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All Journal International Journal of Electrical and Computer Engineering Teknika Jupiter Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal Kompetitif Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) Jurnal Teknologi Informasi dan Bisnis Pengabdian Masyarakat Darmajaya Seminar Nasional Informatika (SEMNASIF) International Journal of Artificial Intelligence Research Prosiding Seminar Nasional Darmajaya Jurnal Pengabdian kepada Masyarakat Jurnal Kedaymas Jurnal Publika Pengabdian Masyarakat Jurnal ABDI MERCUSUAR
Yulmaini Yulmaini
Institut Informatika Dan Bisnis Darmajaya
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Implementation of the C4.5 algorithm for micro, small, and medium enterprises classification Sri Lestari; Yulmaini Yulmaini; Aswin Aswin; Sylvia Sylvia; Yan Aditiya Pratama; Sulyono Sulyono
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6707-6715

Abstract

The coronavirus disease-19 (COVID-19) pandemic has spread to various countries including Indonesia. Thus, implementing large-scale social restrictions (Bahasa: Pembatasan Sosial Berskala Besar (PSBB)) has resulted in the paralysis of the economy in Indonesia. including micro, small, and medium enterprises (MSMEs) have decreased turnover and even went out of business. The Department of Cooperatives and Small and Medium Enterprises (SMEs) in Pesawaran Regency, Lampung, oversees 3,808 MSMEs, whose development should be monitored as a basis for determining policies. However, there are problems in classifying MSMEs according to their categories because they have to check the existing data one by one, so it takes a long time. Therefore, this study proposed the C4.5 algorithm to solve this problem. In addition, this research compared with the naïve Bayes algorithm to find out which algorithm had a good performance and is suitable for this case. The results showed that 91% of MSMEs were included in the micro category, 8% was in a small category, and 1% was in the medium category. Based on the results, it explained that the C4.5 algorithm was bigger than naïve Bayes with a difference in the value of 3.79%. It had an accuracy value of 99.2%. Meanwhile, naive Bayes was 95.41%.
Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques Lestari, Sri; Yulmaini, Yulmaini; Aswin, Aswin; Ma'ruf, Singgih Yulizar; Sulyono, Sulyono; Fikri, Ruki Rizal Nul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3220-3229

Abstract

Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.
Co-Authors - - Suhada, - - -, Murhadi Ade Moussadecq Afnuhazi, Ridhyalla Andayani, Rifka Putri Andi Desfiandi Anuar Sanusi Anuar Sanusi Anuar Sanusi Apri Triansyah Ardi Dwi Saputra Aswin Aswin Aswin Aswin Aswin Aswin Aziz, RZ. Abdul Deni Dwi N.S Deni Dwi N.S Devi Kathina Rani Devi Kathina Rani, Devi Kathina Eka Yuliana Eka Yuliana Eka Yusendra, M. Ariza Elida Purba Elida Purba Fikri, Ruki Rizal Nul Fitri Agustina Fitria - Fitria - Herlina Herlina Herlina Herlina Indriyati, Lia Lia Indriyati M. Ariza Eka Yusendra M. Ariza Eka Yusendra M. Ariza Eka Yusendra, M. Ariza Eka M.Ariza Eka Yusendra Ma'ruf, Singgih Yulizar Mieke Rahayu Muhammad Redintan Justin Murhadi - Mutiara, Suci Mutmainah, Nina Netti Septina Nina Mutmainah Permatasari, Yoan Desi Pratama, Teddy Renjani, Asri Bunga Rio Kurniawan Ruki Rizal Nul Fikri Siti Kholijah Sri Karnila Sri Lestari SRI LESTARI Suci Mutiara Suci Mutiara Sulyono Sulyono, Sulyono Sylvia Sylvia Teddy Pratama Yan Aditiya Pratama Yusendra, M.Ariza Eka Zainal Ansori