Keselamatan Pelayaran merupakan hal yang sangat penting dan menduduki posisi sentral dalam segala aspek di dunia pelayaran. Pengabaian atas Keselamatan Pelayaran cenderung akan mengakibatkan kerugian dari segi materi, medis, pencemaran lingkungan perairan dan kerugian jiwa. Namun melihat kasus kecelakaan kapal yang semakin meningkat dari tahun ke tahun dan korban jiwa yang semakin banyak, keselamatan pelayaran perlu mendapat perhatian yang ekstra. Penelitian ini bertujuan untuk menganalisis pengaruh antara variabel independen terhadap variabel dependen. Dengan populasi Karyawan Operasional Kantor Syahbandar dan Otoritas Pelabuhan Tanjung Wangi dan Nahkoda diambil sampel sebanyak 100 responden. Teknik pengambilan sampel adalah Non-Probability Sampling dengan jenis Quota Sampling. Pengumpulan data menggunakan kuesioner, teknik analisis data yang digunakan adalah dengan analisis regresi linier berganda. Dari hasil penelitian terdapat hubungan antara variabel independen dengan variabel dependen, pengujian hipotesis menggunakan uji t menunjukkan bahwa ketiga variabel independen yaitu Sumber Daya Awak Kapal (X1), Alat Telekomunikasi (X2), dan Pemanduan (X3) yang diteliti terbukti secara individu berpengaruh positif dan signifikan mempengaruhi variabel dependen yaitu Keselamatan Pelayaran (Y). Dengan variabel Sumber Daya Awak Kapal (X1) memiliki pengaruh tertinggi dari pada variabel lain dalam mempengaruhi Keselamatan Pelayaran (Y). Pada uji koefisien determinasi (R Square) nilai Adjust R Square sebesar 0.619 artinya 61.9% peningkatan Keselamatan Pelayaran dipengaruhi oleh variable Sumber Daya Awak Kapal, Alat Telekomunikasi, dan Pemanduan. Sedangkan sisanya 38,1% dijelaskan oleh sebab – sebab yang lain di luar variabel yang diteliti.

The waiting time for ships is one of the problems that must be a concern at Tanjung Intan Port Cilacap and which is very influential for the smooth process of unloading coal from coal carriers to Tanjung Intan Port, Cilacap. This study aims to determine the factors that affect the ship's docking schedule, pilotage services, loading and unloading productivity on the waiting time of the ship. This research was conducted at Tanjung Intan Port, Cilacap. Data was collected through observation, literature study, interviews, documentation and questionnaires. The analysis technique used is multiple linear regression with the help of SPSS V.26 program. Based on the results of research and multiple analysis, the following equations are generated:Y = 0.254 +0.297X1+0.585X2+0.177X3+µ. Based on empirical facts and the results of multiple linear regression equations, it is shown that the Ship docking schedule variable (X1) has t count (3.409) > t table (1.98373), pilotage service variable (X2) has t count (5.809) > t table (1.98373), and loading and unloading productivity (X3) has t count (2.967) > t table (1.98373). As for the value of the adjuster R2 = 0.582. This means that 58.2% of the dependent variable (Y) can be explained by the independent variables, namely Ship docking schedules, guidance services, and loading and unloading productivity. While the rest (100% - 58.2%) = 41.8% is explained by other reasons outside the variables studied. Thus, the results of the study show that the availability of docks, guidance services, and loading and unloading productivity has a positive and significant effect on waiting time for ships at Tanjung Inttan port, Cilacap.

been resolved in Indonesia. This research aims to determine the influence of Port Services, Loading and Unloading Performance, Loading and Unloading Equipment and Natural Factors on Idle Time at the Koja Container Terminal, Tanjung Priok Port, Jakarta. The population in this study were operational employees and ship crew from the deck department division. The sample used in this research was 80 respondents. And using a disproportionate stratified random sampling technique, then the data was analyzed using multiple linear regression with the help of (software) Statistical Package For Social Science (SPSS) Version 25. Based on the research results and data analysis, the regression equation was produced: Y= -1.176+ 0.334X1+ 0.181X2 + 0.318X3 + 0.302X4+ ?. From the equation it is known that the dominant variable influencing Idle Time is the Port Services variable with a regression coefficient of 0.334. The R2 (R square) test obtained a result of (67%), which means that Idle Time at the Koja Container Terminal, Tanjung Priok Port, Jakarta, is influenced by the variables Port Service, Loading and Unloading Performance, Loading and Unloading Equipment and Natural Factors, namely 67%. The conclusion  is  that the independent  variables  (port  services,  loading  and unloading performance, loading and unloading equipment, and natural factors) have a negative and significant effect on the dependent variable Idle Time. This is because the results of the multiple linear regression equation show that t count > t table.

Ship waiting time is one issue that is always getting serious attention in every country. Marine transportation became the main made of transportation in trade. Nearly 90% of foreign trade is done by sea. Freight carried by sea to be very efficient due to large carring capacity and this the role of the port as a place where ships can carry out loading and unloading of goods becomes an important means of supporting adequate facilities and infrastructure. If the performance of the port is not optimal, it will have a direct impact on the service so that it will cause new problems, namely the ship’s waiting time gets higher, so that it  willlead to high costs, which have a direct impact on the price of goods on the market. This research has objective to know analysis the influence of factor ship scheduling, pilotage, and loading and unloading productivity to the waiting time. This research was conducted at the Port of Sei Kolak Kijang as a sample. The data collection done was through a method of observation, the literature study, interview, documentatuion and the questionnaires. Techique the analysis used is linear regression multiple with program assitance SPSS V.20. Based on the results of research and analysis produced equation: Y = 15.326 - 0,220 X1 - 0,178 X2 – 0,308 X3 + µ. Where the decision variable of waiting time (Y), ship scheduling (X1), pilotage (X2), loading and unloading productivity (X3) tested using t test show that the three independent variables significantly influence passenger decision as dependent variable. The Adjusted R Square number of 0,665 indicates that 66,5 percent of waiting time variables can be explained through the three independent variables in the regression equation. While the remaining 33,5 percent is explained by other variables outside the three variables used in this study.