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Analisis Konektivitas Intermoda Feeder Wirawiri di Kota Surabaya (Studi Kasus: Rute Terminal Bratang - Stasiun Pasar Turi) Ayu Ratri Widyarti; Dadang Supriyatno
Jurnal Media Publikasi Terapan Transportasi Vol. 4 No. 1 (April) (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mitrans.v4n1.p445-454

Abstract

Kota Surabaya mengalami masalah mobilitas karena semakin banyaknya aktivitas masyarakat dan ketergantungan pada kendaraan pribadi. Untuk meningkatkan penggunaan transportasi umum, Pemerintah Kota Surabaya menyediakan layanan Feeder WiraWiri yang menghubungkan Terminal Bratang dengan Stasiun Pasar Turi. Penelitian ini bertujuan menganalisis kinerja layanan tersebut berdasarkan jadwal, headway, aksesibilitas, konektivitas, serta persepsi pengguna.Penelitian menggunakan metode kuantitatif deskriptif melalui survei kepada 100 responden, observasi lapangan, dan analisis indikator konektivitas.Hasil menunjukkan nilai konektivitas layanan berada dalam kategori baik (skor 3,95). Jadwal dan headway layanan cukup memenuhi kebutuhan perjalanan, meski masih ada ketidaksesuaian di jam-jam non-sibuk. Aksesibilitas halte dinilai cukup baik, tetapi ada beberapa titik yang perlu diperbaiki fasilitas pejalan kaki. Kesimpulan penelitian menyatakan bahwa Feeder WiraWiri sudah terhubung dengan baik, namun perlu diperbaiki konsistensi headway, fasilitas halte, serta sinkronisasi jadwal antar moda.
Microwave-Assisted Self-Healing of AC-WC Modified with Iron Powder: Mechanical Performance and Healing Rate Amalia Firdaus Mawardi; Machsus Machsus; Dadang Supriyatno; Achmad Faiz Hadi Prajitno; Muhammad Fikri Nadhif; Hazen Masrafat
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3297

Abstract

Conventional asphalt mixtures have limited microwave absorption, reducing the effectiveness of microwave-assisted self-healing. This study evaluates the effect of iron powder (0–10% by mass of fine aggregate) as additional fine aggregate in AC-WC mixtures on mechanical performance and microwave-activated healing behavior. Cylindrical specimens (63 mm × 100 mm; three per mixture) were tested using Marshall Stability and Indirect Tensile Strength (ITS) at 25 °C. Healing efficiency was determined by the ratio of post-heating ITS to initial ITS. Results showed that 10% iron powder increased Marshall stability by 36% compared to the control and achieved the highest healing rate of 76% after the first microwave cycle. However, repeated heating reduced healing performance due to overheating and accelerated binder aging. Iron powder improves early-stage self-healing but requires controlled dosage and heating conditions for long-term durability.
THRESHOLD-BASED ANOMALY DETECTION IN DRY BULK CARGO VOLUME USING SIMULATED LSTM AUTOENCODER RECONSTRUCTION ERROR Irnanda Satya Soerjatmodjo; Dadang Supriyatno; Zidan Fadzil Abdat; Trijeti
International Journal of Civil Engineering and Infrastructure Vol. 5 No. 1 (2025): IJCEI Volume 5 No. 1
Publisher : University Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/ijcei.5.1.49-60

Abstract

This study addresses the challenge of detecting anomalies in annual dry bulk cargo volumes at a major Indonesian port by simulating the reconstruction error typically produced by an LSTM Autoencoder model. Instead of applying deep learning directly, the research utilizes a statistical approximation involving a three-year centered moving average to emulate the expected cargo pattern. The absolute deviation between actual and smoothed values is treated as simulated reconstruction error. A statistical threshold is then calculated based on the mean and standard deviation of these errors to distinguish normal years from anomalous ones. Results indicate that only the year 2023 exceeded the anomaly threshold, suggesting significant irregularity in cargo flow during that period. The proposed method offers a practical and interpretable framework for anomaly detection, particularly in data environments lacking access to machine learning infrastructure. This approach enables port operators and planners to monitor unusual volume fluctuations efficiently and provides a foundation for further integration of data-driven risk management systems. Keywords: Dry Bulk Cargo, Anomaly Detection, Reconstruction Error, LSTM Autoencoder, Moving Average, Threshold Classification, Port Operations.