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All Journal IJIIS: International Journal of Informatics and Information Systems Jurnal Pendidikan dan Teknologi Indonesia
Yarsasi, Sri
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Computer Science & IT Education Engineering

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Analisis Validitas dan Reliabilitas Kuesioner dengan Metode Partial Least Squares Structural Equation Modeling pada Aplikasi SMARTPLS Yarsasi, Sri; Tahyudin, Imam; Hariguna, Taqwa
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.885

Abstract

Validitas dan reliabilitas instrumen merupakan aspek krusial dalam penelitian kuantitatif, karena kualitas pengukuran yang rendah dapat menghasilkan data yang tidak akurat dan mengarah pada kesimpulan yang menyesatkan. Penelitian ini bertujuan untuk mengevaluasi kualitas pengukuran instrumen kuesioner menggunakan pendekatan Partial Least Squares Structural Equation Modeling (PLS-SEM) dengan bantuan perangkat lunak SmartPLS. Metode ini dipilih karena mampu menangani model yang kompleks dan data yang tidak memenuhi asumsi normalitas. Hasil analisis menunjukkan bahwa sebagian besar indikator memiliki nilai outer loading di atas 0,70, nilai Composite Reliability (CR) berada dalam rentang 0,817 hingga 0,914, dan nilai Average Variance Extracted (AVE) melebihi 0,50, yang menunjukkan bahwa instrumen memiliki konsistensi internal dan validitas konvergen yang baik. Namun demikian, terdapat dua indikator dengan nilai outer loading di bawah ambang batas, yaitu X3.2 sebesar 0,612 dan Y2.4 sebesar 0,588, yang perlu dievaluasi ulang. Temuan ini menegaskan bahwa pendekatan PLS-SEM efektif untuk memvalidasi instrumen, terutama dalam penelitian dengan sampel terbatas dan desain eksploratori. Studi ini memberikan kontribusi metodologis terhadap pengembangan instrumen penelitian yang lebih akurat dan adaptif, serta menunjukkan urgensi penggunaan pendekatan statistik modern dalam evaluasi instrumen di berbagai bidang keilmuan.
A Text Classification Approach for Detecting Cyberbullying Risk on Twitter Using Support Vector Machine with Naive Bayes and Random Forest Comparison Yarsasi, Sri; Iskoko, Angga
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.290

Abstract

The rapid development of social media as a means of digital interaction also presents serious challenges in the form of the spread of negative content, including cyberbullying. Cyberbullying is a form of verbal violence committed online and has a significant impact on mental health, especially in adolescents. This research aims to develop a text classification model to detect the risk of cyberbullying using the Support Vector Machine (SVM) algorithm. The data used comes from a collection of cyberbullying-themed tweets. The research stages include text preprocessing (normalization, cleaning, tokenization, stopword removal, and stemming), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), data division into training and testing sets, and model training using linear kernel of SVM. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that this approach is able to identify risky comments quite accurately, with optimal performance on the linear kernel. This research contributes to the development of automated detection systems to create a safer and healthier digital ecosystem, and supports preventive efforts in mitigating cyberbullying online.
Co-Authors Imam Tahyudin Iskoko, Angga Taqwa Hariguna