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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Jurnal Pengembangan Riset dan Observasi Teknik Informatika SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Cahaya Mandalika Bulletin of Computer Science Research
Wahyu, Sawali
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Journal : Jurnal CoreIT

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Sentiment Analysis on Hate Speech Post 2024 Election for Elected President Using a Hybrid Model Machine Learning Handaya, Ken Ken; Wahyu, Sawali
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.31927

Abstract

One of the important events in the democratic life of a country is the general election. In addition, the possibility of hate speech appearing on social media increases as political tensions increase. This hate speech can take the form of negative comments, insults, or even threats against the elected president. This research uses the content of tweets as a data source to analyze public opinion and sentiment towards the elected president. This research aims to analyze sentiment towards hate speech held by twitter users towards the elected president after the 2024 election by building a hybrid model using 3 algorithms, namely k-nearest neighbors, long short-term memory and naive bayes. The results of tests carried out with 12,000 tweet data that show the naive bayes method classification results have an accuracy of 72%, the long short-term memory classification results show an accuracy of 78%, the k-nearest neighbors method accuracy value is 83%, and the hybrid model accuracy value is 78%. Considering the accuracy values of the three algorithm method, by using a hybrid model we can increase the accuracy by combining the three algorithm models. from previously having the lowest accuracy of 72%, by using a hybrid model we can increase the accuracy to 78%.
Design Of The Information System Model For Kodim 1416/Muna Administration Process Using The RAD Method With Whatsapp Based Notification Ruslan, Fisabilia Adipati; Wahyu, Sawali
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.31871

Abstract

Globalization, driven by advancements in information technology, demands fundamental changes across various life aspects, including the operations of companies and organizations. Kodim 1416/Muna faces issues in daily reporting and administration due to reliance on WhatsApp, varying report formats, and delays in administrative approvals. This research aims to design an information system model to address these problems using the Rapid Application Development (RAD) method, encompassing requirements planning, design, and implementation. The designed system is expected to standardize daily reports, reduce the risk of overlooked information, expedite the submission process, and facilitate monitoring by leaders through WhatsApp notifications. System Usability Scale (SUS) testing results show a score of 81, categorized as "Good Performance," indicating user satisfaction with the designed system. Thus, this research contributes to improving operational efficiency and administrative performance at Kodim 1416/Muna.
Public Opinion Sentiment Analysis on Train Transport in Jakarta Using a Hybrid Model Machine Learning Savero, Adriel; Wahyu, Sawali
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.31865

Abstract

Transportation is a key element in smoothing the wheels of the economy and connecting various regions, especially in big cities like Jakarta which has a high population density. This leads to dense and complex traffic conditions. Improving the quality and facilities of public transportation is important to overcome these problems. However, people are still reluctant to use public transportation for various reasons. Therefore, it is important to understand public sentiment towards public transportation in Jakarta. This research focuses on sentiment analysis of train-based transportation, namely KRL, MRT, and LRT. Sentiment analysis is conducted using a hybrid learning model with a voting model method, which combines SVM, logistic regression, and CNN algorithms. The data used is labeled with InSet sentiment dictionary and extracted features using TF-IDF method. The modeling results show that this hybrid model produces 89% accuracy for the KRL dataset, 88% for the MRT dataset, and 81% for the LRT dataset. However, this model still has difficulty in predicting neutral and positive classes. The results of this study show that hybrid learning with the voting model method can provide quite good results in public transportation sentiment analysis, but there is still room for improvement in the classification of neutral and positive sentiments. The findings provide important insights for the development of strategies to improve the quality of public transportation and encourage people to use the service more.
Co-Authors Apriana, Delia Gotama, Jeskel Fornardi Handaya, Ken Ken Hani Dewi Ariessanti Irwanto, Rikky Febi Jefry Sunupurwa Asri Kheng, Tek Pangestu, Danu Faisal Putri, Risanty Amelia Rohman, Ubaedillah Ruslan, Fisabilia Adipati Samditya Putra, Nafiza Savero, Adriel Sulthon, Rifat Wibowo, Sidik Harto Yulhendri Yulhendri