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All Journal Jurnal Sistem dan Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Informatics for Educators and Professional : Journal of Informatics JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknik Informatika UNIKA Santo Thomas WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Teknik Informatika (JUTIF) Mattawang: Jurnal Pengabdian Masyarakat Society: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Sevanam: Jurnal Pengabdian Masyarakat AMMA : Jurnal Pengabdian Masyarakat
I Komang Dharmendra
Institut Teknologi Dan Bisnis STIKOM Bali
Religion Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Public Health Social Sciences Other

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Classifying Public Complaints in Denpasar: a Comparative Study of CNN, RNN, LSTM, and Stacking Deep Learning Models Dharmendra, I Komang; Wijaya, I Made Pasek Pradnyana; Putra, I Made Agus Wirahadi; Atmojo, Yohanes Priyo; Pratiwi, Luh Putu Safitri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4153

Abstract

The process of lodging complaints represents a complex behavioral construct, influenced by the interplay of emotional states, sociocultural factors, and situational contexts. It functions as a pivotal channel for citizens to express dissatisfaction regarding the quality of governmental services. This research aims to optimize public complaint management by leveraging deep learning-based text classification on citizen submissions collected from the Denpasar City Complaint Web Portal. The methodological approach integrates several neural network models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, further enhanced by a Stacking ensemble technique that amalgamates the strengths of each architecture. The dataset consists of 10,302 textual records, categorized into four semantic classes: Complaints, Suggestions, Inquiries, and Information. To improve the robustness and reliability of the classification, advanced preprocessing steps were implemented, including the application of the Synthetic Minority Over-sampling Technique (SMOTE) to alleviate class imbalance and the utilization of Term Frequency–Inverse Document Frequency (TF-IDF) for extracting the most informative textual features. Empirical results demonstrate that the Stacking ensemble model significantly outperforms individual baseline models, achieving an accuracy of 77.83%, with recall and F1-score values of 74.38%. These findings highlight the effectiveness of ensemble deep learning approaches in multiclass complaint classification, thereby supporting improvements in public service delivery and fostering greater governmental transparency. Ultimately, this study contributes to the field of automated text classification by illustrating the potential of advanced neural architectures to enhance citizen participation and institutional accountability.
Co-Authors Adistana Wira Saputra, Gede Agung Ngurah Rai Semadi, I Gusti Budaya, I Gede Bintang Arya Dedy Panji Agustino, Dedy Panji Desak Putu Ayu Aprilia Dinda Sari EDWAR Edwar Edwar Evi Triandini Fathur Mahida Rangga I Gede Harsemadi I Gusti Agung Ngurah Rai Semadi I Gusti Agung Ngurah Rai Semadi I Gusti Ayu Desi Saryanti I Gusti Ngurah Ady Kusuma I Made Agus Wirahadi Putra I Made Ari Santosa I Made Pasek Pradnyana Wijaya I Made Pradipta I Putu Ramayasa, I Putu Ida Ayu Mirah Cahya Dewi Ida Bagus Suradarma Komang Oka Saputra Kusuma, Tubagus Mahendra Luh Putu Ayu Desiani, Luh Putu Ayu LUH PUTU SAFITRI PRATIWI Mahendra, Tubagus Muhamad Samsudin Muhamad Samsudin Ni Kadek Mesi Damayanti Ni Nyoman Utami Januhari, Ni Nyoman Ni Wayan Setiasih Novia Ari Sandra Nyoman Ayu Nila Dewi Nyoman Pramaita Pande, I Made Suandana Astika Putra, I Made Agus Wirahadi Ricky Aurelius Nutanto Diaz, Ricky Aurelius Tubagus Mahendra Kusuma Wijaya, I Made Pasek Pradnyana Yohanes Priyo Atmojo Yohanes Priyo Atmojo, Yohanes Priyo