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All Journal JSM (Jurnal SIFO Mikroskil) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Yayasan Cita Cendikiawan Al Khwarizmi Engineering Science Letter
Ronsen Purba
Universitas Mikroskil
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ENHANCING SENTIMENT ANALYSIS ACCURACY WITH BERT AND SILHOUETTE METHOD OPTIMIZATION Kelvin Kelvin; Frans Mikael Sinaga; Wulan Sri Lestari; Sunaryo Winardi; Khairul Hawani Rambe; Ronsen Purba
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 1 (2025): JITK Issue August2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i1.6392

Abstract

This research is based on the emergence of ChatGPT technology, which has significant implications in various fields. This research aims to design a model that improves sentiment analysis classification accuracy. The methods applied include the use of the Silhouette Coefficient to determine the best cluster parameters before performing data grouping with the Self-Organizing Map (SOM) method. Additionally, the Bidirectional Encoder Representations from Transformers (BERT) model is utilized to perform precise and convergent sentiment classification. The research methodology encompasses several phases, including data preprocessing through natural language processing techniques. Textual data is converted into vector representations, which are then processed using the Silhouette Coefficient to identify the optimal cluster parameters. These parameters are subsequently applied in the Self-Organizing Map method to cluster data, while the Bidirectional Encoder Representations from Transformers model determines public sentiment, categorized as positive, negative, or neutral. The findings of this study indicate that the best cluster parameter is 9, using a batch size of 64 and a maximum sequence length of 128. The highest accuracy achieved using the confusion matrix is 92.06%. Further tests with varying parameters confirm that the Silhouette Coefficient method significantly enhances the convergence and accuracy of classification outcomes. The conclusion of this research is that integrating the Silhouette Coefficient and Bidirectional Encoder Representations from Transformers is effective in optimizing sentiment analysis on large datasets, achieving both accurate and reliable results.
BERT Model Implementation for Dynamic Sentiment Analysis of Pertamina on Social Media X Ronsen Purba; Rivaldi Lubis; Nadya Sikana; Gilbert Fernando Situmorang
Engineering Science Letter Vol. 4 No. 02 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001139

Abstract

This study aims to investigate the dynamics of public sentiment on platform X in response to the Pertamina corruption scandal, exploring how trust and perception shifted before and after the incident. Utilizing BERT-based sentiment classification model trained on real-world social media posts, the model achieved a validation loss of 0.5078 and an F1-score of 82.12%, demonstrating strong predictive performance for large-scale sentiment analysis. Results revealed a significant rise in negative sentiment and a decline in positive sentiment following the public disclosure of the scandal on February 25, 2025, reflecting a deep erosion of public trust in Pertamina. Qualitative thematic analysis further identified a shift from neutral or positive discussions focused on service quality and innovation to emotionally charged critiques emphasizing betrayal, distrust and institutional failure. These findings highlight the value of integrating deep learning classification with qualitative insights to monitor real-time public opinion and institutional reputation. The study underscores the critical need for transparency and effective communication strategies during reputational crises to rebuild public confidence. Limitations include the focus on a single social media platform, suggesting future research should incorporate cross-platform and multilingual analyses. Practically, this research offers actionable insights for corporate crisis management and contributes to understanding social media’s role in shaping public trust and accountability in the digital age.
Peningkatan Kemampuan Berpikir Logis Melalui Pelatihan Python pada SMAS Tri Ratna Sibolga: Evaluasi Pre-Post Test Felix Felix; Ronsen Purba; Heru Kurniawan; Tanti; Juliana Damayanti Manurung
Dedikasi Sains dan Teknologi (DST) Vol. 5 No. 2 (2025): Artikel Pengabdian Nopember 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v5i2.7018

