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All Journal Fundamental Management Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Komputer AMIK BSI Moneter : Jurnal Akuntansi dan Keuangan JIKO (Jurnal Informatika dan Komputer) Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Information Management For Educators And Professionals (IMBI) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Informasi Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen fundamental management journal Journal of Informatics and Information Security
Indah Ariyati
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : JIKO (Jurnal Informatika dan Komputer)

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IMPLEMENTASI PARTICLE SWARM OPTIMIZATION UNTUK OPTIMALISASI DATA MINING DALAM EVALUASI KINERJA ASISTEN DOSEN Indah Ariyati; . Ridwansyah; . Suhardjono
JURNAL INFORMATIKA DAN KOMPUTER Vol 3, No 2 (2018): SEPTEMBER - JANUARI 2019
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.082 KB) | DOI: 10.26798/jiko.v3i2.127

Abstract

The existing complaints on the performance of assistant lecturers show the impact of the absence of better competence, so that an accurate evaluation process on the performance of lecturer assistants based on their duties and obligations in a certain period of time. The evaluation process required an improved model of accuracy which was a formidable challenge in the selection of more efficiency and effectiveness features, in which case we proposed a method of particle swarm optimization to improve the accuracy of neural network methods that experienced problems in the selection of features that were weighted in detailed analysis by particle swarm optimization with neural network learning performance. This study aims to find a complex alternative solution in the evaluation of lecturer's assistant where research is based on parameters obtained from UCI Machine Repository. The final research shows that particle swarm optimization method can in-crease the accuracy of 75.56% from the previous value of 51.75% and increase the kappa value of 0,632 from the previous kappa value 0,276. The result of developing particle swarm optimization toward neural network by increasing the accuracy and kappa value can be used as controlling periodically in evaluating the performance of assistant lecturer.
Co-Authors Azidah, Nanda Ainnoor Daniel , Daniel Daniel Daniel Dema Istiana Endang Retnoningsih Faizah, Siti Farid Haqqon, Muhammad Fitri Wulandari Herlambang Brawijaya Hikmah, Noer Ibnu Satria Fajar Al Afif Imam Nawawi Lala Nilawati M.L.Dian Ela Revita Mahfud, Nuzul Maria Lapriska Dian Ela Revita Nanda Ainnoor Azidah Nandang Iriadi Noer Hikmah Nuraeni Herlinawati Nurcahyani, Devi Priyono Rahayu Swastika Rahmayani, Fanny Rana Fauziyyah Reza Sari, Octaviani Ridwansyah Ridwansyah Ridwansyah Ridwansyah Riestiawan Riestiawan, Riestiawan Rudianto, Biktra Salsabila, Almeyda Sartini Sartini Siti Faizah Sugiono Sugiono Sugiono, Sugiono Suhardjono S Suryadithia, Rachmat Swastika , Rahayu Tyas, Anindya Ikaning Vigi Ayu Pradipta Wina Widiati WITRIANA ENDAH PANGESTI Yuni Eka Achyani, Yuni Eka