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All Journal ComEngApp : Computer Engineering and Applications Journal Fountain of Informatics Journal Format : Jurnal Imiah Teknik Informatika Jurnal Informatika Jurnal Ilmiah FIFO InComTech: Jurnal Telekomunikasi dan Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) IKRA-ITH ABDIMAS JURIKOM (Jurnal Riset Komputer) MULTINETICS Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Jurnal Ilmu Teknik dan Komputer International Journal of Advanced Multidisciplinary IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Pengabdian Pada Masyarakat Kapas: Kumpulan Artikel Pengabdian Masyarakat IRA Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Nasional Jurnal Pengabdian Masyarakat Nauli CSRID
Dwiasnati, Saruni
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Journal : ComEngApp : Computer Engineering and Applications Journal

 1 
Application of Machine Learning in Clustering Maize Producing Regions in Indonesia Eliyani, Eliyani; Dwiasnati, Saruni; Arif, Sutan Mohammad; Avrizal, Reza; Fatimah, Nona
Computer Engineering and Applications Journal Vol 13 No 2 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i2.455

Abstract

Maize is considered an important commodity with promising market prospects. Given the importance of maize, there is a need to increase maize production to meet people's needs and maintain price stability. This study aims to group maize production in Indonesia by region, with the hope of finding areas that have the potential to become maize production centers to reduce dependence on imports. The data used in this research was obtained from the Central Statistics Agency, covering information from 34 provinces during the 2017-2021 period. This analysis uses the K-Means method with the Python programming language. The number of groups is determined using the Elbow Method. The results of this research show that there are three categories of maize production regions: regions with low maize production (below average), regions with medium maize production, and regions with high maize production. A total of 25 provinces are in the low production category, eight provinces are in the medium category, and only East Java is in the high production category.
Application of Machine Learning in Clustering Maize Producing Regions in Indonesia Eliyani; Dwiasnati, Saruni; Arif , Sutan Mohammad; Avrizal, Reza; Fatimah, Nona
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 2 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Maize is considered an important commodity with promising market prospects. Given the importance of maize, there is a need to increase maize production to meet people's needs and maintain price stability. This study aims to group maize production in Indonesia by region, with the hope of finding areas that have the potential to become maize production centers to reduce dependence on imports. The data used in this research was obtained from the Central Statistics Agency, covering information from 34 provinces during the 2017-2021 period. This analysis uses the K-Means method with the Python programming language. The number of groups is determined using the Elbow Method. The results of this research show that there are three categories of maize production regions: regions with low maize production (below average), regions with medium maize production, and regions with high maize production. A total of 25 provinces are in the low production category, eight provinces are in the medium category, and only East Java is in the high production category.
Comparison of Naive Bayes and Support Vector Machine (SVM) Algorithms Regarding The Popularity of Presidential Candidates In The Upcoming 2024 Presidential Election Nurrizky, Fadli; Dwiasnati, Saruni
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 1 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to compare the effectiveness of two classification algorithms, Naive Bayes and Support Vector Machine (SVM), in analyzing the popularity of presidential candidates for the 2024 Presidential Election (Pilpres). The popularity of presidential candidates plays a crucial role in campaign strategies and political decision-making in the modern political era. This research utilizes data from social media, encompassing public sentiment towards presidential candidates and related political issues. The research results indicate that SVM achieves an accuracy rate of 97%, while Naive Bayes achieves 95%, demonstrating the superiority of SVM in predicting the popularity of presidential candidates. In conclusion, the selection of the appropriate algorithm for analyzing complex political data has a significant impact, and the high accuracy rates of both algorithms provide valuable guidance for political decisionmakers and campaign teams in preparation for the upcoming 2024 Pilpres.
Co-Authors Abdillah, Reza Wahyu Akmal, Medina Fibriyanti Alfa, Bonitasari Nurul Arif , Sutan Mohammad ARIF, SUTAN MOHAMMAD Avrizal, Reza Ayu, Kurnia Gusti Aziz, Topaz Malik Baihaqi, Kiki Ahmad Bernadetha, Suzan Da Costa Martins, Ana Maria Ildiberta Darmawan, Bagus Budi Diah Utami, Diah Eliyani Eliyani Eliyani Fatimah, Nona Fauzi Ahmad Muda Halim, Alfian Noer Hidayat, Rahmat Rian Imawan, Sonny Aditya Muhammad Rifqi muhammad rizky, muhammad Nicholas Reinaldo, Ryo Nur, Nur Umar Azaliy Awal Nurfiant, Muhammad Arya Hafsah Nurrizky, Fadli Pertiwi, Anggun Popy Yuliarty, Popy Prihandani, Kamal Prihandi, Ifan Qitala, Shofia Rudiarto, Sabar Saputra, Dony Ramadhan Sumarto, Marco Alfan Triwibowo, Ardy Wawan Gunawan Wijayanti, Atiek Ike Yudo Devianto