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All Journal JUITA : Jurnal Informatika Jurnal Mantik Digital Transformation Technology (Digitech) Scientica: Jurnal Ilmiah Sains dan Teknologi
Yunan Fauzi Wijaya
Universitas Nasional
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Fraud Detection Using Random Forest Classifier, Logistic Regression, and Gradient Boosting Classifier Algorithms on Credit Cards Muhamad Sopiyan; Fauziah Fauziah; Yunan Fauzi Wijaya
JUITA : Jurnal Informatika JUITA Vol. 10 No. 1, May 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1177.532 KB) | DOI: 10.30595/juita.v10i1.12050

Abstract

The following credit card records were used in this study of 284.807 transactions made by credit card holders in Europe for two days from the Kaggle dataset. This is a very poor data set, having 492 transactions, an imbalance of only 0.172% of the 284.807 transactions. The purpose of this study is to obtain the best model and then simulate it by electronically detecting unauthorized financial transactions in bank payment systems. The dataset for this study is unbalanced class data with 99.80% for the major class and 0.2% for the minor class. This type of class-imbalanced data problem is solved by applying method a combination of minority oversampling techniques using Synthetic Minority Oversampling Technique (SMOTE). To determine the most appropriate and accurate classification in solving class balance problems, comparisons were made with the Random Forest Classifier (RFC), Logistic Regression (LGR), and Gradient Boosting Classifier (GBC) algorithms. The test results in this study are the Random Forest Classifier (RFC) algorithm is better than other algorithms because it has the highest accuracy the percentage of data-train is 100% and data-test is 99.99% and the evaluation of the AUC score as a result of algorithm testing is 0.9999.
Effectiveness Use Of Google Classroom Against Employee Classes As An Alternative Form Of Distance Learning: Effectiveness Use Of Google Classroom Against Employee Classes As An Alternative Form Of Distance Learning Yunan Fauzi Wijaya
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.901.pp1243-1249

Abstract

The development of technology in education namely Blended learning adds convenience for students and lecturers in the learning process. This is because blended learning can be done online and remotely. Therefore blended learning is very useful for employee class students in the course process because the schedule of national university employee class meetings is not the same as a regular class. Blended learning becomes one of the alternative learning solutions for the employee class. The blended learning used in this study is Google classroom. Google Classroom is a learning app issued by Google that provides easy access through computers and mobile phones that are very useful for students and lecturers in the learning process. The purpose of this study is to describe blended learning, know google classes as an alternative to learning, and know the effectiveness of using google classroom against the class of employees. In this study, google classroom effective is expected in the process of distance learning students in the class of employees of National University
Analisis Kompleksitas Password Dengan Metode KNN, Naïve Bayes, Decision Tree, Ensemble Methods Dan Linear Regression Eri Mardiani; Nur Rahmansyah; Yunan Fauzi Wijaya; Annisa Amalia Fitri; Rayhan Mustafa; Muhammad Romadhoni Rizki; Komang Mustika Pramesti
Digital Transformation Technology Vol. 3 No. 2 (2023): Artikel Periode September 2023
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v3i2.3513

Abstract

Dalam era digital yang semakin kompleks dan penuh tantangan, keamanan kata sandi menjadi krusial untuk melindungi informasi sensitif dan mencegah potensi ancaman keamanan siber. Kata sandi merupakan lapisan pertama pertahanan dalam banyak sistem keamanan digital, oleh karena itu, pemahaman mendalam tentang metode yang efisien dalam menilai dan memprediksi kompleksitas password sangatlah penting. Ketika dihadapkan dengan data yang sangat kompleks, diperlukan analisis dan representasi visual data agar informasi dapat lebih mudah dipahami. Untuk mengilustrasikan data dengan cara yang interaktif dan dapat dimengerti oleh berbagai kalangan, salah satu software atau alat bantu yang dapat digunakan adalah Orange. Dalam pengolahan data ini menggunakan aplikasi orange, kami menganalisis bagaimana prediksi hubungan antara password dan tingkat kompleksitasnya menggunakan fitur-fitur yang telah dikonstruksi. melakukan analisis data mining melalui penerapan teknik klasifikasi dengan memanfaatkan lima metode algoritma yang berbeda. Dataset yang akan dijadikan dasar proyek berasal dari publikasi data pada situs Kaggle.com.
ANALISIS SENTIMEN TERHADAP PENUTUPAN TIKTOK SHOP MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER PADA MEDIA SOSIAL X Havadz Faradian; Albar Rubhasy; Yunan Fauzi Wijaya
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 2 No. 4 (2024): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.572349/scientica.v2i4.1207

Abstract

Pemanfaatan teknologi informasi telah berkembang pesat, terutama dengan kemunculan media sosial yang memungkinkan masyarakat untuk menyampaikan pendapat secara tak terbatas. Salah satu platform microblogging yang memfasilitasi pengguna dalam berbagi pendapat, emosi, pengalaman, dan topik menarik lainnya adalah X. Di X, beragam topik dibahas oleh pengguna, termasuk tentang penutupan TikTok Shop, sebuah fitur baru dalam dunia belanja yang mencakup berbagai proses mulai dari proses pembelian hingga pengiriman dapat dilakukan tanpa harus menggunakan platform lain. Penelitian pun dilakukan untuk menguji dampak kegunaan yang dirasakan, kemudahan penggunaan yang dirasakan, dan kesesuaian dengan gaya hidup terhadap keinginan membeli melalui perdagangan sosial. Terdapat berbagai sentimen di masyarakat terkait program ini, sehingga klasifikasi pendapat berdasarkan sentimennya diperlukan untuk mengetahui kecenderungan opini terhadap penutupan TikTok Shop, apakah positif atau negatif. Dalam analisisnya, data diperoleh melalui proses scraping menggunakan bahasa pemrograman Python. Sebanyak 253 data berhasil dikumpulkan dari proses scraping, yang kemudian melalui tahap preprocessing seperti cleansing, case folding, tokenizing, normalisasi, filtering, dan stemming.
Co-Authors Albar Rubhasy Annisa Amalia Fitri Eri Mardiani Fauziah Fauziah Havadz Faradian Komang Mustika Pramesti Muhamad Sopiyan Muhammad Romadhoni Rizki Nur Rahmansyah Rayhan Mustafa