, and an RMSE worth of 11126.36 from the total data of 53 countries. And forecasting is carried out in 5 countries with the highest tourist visits. The results obtained are excellent: SVM Confidence of 99.13%, a MAPE value of 2.78%, and an RMSE value of 2783.57.
K-Nearest Neighbor (KNN) untuk Menganalisis Sentimen terhadap Kebijakan Merdeka Belajar Kampus Merdeka pada Komentar Twitter Febby Apri Wenando; Rahman Septiadi; Rahmad Gunawan; Harun Mukhtar; Syahril
Computer Science and Information Technology Vol 3 No 2 (2022): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v3i2.3841

Abstract

On December 11, 2019, the Minister of Education and Culture of the Republic of Indonesia Nadiem Anwar Makarim issued a policy of "Merdeka Belajar". Netizens on Twitter have debated this Merdeka Belajar and became a trending topic. This study tries to analyze the sentiment of tweets about opinions on this policy by classifying whether it is a positive opinion or a negative opinion. The classification method applied is the K-Nearest Neighbor algorithm. In this study, four main processes were carried out, namely text-preprocessing, word-weighting (TF-IDF), classification and validation using k-fold cross validation. Tests were carried out with a dataset of 700 data, training was carried out using 630 training data and 70 testing data. In testing, the highest accuracy of the K-Nearest Neighbor algorithm was obtained at the k-8 value, namely 84.28%. Furthermore, validation is carried out using k-fold cross validation with a value of fold = 10 to get an accuracy of 84.42%.
Co-Authors Amin Hariyanto Arya Hakam Febby Apri Wenando Gunawan, Rahmad Harun Mukhtar Rahman Septiadi Wide Mulyana