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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) MAP (Jurnal Manajemen dan Administrasi Publik)
Prabowo, Toni
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences

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PENGARUH KEPEMIMPINAN TRANSFORMASIONAL DAN PENGELOLAAN SDM TERHADAP KINERJA PERSONEL YANG DIMEDIASI OLEH DISIPLIN KERJA PADA BIRO SDM POLDA NTB Prabowo, Toni
MAP (Jurnal Manajemen dan Administrasi Publik) Vol 8 No 1 (2025): MAP (Jurnal Manajemen dan Administrasi Publik)
Publisher : Program Pascasarjana Universitas Wijaya Putra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37504/map.v8i1.702

Abstract

The purpose of the study was to analyze the Influence of Transformational Leadership and Human Resources Management on Personnel Performance Mediated by Work Discipline at the NTB Police Human Resources Bureau. The type of research is explanatory with a quantitative approach. The research sample was 53 respondents and analyzed with SEM-PLS. The results of the study show that personnel performance is included in the very good category, transformational leadership is included in the very good category, human resource management is included in the very good category, and work discipline is included in the very good category, transformational leadership has a direct and significant effect on work discipline. Human resource management has a direct and significant effect on work discipline. Transformational leadership has a direct and significant effect on personnel performance. Human resource management has a direct and significant effect on personnel performance. Work discipline has a direct and significant effect on personnel performance. Transformational leadership has an indirect and significant effect on personnel performance mediated by work discipline. Human resource management has an indirect and significant effect on personnel performance mediated by work discipline.
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
Co-Authors Ferdillah, Tengku Didi Muhammad Azuan Muhammad Irfan Sarif Sebayang, Aradi