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Sistem Informasi Pengelolaan Daftar Tamu di Wilayah Kerja Badan Pusat Statistik Mesanda, Zery; Taufik Al Afkari Siahaan, Ahmad
Journal of Computer Science and Informatics Engineering Vol 3 No 3 (2024): Juli
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v3i3.725

Sistem Informasi Pengelolaan Daftar Tamu di wilayah kerja Badan Pusat Statistik telah jadi pusat utama rencana kerja praktek ini. Dengan berdasarkan atas keinginan bagi memajukan efisiensi operasional dan memaksimalkan layanan kepada pengunjung, peojek ini bertujuan untuk merancang dan menerapkan sistem yang terintegral bagi pengelolaan daftar tamu di lingkungan BPS. Temuan penting membuktikan bahwa menyatu Sistem Informasi Pengelolaan Daftar Tamu yang terintegrasi dapat menaikkan efisiensi operasional, meningkatkan keamanan data, dan menguatkan ikatan antara BPS atas berbagai bagian yang berkunjung. Projek ini dibuat berdasarkan ketidak kondusipan terhadap tamu yang bertamu dan meluaskan hubungan yang lebih baik atas pengunjung dari beragam kalangan. Dengan demikian, projek ini tidak hanya membagikan pandangan luas tentang pentingnya sistem informasi yang efektif dalam mengelola daftar tamu di BPS, tapi juga memberi rekomendasi tertentu untuk meluruskan sistem yang ada fungsi meningkatkan kemampuan lembaga dan interaksi dengan pengunjung. Kata Kunci : Sistem Informasi, Daftar Tamu, Badan Pusat Statistik

Perbandingan Support Vector Machine dan Naïve Bayes Terkait Kepuasan Pengguna Bus Listrik Kota Medan Mesanda, Zery; Ikhsan, Muhammad
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5556

The city government has introduced an initiative to use electric buses as a cleaner and more sustainable alternative. The success of a public transportation system is not only determined by the availability of fleet and infrastructure, but also by the level of user satisfaction. User satisfaction is an important indicator that reflects the extent to which the service meets users' expectations and needs. Therefore, an in-depth understanding of the factors that influence user satisfaction, as well as the ability to predict and manage them, is key in improving the quality of public transportation services, including usage. In an effort to understand and improve trolleybus user satisfaction in Medan City, an adequate predictive analysis approach is required. By using methods such as Support Vector Machine (SVM) and Naïve Bayes, we can develop predictive models that can identify patterns and trends in user data, thus enabling relevant parties to take appropriate actions to improve services. In this context, the comparison between SVM and Naïve Bayes methods will provide valuable insights into the effectiveness of each method in predicting the satisfaction of electric bus users in Medan City. Based on the comparison results, the Naive Bayes algorithm shows slightly better performance compared to the Support Vector Machine in this sentiment analysis. The accuracy value generated by applying the Naive Bayes method is 58%, while applying the Support Vector Machine method is 57%. Nonetheless, both algorithms provide valuable insights into the sentiment of Medan people towards Electric Buses.

Sistem Informasi Pengelolaan Daftar Tamu di Wilayah Kerja Badan Pusat Statistik Mesanda, Zery; Siahaan , Ahmad Taufik Al Afkari
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 3 No. 3 (2023): November: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v3i3.2390

Sistem Informasi Pengelolaan Daftar Tamu di wilayah kerja Badan Pusat Statistik telah jadi pusat utama rencana kerja praktek ini. Dengan berdasarkan atas keinginan bagi memajukan efisiensi operasional dan memaksimalkan layanan kepada pengunjung, peojek ini bertujuan untuk merancang dan menerapkan sistem yang terintegral bagi pengelolaan daftar tamu di lingkungan BPS. Temuan penting membuktikan bahwa menyatu Sistem Informasi Pengelolaan Daftar Tamu yang terintegrasi dapat menaikkan efisiensi operasional, meningkatkan keamanan data, dan menguatkan ikatan antara BPS atas berbagai bagian yang berkunjung. Projek ini dibuat berdasarkan ketidak kondusipan terhadap tamu yang bertamu dan meluaskan hubungan yang lebih baik atas pengunjung dari beragam kalangan. Dengan demikian, projek ini tidak hanya membagikan pandangan luas tentang pentingnya sistem informasi yang efektif dalam mengelola daftar tamu di BPS, tapi juga memberi rekomendasi tertentu untuk meluruskan sistem yang ada fungsi meningkatkan kemampuan lembaga dan interaksi dengan pengunjung.

Analisis Sentimen Komentar Intagram Pemindahan Ibu Kota Negara Membandingkan Alogritma Support Vecthor Meachine dan Random Forest Mesanda, Zery; Sitompul, Boy Arnol
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 1 (2025): Metrokom : Jurnal Media Teknik Elektro dan Komputer
Publisher : Yayasan Pendidikan Al-Yasiriyah Bersaudara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65371/metrokom.v2i1.59

Social media sentiment analysis has become an important approach in understanding public opinion on strategic issues, including the discourse on the relocation of the national capital. This study aims to compare the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms in classifying the sentiment of public comments on Instagram. A total of 794 comment data were collected using web scraping techniques with Selenium and BeautifulSoup, then divided into 80% training data and 20% test data. The classification process was conducted after the text preprocessing stage, which included case folding, tokenizing, filtering, and stemming. The experimental results show that SVM achieved an accuracy of 75.0% with precision 0.7200, recall 0.7800, and F1-score 0.7488. Meanwhile, Random Forest performed better with an accuracy of 79.4%, precision of 0.7795, recall of 0.8200, and F1-score of 0.7992. Evaluation based on sentiment class shows that SVM can only achieve a correct rate of 75.0% in the positive class and 75.1% in the negative class, while Random Forest excels with 79.4% in the positive class and 79.3% in the negative class. These findings confirm that Random Forest is more optimal and consistent than SVM in sentiment analysis based on social media comments. This study recommends the use of ensemble learning algorithms such as Random Forest in similar studies, as well as further development with larger datasets and deep learning approaches to improve model accuracy and generalization.

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