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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Tech-E Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal Zetroem Nusantara Hasana Journal International Journal Software Engineering and Computer Science (IJSECS) Jurnal Masyarakat Madani Indonesia
Salma, Triana Dewi
Program Studi Informatika
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Nursing Public Health Social Sciences

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Analisis Sentimen Berbasis Transformer: Persepsi Publik terhadap Nusantara pada Perayaan Kemerdekaan Indonesia yang Pertama Triana Dewi Salma; Muhammad Ferdi Kurniawan; Rizqi Darmawan; Amat Basri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3535

Abstract

The inaugural Indonesian Independence Day celebration in the new capital, Nusantara, marked a historic milestone. This study analyzes public sentiment toward this event using the IndoBERT model. Data was collected from Twitter during the celebration period and classified into positive, negative, and neutral sentiments. Three main approaches were employed: IndoBERT as a baseline, IndoBERT fine-tuned with IndoNLU data, and IndoBERT applied to TextBlob-labeled data. Results indicate that the TextBlob-IndoBERT model outperforms the others, effectively processing informal Indonesian text with high accuracy. These findings provide strategic insights for the government in understanding public perception regarding the development of Nusantara and demonstrate the potential of Transformer-based sentiment analysis for the Indonesian language. The study recommends further exploration of factors influencing sentiment and analysis on other social media platforms.
Smart Attendance System: AI Technology for Digital Attendance Using Computer Vision Technology Natsir, Fauzan; Redo Abeputra Sihombing; Triana Dewi Salma; Millati Izzatillah; Ega Shela Marsiani; Farhan Maulana Arramsy; Anuj Kumar
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i1.7569

Abstract

Employee attendance is a crucial aspect of human resource management, particularly in maintaining discipline and ensuring the operational effectiveness of a company. PT KAMM currently uses a fingerprint-based attendance system which, although effective, often encounters issues such as sensor sensitivity to finger conditions, potential device damage caused by continuous physical contact, and employee inconvenience. This research aims to develop a face recognition-based attendance system as a more efficient and hygienic alternative. The dataset comprises 1,400 facial images from 20 PT KAMM employees (20 classes), split into 80% training, 10% validation, and 10% testing data. The method applied combines the Haar Cascade algorithm for face detection and a Convolutional Neural Network (CNN) for face recognition. The CNN architecture consists of four convolutional layers with 32 to 256 filters, ReLU activation, max pooling, flatten, a 512-neuron fully connected layer, dropout of 0.5, and softmax classification. The model was trained for 50 epochs using the Adam optimizer with a learning rate of 0.001 and batch size of 32. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. Results show the system achieved an accuracy of 95.71%, precision of 95.80%, recall of 95.60%, and an F1-score of 95.70%, with an average inference time of 0.12 seconds/frame in real-time. However, the system has limitations: accuracy drops by up to 12% under extreme lighting conditions and when employees wear masks. This study is expected to serve as a reference for other companies seeking to adopt similar face recognition technology for contactless attendance management systems.
Co-Authors Amat Basri Amat Basri Antasari, Novira Dian Anuj Kumar Basri, Amat Darmawan, Rizqi ega shela marsiani Farhan Maulana Arramsy Fauzan Natsir Millati Izzatillah Muhammad Ferdi Kurniawan Muhammad Ferdi Kurniawan Redo Abeputra Sihombing Rizqi Darmawan Rokhanuddin, Sigid Sigid Rokhanuddin Surahmat, Asep Yusuf Sulistyo Nugroho Zulhamdani