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All Journal PROSIDING SEMINAR NASIONAL Jurnal Matematika PIRAMIDA WIDYABHAKTI Jurnal Ilmiah Populer Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya JURNAL MATHEMATIC PAEDAGOGIC SENSITEK Jurnal Manajemen Informatika dan Sistem Informasi MES: Journal of Mathematics Education and Science Jurnal Varian Jurnal Statistika dan Aplikasinya Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Community Empowerment Media Aplikom Jurnal Pengabdian Masyarakat (ABDIRA) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat COVIT Safari : Jurnal Pengabdian Masyarakat Indonesia SmartComp Jurnal KALANDRA Jurnal Pengabdian Nasional (JPN) Indonesia Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat
LUH PUTU SAFITRI PRATIWI
Institut Teknologi Dan Bisnis STIKOM Bali
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Klasifikasi Sentimen untuk Analisis Kepuasan Pelayanan Puskesmas Berbasis Arsitektur LSTM Budaya, I Gede Bintang Arya; Pratiwi, Luh Putu Safitri; Agustino, Dedy Panji
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 4 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i4.5361

Abstract

Transformasi layanan sistem kesehatan di Indonesia menjadi fokus utama pemerintah melalui program nasional satu data kesehatan Indonesia. Dalam program ini, digitalisasi teknologi kesehatan menjadi salah satu pilar transformasi yang penting. Puskesmas, sebagai pusat layanan kesehatan level pertama, menjadi salah satu fokus dalam transformasi ini. Kepuasan pelayanan di Puskesmas menjadi faktor penting dalam mencapai tujuan tersebut. Penelitian ini bertujuan untuk menganalisis pemanfaatan teknik klasifikasi sentimen dalam survei kepuasan pelayanan Puskesmas Dalam penelitian ini, diterapkan pendekatan deep learning dengan menggunakan arsitektur Long-Short Term Memory (LSTM) untuk klasifikasi sentimen.  Hasil penelitian menunjukkan bahwa model LSTM efektif digunakan untuk mengklasifikasikan sentimen. Akurasi keseluruhan model mencapai 76.26%, menunjukkan kemampuan model dalam melakukan prediksi dengan baik. Namun, terdapat perbedaan performa antara kelas sentimen. Sentimen netral memiliki performa yang sedikit lebih rendah dibandingkan dengan sentimen positif dan negatif. Hal ini dapat disebabkan oleh ketidakseimbangan jumlah sampel, kompleksitas sentimen netral, variabilitas dalam teks, dan perbedaan subyektivitas dalam penilaian sentimen.  Penelitian ini menunjukkan potensi penerapan deep learning, khususnya model LSTM, dalam analisis sentimen kepuasan pelayanan Puskesmas. Hasil penelitian ini dapat menjadi dasar untuk integrasi deep learning dalam pengembangan sistem digital kesehatan nasional. 
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 A. A Ayu Meitridwiastiti A.A Ayu Meitridwiastiti Budaya, I Gede Bintang Arya Dedy Panji Agustino Dedy Panji Agustino, Dedy Panji Desak Putu Eka Nilakusmawati EDWAR EDWAR Gede Suwardika Hendayanti, Ni Putu Nanik I Gede Suardika I Gusti Ayu Made Srinadi I Ketut Putu Suniantara I Ketut Putu Suniantara I Komang Dharmendra I Made Hendra M. Pramayasa I Made Hendra Mahajaya Pramayasa I Made Pasek Pradnyana Wijaya I Made Pasek Pradnyana Wijaya I Made Rudita I Wayan Astu Werdistira Ida Ayu Gde Suwiprabayanti Putra Kadek Dwi Pradnyani Novianti Kadek Masakazu Ketut Gus Oka Ciptahadi M. Azman Maricar Made Susilawati Ngakan Putu Rio Wiswambara Ni Luh Gede Kompyang Denia Pradnya Sulasih Ni Made Dwijayani Ni Putu Meina Ayuningsih Ni Putu Meina Ayuningsih Putra, I Made Agus Wirahadi Shofwan Hanief Suniantara, I Ketut Putu Wayan Astu Werdistira Wijaya, I Made Pasek Pradnyana Yohanes Priyo Atmojo, Yohanes Priyo