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Pengenalan Emosi Berdasarkan Suara Menggunakan Algoritma HMM

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 4 No. 3 (2017) · pp. 168–172

DOI
10.25126/jtiik.201743339
ISSN
2528-6579, 2355-7699
Published
2017-08-19
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
en

Abstract

AbstrakPenelitian ini bertujuan mengenali emosi seseorang melalui ucapan menggunakan algoritma HMM. Sistem dibangun dapat mengenali 3 jenis emosi yaitu marah, bahagia dan netral. Fitur yang digunakan dalam sistem ini adalah pitch, energi dan formant. Database yang digunakan adalah suara dari rekaman film. Dari hasil obeservasi probabilitas emosi marah sebesar 0.196, bahagia 0.254 dan netral 0.045. Sistem memiliki tingkat akurasi rata-rata sebesar 86.66%. Rata waktu eksekusi sistem dalam mendeteksi dan mengklasifikasikan emosi sebesar 21.6ms.Kata kunci: suara, emosi, HMM, klasifikasiAbstractThis research aims to recognize human emotions through voice using HMM algorithm. The system can confirm three types of emotions: anger, happiness and neutrality. The features used in this system are pitch, energy and formant. From the results, the emotional probability of angry is 0.196, happy is 0.254 and neutral is 0.045. Base on testing result, the system has an average accuracy of 86.66% and average execution time of the system in detecting and classifying emotions of 21.6ms.Keywords: voice, emotion, HMM, classification

Collaboration network of the authors

36 authors · 80 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-authorOrganisationJoint articles
Dahnial SyauqyUniversity of Brawijaya4
Sabriansyah Rizqika AkbarUniversity of Brawijaya4
Edita Rosana WidasariUniversity of Brawijaya3
Eko SetiawanUniversity of Brawijaya3
Rakhmadhany PrimanandaUniversity of Brawijaya2
Muhammad Fawwaz Dynoeputra IsnandarUniversity of Brawijaya2
Bayu PriyambadhaUniversity of Brawijaya1
Barlian Henryranu P.Affiliation unknown1
Heru NurwarsitoUniversity of Brawijaya1
Denny Sagita RusdiantoUniversity of Brawijaya1
Gembong Edhi SetyawanUniversity of Brawijaya1
Eko Agus SetiawanUniversity of Brawijaya1
Mochamad Afief HamidiUniversity of Brawijaya1
Kasyful AmronUniversity of Brawijaya1
Rizky Kurnia A.M.Affiliation unknown1
Agung Setia BudiUniversity of Brawijaya1
Adhitya BhawiyugaUniversity of Brawijaya1
Muhammad Hanif Azhar EfendiUniversity of Brawijaya1
Lindhu Parang KusumaUniversity of Brawijaya1
Eko SetiawanUniversity of Brawijaya1
Angga Sukma BahariUniversity of Brawijaya1
Mahardeka Tri AnantaUniversity of Brawijaya1
Ryzaldi Ananda FabianaUniversity of Brawijaya1
Syahrul ChilmiUniversity of Brawijaya1
Septiyo Budi PerkasaUniversity of Brawijaya1
Rizal MaulanaUniversity of Brawijaya1
Fakhrul AllaamUniversity of Brawijaya1
Rekyan Regasari Mardi PutriUniversity of Brawijaya1
Irfan Muzakky NurrizqyUniversity of Brawijaya1
Arsal Wildan NaviaddinUniversity of Brawijaya1
JevandikaUniversity of Brawijaya1
Brylliano Maza PutraUniversity of Brawijaya1
Syifa’ Hukma ShabiyyaAffiliation unknown1

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.

Metadata source: OAI-PMH (metadata jurnal) https://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Pengenalan Emosi Berdasarkan Suara Menggunakan Algoritma HMM</dc:title>
	<dc:creator xml:lang="en">Prasetio, Barlian Henryranu</dc:creator>
	<dc:creator xml:lang="en">Kurniawan, Wijaya</dc:creator>
	<dc:creator xml:lang="en">Ichsan, Mochammad Hannats Hanafi</dc:creator>
	<dc:description xml:lang="id">AbstrakPenelitian ini bertujuan mengenali emosi seseorang melalui ucapan menggunakan algoritma HMM. Sistem dibangun dapat mengenali 3 jenis emosi yaitu marah, bahagia dan  netral. Fitur yang digunakan dalam sistem ini adalah pitch, energi dan formant. Database yang digunakan adalah suara dari rekaman film. Dari hasil obeservasi probabilitas emosi marah sebesar 0.196, bahagia 0.254 dan netral 0.045. Sistem memiliki tingkat akurasi rata-rata sebesar 86.66%. Rata waktu eksekusi sistem dalam mendeteksi dan mengklasifikasikan emosi sebesar 21.6ms.Kata kunci: suara, emosi, HMM, klasifikasiAbstractThis research aims to recognize human emotions through voice using HMM algorithm. The system can confirm three types of emotions: anger, happiness and neutrality. The features used in this system are pitch, energy and formant. From the results, the emotional probability of angry is 0.196, happy is 0.254 and neutral is 0.045. Base on testing result, the system has an average accuracy of 86.66% and average execution time of the system in detecting and classifying emotions of 21.6ms.Keywords: voice, emotion, HMM, classification</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2017-08-19</dc:date>
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	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/339</dc:identifier>
	<dc:identifier>10.25126/jtiik.201743339</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 4 No 3: September 2017; 168-172</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 4 No. 3: September 2017; 168-172</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.201743</dc:source>
	<dc:language>en</dc:language>
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	<dc:rights xml:lang="en">Copyright (c) 2017 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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