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All Journal ILKOM Jurnal Ilmiah
Aishiyah Saputri Laswi
Universitas Andi Djemma Palopo
Computer Science & IT

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Perbandingan Algoritma Fitness of Spring dan Algoritma Tabu Search pada Kasus Penjadwalan Perkuliahan Aishiyah Saputri Laswi
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i1.522.39-46

Abstract

The compilation of lecture schedules is very important at universities for the continuity of the teaching and learning process of study programs. Such as at Andi Djemma Palopo University, Informatics Engineering Study Program. Frequently occurring alternate lectures to change course schedules is a complex thing considering the pre-existing schedule is well organized. But with the lectures, it is usually difficult to determine schedules, days, hours, rooms, and classes so that, no alternate lectures are coinciding with other subjects. so that in this Case Study, the researcher compared two methods namely the Fitness Method and the Taboo Search Algorithm to see the performance when determining the replacement schedule. The values obtained from the results of the implementation of the two methods above, are with using the fitness method is 40 while for the taboo search the maximum value obtained is 15, based on the order of the schedule of previous courses and courses that will be replaced. Thus, the value obtained by using the taboo search method is in the normal category compared to the changing fitness method. Thus, the researcher can conclude that the value obtained by the taboo search is smaller so that, it is good to be used to determine scheduling.
Analysis of public opinion on COVID-19 vaccine through social media using Naïve Bayes theory algorithm Aishiyah Saputri Laswi; Munir Yusuf; Ulvah Ulvah; Bungawati Bungawati
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1127.160-168

Abstract

This study aims to analyze various public opinion on the Covid-19 vaccines that appear on social media pages, especially on Facebook and Twitter via # (hastag). The death rate caused by COVID-19 was so high which reached 144,227 people until 2022. The Indonesian government required vaccines for the community starting from children aged 6 years as an effort to prevent the spread of the Covid-19 virus. Unfortunately, the implementation of complete vaccines in Indonesia has only reached 51.3% of the mandatory vaccine population, which is 140 million out of 339 million people. The non-achievement of the target set by the government causes the need to conduct a sentiment analysis on vaccines in Indonesia through social media. Based on the sample data, from 1000 words obtained from 320 opinions there are positive and negative opinions. This data is then analyzed and processed to find out how many positive and negative responses occurred. The data was then processed into several stages to test the level of truth through training data and test data. The results of the data processing were tested using the Naïve Bayes algorithm which resulted in an accuracy value with a precession of 77.08% taken from 90 samples test data, recall with a percentage of 97.87% based on positive data which was predicted to be true with a positive opinion status from 47 samples of test data and 1 positive data status which is still predicted to be negative. Furthermore, the specific percentage value obtained was 65.30% of the 132 test data that are predicted to be true negative.
Co-Authors Bungawati Bungawati Munir Yusuf Ulvah Ulvah