Article Per Year (5 Year)
p-Index From 2021 - 2026
5.161
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Transformatika Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat JIEET (Journal of Information Engineering and Educational Technology) Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Angkasa: Jurnal Ilmiah Bidang Teknologi KACANEGARA Jurnal Pengabdian pada Masyarakat JISKa (Jurnal Informatika Sunan Kalijaga) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Indonesian Journal of Data and Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Innovation in Community Empowerment (JICE) Emerging Information Science and Technology Media Riset Bisnis & Manajemen Gotong Royong : Jurnal Pengabdian, Pemberdayaan Dan Penyuluhan Kepada Masyarakat (J-P3KM) Jurnal INFOTEL Indonesian Journal on Data Science Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Teknomatika: Jurnal Informatika dan Komputer Jurnal Komtika (Komputasi dan Informatika)
Ulfi Saidata Aesyi
Universitas Jenderal Achmad Yani Yogyakarta
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Evaluasi dan Optimalisasi Penyewaan Lapangan Mini Soccer Menggunakan Business Process Improvement anggraini, devianadyah; Anggraini, Deviana Dyah; Aksan, Azzikra Ramadhanti; Maulana Ridwan, Muhamad Fikry; Aesyi, Ulfi Saidata
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 23 No 1 (2025): Januari 2025
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v23i1.159

Abstract

Pengelolaan bisnis secara manual sering kali dianggap tidak efisien. Dalam konteks penyewaan lapangan mini soccer, pengelolaan bisnis secara manual dapat memunculkan berbagai kendala seperti sulitnya mengakses informasi jadwal, lambatnya proses komunikasi, dan potensi kesalahan pencatatan jadwal. Kendala-kendala tersebut tidak hanya menghambat operasional tetapi juga menurunkan kepuasan pelanggan. Oleh karena itu, diperlukan upaya untuk meningkatkan efisiensi dan efektivitas proses bisnis penyewaan lapangan. Hal ini bertujuan untuk mengevaluasi dan mengoptimalkan proses bisnis penyewaan lapangan di salah satu tempat penyewaan mini soccer di Yogyakarta. Metode Failure Mode and Effect Analysis (FMEA) digunakan untuk mengidentifikasi potensi kegagalan dalam proses yang ada, sedangkan pendekatan Business Process Improvement (BPI) diterapkan untuk menyederhanakan dan meningkatkan efisiensi proses bisnis. Dengan mengurangi waktu pemesanan dan penggunaan lapangan hingga 83,6%, prototipe aplikasi yang dikembangkan dapat meningkatkan kecepatan dan akurasi proses bisnis. Pengujian menggunakan metode Single Ease Question (SEQ) menunjukkan bahwa proses bisnis yang disarankan lebih mudah digunakan dan memiliki tingkat kepuasan pengguna yang tinggi. Diharapkan solusi ini akan membantu pengelola lapangan meningkatkan kualitas layanan dan membantu pengambilan keputusan berbasis data.
Zero-Shot Detection of IndoT5-Synthesized Indonesian Scientific Abstracts Using mDeBERTa v3 Aldo Syahputra; Aris Wahyu Murdiyanto; Ulfi Saidata Aesyi
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.457

Abstract

 Introduction: Distinguishing human-written scientific abstracts from AI-synthesized text remains challenging, particularly when machine-generated language appears fluent and formally structured. This study evaluates mDeBERTa v3 in a zero-shot Natural Language Inference (NLI) setting for detecting Indonesian scientific abstracts specifically synthesized using IndoT5-base-paraphrase. Method: A balanced dataset of 2,274 abstracts comprising 1,137 human-written abstracts from SINTA 3 journals and 1,137 IndoT5-synthesized counterparts was analyzed. Seven linguistic features were examined using the Mann–Whitney U test, followed by zero-shot mDeBERTa v3 classification using one-, three-, and five-aspect NLI instruction scenarios. A Random Forest classifier using the same linguistic features was included as a supervised baseline. Results and Discussion: All seven linguistic features differed significantly between classes (p < 0.001), with AI texts showing substantially higher sentence-length variation than human texts. The targeted one-aspect NLI scenario achieved the highest recall of 76.52% but only 53.52% accuracy because 790 human abstracts were misclassified as AI. Increasing instruction complexity further reduced recall. In contrast, Random Forest achieved 91.21% accuracy and an F1-score of 0.9130, confirming that the identified linguistic anomalies are strong learnable signals. Conclusion: Zero-shot mDeBERTa v3 can detect generator-specific structural artifacts but remains insufficiently precise for standalone academic-integrity screening and should be supplemented by supervised methods and human review.
Community perspective analysis of Yogyakarta special region using K-means algorithm Berlina, Laila Indah; Aesyi, Ulfi Saidata; Kharisma, Kharisma
Emerging Information Science and Technology Vol. 5 No. 2 (2024): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i2.24729

