Information Technology Service Management (ITSM) is a framework that emphasizes

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Tujuan dari penelitian ini adalah untuk membangun aplikasi penjadwalan kuliah menggunakan algoritma Particle Swarm Optimization  (PSO). Algoritma  ini dipilih  sebagai  algoritma yang digunakan dalam penelitian  ini karena algoritma  PSO  yang  merupakan  algoritma  heuristik,  adalah  salah  satu  metode  optimasi  yang  menggabungkan localsearch dan global search. Algoritma ini dapat memecahkan masalah dengan membentuk partikel-partikel pada populasi awal secara acak, mengevaluasi nilai  fitness, dan meng-update velocity serta posisi dari partikel. Dengan demikian diharapkan output yang dihasilkan merupakan kombinasi  terbaik dari serangkaian komponen yang ada. Kasus penjadwalan yang digunakan pada penelitian ini adalah penjadwalan mata kuliah di UIN Sultan Syarif Kasim Riau (UIN SUSKA). Permasalahan yang terjadi pada penjadwalan mata kuliah di UIN SUSKA Riau adalah tidak optimalnya pemenuhan beberapa contraint seperti keterbatasan ruangan. Dari hasil pengujian, aplikasi penjadwalan perkuliahan meggunakan algoritma PSO mampu menghasilkan  jadwal perkuliahan yang  sudah  tidak ada bentrok tetapi masih tidak memenuhi dari segi kualitas yaitu jam dimulainya perkuliahan inti yang difokuskan pada jam-jam yang efektif.Kata kunci: Constraint, Fitness,  Particle swarm optimizaton, Penjadwalan

Towards an election year (elections) in 2019 to come, many mass campaign conducted through social media networks one of them on twitter. One online campaign is very popular among the people of the current campaign with the hashtag #2019GantiPresiden. In studies sentiment analysis required hashtag 2019GantiPresiden classifier and the selection of robust functionality that mendaptkan high accuracy values. One of the classifier and feature selection algorithms are Naive Bayes classifier (NBC) with Tri-Gram feature selection Character & Term-Frequency which previous research has resulted in a fairly high accuracy. The purpose of this study was to determine the implementation of Algorithm Naive Bayes classifier (NBC) with each selection and compare features and get accurate results from Algorithm Naive Bayes classifier (NBC) with both the selection of the feature. The author uses the method of observation to collect data and do the simulation. By using the data of 1,000 tweets originating from hashtag # 2019GantiPresiden taken on 15 September 2018, the author divides into two categories: 950 tweets as training data and 50 tweets as test data where the labeling process using methods Lexicon Based sentiment. From this study showed Naïve Bayes classifier algorithm accuracy (NBC) with feature selection Character Tri-Gram by 76% and Term-Frequency by 74%,the result show that the feature selection Character Tri-Gram better than Term-Frequency.

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Sentiment analysis is a method for reviewing products or services to determine opinions or feelings about a product. The results of the analysis can be used by companies as evaluation materials and considerations to improve the products or services provided. This study aims to test the level of public sentiment on the quality of Bank Mandiri services that have received ISO 20000-1 with the application of sentiment analysis using the K-NN algorithm based on ITSM criteria. The initial classification in this study uses the lexicon method by detecting words included in sentiment words, the results of which are included as labels on training data and test data. Formation of the classification with the K-NN algorithm by taking into account the results of the training data indexing and weighting of the test data, with the value of k as the decision maker limit. The trial results of 10 scenarios show that the classification using the K-NN algorithm as a sentiment classification is 98% accuracy value of 50 test data to 600 training data, with 24% getting positive sentiment, 22% negative sentiment and 55% neutral sentiment, with f -measure 95.83%. while in testing 100 the test data obtained 79% accuracy value with 21% getting positive sentiment, 42% negative sentiment and 38% neutral with an f-measure value of 68.42%.