Garuda - Garba Rujukan Digital

Pengenalan Analisis Statistika untuk Meningkatkan Penelitian dan Publikasi Fungsional Statistisi di Jawa Timur Kartika Fithriasari; Nur Iriawan; Adatul Mukarromah; Irhamah; Heri Kuswanto; Wiwiek Setya Winahju; Ulfa Siti Nuraini
Sewagati Vol 5 No 3 (2021)
Publisher : Pusat Publikasi ITS

Jabatan fungsional statistisi memiliki tugas utama yaitu melakukan kegiatan statistik. Kegiatan statistik ini termasuk penyediaan data dan informasi statistik serta analisis dan pengembangan statistik. Jabatan fungsional statistisi dalam menjalankan tugasnya, perlunya peningkatan kemampuan dengan mengikuti pelatihan di bidang statistika. Pelatihan ini juga perlu keluaran yang sesuai dengan yang dibutuhkan fungsional statistisi. Oleh karena itu, pengabdian ini bertujuan untuk membantu meningkatkan kompetensi fungsional statistisi pada berbagai instansi di Jawa Timur dalam mengolah data dan publikasi, diharapkan dengan adanya pelatihan ini bisa meningkatkan kebergunaan informasi yang diperoleh agar dapat tersalurkan dengan baik. Materi yang disampaikan yaitu Statistika Data Driven, Visual dan Analisis Data, serta Karya Ilmiah dan Publikasi. Setelah adanya pelatihan, dilanjutkan dengan pendampingan terhadap fungsional statistisi dalam pengolahan data dan pembuatan karya ilmiah yang disusun oleh fungsional statistisi. Selain itu, manfaat yang dapat diperoleh yaitu terjalinnya kerja sama yang baik antara fungsional statistisi di Jawa Timur dan Departemen Statistika di Institut Teknologi Sepuluh Nopember.

Uncovering Key Topics in Indonesian Political Discourse Through Twitter Analysis After the 2024 Presidential Inauguration Using Clustering methods Hidayatullah, Syarif; Nuraini, Ulfa Siti
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23771

Background: Social media, especially Twitter, plays a key role in political discourse, shaping public opinion. In Indonesia, the 2024 presidential Inauguration , with candidates Prabowo Subianto and Gibran Rakabuming Raka, has generated significant online conversations. Understanding public sentiment and identifying key topics is urgent for analyzing and grouping these discussions, offering insights into political views. Objective: The purpose of this research is to analyze Twitter conversations surrounding the 2024 Indonesian presidential election. The goal is to identify the main topics in these conversations and assess the effectiveness of different clustering algorithms in grouping similar tweets. Methods: This study applies a quantitative approach, using a dataset of 29,905 tweets collected from October 20 to October 25, 2024. The method includes text preprocessing, such as tokenization, stemming, and word weighting. PCA is used for dimensionality reduction. The clustering algorithms K-means, DBSCAN, PAM, and Agglomerative Hierarchical are employed, with performance evaluated based on the Silhouette Score. Results: The results reveal that the Agglomerative Hierarchical Clustering algorithm with Ward linkage and two PCA components produced the highest Silhouette Score of 0.8018. The clustering identified three distinct topics: political leadership, work and collaboration, and unity. Conclusion: This research successfully identified key discussion topics in Twitter conversations about the 2024 Indonesian presidential election. The Agglomerative Hierarchical method with Ward linkage was the most effective clustering algorithm. These findings offer valuable insights into public opinion, and future studies could expand to other social media platforms or investigate the relationship between sentiment and political outcomes.

Pelatihan Pengolahan Dan Visualisasi Data Sebagai Penunjang Peningkatan Pendidikan Menggunakan Microsoft Excel Hasanuddin Al-Habib; Yuliani Puji Astuti; Fadhilah Qalbi Annisa; Riskyana Dewi Intan Puspitasari; Harmon Prayogi; Ulfa Siti Nuraini
Jurnal ABDI: Media Pengabdian Kepada Masyarakat Vol. 10 No. 2 (2025): JURNAL ABDI : Media Pengabdian Kepada masyarakat
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/abdi.v10i2.37764

The aim of the community service to provide training and mentoring in educational data processing using Microsoft Excel. The community service activity involves teachers as participant from Sekolah Indonesia Bangkok, Thailand. The activities are carried out in phases which include planning, coordination with partner school, obtaining necessary permits, developing training module, conducting the training, and evaluating the outcomes. The training consisted of four stages which are a pre-test, training material presentation, a question-and-answer session, and a post-test. The output of the community service was a Microsoft Excel training module that cover both the technical aspects of the software and simple practical application. The effectiveness of the training was evaluated based on the results of the pre-test and post-test. The result showed a significant improvement regarding to participants’ understanding, as evidenced by the increase in the number of correct answers and the average percentage between the pre-test and post-test. The average percentage of correct answers of the participants before the training was around 34% and after the training about 52%. Meanwhile, the average percentage of the pre-test is 62,27% and post-test is 87,91%.

