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All Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Innovative and Creativity
Muhammad Edwin Rachmanudin
Universitas Wijayakusuma Purwokerto
Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Evaluasi Kapasitas dan Level of Service (LOS) pada Kawasan Pendidikan dan Komersial di Purwokerto Muhammad Edwin Rachmanudin; Nabillah Febi Saputri; Bayu Septiaji Wicaksana
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

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Abstract

Penelitian ini bertujuan untuk menganalisis kinerja ruas Jalan Letjend Pol. Soemarto di Purwokerto sebagai kawasan pendidikan dan komersial melalui evaluasi kapasitas jalan dan tingkat pelayanan (Level of Service/LOS). Survei dilakukan pada dua sesi waktu, yaitu pagi (10.20–11.20 WIB) dan sore (16.00–17.00 WIB), dengan menggunakan metode classified count dan analisis berdasarkan Pedoman Kapasitas Jalan Indonesia (PKJI) 2023. Hasil penelitian menunjukkan bahwa volume lalu lintas rata-rata adalah 1.270 smp/jam dengan dominasi sepeda motor (58,5% dalam smp). Kapasitas efektif jalan sebesar 2.288 smp/jam diperoleh setelah mempertimbangkan faktor penyesuaian lebar jalur, hambatan samping, pemisahan arah, dan ukuran kota. Nilai Derajat Kejenuhan (Dj) sebesar 0,56 mengindikasikan kondisi jalan berada pada Level of Service (LOS) A, yaitu arus bebas dengan kecepatan tinggi dan tundaan minimal. Hambatan samping termasuk dalam kategori rendah dengan nilai total antara 110,2–156,3. Hasil ini menunjukkan bahwa ruas jalan masih memiliki cadangan kapasitas sekitar 44% dan berkinerja sangat baik, meskipun berlokasi di kawasan yang secara persepsi dianggap ramai. Studi ini merekomendasikan pemantauan berkala dan pengelolaan hambatan samping untuk mempertahankan kinerja optimal ruas jalan.
Applying Artificial Intelligence Algorithms for Optimalizing The Electricity Distribution Network Susatyo Adhi Pramono; Endang Sri Wahyuningsih; Basuki; Isra’ Nuur Darmawan; Muhammad Edwin Rachmanudin
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 3 (2024): November 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i3.4299

Abstract

Abstract: This research aims to apply artificial intelligence (AI) algorithms to optimize the electricity distribution network, focusing on improving efficiency, responsiveness, and integration of renewable energy. Deep learning and reinforcement learning algorithms are used to learn energy load patterns and adjust distribution in real-time. Through simulation of the electricity distribution network, this research found a 15% increase in energy efficiency, a reduction in response time by 85%, a 10% increase in maximum load capacity, and a 25% increase in the use of renewable energy. Additionally, operational costs of the network decreased by 12% due to automation generated by AI. The innovation of this research lies in the efficient integration of renewable energy sources and load management through more accurate prediction. The research results show that applying AI in the electricity distribution network can provide an effective and sustainable solution to reduce power loss and operational costs, as well as support the use of renewable energy. Therefore, this research contributes to the development of a smarter and more environmentally friendly electricity distribution system.
Co-Authors Basuki Basuki Bayu Septiaji Wicaksana Endang Sri Wahyuningsih, Endang Sri Isra' Nuur Darmawan Nabillah Febi Saputri Susatyo Adhi Pramono, Susatyo Adhi