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All Journal Jurnal Ilmu Komputer dan Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) JMM (Jurnal Masyarakat Mandiri) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI JISKa (Jurnal Informatika Sunan Kalijaga) Scientific Journal of Informatics Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) SinarFe7 Jurnal ELTEK Jurnal Informatika: Jurnal Pengembangan IT
Khadijah Fahmi Hayati Holle
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science Other

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Journal : Scientific Journal of Informatics

 1 
Analisis Sentimen Terhadap Permendikbud Ristek Nomor 30 Tahun 2021 pada Media Sosia Twitter Menggunakan Metode Lexicon-Based dan Multinomial Naïve Bayes Kurniyatul Ainiyah; Khadijah Fahmi Hayati Holle
Jurnal Ilmiah Informatika Vol. 7 No. 1 (2022): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v7i1.29-40

Abstract

Regulation of the Minister of Education, Culture, Research, and Technology (Permendikbud Ristek) Number 30 of 2021 was launched as a form of government efforts in the context of preventing and handling sexual violence in universities. However, it turns out that this regulation has generated various reactions from the community, most of them support it while others reject the ratification of this regulation. Technological developments that occur today encourage people to write their opinions on social media, one of which is Twitter. Tweets discussing this rule can be used to gauge public sentiment. However, considering the number of tweets, the classification process will be difficult to do manually, so it requires a computational system that can automatically classify the sentiments of the existing tweets. From these problems, a system is designed to perform sentiment analysis using the lexicon-based method and Multinomial Naïve Bayes. The results of this sentiment measurement can be useful as data analysis material for the Ministry of Education and Culture, Research and Technology in making decisions regarding this rule. The purpose of this research is to measure the value of accuracy, precision, recall, and f-measure in sentiment analysis using lexicon-based and Multinomial Naïve Bayes methods. The measurement results obtained using a dataset of 470 data are the accuracy value of 71.28%, precision of 70.10%, recall of 78%, and f-measure value of 74.29%.
Perbandingan Metode Klasifikasi Data Mining Untuk Deteksi Keaslian Lowongan Pekerjaan di Medsos Mohammad Malik Fajar; Annisa Rizkiana Putri; Khadijah Fahmi Hayati Holle
Jurnal Ilmiah Informatika Vol. 7 No. 1 (2022): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v7i1.41-48

Abstract

The COVID-19 pandemic has resulted in more and more people losing their jobs. Due to layoffs or bankrupt companies. This has resulted in many people looking for job vacancies. Job vacancies are circulating on social media but there are real and fake ones. Irresponsible people create job vacancies on social media with fraudulent purposes or for personal gain. So, a comparison of data mining classification methods was made for the detection of authenticity of job vacancies on social media. The method used is naive bayes, KNN, and decision tree. In order to find out which method has the highest accuracy value and can be used to classify the authenticity of job vacancies, and fraud on social media can be prevented. Based on this research, the method that has the highest accuracy value is the KNN method. The accuracy value is 94.93%, while the Decision Tree model has an accuracy value of 91.57% and the Naive Bayes model has an accuracy of 84.35%. The KNN method is the best method for classifying the authenticity of job vacancies.
IMPLEMENTASI TEKNIK KRIPTOGRAFI RSA UNTUK PENGAMANAN DATA PENGIRIMAN SMS Ainafatul Nur Muslikah; Hardiana Riski Riswanto; Khamaida Safinah; Khadijah Fahmi Hayati Holle
Jurnal Ilmiah Informatika Vol. 5 No. 1 (2020): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v5i1.749

Abstract

Message sending is one activity that is often used by everyone. However, security in this message delivery system needs to be wary of spying or message piracy during the process of sending messages. Surely someone who sent the message does not know if someone's personal message has been stolen. With this initiative builds a security message using cryptographic RSA algorithm where the message sender or recipient of the message can send the message safely without being known to the message hijacker or spy. Cryptography that uses the RSA algorithm to secure messages. This RSA algorithm message will be decrypted with the public key and to encrypt the message. This application was built on the Android platform because the dominant person has an Android smartphone with a system that runs the length of the message character does not affect the speed at the time of sending the message to the recipient, and there is no limit on the length of the message character during the encryption process so that any length of the massage character can be encrypted well.
Co-Authors Adhitya Prayoga Permana Agung Teguh Almais Agung Teguh Wibowo Almais Agus Zainal Arifin Ainafatul Nur Muslikah Akbar Roihan Alfianita, Rizha Allin Junikhah Annisa Rizkiana Putri Asfilia Nova Anggraini Aziza, Miladina Rizka Cahyo Crysdian Diana Purwitasari Ekaputri, Enggarani Wahyu Fatchurrohman Fatchurrohman Hardiana Riski RIswanto Hayyun Lathifaty Yasri Junikhah, Allin Khamaida Safinah Kurniyatul Ainiyah Kurniyatul Ainiyah Mohammad Malik Fajar Munna, Daurin Nabilatul Mustofa, Ahmad Habibil Nadila Sugianti Nisa Kholifatul Ummah Novrindah Alvi Hasanah Putri, Hikmatul Maulidia Romadhoni, Yuliana Salma Fatia Ulfi Andrian Sari Wibowo, Muhammad Ismail Arjun Yayang Galuh Yessy Fatmasari Yuliana Romadhoni Yunifa Miftachul Arif Yusuf Ansori Zamzam, Muhammad Adib