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All Journal Dinasti Information and Technology SYSTEMIC RESEARCH: Systemic and Information Technology Journal
Adi Muhajirin
Universitas Bhayangkara Jakarta Raya, Indonesia
Computer Science & IT

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System Integration for Judicial Prediction of Case Type and Resolution Duration at Bekasi Religious Court Adi Muhajirin; Nurfiyah Nurfiyah; Arya Haifandika
SYSTEMIC RESEARCH: Systemic and Information Technology Journal Vol. 2 No. 1 (2026): July 2026
Publisher : Greenation Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/systemic.v2i1.69

Abstract

The administration of justice in Religious Courts faces inefficiencies due to manual case classification and unpredictable resolution timelines. This research addresses the gap in systemic judicial management tools by proposing a hybrid machine learning architecture. The objective is to design a systemic information model integrating Deep Learning for case type classification and Ensemble Learning for duration prediction. The method employs CRISP-DM framework combining Long Short-Term Memory (LSTM) for text analysis and XGBoost for temporal regression on 4,461 case records from Bekasi Religious Court. Results indicate a classification accuracy of 93.9% and a Mean Absolute Error (MAE) of 12.4 days for duration prediction. The novelty lies in the systemic integration of dual predictive models into judicial workflows, proposing a digital transformation model for court administration. Conclusions affirm that hybrid AI systems significantly enhance resource planning and administrative efficiency
Komparasi Metode LSTM dan Random Forest dalam Prediksi Waktu Sandar Kapal untuk Optimasi Alokasi Dermaga: Studi Kasus Pelabuhan Tanjung Pandan Andy Achmad Hendharsetiawan; Adi Muhajirin; Alwi Rina Riyanto
Dinasti Information and Technology Vol. 3 No. 2 (2025): Dinasti Information and Technology (October - December 2025)
Publisher : Dinasti Research & Yayasan Dharma Indonesia Tercinta (DINASTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dit.v3i2.2929

Abstract

Efisiensi operasional pelabuhan sangat bergantung pada akurasi prediksi waktu sandar kapal, terutama di Pelabuhan Tanjung Pandan yang memiliki karakteristik tramp trade dengan variasi kapal yang tinggi. Penelitian ini bertujuan membandingkan kinerja metode Long Short-Term Memory (LSTM) dan Random Forest dalam memprediksi durasi sandar kapal sebagai dasar optimasi alokasi dermaga. Menggunakan data operasional periode 2023–2024 (125 observasi), variabel input mencakup Gross Tonnage (GT), Length Overall (LOA), serta tanggal tiba dan berangkat; sedangkan output adalah durasi sandar dalam jam. Data diproses melalui pembersihan, rekayasa fitur, dan normalisasi, lalu dibagi menjadi 80% latih dan 20% uji. Evaluasi dilakukan menggunakan RMSE, MAE, dan R². Hasil menunjukkan bahwa Random Forest mengungguli LSTM dengan RMSE 5,34 jam (vs. 7,82), MAE 4,07 jam (vs. 5,91), dan R² 0,917 (vs. 0,812), mengindikasikan kemampuannya menangkap interaksi non-linear antarfitur statis seperti GT dan LOA lebih efektif dalam konteks operasional pelabuhan ini. Temuan ini merekomendasikan penerapan Random Forest sebagai model prediktif dalam sistem pendukung keputusan alokasi dermaga untuk meningkatkan efisiensi dan mengurangi waiting time kapal
Predicting Vessel Departure Delays at Tanjung Pandan Port Using Supervised Machine Learning : A Comparative Study of Logistic Regression, Decision Tree, and SVM Adi Muhajirin; Andy Achmad Hendharsetiawan; Mukhlis Mukhlis
Dinasti Information and Technology Vol. 3 No. 2 (2025): Dinasti Information and Technology (October - December 2025)
Publisher : Dinasti Research & Yayasan Dharma Indonesia Tercinta (DINASTI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dit.v3i2.2943

Abstract

Operational delays in vessel departure disrupt maritime logistics and increase port dwell time. This study develops predictive models to anticipate departure delays at Tanjung Pandan Port using supervised machine learning. Three algorithms—Logistic Regression, Decision Tree, and Support Vector Machine (SVM)—were trained on 112 verified port calls (2023–2024) with key features: arrival date, scheduled departure date, vessel ownership status (milik vs. keagenan), and document response time. Delay was defined as exceeding the median turnaround time of 58 hours. Data preprocessing included imputation, time-difference engineering (e.g., ΔTIBA–BERANGKAT, response latency), and SMOTE for class balancing. Performance was evaluated using accuracy, precision, recall, and F1-score via 5-fold cross-validation. The Decision Tree model achieved the highest F1-score (0.86) and recall (0.89), identifying response latency > 12 hours, keagenan status, and arrival during neap tide windows as top predictors. SVM showed robust precision (0.88), while Logistic Regression offered the best interpretability of coefficient impact. The models collectively support proactive scheduling interventions-e.g., digital clearance acceleration or priority berthing for high-risk vessels—to mitigate delays. This study contributes the first ML-based delay prediction framework for shallow-draft, tramp-operated Indonesian ports.
Co-Authors Alwi Rina Riyanto Andy Achmad Hendharsetiawan Arya Haifandika Mukhlis Mukhlis Nurfiyah