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All Journal ComEngApp : Computer Engineering and Applications Journal Syntax Jurnal Informatika Fountain of Informatics Journal Format : Jurnal Imiah Teknik Informatika Jurnal Informatika Jurnal Ilmiah FIFO Faktor Exacta Jurnal Pengabdian Pada Masyarakat InComTech: Jurnal Telekomunikasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) IKRA-ITH ABDIMAS JURIKOM (Jurnal Riset Komputer) IJISCS (International Journal Of Information System and Computer Science) MULTINETICS Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Jurnal Ilmu Teknik dan Komputer Jurnal Pengabdian Masyarakat Asia Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia International Journal of Advanced Multidisciplinary Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Systems Engineering and Information Technology IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Pengabdian Pada Masyarakat Media Abdimas Kapas: Kumpulan Artikel Pengabdian Masyarakat Sinergi: Jurnal Pengabdian Kepada Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Nasional Jurnal Pengabdian Masyarakat Nauli CSRID KOMPUTASI Global Science: Journal of Information Technology and Computer Science
Saruni Dwiasnati
Universitas Mercu Buana
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Detection of Rice Leaf Pests Based on Images with Convolution Neural Network in Yollo v8 Fauzi, Ahmad; Baihaqi, Kiki Ahmad; Pertiwi, Anggun; Devianto, Yudo; Dwiasnati, Saruni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2008

Abstract

Detection of rice leaf pests is important in agriculture because it can help farmers determine appropriate preventive measures. One method that can be used to detect rice leaf pests is digital image processing technology. In this research, proof of suitability for solving this case was carried out between the Convolutional Neural Network (CNN) algorithm which was run offline with R-CNN and YOLOv8 for detecting rice leaf pests. At the data preparation stage, images of rice leaves were taken from various sources with a total of 100 images taken from website data and 10 images taken from the research site. Next, preprocessing and data augmentation are carried out to improve image quality and increase data variation. At the model training stage, a training and evaluation process is carried out using two types of algorithms, namely R-CNN and YOLOv8. The accuracy of the testing results using the same data using Yolov8 obtained 87.0% accuracy and 79% precision, while using R-CNN the results obtained were 85% for accuracy and 75% for precision with data divided into 80 training data 20 validation data and 10 testing data. Labeling the dataset uses Makesensei which has been completely standardized, with the resulting parameters being the spots on rice leaves.
Implementasi Absensi Karyawan Menggunakan Algoritma Haversine dengan Global Posisitioning System Berbasis Android Dwiasnati, Saruni; Antono, Fajar
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 6 No 1 (2022)
Publisher : Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v6i1.459

Abstract

Perusahaan yang berkembang merupakan perusahaan yang memiliki tata kelola perusahaan yang baik yaitu dengan penerapan presensi dan manajemen jam kerja karyawan. Perkembangan perusahaan juga didukung oleh perkembangan teknologi dimana teknologi informasi saat ini berkembang pesat dan cepat yaitu teknologi mobile. Komunikasi mobile dapat mempermudah masyarakat untuk memperoleh informasi yang cepat dengan akses internet. PT. Bangsawan Cyberindo merupakan perusahaan yang masih menerapkan presensi dan manajemen jam karyawan secara manual. Namun dalam pelaksanaannya, pihak manajemen masih kesulitan dalam memantau dan merekapitulasi data presensi dan jam kerja karyawan. Sehingga perusahaan membutuhkan suatu sistem yang tepat dan berguna agar dapat membantu pihak manajemen. Dengan demikian, Penerapan digitalisasi sistem presensi ini menggunakan fungsi Global Posisitiong System (GPS) pada perangkat Android dan menerapkan algoritma Haversine Formula untuk perhitungan jarak antara titik posisi kantor ke titik posisi user. Untuk menampilkan peta lokasi kantor pusat dan cabang terdekat, sistem terintegrasi dengan Google Map. Hasil pengujian dapat disimpulkan bahwa perhitungan jarak antara sistem dengan perhitungan yang dilakukan secara manual hanya berbeda ± 0,0018 meter.
Penerapan Data Science untuk Mendukung Transformasi Digital UMKM di Kelurahan Kembangan Utara Dwiasnati, Saruni; Devianto, Yudo; Gunawan, Wawan; Yuliarty, Poppy
Kapas: Kumpulan Artikel Pengabdian Masyarakat Vol 4, No 2 (2025)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/ks.v4i2.4382

Abstract

The implementation of Data Science to support the digital transformation of SMEs (Small and Medium Enterprises) in Kelurahan Kembangan Utara aims to help local SMEs enhance their competitiveness through the utilization of digital technology. In the era of digitalization, SMEs need to adapt to changes in order to remain relevant and grow. Through the Data Science approach, this program focuses on utilizing data for market analysis, trend prediction, and business process optimization. Training provided to SME owners includes the application of data analysis algorithms, the creation of product recommendation systems, and the use of digital platforms that can improve operational efficiency and expand market reach. By integrating data-driven decision-making, SME owners can make more accurate decisions, increase sales, and open up new business opportunities. This program not only provides insights into the importance of digitalization but also equips participants with practical skills in using technologies relevant to the local market's needs. The expected outcome of this program is the improvement of the digital capacity of SMEs in Kelurahan Kembangan Utara, which in turn can contribute to the empowerment of the local economy.
Mapping Public Sentiment on Generative AI via Twitter NLP and Topic Modeling* Noronha, Marcelino Caetano; Dwiasnati, Saruni; Helena P Panjaitan, Cherlina
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.183

Abstract

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *
Co-Authors Abdillah, Reza Wahyu Agung Susilo Yuda Irawan Ahmad Fauzi Akmal, Medina Fibriyanti Alfa, Bonitasari Nurul Anissa Lestari Antono, Fajar Arif , Sutan Mohammad ARIF, SUTAN MOHAMMAD Avrizal, Reza Ayu, Kurnia Gusti Aziz, Topaz Malik Baihaqi, Kiki Ahmad Bambang Jokonowo Bambang Sukowo Bernadetha, Suzan Da Costa Martins, Ana Maria Ildiberta Darmawan, Bagus Budi Devi Fitrianah Diah Utami, Diah Elga Ramdani Eliyani Eliyani Eliyani Fadli Nurrizky Fajar Antono Fatimah, Nona Fatonah, Nenden Siti Fauzi Ahmad Muda Halim, Alfian Noer Hanny Hikmayanti Handayani Haris Yusuf Bakhtiar Helena P Panjaitan, Cherlina Herfandi Herfandi Herry Derajad Wijaya Herry Derajad Wijaya Hidayat, Rahmat Rian Imawan, Sonny Aditya Jumaryadi, Yuwan Lokesworo M. Fajrun Nadhif Muhammad Rifqi muhammad rizky, muhammad Nia Kusuma Wardhani Nicholas Reinaldo, Ryo Noronha, Marcelino Caetano Nur, Nur Umar Azaliy Awal Nurfiant, Muhammad Arya Hafsah Nurrizky, Fadli Pertiwi, Anggun Popy Yuliarty, Popy Prihandani, Kamal Prihandi, Ifan Qitala, Shofia Reza Avrizal Rifqi Muhammad Riri Fajriah Rudiarto, Sabar Saputra, Dony Ramadhan Sarima Lumbansiantar Shofia Qitala Sukarna, Royan Habibie Sukma Wardhana Sumarto, Marco Alfan Surya Perdana Triwibowo, Ardy Wardhana, Ariyani Wardhani, Elisa Wawan Gunawan Wawan Gunawan Wijayanti, Atiek Ike Yudo Devianto Yuliarty, Poppy