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All Journal Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Saprina Putri Utama Ritonga
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Computer Science & IT Industrial & Manufacturing Engineering

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Analisis Sentimen Masyarakat terhadap Penggunaan Teknologi AI dengan Metode Machine Learning Nur Aisyah Pandia; Putri Ramadani; Saprina Putri Utama Ritonga; Fatwa Aulia; Mhd.Furqan
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1198

Abstract

This study discusses public perceptions of the increasingly widespread use of machine-based technology in everyday life. One approach to understanding this perception is through sentiment analysis conducted on public opinion on social media. Using machine learning methods, this study classifies public sentiment into three categories: positive, negative, and neutral. Data was collected through the Twitter social media stage and processed using the CRISP-DM approach. Three algorithms were used in the classification, namely Bolster Vector Machine (SVM), Credulous Bayes, and Choice Tree. The evaluation results showed that SVM provided the highest accuracy in classifying sentiment data. The majority of public opinion was neutral, but there were concerns regarding social and ethical impacts. This study contributes to a general understanding of public perceptions of machine-based technology that are increasingly dominating various sectors.
Deteksi Sampah Plastik di Lantai Menggunakan Thresholding dan Countour Detection Saprina Putri Utama Ritonga; Asro Hayati Berutu; Anggi Jelita Sitepu; Supiyandi, Supiyandi
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1236

Abstract

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.
Co-Authors Anggi Jelita Sitepu Asro Hayati Berutu Fatwa Aulia Mhd.Furqan Nur Aisyah Pandia Putri Ramadani Supiyandi Supiyandi