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EARLY DETECTION OF ACADEMIC DEPRESSION USING SMARTPHONE-BASED MACHINE LEARNING MODELS

Jurnal Ilmiah Ilmu Terapan Universitas Jambi · Vol. 9 No. 3 (2025) · hlm. 1104–1116

DOI
10.22437/jiituj.v9i3.46375
ISSN
2580-2259, 2580-2240
Terbit
2025-08-10
Penerbit
LPPM Universitas Jambi
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Mental health in developing countries is a common and complex problem. The problem continues to increase and is closely related to low self-confidence, negative interpersonal relationships, and academic depression. This can affect students' ability to complete academic assignments on a university scale. An AI-based early detection application can potentially improve mental health services related to treatment access. This system can help identify users who may be depressed based on the language used, especially for those who are reluctant to seek professional solutions due to the negative stigma of mental health. This study uses a qualitative descriptive method involving observation, in-depth analysis of group conversations, and early detection of academic depression by identifying conversation patterns between students and counselors as the basis for developing a smartphone-based application. This study produced a dataset of 395 depression-level data entries used as training data to develop a machine-learning model. A prototype of an academic depression detection application has been developed as a mobile-based application.

Kata kunci

Jaringan kolaborasi para penulis

40 penulis · 128 tautan kepenulisan bersama · 57 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Universitas Bina Darma
  • Universitas Jambi
  • Sriwijaya University
  • Universitas Labuhanbatu
  • Universitas 17 Agustus 1945 Jakarta
  • Universitas Katolik Musi Charitas
  • Sebelas Maret University
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Rezki SyaputraUniversitas Bina Darma5
Ahmad SyaziliUniversitas Labuhanbatu3
Ahmad RusliUniversitas Labuhanbatu3
Dedek JulianUniversitas Bina Darma3
Bosya PerdanaAfiliasi tidak diketahui2
Dhimas Rosanto SaputraAfiliasi tidak diketahui2
Prihambodo Hendro SaksonoUniversitas Bina Darma2
Deni ErlansyahUniversitas Bina Darma2
Ferdi AdityaUniversitas Bina Darma2
Padel Mohammad AgamUniversitas Bina Darma2
Maria Bellaniar IsmiatiUniversitas Katolik Musi Charitas1
JemakmunAfiliasi tidak diketahui1
Mukran RoniAfiliasi tidak diketahui1
Deshinta Arrova DewiINTI International University1
Sulaiman HelmiUniversitas Bina Darma1
Malik CahyadinSebelas Maret University1
Mashal AlqudahAfiliasi tidak diketahui1
Shopia RahmawatiUniversitas Jambi1
Ella MasitaUniversitas Jambi1
Marzul HidayatUniversitas Jambi1
Fortunasari FortunasariUniversitas Jambi1
Hani Atus SholikhahSriwijaya University1
Wuttipong WongprakobThammasat University1
Lenny MarzulinaUniversitas Islam Negeri Raden Fatah Palembang1
Linda UnsrianaBinus University1
Desy MisnawatiUniversitas Bina Darma1
Novita DamayantiUniversitas Prof Dr Moestopo1
Rahmat GernowoDiponegoro University1
Surya Negara EdiAfiliasi tidak diketahui1
Ayu Puspita Indah SariAfiliasi tidak diketahui1
Cita Hikmah YantiAfiliasi tidak diketahui1
M SuardiAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://online-journal.unja.ac.id/index.php/JIITUJ/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">EARLY DETECTION OF ACADEMIC DEPRESSION USING SMARTPHONE-BASED MACHINE LEARNING MODELS</dc:title>
	<dc:creator xml:lang="en">Negara, Edi Surya</dc:creator>
	<dc:creator xml:lang="en">Hermawan, Latius</dc:creator>
	<dc:creator xml:lang="en">Mayrita, Hastari</dc:creator>
	<dc:creator xml:lang="en">Arisandy, Desy</dc:creator>
	<dc:creator xml:lang="en">Farozi, Mohamad</dc:creator>
	<dc:creator xml:lang="en">Ramadan, Rahmat</dc:creator>
	<dc:creator xml:lang="en">Ariana, Sunda</dc:creator>
	<dc:creator xml:lang="en">Andryani, Ria</dc:creator>
	<dc:subject xml:lang="en">Depression</dc:subject>
	<dc:subject xml:lang="en">Detection</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">Smartphone</dc:subject>
	<dc:description xml:lang="en">Mental health in developing countries is a common and complex problem. The problem continues to increase and is closely related to low self-confidence, negative interpersonal relationships, and academic depression. This can affect students' ability to complete academic assignments on a university scale. An AI-based early detection application can potentially improve mental health services related to treatment access. This system can help identify users who may be depressed based on the language used, especially for those who are reluctant to seek professional solutions due to the negative stigma of mental health. This study uses a qualitative descriptive method involving observation, in-depth analysis of group conversations, and early detection of academic depression by identifying conversation patterns between students and counselors as the basis for developing a smartphone-based application. This study produced a dataset of 395 depression-level data entries used as training data to develop a machine-learning model. A prototype of an academic depression detection application has been developed as a mobile-based application.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Jambi</dc:publisher>
	<dc:date>2025-08-10</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://online-journal.unja.ac.id/JIITUJ/article/view/46375</dc:identifier>
	<dc:identifier>10.22437/jiituj.v9i3.46375</dc:identifier>
	<dc:source xml:lang="en">Jurnal Ilmiah Ilmu Terapan Universitas Jambi; Vol. 9 No. 3 (2025): Volume 9, Nomor 3, September 2025; 1104-1116</dc:source>
	<dc:source>2580-2259</dc:source>
	<dc:source>2580-2240</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/JIITUJ/article/view/46375/21891</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Edi Surya Negara, Latius Hermawan, Hastari Mayrita, Desy Arisandy, Mohamad Farozi, Rahmat Ramadan, Sunda Ariana, Ria Andryani</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>