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All Journal JURIKOM (Jurnal Riset Komputer) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer
Fahraini, Fakhita
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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PENDETEKSIAN DAN PENGHITUNGAN KENDARAAN MENGGUNAKAN ALGORITMA COUNTER DALAM KONTEKS PENGOLAHAN CITRA Supiyandi; Hidayah, Adinda Fita; Luthfie Budie; Rasendriya, Rafif; Berutu, Nurhalijah; Fahraini, Fakhita
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 11 No. 2 (2024): Prosisko Vol. 11 No. 2 September 2024
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v11i2.8918

Abstract

Peningkatan jumlah kendaraan di jalan raya telah menciptakan tantangan besar dalam manajemen lalu lintas. Pengelolaan yang efisien memerlukan sistem yang dapat mendeteksi dan menghitung jumlah kendaraan secara real-time. Algoritma Counter, yang menggabungkan teknik background subtraction dan object tracking, menawarkan solusi potensial untuk tantangan ini. Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi efektivitas algoritma tersebut dalam konteks pengolahan citra digital. Hasil penelitian menunjukkan bahwa algoritma Counter mampu mendeteksi dan menghitung kendaraan dengan akurasi tinggi di berbagai kondisi pencahayaan dan kepadatan lalu lintas
PENDETEKSIAN DAN PENGHITUNGAN KENDARAAN MENGGUNAKAN ALGORITMA COUNTER DALAM KONTEKS PENGOLAHAN CITRA Supiyandi; Hidayah, Adinda Fita; Luthfie Budie; Rasendriya, Rafif; Berutu, Nurhalijah; Fahraini, Fakhita
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 11 No. 2 (2024): Prosisko Vol. 11 No. 2 September 2024
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v11i2.8918

Abstract

Peningkatan jumlah kendaraan di jalan raya telah menciptakan tantangan besar dalam manajemen lalu lintas. Pengelolaan yang efisien memerlukan sistem yang dapat mendeteksi dan menghitung jumlah kendaraan secara real-time. Algoritma Counter, yang menggabungkan teknik background subtraction dan object tracking, menawarkan solusi potensial untuk tantangan ini. Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi efektivitas algoritma tersebut dalam konteks pengolahan citra digital. Hasil penelitian menunjukkan bahwa algoritma Counter mampu mendeteksi dan menghitung kendaraan dengan akurasi tinggi di berbagai kondisi pencahayaan dan kepadatan lalu lintas
Analisis Sentimen Masyarakat Indonesia terhadap Keterlibatan Bill Gates dalam Program Vaksin TBC di Media Sosial X Menggunakan SVM Fahraini, Fakhita; Sriani, Sriani
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9431

Abstract

The involvement of Bill Gates in the development program of the tuberculosis (TB) vaccine has generated diverse responses among the Indonesian public, particularly as expressed through social media platform X, making it an important issue to examine since public sentiment may influence public trust in health programs and the success of TB prevention efforts. This study aims to analyze public sentiment in Indonesia toward Bill Gates’ involvement in the TB vaccine program based on social media data from platform X using the Support Vector Machine (SVM) algorithm. The research data consist of 784 Indonesian-language tweets collected through web scraping techniques using keywords related to the TB vaccine issue and Bill Gates. The collected data then underwent several text preprocessing stages, including data cleaning, case folding, tokenization, word normalization, stopword removal, and stemming to improve text quality and consistency. Feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the dataset was split into 80% training data and 20% testing data. The classification model was built using an SVM algorithm with a linear kernel to optimally separate positive and negative sentiment classes. The experimental results show that the SVM model combined with TF-IDF achieved an accuracy of 96.8%, with a precision of 98.3%, a recall of 79.2%, and an F1-score of 86.0%. Sentiment distribution analysis indicates that the majority of tweets were dominated by negative sentiment, while positive sentiment appeared in a smaller proportion. The main contribution of this study lies in the application of social media–based sentiment analysis specifically to the TB vaccine issue associated with a global public figure in the Indonesian context, as well as in demonstrating that the combination of SVM and TF-IDF is effective in accurately classifying public opinion. These findings are expected to serve as a data-driven reference for government institutions and public health stakeholders in designing more effective public communication strategies.
Co-Authors Berutu, Nurhalijah Hidayah, Adinda Fita Luthfie Budie Rasendriya, Rafif Supiyandi Supiyandi