Abstract

This study explores community perspectives on Yogyakarta, a culturally rich region in Indonesia known as "Jogja Istimewa," "Student City," and "City of Tourism." Given the potential challenges faced by the region, the research employs the K-Means Algorithm to analyze opinions gathered from Twitter, offering a novel alternative to traditional surveys. Using a data crawling method, relevant tweets about Yogyakarta were collected and processed through preprocessing and TF-IDF to enhance word significance. The findings reveal diverse community views regarding job opportunities, culture, tourism, religious activities, stakeholder involvement, and security. The application of K-Means clustering effectively highlights the multifaceted perspectives of Yogyakarta's residents, providing valuable insights for understanding the region’s socio-cultural dynamics. 
Perbandingan LSTM dengan Support Vector Machine dan Multinomial Na ve Bayes pada Klasifikasi Kategori Hoax Puji Winar Cahyo; Ulfi Saidata Aesyi
Jurnal Transformatika Vol. 20 No. 2 (2023): January 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i2.5880

Abstract

Hoax is fake news, now massively spread through social media. The impact of hoaxes is that people's misperceptions in understanding of news are very high. With the existence of hoaxes are spreading through social media, it requires the public to think smart when receiving the news. Currently, many ways to prevent hoaxes, right now we have Fact Checker Directory Platform which is a truth platform sourced from several fact check sites. On the truth check platform, every news detected as hoaxes has been categorized into specific type of hoax, manually by the validator. For this reason, this research attempts to automatically categorize the types of hoaxes using comparation of Deep Learning with Machine Learning classifications. Deep Learning uses Long Short Term Memory Network (LSTM), while Machine Learning uses Support Vector Machine (SVM) and Multinomial Naive Bayes. Through the build model process, SVM produces the best accuracy quality of 0.74, Multinomial Na ve Bayes produces an accuracy quality of 0.62 while LSTM displays 0.49. The results of low accuracy in LSTM need to be evaluated on model architecture and data normalization during preprocessing.
Co-Authors Ahmad Hanafi Aksan, Azzikra Ramadhanti Aldo Syahputra Anggraini, Deviana Dyah anggraini, devianadyah Ardilla Mega Agustin Arianti, Dennis Fitri Salsabilla Arif Himawan Arif Himawan, Arif Aris Wahyu Murdiyanto Arumsari, Dewi Asnawi, Choerun Bagas Dwi Santosa Bagas Dwi Santosa Berlina, Laila Indah Cahayana, Nika Nur Dedy Hariyadi Faisal Dwi Nurwenda Herdiesel Santoso Irmma Dwijayanti Khabani, Muhamad Zul Kharisma Kharisma Kharisma, Kharisma Kusumaningtyas, Kartikadyota Kusumaningtyas, Netania Indi Lahitani, Alfirna Rizqi Lintang Sumirat Maria Gratiana Dian Jatiningsih Maulana Ridwan, Muhamad Fikry Muhammad Rifqi Maarif, Muhammad Rifqi Netania Indi Kusumaningtyas Noviana Wulandari Nurul Fatimah Nurul Fatimah Puji Winar Cahyo Puji Winar Cahyo Putri Pusvitasari Retantyo Wardoyo Risky Firmansyach Riyanto Tri Kurniawan Riyanto Tri Kurniawan Rizqi Lahitani, Alfirna Santosa, Bagas Dwi Taufaldisatya Wijatama Diwangkara Taufaldisatya Wijatama Diwangkara