Open access Jurnal SINTA 2
Penggunaan Metode Naïve Bayes Classifier pada Analisis Sentimen Penilaian Masyarakat Terhadap Pelayanan Rumah Sakit di Malang
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 11 No. 5 (2024) · pp. 993–1000
- ISSN
- 2528-6579, 2355-7699
- Published
- 2024-10-31
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Bahasa Indonesia
Peran rumah sakit dalam kehidupan masyarakat sangatlah penting terkait tingkat kepuasan Masyarakat terhadap pelayanan, fasilitas, dan aspek lainnya. Opini dan penilaian masyarakat turut menjadi penilaian terhadap kinerja pelayanan rumah sakit. Pada Google Maps Reviews banyak ulasan dari berbagai rumah sakit.Penilaian yang sangat besar dapat kita lihat pada Google Maps Reviews akan memakan waktu bagi masyarakat. Keluhan-keluhan Masyarakat disekitar penulis terhadap pelayanan rumah sakit di Malang menjadikan penilaian pelayanan rumah sakit di Malang menjadi objek dari penelitian dasar ini. Penulis memanfaatkan algoritma Naïve Bayes Classifier dan Cross Validation untuk mengkategorikan penilaian berdasarkan sentimen positif dan negatif serta aspek agar mempermudah pengkategorian. Aspek yang dipergunakan tersebut adalah aspek penanganan, fasilitas, administrasi, dan biaya. Penulis juga menggunakan analisis Root Cause untuk mempermudah masyarakat dan pihak terkait dalam menemukan masalah dan rekomendasi pemecahan masalah. Awalnya data di proses dengan text preprocessing lalu pembobotan kata TF-IDF, pelabelan data, penerapan algoritma Naïve Bayes Classifier dan mengambil sentimen negatif untuk menentukan Root Cause. Hasil pengujian dengan menggunakan Cross Validation dengan fold k-9 memiliki nilai accuracy 82,97%, precision sebesar 83,13%, recall 82,93%, dan f-measure sebesar 82,92%. Hasil uji dengan menggunakan 20% data tes diperoleh akurasi 90%. Abstract The role of hospitals in society is crucial in terms of the level of satisfaction that the community derives from their services, facilities, and other aspects. Public opinions and assessments also contribute to evaluating hospital service performance. On Google Maps Reviews, there are numerous reviews from various hospitals. A significant evaluation can be observed on Google Maps Reviews, which might take time for the community. The complaints of the community around the writer regarding the hospital services in Malang make the assessment of hospital services in Malang the subject of this basic research. The author utilizes the Naive Bayes Classifier algorithm and Cross Validation to categorize assessments based on positive and negative sentiments, as well as 4 aspects to facilitate categorization. The author also employs Root Cause analysis to aid the public and relevant parties in identifying issues and providing problem-solving recommendations. After processing the data through text preprocessing and TF-IDF word weighting, data labeling, applying the Naive Bayes Classifier algorithm, and extracting negative sentiments to determine the Root Cause in negative hospital sentiments. Based on this process, applying Cross Validation with k-9 folds yields the highest values: an accuracy of 82.97%, precision of 83.13%, recall of 82.93%, and an f-measure of 82.92%. Through the sentiment classification and Cross Validation process, the accuracy results in 90% for hospital reviews with the highest number of assessments divided into 2 sentiments and 4 aspects: positive and negative sentiments, as well as treatment, facilities, administration, and costs.
