Article Per Year (5 Year)
p-Index From 2020 - 2025
1.554
P-Index
This Author published in this journals
All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika)
Armansyah, A
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Social Sciences

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

 1 
Algoritma Genetika Untuk Perancangan Aplikasi Penjadwalan Mata Pelajaran Furqan, Mhd; Armansyah, A; Ananda, Rizki
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.476

Abstract

The schedule is one of the important activities to help the teaching and learning process in schools, the schedule planning process is still done manually so there are still conflicting schedules between classes. because of the large number of classes and a lot of time ordering a certain day so that sometimes up to 3 times the revision schedule, and the implementation of learning becomes late. To overcome this, one of the appropriate ones is used so that the scheduling process can run well. One of the algorithms used for scheduling the genetic algorithm is one of the improvement algorithms that can be used in various types of problems such as scheduling, the schedule will be tested on classes that clash, which are selected randomly. random or random in each class, the test will be asked to input or fill in the crossover probability number = 0.70 and mutation probability = 0.40 and the number of generations = 1000, then executed. After that it will occur and program execution in the form of selection, crossover, and mutation that will occur in the background of the screen, so that the results of applying 17 classes and 1 laboratory room using the genetic algorithm method can be used to compile a list of lessons.
Optimasi Sentimen Analisis Informatif dan Tidak Informatif dari Tweet di BMKG Menggunakan Algoritma Naive Bayes dan Metode Teknik Pengambilan Sampel Minoritas Sintetis Hidayatulloh, Muhammad Yusuf; Sunanto, Anto; Armansyah, A; Gevin, Muhammad Farrell Afelino; Saputra, Dedi Dwi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.565

Abstract

The emergence of computer-based and mobile-based social networks seems to have received high attention from the public. Evidenced by the increasing number of social networks that appear. Friendster, Facebook, Twitter, Linkd In and many others. Twitter is one of the social media used to find information, Twitter users generally report every activity. They are even more helped by the existence of increasingly sophisticated cellphones. The system created in this study to optimize the analysis of informative and uninformative sentiment using a rapid miner application with the Naïve Bayes, Naïve Bayes + Adaboost, SVM, and SVM PSO methods using data taken from twitter @infoBMKG. The research method used is the collection of tweet data from twitter taken by the Crawling method. The data taken is tweets in Indonesian with a total of 1,000 tweets from the @infoBMKG twitter account. The results of the nave Bayes algorithm test carried out in this study were to measure the performance of accuracy, precision, recall, AUC from the results of the training and submission of datasets that had gone through the data preprocessing process. From the results of the research that has been done, it is proven that the optimization of informative and uninformative sentiment analysis from tweets on BMKG's twitter gets good results using the Support Machine Vector method with higher Accuracy, Recall, and AUC values than other methods.
Co-Authors Adha, Rifki Mahsyaf Gevin, Muhammad Farrell Afelino Hidayatulloh, Muhammad Yusuf Hsb, Dinda Umami Kurniawan, Riski Askia Mhd Furqan Rizki Ananda Saputra, Dedi Dwi Sunanto, Anto