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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Indonesian Journal of Innovation Multidisipliner Research Scientific Journal of Informatics Jurnal Riset Pendidikan dan Karakter Indonesian Journal of Innovation Multidisipliner Research INOVTEK Polbeng - Seri Informatika
Fitri Insani
Bogor Agricultural University
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Jurnal CoreIT

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Pembentukan Model Regresi Linier Menggunakan Algoritma Genetika untuk Prediksi Parameter Indeks Standar Pencemar Udara(ISPU) Insani, Fitri; Darlianti, Sri Indah
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 5, No 2 (2019): Desember 2019
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (568.836 KB) | DOI: 10.24014/coreit.v5i2.9157

Abstract

Prediksi merupakan upaya untuk mengetahui suatu peristiwa di masa yang akan datang. Pada kasus prediksi data yang dipelajari merupakan data historis, agar data tersebut dapat mengasilkan informasi berupa hasil prediksi maka digunakan suatu model yaitu regresi linier, dalam proses pembentukan model regresi linier digunakan pendekatan kecerdasan buatan algoritma genetika. Algoritma genetika digunakan untuk mendapatkan nilai koefisien terbaik pada persamaan regresi linier. Penelitian ini menggunakan data parameter Indeks Standar Pencemar Udara(ISPU), terdapat lima parameter ISPU meliputi Sulfur dioksida (SO2), Partikulat (PM10), Karbondioksida (CO), Ozon (O3), dan Nitrogen dioksida (NO2) sehingga dibangun lima model prediksi. Berdasarkan hasil pengujian menggunakan data harian bulan Januari 2016 sampai dengan Desember 2016, algoritma genetika mampu menentukan nilai koefisien yang digunakan pada model regresi linier dalam memprediksi parameter ISPU dengan kesalahan prediksi untuk parameter SO2 yaitu 2,33958%, kesalahan prediksi parameter PM10 6,623923%, kesalahan prediksi parameter CO 2,62279%, kesalahan prediksi parameter O3 6,34495%, dan kesalahan prediksi parameter NO2 2,927575%
SMS Phishing Detection Model with Hyperparameter Optimization in Machine Learning Abdillah, Rahmad; Insani, Fitri
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
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.v11i1.35547

Abstract

Phishing is one of the growing cybersecurity threats, including through SMS, known as smishing. This research aims to build a model for SMS phishing detection using a machine learning approach optimized through hyperparameter tuning techniques. The data used is obtained from personal SMS messages collected through questionnaires, which are then labeled by information security experts. The SMS text is cleaned using Natural Language Processing (NLP) techniques and represented using the TF-IDF method. Ten classification algorithms are tested in this study: K-NN, Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost, Bagging, ExtraTree, Gradient Boosting, and XGBoost. Hyperparameter optimization is performed using Grid Search and Optuna, and performance is evaluated using accuracy, F1-score, and ROC-AUC metrics. The results show that the SVM and Logistic Regression models performed the best, achieving accuracy up to 98.5%. Hyperparameter optimization techniques have proven effective in improving the performance of SMS phishing classification models. This research is expected to contribute to the development of accurate and efficient SMS phishing detection systems.
Co-Authors Abdul Aziz Afriyanti, Liza Ahmad Fauzan Al Fiqri, M. Faiz Ananda, Rahmadan Alam Ardan Aqilah, M Alfandri Baehaqi Berliana, Trisia Intan Damayanti, Elok Darlianti, Sri Indah Elin Haerani Elvia Budianita Fachry Abda El Rahman Fadhilah Syafria Fahrozi, Aqshol Al Fakhira, Adzra Febi Yanto Fitria Novitasari Gusti, Siska Kurnia Hamwar, Syahbudin Hasibuan, Aldiansyah Pramudia Iis Afrianty Jasril Jasril Jasril Jasril Kurnia Rahman, Fikri kurnia, fitra Lestari Handayani Miftah Farid Muhammad Abdillah Muhammad Affandes Muhammad Fikry Muhammad Irsyad Muhammad Ravil Mulyadi, Syahrul Najmi, Risna Lailatun Nazir, Alwis Nova Yohana Novriyanto Novriyanto Nurfadilah, Nova Siska Okfalisa Okfalisa Oktavia, Lola Pizaini Pizaini Pratama, Dandi Irwayunda Rahmad Abdillah Reni Susanti Reski Mai Candra Rossa, Rona Ruri Hartika Zain Saputra, Nugroho Wahyu Setiawan, Adil Subhi, Yazid Abdullah Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Tri Prastio Nugroho Utari, Roid Fitrah Vernando, Julio Vitriani, Yelfi Vusuvangat, Imam Wiranti, Lusi Diah Yusra Zahri, Firman