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All Journal Jurnal Sains & Teknologi Fakultas Teknik Universitas Darma Persada
Afifa, Linda Nur
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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ANALISIS SENTIMEN TANGGAPAN PELANGGAN INDIHOME DI PLATFORM SOSIAL MEDIA FACEBOOK DAN TWITTER MENGGUNAKAN SUPPORT VECTOR MESIN DAN PENDEKATAN KLASIFIKASI NAÏVE BAYES (STUDI KASUS: PT. TELKOM INDONESIA) Norman, Suzuki Syofian; Kusuma, Dhino Rahmad; Afifa, Linda Nur
Jurnal Sains & Teknologi Fakultas Teknik Universitas Darma Persada Vol. 13 No. 1 (2023): Jurnal Sains & Teknologi
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70746/jstunsada.v13i1.221

Abstract

In the digital era, the internet has become an inseparable part of everyday life, including the ease of finding information and sharing opinions through social media such as Twitter and Facebook. On these two platforms, users can provide reviews about products, including IndiHome services. The large number of reviews on social media reflects the high level of feelings users have for the service. However, currently PT. Telkom Indonesia does not fully know the opinions and reviews of IndiHome customers on social media, both positive and negative. This study aims to improve understanding of the positive and negative opinions of customers towards IndiHome and to compare the effectiveness of the Support Vector Machine and Naive Bayes algorithms in sentiment analysis. Thus, PT. Telkom Indonesia can take the necessary steps to increase public trust in IndiHome and evaluate the performance of the classification results using the Support Vector Machine and Naïve Bayes methods. The data used in this study amounted to 5000 data, but after the data preparation stage, the remaining 2000 data. From the data that has gone through the preparation stage, there are 638 data with positive sentiment and 1341 data with negative sentiment. The test results on the Support Vector Machine model achieve an accuracy of 91%, while the Naive Bayes model achieves an accuracy of 85%.
PREDIKSI KELULUSAN MAHASISWA PROGRAM STUDI TEKNOLOGI INFORMASI UNIVERSITAS DARMA PERSADA DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN DECISION TREE Badria, Badria; Afifa, Linda Nur
Jurnal Sains & Teknologi Fakultas Teknik Universitas Darma Persada Vol. 14 No. 1 (2024): Jurnal Sains & Teknologi
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70746/jstunsada.v14i1.504

Abstract

Penelitian ini mengembangkan sistem prediksi kelulusan yang digunakan untuk mengetahui hasil prediksi dengan mudah dan tepat dengan menerapkan metode klasifikasi. Lokasi penelitian ini adalah Program Studi Teknologi Informasi Universitas Darma Persada dengan menerapkan algoritma Support Vector Machine. Dalam melakukan prediksi terdiri dari beberapa kriteria yang sudah ditetapkan seperti persentase kehadiran, riwayat tagihan mahasiswa semester 1 hingga semester 4 serta Indeks Prestasi Semester (IPS) 1 hingga 4. Dengan menerapkan metodologi yang mencakup proses mengumpulkan data, pemodelan, evaluasi serta mengimplementasi model prediksi. Penelitian ini mendapatkan hasil bahwa algoritma SVM memberikan kinerja yang baik dalam kelulusan mahasiswa dengan tingkat akurasi yang tinggi yaitu 97%. Dalam menerapkan model prediksi ini, diharapkan pihak universitas dapat bertindak aktif dalam peningkatan keberhasilan akademik mahasiswa serta bertindak dalam pengurangan tingkat Drop Out mahasiswa. Hal tersebut dapat membantu dalam meingkatkan kualitas pendidikan dan akreditasi Universitas Darma Persada.
Co-Authors Badria, Badria Kusuma, Dhino Rahmad Norman, Suzuki Syofian