KINERJA QUAY CONTAINER CRANE DALAM KEGIATAN BONGKAR MUAT PETIKEMAS DI KSO TERMINAL PETIKEMAS KOJA JAKARTA UTARA

Tujuan penelitian ini adalah untuk mengetahui Pengaruh Kinerja Quay Container Crane Terhadap Kelancaran Kegiatan Bongkar Muat Petikemas di Terminal Petikemas Koja, Jakarta Utara. Teknik pengumpulan data melalui wawancara dan penyebaran kuesioner. Jenis penelitian deskriptif kuantitatif, sumber data yang digunakan adalah data primer dan data sekunder, teknik analisis data dengan analisis regresi linear sederhana, koefisien korelasi, koefisien penentu, uji hipotesis, uji validitas, dan uji reliabilitas. Populasi penelitian sebanyak 37 orang operator QCC dan sampel penelitian sebanyak 37 orang. Teknik sampling menggunakan sampel jenuh. Hasil analisis menunjukan adanya pengaruh dari kinerja Quay Container Crane dan kelancaran kegiatan bongkar muat yang ditunjukan dengan persamaan garis regresi linear sederhana yaitu: Y = 9,679 + 0,696X, artinya, jika terjadi perubahan kinerja QCC (variabel X) bertambah, maka kelancaran kegiatan bongkar muat akan meningkat sebesar 0,696 dengan konstanta (a) 9,679. Analisis Koefisien Korelasi (r) = 0,610 artinya kinerja QCC (variabel X) dengan kelancaran kegiatan bongkar muat memiliki pengaruh hubungan yang kuat dan positif. Analisis Koefisien Penentu (KP) sebesar 37,2%. Berdasarkan hasil uji hipotesis menunjukan thitung > ttabel atau 5,746 > 2,030, sehingga H0 ditolak dan Ha diterima, artinya adanya pengaruh kinerja QCC terhadap kelancaran kegiatan bongkar muat.

Key Performance Indicators Analysis for Quay Container Crane Performance Assessment (Case Study at Jakarta International Container Terminal)

<p><span lang="PT-BR">PT Jakarta International Container Terminal (JICT) is the largest container port in Indonesia. Currently, JICT capacity is 2.5 million TEUs (Twenty-foot Equivalent Units) per year, it continues to strive to improve international services and is supported by adequate container loading and unloading equipment. The requirement to establish common standards in different types of container port equipment and identify performance indicators to assess the performance of container handling equipment has increased. Although the Quay Container Crane (QCC) operating system may be different at each container terminal, there are similarities in its main movements, namely: Main Hoist, Trolley, Gantry, and Boom. By knowing the clock metric for each movement, it is possible to determine the Key Performance Indicator (KPI) that has been adopted and assess the performance of the Quay Container Crane (QCC). The results of the study identified that the value of MMBF (Mean Move Between Failures) decreased due to the accumulation of long-lasting heavy load operations, while the number of maintenance activities for machine parts and working hours continued to increase. Key Performance Indicator (KPI) as a management tool can guide QCC inspections and the results can provide useful insights for improving the performance of equipment and container loading and unloading operations in the future.</span></p>

DRONE-BASED CONTAINER CRANE INSPECTION: CONCEPT, CHALLENGES AND PRELIMINARY RESULTS

Container crane inspection is a very important task to maintain their uninterrupted operation. Nevertheless, this is a costly and time-consuming activity if performed manually. Recently, image-based detection of surface damages or changes using drones has gained increasing interest in industry; especially when objects of interest have a complex structure like container cranes. One main aim of this paper is a single-epoch image analysis which will also serve later for multi-epoch processing. It provides reliable information about current defects that may lead to big damages if not inspected by experts. Naïve Bayes classifier is employed to classify the images in different classes of which critical defects and especially rust is important. The preliminary results show that the precision on the target class reached about 99%. However, 87% percent recall in this class is not enough and it should be improved for this application.Having a large dataset requires an efficient data management system to provide users and decision makers with the information needed. In addition, in order to foster full automation, the aforementioned image analysis component should have a direct connection to the database and thus is able to query image and semantic information. We therefore introduce the second aim of our research, that is a concept for database design. Here, not only the raw data and the final results are integrated but also the intermediate results. At the same time, the database concept is connected to an integrated client interface that allows retrieving data of interest in a virtual globe.

Load Position Detection of Container Crane Using Camera

In this study, the authors proposed an image processing algorithm to detect (measure) the rope length of container crane (distance from camera system to container spreader) and sway angle of the spearder (container). This measurement will be the main input to design the anti-sway control system for container cranes. The image processing algorithm includes the main steps: converting from BGR color space to HSV color space, then, binary image is used to extract the marker area. Next, the Canny boundary detection technique is applied to determine the boundary of the markers in the container spreader. The center location of each marker is determined and used to calculate the distance from the camera system to the container spreader is calculated. The rope length accuracy by the image processing algorithm is 99,79%. It is satisfied for crane control purpose.

Klasifikasi Jenis Pemeliharaan dan Perawatan Container Crane menggunakan Algoritma Machine Learning

Pada industri jasa pelayanan peti kemas, Terminal Nilam merupakan pelanggan dari PT. BIMA, yang secara khusus bergerak dibidang jasa perbaikan dan perawatan alat berat. Terminal ini menjadi sentral tempat untuk melakukan aktifitas bongkar muat peti kemas domestik yang memiliki empat buah container crane untuk melayani dua kapal. Proses perawatan alat berat seperti container crane yang selama ini beroperasi, agaknya kurang memperhatikan data pengelompokkan atau klasifikasi jenis perawatan yang dibutuhkan oleh alat berat tersebut. Di kemudian hari, alat berat dapat menunjukkan kinerja yang tidak maksimal bahkan dapat berujung pada kecelakaan kerja. Selain itu, kelalaian perawatan container crane juga dapat menyebabkan pembengkakan biaya perawatan lanjut. Target produksi bongkar muat dapat berkurang dan juga keterlambatan jadwal kapal sandar sangat mungkin terjadi. Metode pembelajaran menggunakan mesin atau biasa disebut dengan Machine Learning (ML), dengan mudah dapat melenyapkan kemungkinan-kemungkinan tersebut. ML dalam penelitian ini, kami rancang agar bekerja dengan mengidentifikasi lalu mengelompokkan jenis perawatan container crane yang sesuai, yaitu ringan atau berat. Metode ML yang pilih untuk digunakan dalam penelitian ini yaitu Random Forest, Support Vector Machine, k-Nearest Neighbor, Naïve Bayes, Logistic Regression, J48, dan Decision Tree. Penelitian ini menunjukkan keberhasilan ML model tree dalam melakukan pembelajaran jenis data perawatan container crane (numerik dan kategoris), dengan J48 menunjukkan performa terbaik dengan nilai akurasi dan nilai ROC-AUC mencapai 99,1%. Pertimbangan klasifikasi kami lakukan dengan mengacu kepada tanggal terakhir perawatan, hour meter, breakdown, shutdown, dan sparepart.