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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Muhammad Azuan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Analisis Sentimen Masyarakat terhadap Isu Korupsi Dana Bencana di Indonesia Menggunakan Metode Bidirectional Long Short-Term Memory (Bi-LSTM) Prabowo, Toni; Muhammad Irfan Sarif; Sebayang, Aradi; Ferdillah, Tengku Didi; Muhammad Azuan
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.756

Abstract

Corruption of disaster relief funds and social assistance is a critical issue that undermines social justice and public trust in government integrity in Indonesia. This phenomenon has triggered a massive wave of opinions on social media, necessitating deep computational analysis to objectively understand public perception dynamics. This study aims to implement and evaluate the performance of a Deep Learning algorithm, specifically Bidirectional Long Short-Term Memory (Bi-LSTM), in classifying public sentiment related to the issue of disaster fund corruption. The dataset comprises 1,358 textual data points categorized into negative, neutral, and positive sentiments, with a significant dominance of the negative class (926 entries). The proposed model architecture integrates a 300-dimensional embedding layer, a Bi-LSTM layer to capture bidirectional context, and a combination of Global Max Pooling and Global Average Pooling for optimal feature extraction. The experimental results demonstrate that the model achieved an accuracy of 0.75, with a Weighted F1-score of 0.76 and a Macro F1-score of 0.65. Confusion Matrix analysis reveals that the model is highly effective in identifying negative sentiments but faces challenges in distinguishing minority classes due to data imbalance and linguistic ambiguities such as sarcasm. These findings provide deep insights for policymakers regarding public sentiment and demonstrate both the potential and limitations of the Bi-LSTM method in processing informal and sarcastic Indonesian text within the context of political and corruption discourse. Keywords: Sentiment Analysis, Bi-LSTM, Disaster Fund Corruption, Deep Learning, Natural Language Processing
Analisis Kinerja Deep Learning Berbasis Convolutional Neural Network (CNN) untuk deteksi Dini SQl Injection Studi Kasus: Datacenter Diskominfo Binjai Khairul; Muhammad Azuan; Prabowo, Toni; Aradi Sebayang; Tengku Didi Ferdillah
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.964

Abstract

Serangan SQL Injection (SQLi) merupakan ancaman kritis bagi keamanan layanan publik berbasis web di lingkungan pemerintahan. Metode deteksi tradisional sering kali mengalami keterbatasan dalam menangani payload yang disamarkan (obfuscated) dan memerlukan rekayasa fitur manual yang kompleks. Penelitian ini bertujuan untuk menganalisis kinerja arsitektur Deep Learning berbasis 1D-Convolutional Neural Network (1D-CNN) untuk deteksi dini serangan SQLi, dengan studi kasus pada Datacenter Diskominfo Kota Binjai. Metodologi penelitian mencakup pemrosesan dataset sebanyak 19.078 baris log akses server web riil yang dibagi menggunakan metode Hold-out Validation dengan proporsi 80% data latih dan 20% data uji. Arsitektur 1D-CNN dirancang untuk melakukan pemrosesan teks sekuensial guna mengekstrak fitur leksikal lokal secara otomatis langsung dari log mentah. Hasil evaluasi menunjukkan performa klasifikasi yang sangat superior dengan tingkat Akurasi 100%, Presisi 99%, dan Recall 97% pada identifikasi serangan. Penelitian ini menyimpulkan bahwa model 1D-CNN sangat handal dan efisien untuk diimplementasikan sebagai sistem peringatan dini (early warning system) tanpa mengganggu kinerja operasional layanan publik di lingkungan Pemerintah Kota Binjai.
Co-Authors Aradi Sebayang Ferdillah, Tengku Didi Khairul Muhammad Irfan Sarif Prabowo, Toni Sebayang, Aradi Tengku Didi Ferdillah