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All Journal Jurnal Teknologi Informasi MURA Jurnal Teknologi Informasi Mura Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi
Kadir, Nurul Tiara
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Other

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An Explainable Spatio-Temporal Decision Support System (DSS) Using XGBoost And SHAP For Urban Complaint Trend Prediction Sakmar, Moeng; Darmawan, Agus; Shofo, Puteri Awaliatus; Kadir, Nurul Tiara
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 1 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i1.44562

Abstract

The increase in the volume of public complaints in urban areas requires an accurate and explainable decision support system. This study developed an Explainable Decision Support System (xDSS) based on the Extreme Gradient Boosting (XGBoost) algorithm combined with the SHapley Additive Explanations (SHAP) method to predict spatial and temporal trends in public complaints in DKI Jakarta Province. The research data was obtained from the Satu-Data Jakarta portal and included multi-year complaint reports that were processed through aggregation, temporal feature engineering, and regression-based metric evaluation. The results show that the XGBoost model has high predictive performance with an R² value of 0.8425, MAE of 2.9858, and RMSE of 4.9928, indicating the model’s ability to explain more than 84% of the variation in the actual number of complaints. SHAP analysis revealed that temporal features such as complaint_lag1 and complaint_ma3 had the most dominant influence, while external variables such as rainfall (rainfall_mm) and population density (population_density) also made positive contributions. These results indicate that the dynamics of public complaints are influenced by a combination of historical factors and environmental conditions. Practically, this xDSS system can provide accurate predictions and transparent interpretations, thereby supporting the implementation of Smart Governance and evidence-based policy. This approach strengthens the application of Explainable Artificial Intelligence (XAI) in public service governance by providing accurate, ethical, and auditable models to support strategic decision-making in the era of digital government.
ANALISIS SENTIMEN THREADS KULINER YOGYAKARTA MENGGUNAKAN FINE-TUNED SENTENCE TRANSFORMER Kadir, Nurul Tiara; Rukhmanti Udhayana Hr, Khadijah Febriana; Wahyu Setyaningsih, Putry; Muthia, Muthia
Jurnal Teknologi Informasi Mura 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.
Evektivitas Xgboost Lightgbm dan Catboost pada Dataset Imbalanced Predictive Maintenance Moeng Sakmar; Kadir, Nurul Tiara; 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 Kadir, Nurul Tiara; Rukhmanti Udhayana Hr, Khadijah Febriana; Wahyu Setyaningsih, Putry; Muthia, Muthia
Jurnal Teknologi Informasi Mura 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 Agus Darmawan Moeng Sakmar Muthia, Muthia Puteri Awaliatush Shofo Rukhmanti Udhayana Hr, Khadijah Febriana Sakmar, Moeng Shofo, Puteri Awaliatus Wahyu Setyaningsih, Putry