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All Journal Jurnal Manajemen Informatika Jayakarta Dinasti Information and Technology
Andy Achmad Hendharsetiawan
Universitas Bhayangkara Jakarta Raya, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Materials Science & Nanotechnology

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SISTEM PENJADWALAN MATA PELAJARAN MENGGUNAKAN ALGORITMA GENETIKA SMK PGRI RAWALUMBU Aida Fitriyani; Hendarman Lubis; Andy Achmad Hendharsetiawan; Johanes Andi Lawrence Hutahaean
Jurnal Manajamen Informatika Jayakarta Vol 5 No 2 (2025): Jurnal Manajemen Informatika Jayakarta (JMI Jayakarta)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v5i2.1881

Abstract

Subject Scheduling System Using the Genetic Algorithm at SMK PGRI Rawalumbu. Bekasi. Faculty of Computer Science. Bhayangkara University, Jakarta Raya. 2024. The aim of this research is to reduce the time required to develop and test schedules. Ensure there are no clashes in the teaching schedule. Utilizing web -based technology for easy and flexible data access. Helps schools compile subject schedules that suit their needs, reduce clashes, and increase the efficiency of automatic schedule creation Research Framework Method or strategy for planning and implementing research that focuses on collecting and analyzing quantitative data. This framework is intended to regulate the procedures required in research and provide an overview of the detailed steps that will be part of the research. A genetic algorithm is an algorithm that uses an understanding of natural evolution to solve problems. By using natural selection theory, genetic algorithms can optimize scheduling system uses a genetic algorithm cycle flow diagram, which includes initial population generation, fitness, level assessment, selected individuals, cross-over, mutation, and regeneration.
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 Adi Muhajirin Aida Fitriyani Alwi Rina Riyanto Hendarman Lubis Johanes Andi Lawrence Hutahaean Mukhlis Mukhlis