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All Journal Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Ilmu Manajemen Dan Kewirausahaan (JIMK)
Nst, Ely Nurhalizah
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Computer Science & IT Decision Sciences, Operations Research & Management

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SYSTEMATIC LITERATURE RIVIEW: TRANSFORMASI STRATEGI WIRAUSAHA BERBASIS AI DALAM MENGHADAPI TANTANGAN DIGITAL MARKETING Nst, Ely Nurhalizah; Veri, Jhon
JURNAL ILMU MANAJEMEN DAN KEWIRAUSAHAAN (JIMK) Vol 5 No 2 (2024): Desember
Publisher : Program Studi Manajemen Fakultas Ekonomi dan Bisnis, Universitas Muslim Nusantara Al-Washliyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32696/jimk.v5i2.3884

Abstract

Penerapan Artificial Intelligence (AI) dalam strategi pemasaran digital kini menjadi solusi penting bagi pengusaha di era teknologi yang terus berkembang. Digital marketing yang dulu hanya mengandalkan teknologi dasar, seperti iklan online, kini membutuhkan integrasi AI untuk menghadapi tantangan pasar yang semakin kompleks. Penelitian ini menggunakan metode Systematic Literature Review (SLR) untuk menganalisis 49 jurnal yang relevan mengenai transformasi pemasaran digital berbasis AI. Data yang terkumpul kemudian disaring dengan Covidence dan divisualisasikan menggunakan VOSviewer. Dari 49 jurnal yang dianalisis, 10 studi dipilih berdasarkan relevansi dan kualitas. Hasil penelitian menunjukkan bahwa AI dapat meningkatkan efektivitas pemasaran dengan menganalisis data konsumen, mempersonalisasi kampanye, dan mengotomatiskan interaksi dengan pelanggan. Selain itu, AI juga membantu dalam memprediksi tren pasar dan pengambilan keputusan berbasis data menggunakan teknik seperti Structural Equation Modeling (SEM). Integrasi AI dengan media sosial dan e-commerce dapat memperkuat citra merek, meningkatkan daya saing, dan efisiensi operasional. Dengan demikian, transformasi pemasaran berbasis AI sangat dibutuhkan oleh pengusaha untuk tetap bersaing di pasar digital yang terus berkembang dan memastikan pertumbuhan bisnis yang berkelanjutan.
PENERAPAN MACHINE LEARNING MENGGUNAKAN ALGORITMA DECISION TREE UNTUK PREDIKSI TINGKAT KELULUSAN MAHASISWA Nst, Ely Nurhalizah; Sumijan; Nurcahyo, Gunadi Widi
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/ctjbgh21

Abstract

Students are an integral part of higher education institutions, where graduation rates serve as a key indicator of academic quality and institutional effectiveness. To maintain accreditation and academic standards, universities must optimize student graduation rates. Evaluating the factors influencing graduation is crucial in identifying patterns and key determinants that contribute to academic success. This study aims to predict student graduation using Machine Learning, specifically the C5.0 Decision Tree algorithm. The findings indicate a high reliability in predicting student graduation, with an accuracy of 91.35%. The model's ability to identify on-time graduates is reflected in a recall of 93.85% for the On-Time category and 87.18% for the Delayed category. The prediction accuracy is further demonstrated by a precision of 92.42% for the On-Time category and 89.47% for the Delayed category. The F1-Score, which represents the balance between recall and precision, reaches 93.12% for the On-Time category and 88.32% for the Delayed category. These evaluation metrics indicate that the C5.0 algorithm effectively classifies students based on their likelihood of graduating with high accuracy. The predictions generated can serve as a reference for universities to identify at-risk students early, allowing the implementation of appropriate academic strategies to improve graduation rates, accreditation, and institutional quality.
Co-Authors Gunadi Widi Nurcahyo Sumijan Veri, Jhon