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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics JURNAL PENDIDIKAN TAMBUSAI Indonesian Journal of Applied Informatics EDUKATIF : JURNAL ILMU PENDIDIKAN Jurnal Paedagogy Journal of English Language and Education Brilliance: Research of Artificial Intelligence Jurnal Pendidikan Sains dan Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Digital Transformation Technology (Digitech) Nuansa Informatika JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer Jurnal Pendidikan Teknologi Informasi dan Komunikasi Journal of Computers for Society Cadika Journal.
Rani Megasari
Universitas Pendidikan Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Analisis Sentimen dan Pemodelan Topik pada Post tentang Merek Teknologi di X Menggunakan Fine-tuning IndoBERT dan BERTopic Muhammad Rayhan Nur; Yudi Wibisono; Rani Megasari
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.508

Abstract

Media sosial telah menjadi wadah bagi konsumen untuk menyampaikan persepsi dan opini. Opini yang beredar tersebut berpotensi menjadi sumber data yang berharga bagi brand, termasuk Xiaomi, dalam memahami persepsi publik terhadap produk mereka. Penelitian ini bertujuan untuk menganalisis sentimen dan mengidentifikasi topik diskusi pada unggahan (post) mengenai merek teknologi Xiaomi di platform X (sebelumnya Twitter) dengan pendekatan berbasis Transformer. Dua metode utama yang digunakan adalah fine-tuning IndoBERT untuk model klasifikasi sentimen dan BERTopic untuk pemodelan topik. Data yang berhasil dikumpulkan sebanyak 10.130 post dari bulan Mei 2023 hingga Mei 2025 yang dilanjutkan menuju tahapan praproses serta pelabelan. Model klasifikasi dilatih dengan berbagai kombinasi konfigurasi hyperparameter, dengan hasil pengujian terbaik menghasilkan nilai accuracy 79,8%, precision 73,0%, recall 67,7%, dan f1-score (macro) sebesar 0,699. Distribusi sentimen dalam data menunjukkan dominasi sentimen netral, sedangkan BERTopic berhasil menghasilkan 16 cluster topik dengan rata-rata nilai coherence (C_v) sebesar 0,5437. Topik paling dominan dengan jumlah anggota cluster terbanyak membahas mengenai produk Xiaomi Series dan Poco. Sementara itu, topik dengan persentase sentimen negatif tertinggi berkaitan dengan layanan service center dan sentimen positif tertinggi mengenai produk komputer tablet (tab) Xiaomi. Penggabungan hasil analisis sentimen dan topik memberikan pemahaman yang lebih mendalam terhadap isu yang dibicarakan serta persepsi konsumen. Penelitian ini membuktikan bahwa kombinasi IndoBERT dan BERTopic efektif dalam menganalisis opini konsumen di media sosial serta memberikan wawasan strategis yang relevan bagi perusahaan untuk mengidentifikasi kekuatan dan potensi peningkatan yang dapat dilakukan.
Unsupervised Clustering of Handwritten Essay Answer Images Using Vision Transformer Mohamad Asyqari Anugrah; Yaya Wihardi; Rani Megasari
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.517

Abstract

This study explores the use of deep clustering methods to automatically group handwritten essay answer sheets based on their visual patterns. Feature extraction was performed using three backbone models: ResNet-50, Vision Transformer (ViT-base), and Tr-OCR. These features were then clustered using two unsupervised algorithms—K-means (with k=5) and HDBSCAN (with minimum cluster size = 10). To enhance clustering performance, a deep clustering approach was implemented by applying K-means iteratively to refine feature representations. Evaluation was conducted both quantitatively, using Silhouette Score, Davies-Bouldin Index, and Calinski- Harabasz Score, and qualitatively, through t-SNE visualizations and cluster content inspection. The ViT and Tr-OCR backbones outperformed CNN-based ResNet-50, achieving higher cluster cohesion and separation. Notably, the final clustering result using ViT with HDBSCAN reached a Silhouette Score of 0.772, Davies-Bouldin Index of 0.369, and Calinski-Harabasz Score of 408.006. The findings indicate that vision transformer-based models are more effective for unsupervised grouping of handwritten visual data. This approach can assist educators in accelerating and objectifying the grading process and may serve as a foundation for future automated essay evaluation systems integrating OCR and NLP techniques.
Perancangan Strategic Dashboard Pemetaan Alumni Menggunakan Metode PureShare Dicki Fathurohman; Rani Megasari; Muhammad Nursalman
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.518

Abstract

Alumni play a crucial role as university representatives in society and as a bridge connecting networks and sharing information with current students and new graduates. However, limited up-to-date alumni data and low participation in data updates hinder universities from fully leveraging alumni potential. This study aims to develop a strategic alumni mapping dashboard for Universitas Pendidikan Indonesia (UPI) using the PureShare method, which emphasizes alignment between organizational goals and user needs. The development process includes planning and design, system and data review, prototype creation, refinement, and release. During planning, objectives and key results were identified as the foundation for dashboard design. The data review stage revealed several missing key data, such as income, employment status, and further study data, which were subsequently added to complete the database. The prototype was tested through blackbox testing and refined based on user feedback. The final dashboard presents interactive visualizations, including alumni growth trends, distributions by domicile, employment, entrepreneurship, and further study. This dashboard serves as a monitoring and strategic decision-making tool for UPI’s Alumni Association, supporting the achievement of university Key Performance Indicators, particularly graduate success. This research highlights the importance of participatory approaches and user-centered design in enhancing the effectiveness of alumni mapping dashboards.
Co-Authors Adeliani, Cindy Sri Meidina Ali Nurdin, Enjang Aliyya, Farrel Rahma Andriansyah, Rasyid Ardyanti, Christina Prilla Rosaria Asep Wahyudin Destian, Rangga Dicki Fathurohman Dwi Cahyono, Muhamad Yasirwan Dwi Hendratmo Widyantoro Eddy Prasetyo Nugroho Eka Fitrajaya Rahman Eki Nugraha Emir Mauludi Husni Emir Mauludi Husni Erry Fuadillah Farizi, Syahandhika Naufal Hazmi Ramadhan Adli ISKANDAR, AYSHA ALIA Jullfikar, Raisyad KUSPRIYANTO Lala Septem Riza M. Munir Marantika, Maya Mia Karisma Haq Misbah, Indra Mohamad Asyqari Anugrah Muhammad Nursalman Muhammad Rayhan Nur Muldani, Henhen Imam Munir Munir Munir Munir, Munir Nazir, Shah Nursalman, Muhammad Ranggana, Alfaza Rasim, Rasim Resky Ramadhandi Santoso Rosa Ariani Sukamto Vani Aprianto Wahyudin , Wahyudin Wahyudin Wahyudin Wibisono, Yudi Wihardi, Yaya Yudi Ahmad Hambali