Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi Mura Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi
Nurul Tiara Kadir
Universitas Jenderal Soedirman
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Evektivitas Xgboost Lightgbm dan Catboost pada Dataset Imbalanced Predictive Maintenance Moeng Sakmar; Nurul Tiara Kadir; Puteri Awaliatush Shofo; Agus Darmawan
Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Vol. 3 No. 1 (2026): SINTA: JANUARI
Publisher : Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/sinta.v3i1.145

Abstract

In the era of Industry 4.0, unexpected machine failures have become a critical challenge, triggering unplanned downtime and significant financial losses for the manufacturing sector. A fundamental obstacle in the development of Machine Learning-based Predictive Maintenance systems is data imbalance, where damage incidents occur much less frequently than normal conditions, causing models to become biased and fail to recognize vital anomalies. This study aims to evaluate the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in optimizing failure detection performance on the AI4I 2020 dataset. It uses a comparative approach with three Gradient Boosting algorithms: XGBoost, LightGBM, and CatBoost. This study highlights the Accuracy Paradox phenomenon in scenarios without resampling, where high spurious accuracy masks the model's inability to detect failures or low Recall. The findings of this study show that the integration of SMOTE successfully reconstructs the model's decision boundaries, thereby significantly increasing sensitivity to minority classes. Based on an in-depth analysis using the Confusion Matrix, the XGBoost algorithm combined with SMOTE was identified as the most optimal model, as it effectively balanced critical trade-offs by achieving a high Recall to ensure asset safety, while minimizing false alarms (False Positives) that impact technician work efficiency, compared to its competitors. This study concludes that addressing data imbalance is a deterministic step in building a predictive maintenance system that is not only technically precise but also reliable and safe for implementation in real industrial ecosystems.
ANALISIS SENTIMEN THREADS KULINER YOGYAKARTA MENGGUNAKAN FINE-TUNED SENTENCE TRANSFORMER Nurul Tiara Kadir; Khadijah Febriana Rukhmanti Udhayana Hr; Putry Wahyu Setyaningsih; Muthia Muthia
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2894

Abstract

Media sosial menjadi sumber penting dalam memahami persepsi wisatawan terhadap destinasi kuliner. Salah satu platform yang relatif baru dan belum banyak dieksplorasi dalam penelitian akademik adalah Threads, yang menyediakan percakapan berbasis teks. Penelitian ini bertujuan menganalisis sentimen pengguna Threads terhadap wisata kuliner Yogyakarta serta mengevaluasi potensi Threads sebagai sumber data alternatif dalam pemetaan persepsi pengguna. Data dikumpulkan melalui proses scraping dan mendapatkan 1.002 postingan Threads dengan kata kunci Kuliner Jogja pada periode awal Oktober 2024 hingga awal Desember 2025. Metode penelitian mencakup tahapan text mining, pra-pemrosesan teks, serta penerapan model kecerdasan buatan fine-tuned Sentence Transformer untuk klasifikasi sentimen ke dalam kategori positif, netral, dan negatif. Selain itu, dilakukan ekstraksi entitas untuk mengidentifikasi objek kuliner yang paling sering dibicarakan. Hasil analisis menunjukkan bahwa persepsi terhadap kuliner Yogyakarta di Threads didominasi sentimen positif sebesar 57.8%, diikuti sentimen netral sebesar 38.3%, sementara sentimen negatif relatif kecil yaitu 3.9%. Dominasi istilah seperti warung, soto, dan ayam mengindikasikan kuatnya daya tarik kuliner tradisional dalam persepsi wisata kuliner Yogyakarta. Penelitian ini menunjukkan efektivitas fine-tuned Sentence Transformer dengan data latih terbatas pada domain kuliner berbahasa Indonesia.
Co-Authors Agus Darmawan Khadijah Febriana Rukhmanti Udhayana Hr Moeng Sakmar Muthia Muthia Puteri Awaliatush Shofo Putry Wahyu Setyaningsih