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All Journal ComEngApp : Computer Engineering and Applications Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika)
Rizky, Aditya Saiful
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Computer Science & IT Education Engineering

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Emotion Classification in Indonesian Text Using IndoBERT Rizky, Aditya Saiful; Hidayat, Erwin Yudi
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.494

Abstract

Mental health issues have become a challenge that affects many individuals around the world. A 2018 WHO report noted an increase in deaths by suicide, with a frequency of one case every 40 seconds. The Ipsos Global 2023 survey showed that 44% of respondents in 31 countries are concerned about mental health, while 30% identified stress as a major issue. In Indonesia, the mental health situation is also a serious concern. The 2022 I-NAMHS survey found that 34.9% of adolescents face mental health problems, but only 2.6% of them utilize counseling services. Emotion detection in text is challenging due to the absence of facial expressions or voice modulation. This study aims to classify emotions in Indonesian text using the IndoBERT model. The dataset used consists of 5079 tweets with five emotion labels: Angry, Fear, Joy, Love, and Sad. Parameter variations include the composition of training, validation, and test data split (80:10:10, 75:15:15, and 60:20:20), as well as the combination of learning rate (1e-2 to 1e-7) and batch size (8, 16, and 32). The model was trained for 25 epochs with the application of early stop and patience for 5 epochs. The experimental results showed that the composition of data split 80:10:10, learning rate 1e-6, and batch size 8 resulted in optimal classification. Although some experiments showed indications of overfitting, this research has important implications in the early detection of emotions and can help in mental health treatment efforts.
Emotion Classification in Indonesian Text Using IndoBERT Rizky, Aditya Saiful; Hidayat, Erwin Yudi
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mental health issues have become a challenge that affects many individuals around the world. A 2018 WHO report noted an increase in deaths by suicide, with a frequency of one case every 40 seconds. The Ipsos Global 2023 survey showed that 44% of respondents in 31 countries are concerned about mental health, while 30% identified stress as a major issue. In Indonesia, the mental health situation is also a serious concern. The 2022 I-NAMHS survey found that 34.9% of adolescents face mental health problems, but only 2.6% of them utilize counseling services. Emotion detection in text is challenging due to the absence of facial expressions or voice modulation. This study aims to classify emotions in Indonesian text using the IndoBERT model. The dataset used consists of 5079 tweets with five emotion labels: Angry, Fear, Joy, Love, and Sad. Parameter variations include the composition of training, validation, and test data split (80:10:10, 75:15:15, and 60:20:20), as well as the combination of learning rate (1e-2 to 1e-7) and batch size (8, 16, and 32). The model was trained for 25 epochs with the application of early stop and patience for 5 epochs. The experimental results showed that the composition of data split 80:10:10, learning rate 1e-6, and batch size 8 resulted in optimal classification. Although some experiments showed indications of overfitting, this research has important implications in the early detection of emotions and can help in mental health treatment efforts.
Klasifikasi Emosi Pada Teks Bahasa Indonesia Menggunakan IndoBERT Rizky, Aditya Saiful; Hidayat, Erwin Yudi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6602

Abstract

Masalah kesehatan mental telah menjadi tantangan yang mempengaruhi banyak individu di seluruh dunia. Laporan WHO tahun 2018 mencatat peningkatan angka kematian akibat bunuh diri, dengan frekuensi satu kasus setiap 40 detik. Survei Ipsos Global 2023 menunjukkan bahwa 44% responden di 31 negara mengkhawatirkan kesehatan mental, sementara 30% mengidentifikasi stres sebagai isu utama. Di Indonesia, situasi kesehatan mental juga menjadi perhatian serius. Survei I-NAMHS 2022 menemukan bahwa 34,9% remaja menghadapi masalah kesehatan pada mental, tetapi hanya 2,6% dari mereka yang memanfaatkan layanan konseling. Deteksi emosi dalam teks menjadi tantangan karena tidak adanya ekspresi wajah atau modulasi suara. Penelitian ini bertujuan untuk mengklasifikasikan emosi dalam teks berbahasa Indonesia menggunakan model IndoBERT. Dataset yang digunakan terdiri dari 5079 tweet dengan lima label emosi: Marah (Angry), Takut (Fear), Senang (Joy), Cinta (Love), dan Sedih (Sad). Variasi parameter meliputi komposisi pembagian data latih, validasi, dan uji (80:10:10, 75:15:15, dan 60:20:20), serta kombinasi learning rate (1e-2 hingga 1e-7) dan batch size (8, 16, dan 32). Model dilatih selama 25 epoch dengan penerapan early stop dan patience selama 5 epoch. Hasil eksperimen menunjukkan bahwa komposisi pembagian data 80:10:10, learning rate 1e-6, dan batch size 8 menghasilkan klasifikasi yang optimal. Meskipun beberapa percobaan menunjukkan indikasi overfitting, penelitian ini memiliki implikasi penting dalam deteksi dini emosi dan dapat membantu dalam upaya penanganan kesehatan mental.
Co-Authors Erwin Yudi Hidayat