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Exploring Data Analytics in Attendance Systems: Unveiling Machine Learning Techniques, Patterns, Practices, and Emerging Trends

Scientific Journal of Informatics · Vol. 11 No. 2 (2024) · hlm. 325–340

DOI
10.15294/sji.v11i2.3438
ISSN
2407-7658, 2460-0040
Terbit
2024-05-16
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Purpose: The research aims to identify patterns and trends in attendance management through the application of reward and punishment systems as innovative solutions for improving employee attendance and well-being. Methods: This research utilizes a descriptive analysis approach with the application of Machine Learning (ML) techniques to enhance the accuracy of attendance pattern prediction and ML models for the classification of emerging trends and patterns. Research data were obtained through the company's attendance system and divided into two segments (80% for training and 20% for testing) while maintaining a balanced class proportion, then processed using SPSS and Python software with the Scikit-learn library. Result: The results of the study show that employee attendance is increased from 86.52% to 90.44% when the reward and punishment method is applied to the employee attendance system. Proper reward allocation can increase employee motivation to adhere to work schedules and consistently attend, while punishment tends to lead to lower attendance rates. Novelty: This research emphasizes the optimization of attendance management through data analytics approaches and the implementation of advanced technology in attendance systems with the application of ML techniques to analyze attendance data comprehensively and detect significant patterns.

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40 penulis · 158 tautan kepenulisan bersama · 73 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Satya Wacana Christian University
  • Universitas Raharja
  • Universitas Buana Perjuangan Karawang
  • Atma Jaya Catholic University of Indonesia
  • Universitas Buddhi Dharma
  • Universitas Sains dan Teknologi Komputer
  • State University of Semarang
  • 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
Irwan SembiringSatya Wacana Christian University16
Untung RahardjaUniversitas Raharja10
Qurotul AiniUniversitas Raharja8
Kristoko Dwi HartomoSatya Wacana Christian University5
Hendry HendrySatya Wacana Christian University4
Ninda LutfianiUniversitas Raharja4
Sutarto WijonoSatya Wacana Christian University4
Ade IrianiSatya Wacana Christian University3
Nuke Puji Lestari SantosoUniversitas Raharja2
Adi SetiawanSatya Wacana Christian University2
Ivanna K. TimotiusSatya Wacana Christian University2
April Firman DaruState University of Semarang2
Po Abas SunaryaUniversitas Raharja2
Yerik Afrianto SinggalenAtma Jaya Catholic University of Indonesia2
Eko SediyonoSatya Wacana Christian University2
Iwan SetiawanInstitut Kemandirian Nusantara2
Ahmad FauziSTMIK IKMI Cirebon2
Baenil HudaSatya Wacana Christian University2
April Lia HanantoUniversitas Buana Perjuangan Karawang2
Tukino TukinoUniversitas Buana Perjuangan Karawang2
Krismiyati KrismiyatiSatya Wacana Christian University2
Theophilus WellemSatya Wacana Christian University2
Mars Caroline WibowoUniversitas Sains dan Teknologi Komputer2
HenderiAfiliasi tidak diketahui2
Sri Yulianto Joko PrasetyoSatya Wacana Christian University1
Sulistio SulistioUniversitas Raharja1
Andreas Ardian FebriantoSatya Wacana Christian University1
Regina LionnieMercu Buana University1
Septian AbednegoSatya Wacana Christian University1
Gunawan DewantoroSatya Wacana Christian University1
Eric Megah PerdanaSatya Wacana Christian University1
Eko Nur HermansyahSatya Wacana Christian University1
Merryana LestariSatya Wacana Christian University1
Fian Yulio SantosoSatya Wacana Christian University1
Benny DaniawanUniversitas Buddhi Dharma1

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://journal.unnes.ac.id/journals/sji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Exploring Data Analytics in Attendance Systems: Unveiling Machine Learning Techniques, Patterns, Practices, and Emerging Trends</dc:title>
	<dc:creator xml:lang="en">Santoso, Joseph Teguh</dc:creator>
	<dc:creator xml:lang="en">Manongga, Danny</dc:creator>
	<dc:creator xml:lang="en">Setyawan, Iwan</dc:creator>
	<dc:creator xml:lang="en">Purnomo, Hindriyanto Dwi </dc:creator>
	<dc:creator xml:lang="en">Hendry</dc:creator>
	<dc:subject xml:lang="en">Attendance management</dc:subject>
	<dc:subject xml:lang="en">Reward and punishment</dc:subject>
	<dc:subject xml:lang="en">Data analytics</dc:subject>
	<dc:subject xml:lang="en">Implementation of technology in attendance systems</dc:subject>
	<dc:description xml:lang="en">Purpose: The research aims to identify patterns and trends in attendance management through the application of reward and punishment systems as innovative solutions for improving employee attendance and well-being.
Methods: This research utilizes a descriptive analysis approach with the application of Machine Learning (ML) techniques to enhance the accuracy of attendance pattern prediction and ML models for the classification of emerging trends and patterns. Research data were obtained through the company's attendance system and divided into two segments (80% for training and 20% for testing) while maintaining a balanced class proportion, then processed using SPSS and Python software with the Scikit-learn library.
Result: The results of the study show that employee attendance is increased from 86.52% to 90.44% when the reward and punishment method is applied to the employee attendance system. Proper reward allocation can increase employee motivation to adhere to work schedules and consistently attend, while punishment tends to lead to lower attendance rates.
Novelty: This research emphasizes the optimization of attendance management through data analytics approaches and the implementation of advanced technology in attendance systems with the application of ML techniques to analyze attendance data comprehensively and detect significant patterns.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2024-05-16</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/3438</dc:identifier>
	<dc:identifier>10.15294/sji.v11i2.3438</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 11 No. 2: Regular Issue; 325-340</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/3438/369</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>