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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika
Marcel Alezandro Sihombing
Universitas HKBP Nommensen Pematang Siantar
Computer Science & IT

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Analisis Kualitatif Persepsi Mahasiswa terhadap Penggunaan Machine Learning sebagai Pendukung Pembelajaran Alex Septama Sihite; Judea Tirta Jordan Simamora; Octav Kornelius Hutagaol; Marcel Alezandro Sihombing
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7095

Abstract

The development of Artificial Intelligence (AI), particularly Machine Learning (ML), has had a significant impact on the world of education as a supporter of the learning process. This study aims to analyze students' perceptions of the use of Machine Learning as a supporter of Artificial Intelligence-based learning. The study used a descriptive qualitative approach with data collection techniques through Likert scale questionnaires and interviews. The research respondents were 50 students who had used Machine Learning-based applications in learning activities. Data analysis was carried out through data reduction, data presentation, and drawing conclusions. The results showed that the majority of students had a positive perception of the use of Machine Learning. 80% of respondents agreed with the use of Machine Learning in learning, 12% were neutral, and 8% disagreed. Students considered that Machine Learning facilitates understanding of material, accelerates task completion, increases learning motivation, and supports independent learning. However, some students were still concerned about the potential for dependence on technology and reduced critical thinking skills if used excessively. The results of the study indicate that Machine Learning has great potential as a supporter of learning in higher education if used wisely and balanced with critical thinking skills and digital literacy. Keywords: Artificial Intelligence, Machine Learning, Student Perception, Learning, Technology Acceptance Model.
Analisis Penerapan Algoritma Decision Tree, Random Forest, dan Naive Bayes pada Pengolahan Data untuk Sistem Cerdas Judea Tirta Jordan Simamora; Alex Septama Sihite; Octav Kornelius Hutagaol; Marcel Alezandro Sihombing
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7099

Abstract

The rapid growth of information technology has led to a significant increase in the volume of data generated across various sectors. This condition creates challenges in data processing, particularly in obtaining accurate, efficient, and timely information to support intelligent decision-making. Machine learning, as a branch of artificial intelligence, has emerged as one of the most widely used approaches for addressing these challenges due to its ability to learn patterns from data and generate predictions automatically. However, the successful implementation of machine learning is influenced by several factors, including data quality, algorithm selection, and model evaluation processes. This study aims to analyze the application of machine learning in data processing for intelligent systems through a literature review approach. The research method was conducted by collecting, selecting, reviewing, and synthesizing relevant scientific literature from journals, books, conference proceedings, and academic publications. The analysis employed a qualitative descriptive approach to identify commonly used algorithms, implementation benefits, challenges, and recent development trends in machine learning applications. The results indicate that algorithms such as Decision Tree, Random Forest, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes are among the most frequently applied methods in intelligent data processing systems. The findings also show that machine learning contributes significantly to improving prediction accuracy, accelerating data analysis, automating decision-making processes, and enhancing system adaptability across various domains, including healthcare, education, business, finance, and information systems. Nevertheless, several challenges remain, such as low-quality datasets, imbalanced data, high computational requirements, overfitting, underfitting, and limited model interpretability. This study contributes by providing a comprehensive overview of machine learning applications, highlighting the importance of data preprocessing and appropriate algorithm selection, and identifying future research opportunities related to efficient, transparent, and explainable intelligent systems. Keywords : Machine Learning, Data Processing, Intelligent Systems, Artificial Intelligence.
Analisis Perkembangan Machine Learning di Indonesia Berdasarkan Publikasi Ilmiah Oktav Kornelius Hutagaol; Judea Tirta Jordan Simamora; Alex Septama Sihite; Marcel Alezandro Sihombing
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7118

Abstract

Machine Learning is one of the fastest-growing branches of Artificial Intelligence and has been widely applied in various sectors, including education, healthcare, agriculture, industry, finance, and government. The increasing number of scientific publications indicates that this technology is increasingly utilized as a data-driven decision-making solution. This study aims to analyze the development of Machine Learning research in Indonesia based on scientific publications published in national and international journals. The research employs the Systematic Literature Review (SLR) method by identifying, selecting, evaluating, and synthesizing relevant scientific articles. The literature search was conducted through several academic databases, including Google Scholar, Garuda, SINTA, IEEE Xplore, and Scopus, using predetermined inclusion and exclusion criteria. The findings reveal that the number of Machine Learning publications in Indonesia has consistently increased over recent years. Education, healthcare, and industry are the dominant application areas, while the most frequently used algorithms include Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine, and K-Nearest Neighbor. Despite its significant potential to support digital transformation, the development of Machine Learning in Indonesia still faces several challenges, including limited datasets, data quality, computational infrastructure, and the availability of skilled human resources. This study is expected to provide valuable references for researchers and practitioners in understanding the research trends and future directions of Machine Learning development in Indonesia. Keyword : Machine Learning, Artificial Intelligence, Systematic Literature Review, Scientific Publication, Indonesia.
Co-Authors Alex Septama Sihite Judea Tirta Jordan Simamora Octav Kornelius Hutagaol Oktav Kornelius Hutagaol