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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Sains dan Ilmu Terapan JATIKOM: Jurnal Aplikasi dan Teori Ilmu Komputer
Rani Megasari
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Journal : Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)

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Pengembangan Sistem Monitoring Prestasi Mahasiswa Berbasis Data Management Framework Mia Karisma Haq; Rani Megasari; Prasetyo Nugroho, Eddy
Jurnal Komputer Teknologi Informasi Sistem Informasi (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.v4i2.485

Abstract

The issue of limited structured data has often hindered the role of academic advisors in effectively monitoring students’ participation and achievements. At Universitas Pendidikan Indonesia (UPI), student achievement data, both academic and non-academic, is still largely processed manually through forms or online messaging groups, which does not support comprehensive analysis. This study aims to develop a Student Achievement Monitoring System based on the Data Management Framework (DAMA-DMBOK) to ensure that data management is standardized, integrated, and supports data-driven decision-making. The research method includes data collection through literature studies, observation, and interviews; designing the data architecture; formulating key performance indicators (KPIs); developing data visualization and reporting features; and evaluating data management maturity using the Data Management Maturity Assessment (DMMA). The implementation results show that the system has successfully increased the maturity level of data management in key areas such as Data Modeling and Design, Data Storage and Operations, Data Integration & Interoperability, Metadata Management, and Business Intelligence, reaching the Optimizing level. With its analytical dashboard, reporting features, and dynamic data filters, the system supports academic advisors in monitoring student achievement more accurately, continuously, and in a well-documented manner. This study is expected to serve as a reference for developing more adaptive and integrated student achievement monitoring systems at the study program level in higher education institutions.
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 Informasi (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 Informasi (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.
Co-Authors Abdul Aziz Priatna Andy Agustian Anton Winarko Eki Nugraha Elisa Rosa Irsad Harfiansah Shodiq Irsyad Prawira Jajang Kusnendar Lala Septem Riza Mia Karisma Haq Mohamad Asyqari Anugrah Muhammad Nursalman Muhammad Rayhan Nur Prasetyo Nugroho, Eddy Rangga Destian Rasim Rasim W. Wahyudin Wibisono, Yudi Wihardi, Yaya