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All Journal Jurnal Pengabdian Teknologi, Ekonomi dan Humaniora Jurnal Sains & Teknologi Fakultas Teknik Universitas Darma Persada
Afifa, Linda Nur
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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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.
Enhancing Teachers Proficiency with Digital Interactive Learning Resources for Differentiated Instruction Yahya, Yahya; Febriana Sesunan, Mira; Syamsiyah, Nur; Asbanu, Husen; Pratama, Juan; Nur Afifa, Linda; Darsono
JEPTIRA Vol 3 No 1 (2025): JOURNAL OF COMMUNITY ENGAGEMENT JEPTIRA
Publisher : Fakultas Teknik Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/jeptira.v3i1.93

Abstract

The course seeks to enhance educators' competencies in creating customized learning experiences with digital interactive technologies, namely platforms like Wordwall and Quizizz. The event occurred on February 11, 2025, in the Bekasi region, involving high school and vocational school educators. The training materials encompassed the concept of diversified learning, an introduction to the Wordwall and Quizizz platforms, and exercises for developing interactive media that align with suitable teaching methodologies. The activity's outcomes demonstrated a notable enhancement in instructors' competencies in the creative and contextual utilization of digital media, alongside heightened understanding of the need of adaptive learning services tailored to student needs. This initiative enhances the transformation of digital education at the secondary school level.
Co-Authors Badria, Badria DARSONO Husen Asbanu Kusuma, Dhino Rahmad Mira Febriana Sesunan Nur Syamsiyah Pratama, Juan Suzuki Syofian Yahya Yahya