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All Journal Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Media Teknik Elektro dan Komputer
Hasibuan, Nazwa Aliya Muthmainnah
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Sentimen Opini Publik tentang Kerusakan Jalan di Sumatera Utara Menggunakan Metode Naive Bayes Berbasis Pengawasan Lemah (Berbasis Leksikon) Dharmawan, Kaka Davi; Hasibuan, Nazwa Aliya Muthmainnah
Jurnal Media Teknik Elektro dan Komputer Vol 2 No 2 (2025): 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.v2i2.131

Abstract

Road infrastructure is a vital aspect of regional development that often receives public attention in online media, especially in North Sumatra. Manual monitoring of public opinion on this issue is inefficient due to the large volume of data and the imbalance of sentiment, which is dominated by complaints. This study aims to develop an automatic sentiment analysis model using a Weak Supervision approach that combines the Lexicon-Based method for automatic labelling and the Multinomial Naive Bayes algorithm to classify public opinion into three distinct categories: positive, negative, and neutral. Data was collected through web scraping techniques from various online news portals. To overcome data class imbalance, this study applied the Synthetic Minority Over-sampling Technique (SMOTE) to the training data. Test results on the test data showed that the model was able to achieve an accuracy of 70.93%. The model performed very well in detecting negative sentiment with a Precision value of 0.86, and was able to recognize positive sentiment with a Recall of 0.70 thanks to the application of SMOTE. Based on these results, the Naïve Bayes model can be used effectively to classify public sentiment towards road damage. In addition, these findings serve as strategic references and recommendations for stakeholders, such as the Inspectorate, to formulate relevant and data-driven policies in infrastructure improvement and regional development efforts.
IMPLEMENTASI METODE WATERFALL DALAM PENGEMBANGAN SISTEM INFORMASI REKRUTMEN ASISTEN LABORATORIUM ILMU KOMPUTER BERBASIS WEB Dharmawan, Kaka Davi; Fahlome, Dodyk; Arrahman, Said; Alya, Dea; Hasibuan, Nazwa Aliya Muthmainnah; Hasibuan, Naina Nazwa; wati, Mila; Harahap, Tiara Bela; Mahfuza, Salsabila; Zufria, Ilka
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 2 (2025): Desember 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i2.7183

Abstract

Perkembangan teknologi informasi mendorong institusi pendidikan untuk mengadopsi sistem administrasi yang lebih efisien dan terintegrasi. Proses rekrutmen asisten laboratorium di Program Studi Ilmu Komputer sebelumnya dilakukan secara manual melalui Google Form dan via WhatsApp, yang mengakibatkan data yang tidak teratur, sulitnya mengetahui status seleksi, serta keterlambatan rekapitulasi laporan akhir. Penelitian ini bertujuan merancang dan membangun sistem informasi rekrutmen asisten laboratorium berbasis web menggunakan metode Waterfall. Hasil implementasi menunjukkan bahwa sistem mampu meningkatkan efisiensi pengelolaan rekrutmen dari sisi admin sebesar 66,67%, dengan waktu rekapitulasi nilai berkurang dari rata-rata tiga hari menjadi satu hari, serta mengurangi beban kerja administratif secara signifikan melalui otomasi proses. Dari sisi pendaftar, efisiensi proses pendaftaran dan pelacakan status meningkat sekitar 50% dibanding metode sebelumnya, berkat integrasi fitur pendaftaran akun, unggah dokumen, dan pemantauan status secara real-time. Sistem ini terbukti mempercepat alur seleksi, meminimalkan risiko duplikasi data, serta meningkatkan transparansi informasi bagi semua pihak yang terlibat.
Co-Authors Alya, Dea Arrahman, Said Dharmawan, Kaka Davi Fahlome, Dodyk Harahap, Tiara Bela Hasibuan, Naina Nazwa Ilka Zufria Mahfuza, Salsabila Mila Wati, Mila