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All Journal Jupiter Aisyah Journal of Informatics and Electrical Engineering Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma IJOEM: Indonesian Journal of Elearning and Multimedia Finger : Jurnal Ilmiah Teknologi Pendidikan Jurnal Pengabdian Masyarakat Bangsa Jurnal Pendidikan Teknologi Informasi dan Komunikasi BACA: Jurnal Dokumentasi dan Informasi Jurnal Pendidikan Teknologi Informasi (J-Diteksi) Jurnal Rekayasa Perangkat Lunak
Dita Septasari
Universitas Aisyah Pringsewu
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Sentiment Analysis of WhatsApp User Reviews as Information Evaluation for Digital Services Nur Aminudin; Agus Wantoro; Dita Septasari
BACA: Jurnal Dokumentasi dan Informasi Vol. 47 No. 1 (2026): BACA: Jurnal Dokumentasi dan Informasi (June)
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/baca.2026.14917

Abstract

This study examines user reviews of the WhatsApp application as digital information objects that reflect user perceptions of digital information service quality. The rapid growth of communication platforms has generated large volumes of user-generated content, which requires systematic analysis and functions as a form of digital documentation. This research aims to evaluate how machine learning and deep learning approaches can support information evaluation through sentiment analysis of user reviews. A publicly available dataset of WhatsApp user reviews obtained from Kaggle was used as the data source. The research methodology consisted of text preprocessing, feature representation, sentiment classification, and performance evaluation. Support Vector Machine (SVM) was employed as a baseline machine learning method, while Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models represented deep learning approaches. The experimental results show that deep learning models outperform the traditional approach, with CNN achieving the best performance across accuracy, precision, recall, and F1-score metrics. These findings indicate that deep learning-based sentiment analysis is effective in transforming large-scale user reviews into actionable information for evaluating digital information services. This study contributes to documentation and information science by demonstrating the role of artificial intelligence in analyzing user-generated digital documentation to support evidence-based decision-making in digital service development.
IoT-Based Cup Sealer Machine Automation Using Nodemcu ESP32 Nur Aminudin; Budi Usmanto; Dwi Feriyanto; Dita Septasari; Tahta Herdian Andika; Adamu Abubakar Muhammad
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 1 (2025): Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jentik.v4i1.442

Abstract

Background of study: The development of the food and beverage industry demands innovation in efficient and reliable packaging processes. Conventional cup sealing machines often face limitations in speed and precision, necessitating technology-based solutions. Aims and scope of paper: This objective of the study is to design and implement an automated cup sealer system based on the Internet of Things (IoT), using the NodeMCU ESP32, capable of performing sealing and real-time monitoring. The system integrates a flowmeter sensor to detect the presence of cups, a stepper motor for the sealing process, and an LCD display along with WiFi connectivity for monitoring production data. Methods: The methodology involves hardware design, control system programming, and performance testing of the device under various temperature and motor speed parameters. Result: The results show that the system can increase production efficiency by up to six times compared to the manual method, with a capacity of 300 cups per hour and a sealing success rate of 95% at an optimal temperature of 100°C and a motor speed of 10 RPM. Synchronization among components was enhanced through sensor calibration and algorithm development. Conclusion: In conclusion, this automated system not only improves efficiency and accuracy but also offers flexibility and IoT-based control, making it highly relevant for small and medium-sized industries.
AHP-BASED SMART SYSTEM UNTUK PEMILIHAN SISWA TERBAIK TKJ SMK MUHAMMADIYAH PAGELARAN: AHP-BASED SMART SYSTEM UNTUK PEMILIHAN SISWA TERBAIK TKJ SMK MUHAMMADIYAH PAGELARAN Dita Septasari; Ulfa Isni Kurnia; Khoirul Anam; Elvi Puspita Sari
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 18 No 1 (2026): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18150173

Abstract

Abstrak Pemilihan siswa terbaik di SMK Muhammadiyah Pagelaran, khususnya kelas 12 jurusan Teknik Komputer dan Jaringan (TKJ), memerlukan sistem yang objektif dan terukur. Penilaian yang hanya berdasarkan nilai akademik sering kali tidak mencerminkan keseluruhan potensi siswa. Penelitian ini mengimplementasikan metode Analytical Hierarchy Process (AHP) untuk membangun sistem pendukung keputusan (SPK) yang menilai siswa berdasarkan empat kriteria utama: nilai akademik, kedisiplinan, prestasi non-akademik, dan keaktifan. Proses AHP dilakukan melalui pembobotan kriteria dan perhitungan nilai tertimbang setiap siswa. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan keputusan yang konsisten dan objektif, dengan siswa bernama Anisa Nurhaliza memperoleh skor tertinggi. Penerapan metode AHP terbukti meningkatkan transparansi dan efisiensi dalam proses pemilihan siswa terbaik di lingkungan sekolah.
Deep Learning Framework for Automatic Tagging of Multimedia Content Ikna Awaliyani; Nur Aminudin; Dita Septasari; Ulfa Isni Kurnia
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 18 No 1 (2026): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18168720

Abstract

Penandaan otomatis konten multimedia merupakan komponen penting dalam manajemen aset digital, rekomendasi konten, dan sistem pengambilan visual skala besar. Namun, kompleksitas konteks visual, variasi objek, dan karakteristik multi-label membuat tugas ini menantang untuk pendekatan konvensional. Studi ini mengusulkan kerangka kerja pembelajaran mendalam ujung ke ujung yang mengintegrasikan Vision Transformer (ViT) sebagai ekstraktor fitur utama dengan kepala klasifikasi multi-label adaptif, termasuk modul fusi multimoda opsional untuk memanfaatkan hubungan semantik antara gambar dan teks. Eksperimen dilakukan pada dataset skala besar seperti MS-COCO, NUS-WIDE, dan Open Images Dataset menggunakan strategi pelatihan termasuk augmentasi data, fine-tuning progresif, dan fungsi kehilangan adaptif. Model yang diusulkan mencapai peningkatan yang konsisten, mengungguli baseline CNN sebesar 4–6% dan arsitektur ViT murni sebesar 2–3%, dengan Presisi Rata-rata (mAP) rata-rata 0,78 dan skor F1 0,82. Integrasi multimoda semakin meningkatkan kinerja pada label abstrak seperti aktivitas, luar ruangan, dan acara. Temuan ini menunjukkan bahwa Vision Transformers, dikombinasikan dengan pembelajaran multimoda, secara signifikan meningkatkan akurasi dan kualitas semantik penandaan konten multimedia otomatis.
Co-Authors Abdul Aziz, RZ. Adamu Abubakar Muhammad Agus Wantoro Ariyanti, Septika Budi Usmanto Dwi Feriyanto Dwi Feriyanto Elvi Puspita Sari Ikna Awaliyani Khoirul Anam Mukaromah, Hafsah Nur Aminudin Nurul Hidayat Rahman Yusuf Shima Asadi Tahta Herdian Andika Ulfa Isni Kurnia