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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Martabe : Jurnal Pengabdian Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) Unes Law Review JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Tunas Jurnal Teknik Informatika (JUTIF) IJISTECH JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Sosial dan Teknologi Jurnal Jendela Pendidikan Jurnal Abdimas Bina Bangsa International Journal of Basic and Applied Science J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Penelitian Inovatif Jurnal Ilmu Komputer dan Informatika EXPLORER Prosiding Snastikom BEES: Bulletin of Electrical and Electronics Engineering Jurnal Ilmu Komputer dan Teknologi (IKOMTI) JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Teknik Mesin, Industri, Elektro dan Informatika Eduvest - Journal of Universal Studies Bulletin of Artificial Intelligence Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Widodo Saputra
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Journal : explorer

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Integrasi Strategi Pre-processing Data untuk Optimalisasi Akurasi Algoritma Backpropagation Widodo Saputra; Saifullah Saifullah; Eka Irawan; Anjar Wanto
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2743

Abstract

Backpropagation is one of the artificial neural network algorithms widely used in classification and prediction processes due to its ability to recognize data patterns accurately. However, the performance of this algorithm is highly influenced by the quality of the input data. Unstructured data, differences in data scales, missing values, and irrelevant features can reduce the model’s accuracy. This study aims to analyze the effect of integrating data pre-processing strategies to optimize the accuracy of the Backpropagation algorithm. The dataset used in this research was obtained from the Badan Pusat Statistik (BPS) in the form of Open Unemployment Rate data for the population aged 15 years and above in North Sumatra Province from 2019 to 2024. The applied pre-processing stages included data cleaning, normalization, missing value handling, and feature reduction. The research method was conducted by comparing the model testing results using standard pre-processing and partial pre-processing on several network architectures. The results showed that the implementation of pre-processing strategies was able to improve the performance of the Backpropagation model. The highest accuracy value was obtained in the 3-56-1 architecture with an increase from 80.00% to 85.88%. In addition to improving accuracy, the model training process became more stable and the error convergence was achieved faster. Therefore, the integration of data pre-processing strategies has proven to be effective in optimizing the accuracy of the Backpropagation algorithm for numerical data-based prediction problems
Optimasi Support Vector Machine Menggunakan Particle Swarm Optimization pada Analisis Sentimen Ulasan Shopee COD Eka Irawan; Wendi Robiansyah; Widodo saputra; Anjar Wanto
Explorer Vol 6 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i2.2744

Abstract

The Cash on Delivery (COD) service provided by the Shopee e-commerce platform often elicits a large volume of user reviews that exhibit unconventional language structures, prompting the need for a precise and automated sentiment analysis mechanism. This research endeavor seeks to categorize sentiments expressed in Shopee reviews as either positive or negative by leveraging the Support Vector Machine (SVM) algorithm, which has been fine-tuned using Particle Swarm Optimization (PSO). A key obstacle in text analysis lies in the vast feature space, which can impair model efficacy. Thus, PSO is utilized as a feature selection technique to identify the most pertinent set of terms from the TF-IDF feature extraction. The findings reveal that the integration of PSO successfully decreased feature dimensionality by 45% from the initial set of 1,000 features. Despite the substantial reduction in features, the SVM-PSO model achieved an enhanced accuracy of 81.21%, surpassing the baseline model's 78.79%. With an AUC value of 0.845, it is evident that the model retains stability and effectiveness in discerning sentiment even with a considerably reduced feature set. This investigation illustrates the efficacy of PSO optimization in eliminating extraneous features and refining the model's focus on sentiment-carrying vocabulary.
Co-Authors A'an Cun Abdi Rahim Damanik Abdi Rahim Damanik Agus Perdana Windarto Ahmad Yani Aldo Daniel Purba Almaida, Zulia Anggi Martua Valentino Sianipar Arif Ramadhan Siregar Azhar Fadilah Zuhri Bahrudi Efendi Damanik Bahrudi Efendi Damanik Bahrudi Efendi Damanik Berkat Iman Setia Dawolo Bintang, Daud Budi Paul Sitompul Damanik, Abdi Rahim Dedy Hartama Dedy Hartama Dedy Hartama Dedy Kristianto Lumbantobing Dewi, Rafiqa Dhea Halimah Eka Irawan Eka Irawan Fahmi Firzada Firzada, Fahmi Hardinata, Jaya T Hartama, Dedy Hendry Qurniawan Hendry Qurniawan Heru Satria Tambunan Heru Satria Tambunan Iin Parlina Iin Parlina Iin Parlina Ika Purnama Sari Indra Gunawan INDRA GUNAWAN Indra Gunawan Indra Gunawan Indra Gunawan Irfan Sudahri Damanik Jaya Tata Hardinata Khairwa Bakhsar Kusuma, Rizky Tri Lulu Apriliani Lumbantobing, Dedy Kristianto Luvita Yolanda Hutabarat M Safii M. Safii Madani, Aulya Fani Muhammad Ridwan Lubis Muhammad Ridwan Lubis Muhammad Ridwan Lubis Nasution, Della Fatricia Nasution, Zulaini Masruro Nur Hasanah Lubis Okprana, Harly Oktaviani, Selli Paul V M Poningsih Poningsih Poningsih, Poningsih Riahta Ulina Br. Barus Rika Nur Adiha Robertus Silalahi Robiansyah, Wendi Rut Indra Lita Sinaga Saifullah Saifullah Sibarani, Horainim Silaban, Herlan F Sitompul, Wati Rizky Pebrianti Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun, Solikhun Sri Muliani Damanik Sri Nuraini Sri Rahayu Ningsih Sumarno Sumarno . Sumarno Sumarno Sumarno Sumarno Sundari Retno Andani Sundari Retno Andani Surya Darma Surya Darma Veithzal Rivai Zainal Wanto, Anjar Wendi Robiansyah Yovan Bastian Zulaini Masruro Nasution Zulia Almaida Siregar