English
Peran rumah sakit dalam kehidupan masyarakat sangatlah penting terkait tingkat kepuasan Masyarakat terhadap pelayanan, fasilitas, dan aspek lainnya. Opini dan penilaian masyarakat turut menjadi penilaian terhadap kinerja pelayanan rumah sakit. Pada Google Maps Reviews banyak ulasan dari berbagai rumah sakit.Penilaian yang sangat besar dapat kita lihat pada Google Maps Reviews akan memakan waktu bagi masyarakat. Keluhan-keluhan Masyarakat disekitar penulis terhadap pelayanan rumah sakit di Malang menjadikan penilaian pelayanan rumah sakit di Malang menjadi objek dari penelitian dasar ini. Penulis memanfaatkan algoritma Naïve Bayes Classifier dan Cross Validation untuk mengkategorikan penilaian berdasarkan sentimen positif dan negatif serta aspek agar mempermudah pengkategorian. Aspek yang dipergunakan tersebut adalah aspek penanganan, fasilitas, administrasi, dan biaya. Penulis juga menggunakan analisis Root Cause untuk mempermudah masyarakat dan pihak terkait dalam menemukan masalah dan rekomendasi pemecahan masalah. Awalnya data di proses dengan text preprocessing lalu pembobotan kata TF-IDF, pelabelan data, penerapan algoritma Naïve Bayes Classifier dan mengambil sentimen negatif untuk menentukan Root Cause. Hasil pengujian dengan menggunakan Cross Validation dengan fold k-9 memiliki nilai accuracy 82,97%, precision sebesar 83,13%, recall 82,93%, dan f-measure sebesar 82,92%. Hasil uji dengan menggunakan 20% data tes diperoleh akurasi 90%. Abstract The role of hospitals in society is crucial in terms of the level of satisfaction that the community derives from their services, facilities, and other aspects. Public opinions and assessments also contribute to evaluating hospital service performance. On Google Maps Reviews, there are numerous reviews from various hospitals. A significant evaluation can be observed on Google Maps Reviews, which might take time for the community. The complaints of the community around the writer regarding the hospital services in Malang make the assessment of hospital services in Malang the subject of this basic research. The author utilizes the Naive Bayes Classifier algorithm and Cross Validation to categorize assessments based on positive and negative sentiments, as well as 4 aspects to facilitate categorization. The author also employs Root Cause analysis to aid the public and relevant parties in identifying issues and providing problem-solving recommendations. After processing the data through text preprocessing and TF-IDF word weighting, data labeling, applying the Naive Bayes Classifier algorithm, and extracting negative sentiments to determine the Root Cause in negative hospital sentiments. Based on this process, applying Cross Validation with k-9 folds yields the highest values: an accuracy of 82.97%, precision of 83.13%, recall of 82.93%, and an f-measure of 82.92%. Through the sentiment classification and Cross Validation process, the accuracy results in 90% for hospital reviews with the highest number of assessments divided into 2 sentiments and 4 aspects: positive and negative sentiments, as well as treatment, facilities, administration, and costs.
Keywords
Collaboration network of the authors
40 authors · 76 co-authorship links
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Indriati . | Affiliation unknown | 2 |
| Nanang Yudi Setiawan | University of Brawijaya | 2 |
| Marji . | University of Brawijaya | 1 |
| Lailil Muflikhah | University of Brawijaya | 1 |
| Putri Nur Fadila | University of Brawijaya | 1 |
| Zikfikri Yulfiandi Rachmad | University of Brawijaya | 1 |
| Achmad Arwan | University of Brawijaya | 1 |
| Mahendra Data | University of Brawijaya | 1 |
| Ibnu Aqli | University of Brawijaya | 1 |
| Suherni Prahesti | University of Brawijaya | 1 |
| Heru Nurwasito | University of Brawijaya | 1 |
| Sutrisno Sutrisno | University of Brawijaya | 1 |
| Rozaq Akbar | University of Brawijaya | 1 |
| Marji Marji | Affiliation unknown | 1 |
| Novanto Yudistira | University of Brawijaya | 1 |
| Agus Wahyu Widodo | University of Brawijaya | 1 |
| Candra Dewi | University of Brawijaya | 1 |
| Indriati Indriati | University of Brawijaya | 1 |
| Dany Primanita Kartikasari | University of Brawijaya | 1 |
| Roisul Setiawan | University of Brawijaya | 1 |
| Widhy Hayuhardhika Nugraha Putra | University of Brawijaya | 1 |
| Issa Arwani | University of Brawijaya | 1 |
| Ghazian Hamdi | University of Brawijaya | 1 |
| Imam Cholissodin | University of Brawijaya | 1 |
| Eriq Muhammad Adams Jonemaro | University of Brawijaya | 1 |
| Willyan Eka Ksatria | University of Brawijaya | 1 |
| Annisa Sukmawati | University of Brawijaya | 1 |
| Muhammad Farid Muzayyani | University of Brawijaya | 1 |
| Alland Rifqy Putra | Affiliation unknown | 1 |
| Alland Rifqy Putra | University of Brawijaya | 1 |
| Muhammad Fariz Firdaus | University of Brawijaya | 1 |
| Dinar Fairus Salsabillah | University of Brawijaya | 1 |
| Satrio Hadi Wijoyo | University of Brawijaya | 1 |
| Eka Bayu Satriawan | Affiliation unknown | 1 |
| Eka Bayu Satriawan | Affiliation unknown | 1 |
| Raihan Winurputra | Affiliation unknown | 1 |
| Raihan Winurputra | University of Brawijaya | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.