Abstract

Kemampuan berpikir logis merupakan keterampilan penting dalam mendukung proses pemecahan masalah, pengambilan keputusan, dan pemahaman konsep mata pelajaran. Namun, sistem pembelajaran di sekolah masih banyak menekankan metode hafalan, yang menyebabkan siswa kurang terlatih dalam berpikir kritis dan analitis. Oleh karena itu, kegiatan Pengabdian kepada Masyarakat ini dilakukan dengan tujuan untuk memperkuat kemampuan berpikir logis siswa melalui pelatihan dasar pemrograman Python. Kegiatan ini dilaksanakan di SMAS Tri Ratna Sibolga dan melibatkan siswa kelas XII IPA 33 orang dan XII IPS 29 orang sebagai peserta. Materi pelatihan mencakup pengenalan logika pemrograman, struktur algoritma sederhana, serta latihan menyusun solusi dari studi kasus menggunakan pendekatan logis. Kegiatan berlangsung selama dua hari dan disampaikan melalui metode ceramah, diskusi, dan latihan berbasis konsep pemrograman. Pelaksanaan dimulai dengan pre-test untuk mengukur pemahaman awal siswa, kemudian dilanjutkan dengan penyampaian materi, praktik, dan post-test untuk mengevaluasi hasil. Hasil kegiatan menunjukkan adanya peningkatan skor rata-rata peserta baik dalam aspek pemahaman logika pemrograman sebesar 5,83% dan kemampuan logika dasar sebesar 28,37%. Selain itu, siswa menunjukkan minat yang tinggi dalam mengikuti pelatihan dan berpartisipasi aktif selama kegiatan berlangsung berupa pengisian post test dan test yang lengkap mencapai 74,19%. Kegiatan ini tidak hanya memberikan manfaat bagi siswa, tetapi juga mempererat kerja sama antara universitas dan sekolah mitra. Secara keseluruhan, kegiatan ini berhasil mencapai tujuannya dan memberikan kontribusi positif dalam pengembangan keterampilan berpikir logis siswa di era digital.
Pengamanan Citra Warna Menggunakan Modified Reversible-Enhanced Stego Block Chaining dan Generator Modulo David David; Nicholas Ong; Ronsen Purba; Wulan Sri Lestari
Jurnal Sifo Mikroskil Vol. 27 No. 1 (2026): JSM VOLUME 27 NOMOR 1 TAHUN 2026
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v27i1.1866

Abstract

Keamanan komunikasi digital menjadi aspek yang semakin krusial seiring berkembangnya teknik analisis dan deteksi steganografi, sehingga diperlukan pendekatan penyisipan pesan yang lebih adaptif dan tidak mudah diprediksi. Penelitian ini mengusulkan metode Modified Reversible-Enhanced Stego Block Chaining (MRESBC) dengan dukungan Generator Modulo dan algoritma Diffie-Hellman Key Exchange (DHKE) untuk meningkatkan keamanan penyisipan pesan rahasia dalam citra warna. Metode ini memodifikasi teknik RESBC dengan menambahkan mekanisme pengacakan posisi sisip menggunakan generator modulo dan variasi bit penyisipan, serta menerapkan algoritma DHKE guna memperkuat pertukaran kunci secara aman dan sebagai seed awal dari generator modulo. Selain itu, data pesan dikompresi menggunakan algoritma Lempel-Ziv-Welch (LZW) guna mengoptimalkan kapasitas penyimpanan. Penelitian ini mengembangkan aplikasi prototipe dan pengujian dilakukan terhadap citra RGB 24-bit menggunakan metrik MSE, PSNR, dan SSIM. Hasil menunjukkan nilai PSNR terbaik sebesar 59,530 dB pada penyisipan 1-bit LSB dan terendah 36,583 dB pada 4-bit LSB dengan 3 stage, seluruhnya di atas ambang batas 30 dB. Nilai SSIM mencapai 1,0 pada seluruh skenario, mengindikasikan rekonstruksi citra pesan yang sempurna. Metode yang diusulkan terbukti mampu meningkatkan keamanan steganografi tanpa mengorbankan kualitas visual secara signifikan.
Co-Authors David David Felix Gilbert Fernando Situmorang Heru Kurniawan Juliana Damayanti Manurung Kelvin Kelvin Khairul Hawani Rambe Nadya Sikana Nicholas Ong Rivaldi Lubis Tanti Winardi, Sunaryo Wulan Sri Lestari