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Andi Ernawati
Universitas Pembangunan Pancabudi
Computer Science & IT

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Application of the C45 Algorithm to Predict Student Academic Scores Andi Ernawati; Zulham Sitorus; Ananda Aulia; Ayu Ofta
Bulletin of Information Technology (BIT) Vol 5 No 2 (2024): Juni 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i2.1251

Abstract

Student grades are the results of teaching and learning activities on a campus. So you can know your target for completing your studies. This research uses the C4.5 Algorithm which can help predict the results of student assessments. The dataset consists of student achievement index, place of residence, discipline, lecturer's role in lectures. From 40 datasets we have obtained a decision on student academic achievement and obtained performance results from accuracy results of 86.36% with class precision predicate Yes=84.62%, No=88.89% and class recall Yes=91.67%, No=80.00%.
Implementation of E-Commerce System as SME Development Strategy in the Digital Era Maulian Saputra; Susilawati; Siti Nurhaliza Sofyan; Ananda Aulia; Andi Ernawati; Ayu Oftasari; Rian Farta wijaya
Bulletin of Information Technology (BIT) Vol 5 No 3: September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i3.1545

Abstract

The implementation of e-commerce systems has become one of the main strategies in the development of Small and Medium Enterprises (SMEs) in the digital era. E-commerce allows SMEs to expand market reach, improve operational efficiency, and strengthen relationships with consumers through better data access. In addition, this digital platform offers benefits such as distribution cost savings, business process automation, and improved customer service. However, challenges in e-commerce adoption for SMEs include limited digital literacy, uneven technology infrastructure, and cybersecurity issues. To achieve the full potential of e-commerce, support from the government and private sector in the form of adequate policies, infrastructure, and training is required. This research aims to identify the benefits, challenges and solutions in implementing e-commerce for SMEs, in order to improve their competitiveness in an increasingly competitive global market.
Analisis Data Mining Pola Penggunaan Seluler dan Klasifikasi Perilaku Pengguna di Berbagai Perangkat Menggunakan Metode C4.5 Andi Ernawati; Sri Wahyuni
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1689

Abstract

Along with the development of digital technology, the use of mobile devices is increasing rapidly and affects user behaviour in accessing information and interacting with digital applications. This research aims to analyse mobile device usage patterns and classify user behaviour across various devices by utilising the C4.5 data mining method. The data used in this study was obtained from the Kaggle.com platform which provides a dataset of mobile device usage patterns, including variables such as frequency of application use, duration of device use, and type of application accessed. The research stages include data collection, data pre-processing to ensure quality, and analysis using the C4.5 algorithm. The C4.5 algorithm was chosen due to its ability to build a decision tree model that can classify user behaviour with a good level of accuracy. The results of this study show that there are certain patterns in mobile device usage that can be linked to demographic characteristics and user preferences for device types and applications. The resulting decision tree model is able to classify user behaviour with an accuracy rate of 41.71%%, and shows that social media applications and streaming applications are the most frequently used categories on mobile devices. This research is expected to provide insights for app developers and digital marketers in understanding user behaviour and optimising mobile-based interaction strategies. In addition, the results of this study also contribute to the application of the C4.5 method for analysing mobile technology usage patterns in the context of big data. Keywords: Data Mining, C4.5, Mobile Usage Pattern, User Behaviour Classification,Rapidminer Decision Tree...
Co-Authors Ananda Aulia Ananda Aulia Ayu Ofta Ayu Oftasari Maulian Saputra Rian Farta Wijaya Siti Nurhaliza Sofyan Sitorus, Zulham Sri Wahyuni Susilawati