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Color Complexity and Sharpness for Instagram Engagement Classification in University Accounts

Proceedings Academic Universitas Jambi · Vol. 2 No. 2 (2026) · hlm. 279–290

DOI
10.22437/proca.v2i2.55582
Terbit
2026-07-15
Penerbit
LPPM Universitas Jambi
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Understanding the visual factors that drive engagement on social media platforms is essential for effective content strategy. This study investigates the classification of Instagram post engagement using interpretable visual and temporal features extracted from university social media content. We analyzed 638 Instagram posts from two university accounts (Faculty of Science and Technology and Student Executive Board of Health Sciences), extracting six visual features (colorfulness, color complexity, dominant hue, contrast, symmetry, and sharpness) and three temporal features (posting hour, day of week, and weekend indicator). A binary classification approach was employed to categorize posts into low and high engagement classes based on median likes. Four machine learning classifiers were evaluated using stratified 5-fold cross-validation. The Random Forest classifier achieved the highest performance with an F1-score of 0.714, accuracy of 69.9 percent, and AUC-ROC of 0.775. Feature importance analysis revealed that color complexity contributed 22.4 percent to the classification decision, followed by sharpness at 21.9 percent and contrast at 16.4 percent. These findings suggest that visually complex images with higher color diversity and sharp details tend to generate greater engagement on university Instagram accounts. The interpretable nature of the extracted features provides actionable insights for social media content creators in academic institutions.

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  • Universitas Jambi
  • National Research and Innovation Agency
  • State University of Semarang
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Reni AryaniAfiliasi tidak diketahui3
Mauladi MauladiAfiliasi tidak diketahui2
Nindy Raisa HanumAfiliasi tidak diketahui2
Yogi PerdanaAfiliasi tidak diketahui2
Fitra WahyuniAfiliasi tidak diketahui2
Rahmad AsharAfiliasi tidak diketahui2
Maryetta HanaAfiliasi tidak diketahui1
Minata Putra MahaartaAfiliasi tidak diketahui1
Muhammad Reno FadillahAfiliasi tidak diketahui1
Johanda Putra HendraAfiliasi tidak diketahui1
Dewi LestariNational Research and Innovation Agency1
Mohammad IlhamiAfiliasi tidak diketahui1
Yolla NoverinaAfiliasi tidak diketahui1
Hasby KuswantoAfiliasi tidak diketahui1
Lina Kartika ParamitaAfiliasi tidak diketahui1
Jodion SiburianAfiliasi tidak diketahui1
Ayudita ArdilaAfiliasi tidak diketahui1
Jodion SiburianUniversitas Jambi1
Jefri MarzalUniversitas Jambi1
Ayudita ArdilaUniversitas Jambi1
Gunawan GunawanState University of Semarang1
Maison MaisonAfiliasi 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/PROCA/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Color Complexity and Sharpness for Instagram Engagement Classification in University Accounts</dc:title>
	<dc:creator xml:lang="en">Waladi, Akhiyar</dc:creator>
	<dc:creator xml:lang="en">Marzal, Jefri</dc:creator>
	<dc:creator xml:lang="en">A, Muhammad Razi</dc:creator>
	<dc:creator xml:lang="en">Yudistira, Miranty</dc:creator>
	<dc:creator xml:lang="en">Iftitah, Hasanatul</dc:creator>
	<dc:creator xml:lang="en">Alghifari, Hamzah</dc:creator>
	<dc:subject xml:lang="en">Instagram engagement</dc:subject>
	<dc:subject xml:lang="en">visual features</dc:subject>
	<dc:subject xml:lang="en">temporal features</dc:subject>
	<dc:subject xml:lang="en">machine learning classification</dc:subject>
	<dc:subject xml:lang="en">social media analytics</dc:subject>
	<dc:subject xml:lang="en">color complexity</dc:subject>
	<dc:description xml:lang="en">Understanding the visual factors that drive engagement on social media platforms is essential for effective content strategy. This study investigates the classification of Instagram post engagement using interpretable visual and temporal features extracted from university social media content. We analyzed 638 Instagram posts from two university accounts (Faculty of Science and Technology and Student Executive Board of Health Sciences), extracting six visual features (colorfulness, color complexity, dominant hue, contrast, symmetry, and sharpness) and three temporal features (posting hour, day of week, and weekend indicator). A binary classification approach was employed to categorize posts into low and high engagement classes based on median likes. Four machine learning classifiers were evaluated using stratified 5-fold cross-validation. The Random Forest classifier achieved the highest performance with an F1-score of 0.714, accuracy of 69.9 percent, and AUC-ROC of 0.775. Feature importance analysis revealed that color complexity contributed 22.4 percent to the classification decision, followed by sharpness at 21.9 percent and contrast at 16.4 percent. These findings suggest that visually complex images with higher color diversity and sharp details tend to generate greater engagement on university Instagram accounts. The interpretable nature of the extracted features provides actionable insights for social media content creators in academic institutions.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Jambi</dc:publisher>
	<dc:date>2026-07-15</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/PROCA/article/view/55582</dc:identifier>
	<dc:identifier>10.22437/proca.v2i2.55582</dc:identifier>
	<dc:source xml:lang="en">Proceedings Academic Universitas Jambi; Vol. 2 No. 2 (2026): The 3rd Jambi International Conference On Engineering, Science and Technology (JICEST); 279-290</dc:source>
	<dc:source>10.22437/proca.v2i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/PROCA/article/view/55582/25006</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 PROCA UNJA</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>