Assessing Financial Risks at PT Pertamina: Insights from Monte Carlo and Altman Z-Score Hidayatullah, Syarif; Shidqiyyah, Nuhaa Salsabila; Pratama, Maheza Fiko; Fauziah, Dian Ayu; Nuraini, Ulfa Siti
Manajemen dan Bisnis Vol 24, No 1 (2025): Vol 24, No 1 (2025): March 2025
Publisher : Department of Management - Faculty of Business and Economics. Universitas Surabaya.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24123/mabis.v24i1.864

Pandemi COVID-19 menyebabkan penurunan permintaan dan pasokan BBM yang signifikan, dengan harga minyak PT Pertamina turun menjadi USD 20 per barel pada tahun 2020. Mengingat situasi tersebut, penelitian ini menganalisis manajemen risiko keuangan PT Pertamina dari tahun 2012 hingga 2023, menggunakan simulasi Monte Carlo, analisis sensitivitas, dan model regresi untuk mengevaluasi dampak berbagai risiko terhadap indikator keuangan seperti total pendapatan, ROA, ROE, NPM, dan EBIT. Simulasi Monte Carlo menunjukkan fluktuasi laba bersih yang cukup besar karena volatilitas harga minyak dan perubahan nilai tukar. Analisis sensitivitas mengidentifikasi risiko operasional sebagai hal yang penting bagi keberlanjutan keuangan, sementara risiko pasar positif dalam kondisi tertentu. Analisis regresi menunjukkan bahwa fluktuasi nilai tukar secara signifikan berdampak pada Total Pendapatan, tetapi harga minyak memiliki efek minimal pada ROA dan ROE. Analisis Altman Z-Score mengungkapkan tren kesehatan keuangan yang menurun, terutama antara tahun 2016 dan 2020, dengan peningkatan risiko kebangkrutan. Meskipun PT Pertamina mengalami pemulihan pada 2021-2023, PT Pertamina tetap berada di zona berisiko tinggi, membutuhkan langkah-langkah strategis seperti diversifikasi bisnis, optimalisasi rantai pasokan, dan kebijakan nilai tukar proaktif untuk memastikan stabilitas jangka panjang.

Pelatihan Informatika Guru SMA/K di Surabaya Annisa, Fadhilah Qalbi; Nuraini, Ulfa Siti; Kurniawan, Ibnu Febry; Prayogi, Harmon; Wintarti, Atik
Sewagati Vol 9 No 3 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i3.2623

Guru SMA dan SMK perlu menyesuaikan diri dengan perubahan kurikulum yang berlaku saat ini. Dalam Kurikulum Merdeka, informatika menjadi mata pelajaran wajib yang harus diajarkan di sekolah. Oleh sebab itu, diperlukan pelatihan bagi guru informatika di Surabaya untuk memperkuat pengetahuan dan keterampilan teknis sesuai dengan tren yang berkembang. Kegiatan ini meliputi pemaparan materi dan praktik yang berfokus pada pokok bahasan algoritma dan pemrograman komputer dan analisis data. Materi yang disampaikan meliputi logika komputer, algoritma dan pemrograman, searching dan sorting, serta analisis dan visualisasi data. Setelah pelatihan, peserta diminta menerapkan materi yang telah dipelajari dalam sebuah proyek sederhana, kemudian mempresentasikan hasilnya. Pelatihan ini menunjukkan adanya peningkatan pemahaman pada Guru SMA dan SMK Surabaya terhadap informatika, yang diukur melalui perbandingan hasil pre-test dan post-test. Selain itu, manfaat lainnya yaitu terjadinya kerja sama yang baik antara Guru SMA dan SMK Surabaya dengan Prodi S-1 Sains Data, Universitas Negeri Surabaya.

I-Regs (Internet-Regression Analysis) as a Statistical Innovation in Nonparametric Regression Modeling Dani, Andrea; Budiantara, I Nyoman; Nuraini, Ulfa Siti; Yustanti, Wiyli; Sifriyani; Putra, Fachrian Bimantoro
Journal of Education Technology and Information System Vol. 1 No. 02 (2025): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jetis.v1i02.35288

This research develops an information system based on the R-Shiny Dashboard, allowing users to perform nonparametric regression modeling. Internet-Regression Analysis (I-Regs) is the name of a dashboard that has been successfully developed. I-Regs provides a complete model library in regression analysis modeling, including parametric, nonparametric, and semiparametric regression. It is hoped that I-Regs can become a valuable tool for researchers, practitioners, and students in modeling regression analysis and solving various data analysis problems.

