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Journal : INTECOMS: Journal of Information Technology and Computer Science

Sistem Pendukung Keputusan Penentuan Dosen Pembimbing Magang Dengan Menggunakan Metode Ahp Rating Mode Bayu Laksono Wahyu Arminsyah; Kamal Prihandani; Ultach Enri
INTECOMS: Journal of Information Technology and Computer Science Vol 4 No 2 (2021): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v4i2.2661

Abstract

Pada saat ini penentuan dosen pembimbing magang di FASILKOM UNSIKA masih dilaksanakan secara manual, tentu saja hal ini kurang efisien dan efektif serta mempunyai kemungkinan human error. Penentuan dosen pembimbing magang juga tidak mudah karena harus mempertimbangkan juga antara tema/topik magang mahasiswa dengan kemampuan dosen. Tujuan penelitian ini adalah untuk mengimplementasikan metode AHP rating mode untuk membantu menentukan dosen pembimbing magang. Metode penelitian yang digunakan adalah model waterfall dari SDLC, yang terdiri dari analisis kebutuhan, desain, pengkodean, pengujian dan pemeliharaan. Sistem pendukung keputusan akan dirancang berbasis web dengan menggunakan bahasa pemrograman PHP dan dengan DBMS MySQL. Berdasarkan hasil dari pengujian terhadap sistem oleh tim magang FASILKOM UNSIKA dapat disimpulkan bahwa sistem pendukung keputusan ini dibutuhkan dan membantu dalam menentukan dosen pembimbing magang di FASILKOM UNSIKA.
Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine Muhammad Diki Hendriyanto; Azhari Ali Ridha; Ultach Enri
INTECOMS: Journal of Information Technology and Computer Science Vol 5 No 1 (2022): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v5i1.3708

Abstract

MOLA is one of the video streaming platform applications on the google play store. The mola application has been downloaded 5 million times but only has a 3.2 rating. On the Google Play Store app rating is followed by user reviews of the app. There are quite a lot of reviews that are unstructured and contain opinions from users about their satisfaction with the application so that it is often taken into consideration by potential users to choose the application used. Based on this, sentiment analysis was carried out using the Support Vector Machine algorithm to find out how the sentiments of users towards the MOLA application on the google play store were carried out. This study uses the Knowledge Discovery in Database (KDD) method. The data used is a review of the MOLA application with as many 520 data consisting of 312 positive reviews and 208 negative reviews. The best results are obtained in scenario 1 (90:10) using the RBF (Radial Basis Function) kernel which produces 92.31% accuracy, 96.3% precision, 89.66% recall, and 92.86% f1-score. Keywords: Sentiment Analysis, Support Vector Machine, MOLA