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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Vocatech : Vocational Education and Technology Journal JATI (Jurnal Mahasiswa Teknik Informatika) JTIK (Jurnal Teknik Informatika Kaputama) Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) TECHSI - Jurnal Teknik Informatika Jurnal Teknologi Informasi Heca Journal of Applied Sciences Infolitika Journal of Data Science
Suryadi Suryadi
Universitas Teuku Umar Fakultas Teknik Program Studi Teknologi Informasi
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Sistem Pendukung Keputusan Rating Mitra Pada Badan Pusat Statistik Kabupaten Aceh Barat Menggunakan Metode Simple Additive Weighting (SAW) Astrianda, Nica; Away, Asmaul Husna; Suryadi, Suryadi
VOCATECH: Vocational Education and Technology Journal Vol 6, No 1 (2024): October
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v6i1.188

Abstract

AbstractStatistics Indonesia (BPS) is a non-ministerial government responsible for ensuring the availability of data for use by the government and the public. The data collection process at BPS is conducted through various methods, including surveys, which require substantial manpower. Therefore, BPS employs partners for the data collection process. Partners are recruited through the Sobat BPS application; however, there is often no assessment of these partners within the application, making it difficult for BPS to select high-performing partners. This issue allows for the potential re-selection of irresponsible partners. This study successfully designed and implemented an effective decision support system for evaluating BPS partners using the Simple Additive Weighting (SAW) method. The evaluation results show that this system not only provides accurate and objective assessments but also improves the efficiency of the partner selection process. With 100% accuracy in testing, this system can be adopted as a new standard to ensure that selected partners perform well, thereby supporting BPS in carrying out its duties more effectively and responsibly in the future.AbstrakBadan Pusat Statistik (BPS) merupakan lembaga pemerintah nonkementerian yang bertugas menjamin ketersediaan data untuk digunakan oleh pemerintah dan masyarakat. Proses pengumpulan data di BPS dilakukan melalui berbagai metode, termasuk survei, yang membutuhkan tenaga lebih besar. Oleh karena itu, BPS menggunakan jasa mitra untuk proses pendataan. Mitra direkrut melalui aplikasi Sobat BPS, namun seringkali tidak ada penilaian terhadap mitra pada aplikasi tersebut, menyulitkan BPS dalam menyeleksi mitra berkinerja baik. Masalah ini menyebabkan mitra yang tidak bertanggung jawab dapat terpilih Kembali. Penelitian ini merancang "Sistem Pendukung Keputusan Rating Mitra Pada Badan Pusat Statistik Kabupaten Aceh Barat Menggunakan Metode Simple Additive Weighting (SAW)" untuk menyeleksi mitra berkinerja baik atau buruk agar tidak terulang pada pekerjaan selanjutnya. Hasil penelitian menunjukkan bahwa sistem ini menghasilkan keputusan yang akurat, cepat, objektif, dan dapat menjadi standar baru bagi BPS dalam menyeleksi mitra berkinerja baik. Pengujian perhitungan pada sistem sesuai dengan perhitungan manual menggunakan metode SAW menunjukkan akurasi sebesar 100%.
Leveraging Machine Learning for Sentiment Analysis in Hotel Applications: A Comparative Study of Support Vector Machine and Random Forest Algorithms Suryadi, Suryadi; Syahputra , Dedek; Astrianda, Nica; Syahputra, Rizki Agam; Suhendra, Rivansyah
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4877

Abstract

This research aims to conduct sentiment analysis on user reviews of hotel booking applications such as Trivago, Tiket, Booking, Traveloka, and Agoda, collected from the Google Play Store. The dataset used consists of 5,000 user reviews, with 80% of the data allocated for training and 20% for testing. Two algorithms applied in this study are Support Vector Machine (SVM) and Random Forest, with performance evaluation based on accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm delivers the best performance on the Trivago application with 94% accuracy, 94% precision, 100% recall, and a 97% F1-score. Random Forest proves to be more effective in handling diverse review data, while the Support Vector Machine (SVM) algorithm also produces good results in sentiment classification. This research contributes to the development of sentiment analysis based on user reviews, which can be utilized by app developers and hotel management to improve service quality and user experience.
Sentiment Analysis on Tabungan Perumahan Rakyat (TAPERA) Program by using Support Vector Machine (SVM) Syahputra, Rizki Agam; Arifin, Riski; ., Suryadi; Iqbal, Muhammad
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8694

Abstract

This study aims to analyze public sentiment towards the Housing Savings Program (TAPERA) using the Support Vector Machine (SVM) algorithm. The dataset comprises 16,061 reviews about TAPERA which was gathered from web scrapping and YouTube API. The sentiment analysis results indicate that 99.8% of the reviews are negative, while only 0.2% are positive. The SVM model applied in this study achieved a very high accuracy rate of 99.81%. This indicates that the model is highly effective in classifying sentiments, particularly in identifying negative sentiments. The resulting confusion matrix shows the model's excellent performance in detecting negative sentiments, with no False Positives (FP) and a very high number of True Negatives (TN). However, the model exhibits weaknesses in detecting positive sentiments, as indicated by the presence of several False Negatives (FN) and the absence of True Positives (TP). The findings of this study suggest that the public generally holds a very negative view of the TAPERA program. This insight is crucial for program administrators to consider as they evaluate and improve the program based on negative feedback received from the public. Overall, this research provides important insights into public perceptions of TAPERA and underscores the need for better modeling for more representative sentiment analysis. These findings can serve as a basis for policymakers in designing more effective communication strategies and program improvements to increase public acceptance of TAPERA.
Co-Authors Abdurrahman Ridho Andini, Mirna Ria Arie Budiansyah Arifin, Riski Away, Asmaul Husna Candra, Rudi Arif Chandra, Radja Aditya Earlia, Nanda Husdayanti, Noviana Ikhsan Ikhsan Ilham Juliwardi Ilham, Dirja Nur Juliawardi, Ilham Kasmawati Kasmawati Maulana, Aga Muhammad Ardiansyah Muhammad Iqbal Muhammad Subianto Mukhlizar, Mukhlizar Murhaban Murhaban Murhaban Nica Astrianda Niode, Nurdjannah Jane Nurul Helda Rinaldi Idroes Rizki Agam Syahputra Saifullah Saifullah Sanusi Sanusi Saputra Lubis, Doni Sasmita, Novi Reandy Shahrul Suhendra, Rivansyah Syahputra , Dedek Teuku Rizky Noviandy