Towards Sustainable and Trustworthy Digital Infrastructure: Benchmarking RSA and ECDSA Digital Signature Algorithms in Support of SDGs 9 and 16 Yuliani Puji Astuti; Ulfa Siti Nuraini
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.212

Objective: This study aims to evaluate and compare the performance of the Rivest–Shamir–Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA) digital signature schemes in terms of key generation, signing, verification, and storage efficiency. The research supports the advancement of secure digital communication systems aligned with Sustainable Development Goals (SDGs) 9 and 16, which emphasize innovation, resilient digital infrastructure, and trustworthy institutions. Method: A quantitative experimental approach was employed on a Windows AMD64 platform using Python. Five cryptographic configurations were evaluated: RSA-2048, RSA-4096, ECDSA P-256, ECDSA P-384, and ECDSA P-521. Performance tests were conducted on payload sizes of 1 KB, 10 KB, and 100 KB. Each cryptographic operation, including key generation, signing, and verification, was repeated 100 times to ensure measurement consistency and reliability. Results: The findings indicate that ECDSA significantly outperforms RSA in several performance aspects. ECDSA P-256 reduced signature storage requirements by 72.3%, generated keys nearly 13,000 times faster than RSA-2048, and signed 10 KB payloads approximately 48 times faster. ECDSA P-384 also demonstrated strong performance while providing a higher security level. Although RSA-2048 remains suitable for legacy systems, its efficiency is lower than ECDSA-based alternatives. Novelty: This study provides a comprehensive comparative evaluation of multiple RSA and ECDSA variants across different payload sizes and operational metrics, offering practical recommendations for selecting digital signature algorithms. The results highlight ECDSA P-256 as the optimal choice for 128-bit security requirements and ECDSA P-384 for applications requiring stronger 192-bit security.

Towards Sustainable and Trustworthy Digital Infrastructure: Benchmarking RSA and ECDSA Digital Signature Algorithms in Support of SDGs 9 and 16 Yuliani Puji Astuti; Ulfa Siti Nuraini
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.212

Objective: This study aims to evaluate and compare the performance of the Rivest–Shamir–Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA) digital signature schemes in terms of key generation, signing, verification, and storage efficiency. The research supports the advancement of secure digital communication systems aligned with Sustainable Development Goals (SDGs) 9 and 16, which emphasize innovation, resilient digital infrastructure, and trustworthy institutions. Method: A quantitative experimental approach was employed on a Windows AMD64 platform using Python. Five cryptographic configurations were evaluated: RSA-2048, RSA-4096, ECDSA P-256, ECDSA P-384, and ECDSA P-521. Performance tests were conducted on payload sizes of 1 KB, 10 KB, and 100 KB. Each cryptographic operation, including key generation, signing, and verification, was repeated 100 times to ensure measurement consistency and reliability. Results: The findings indicate that ECDSA significantly outperforms RSA in several performance aspects. ECDSA P-256 reduced signature storage requirements by 72.3%, generated keys nearly 13,000 times faster than RSA-2048, and signed 10 KB payloads approximately 48 times faster. ECDSA P-384 also demonstrated strong performance while providing a higher security level. Although RSA-2048 remains suitable for legacy systems, its efficiency is lower than ECDSA-based alternatives. Novelty: This study provides a comprehensive comparative evaluation of multiple RSA and ECDSA variants across different payload sizes and operational metrics, offering practical recommendations for selecting digital signature algorithms. The results highlight ECDSA P-256 as the optimal choice for 128-bit security requirements and ECDSA P-384 for applications requiring stronger 192-bit security.

Higher-order SEM-PLS Modeling of School Readiness Among Indonesian Senior High School Students Dinda Galuh Guminta; Hasanuddin Al-Habib; Ulfa Siti Nuraini; Kartika Chandra Dewi
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 23 No. 1 (2026)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2026.v23.i1.18060

Improving the quality of secondary education is a strategic priority in Indonesia due to its impact on human wellbeing. Secondary education faces challenges in improving the quality of learning and students' psychological readiness. School preparation is widely acknowledged as a multifaceted concept that highlights the significance of social-emotional engagement, self-regulation, and cognitive-motivational engagement. Therefore, this study aims to examine the construct of school readiness in Indonesia senior high school students using higher-order SEM-PLS. Results indicate that behavioral regulation is the strongest predictor of academic achievement (0.385) and functions as the central mechanism transmitting social-emotional engagement into academic outcomes. However, cognitive-motivational factors exert a competitive mediating effect, indicating that academic grades do not always improve among students who show strong cognitive-motivational engagement (-0.130). The results of this study highlight the important role of behavioral regulation in academic achievement and indicate that social-emotional engagement exerts the strongest overall influence on cognitive-motivational engagement, thereby supporting a multidimensional integrated model of school readiness.

Comparative Analysis of Random Forest, Support Vector Machine, and K-Nearest Neighbor with Image Feature Extraction for Rice Leaf Disease Detection Nadia Nafista; Wawu Tri Ambodo; Analicia; Ulfa Siti Nuraini
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112856

Plant diseases and pest infestations have caused a decline in global food production of up to 40%, including in Indonesia, making an efficient and accurate disease detection system essential to support food security. This study proposes a supervised learning approach to detect rice leaf diseases based on image processing. Leaf images are processed through the stages of image segmentation, normalization, Gaussian blur, Canny edge detection, visualization of diseased areas, and hybrid feature extraction. Supervised learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were trained and compared. Test results show that Random Forest delivers the best performance with an accuracy of 97%, outperforming SVM and KNN. These findings indicate that the proposed approach can serve as an effective and reliable solution for the automatic detection of rice leaf diseases.

Title

Found 13 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 13 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 13 Documents
Search