Threats to aviation security have increased, firearms technology has developedrapidly, including the existence of firearms using a magazine in which there are 4 (four) rounds of ammunition and firearms with revolver type with titanium material which is light, strong and can store 5 (five) rounds of ammunition so that it can shoot with a certain amount of ammunition contained in it. In addition to thedevelopment of firearms, there are also sharp weapons that are not made of metal (non-metallic knife) where the developmentof this sharp weapon is in stark contrast to the technology of security equipment used at terminal 3 Soekarno-Hatta International Airport, the majority of which still use metal detectors. With the development of weapons technology, both firearms and sharp weapons, it is this equipment, procedures and capabilities of aviation security personnel, especially at Terminal 3 of Soekarno- Hatta Airport, that can be improved to anticipate technologicaldevelopments in aviation crimes, so that aviation security and safety is the goal. in aviationin Indonesia can be realized. The Ministry of Transportation seeks to assist every airport operator in determining standards for each personel, equipment and procedures to anticipate increasing aviation security threats through the Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 28 of 2021 concerning the National Aviation Security Education and Training Program.

This study aims to determine how the perimeter conditions and solutions to the obstacles that exist in the Budiarto Airport Management Unit (UPBU). The study used a descriptive method and data collection used was the method of interviewing, observing and documenting. The data analysis technique used is data reduction, data display, and conclusing drawing. This study used several respondents as a sample, one airport security officer and four village/sub-district officials in four villages surrounding the Budiarto Airport Management Unit (UPBU). This research shows that the perimeter security system at Budiarto Airport is still not ready and tight, because avsec (aviation security) is only located or standing guard at the gate only, the results of interviews with the surrounding village chief also showed that there are still many residents who are still approaching the airport security area because they still do not understand the meaning of warning signs on the perimeter.

Logistics processes that only make one trip in the transportation of goods result in inefficient logistics costs. Truku is an integrated digital logistics solution that provides added value to the company. the current condition of the truck application is still unable to optimally support services to users, especially the automatic payment transaction feature. The method used in this study is a conceptual model of Customer Knowledge Management (CKM) based on the Customer Relationship Management (CRM) phase and the Knowledge Management (KM) process. The results of the comparison of the features that have been formulated with the features available in the Truku application then become a reference for determining the proposed features that are recommended to PT Biru Samudera Selatan to be implemented in the Truku logistics application. So that later it can facilitate the development of application features based on the needs of the users of the Truku logistics application.

Indonesia is a maritime country that has large fishery resource potential and is spread over 11 Fishery Potential Areas (WPP). Therefore this research was conducted in order to obtain alternative strategies that can be implemented in an effort to increase the supply of tuna in the city of Padang using a system dynamic method approach. Based on the results obtained, increasing the amount of tuna supply can be done through existing policies in each scenario. The policies that can be carried out are: providing a fuel subsidy policy if scenario 1 is implemented, investing in the procurement of longline tuna vessels, licensing policies for increasing company capacity, and providing a fuel subsidy policy if scenario 2 is implemented, investing in the procurement of traditional vessels, investing in the procurement of longline tuna vessels, policies licensing for company capacity building, and providing fuel subsidy policies if scenario 3 is implemented. Determining the policies to be implemented for the next few years, this depends on policy makers who have considered the advantages and disadvantages of each scenario.

Determining the needs of retailers in implementing halal logistics can be identified using the Fuzzy QFD (Quality Function Deployment) method. Assessment of retailer needs was carried out by 3 sources. Then the results of the assessment are translated into Fuzzy numbers and then the HOQ matrix is designed. Based on the research conducted, obtained 7 retailer needs and 13 technical characteristics that influence the implementation of halal logistics. Then from 4 kinds of levels of relationship between each technical characteristic, 2 kinds of assessments were obtained, namely the level of strong positive and weak positive relationships. Assessment of the strength of the relationship between each retailer's needs and logistics activities in the context of implementing the halal logistics system for the value of the "Very Strong" relationship can be seen in the relationship between special packaging and ordering costs and special packaging with guaranteed goods condition. As for the value of the strength of the "Medium" relationship, it can be seen in the relationship between the concoction according to the wishes of consumers with the ordering fee and the halal guarantee fee with the ordering fee.

Logistic issues are still become a serious matter for Indonesian society. This problem should be seriously considered by Indonesia. Many sectors that support logistic growth should be arranged properly in order to increase the value of performance of logistic in Indonesia. Agriculture sector is one of the sectors that has important role in national development integration. Unfortunately, production growth for this sector is still relatively low compared to some other sectors with value 1.85%. One of the agricultural sectors in Indonesia was palm oil plantations that produces cooking oil. Currently, Indonesia was suspected by having problems on the cooking oil distribution. Cooking oil distribution margins are tending to increase, while margin is one of the efficiency indicators in distribution system. The increasing of distribution margin indicates that the distribution of the commodity is inefficient. Thus, we need to improve efficiency and effectiveness in all logistics activities. The issue is, what are the relevant indicators to manage the logistics of cooking oil in an effort to improve the efficiency and effectiveness. Determination of performance indicators can be done by getting the opinion and consensus of the experts who understand the logistics of cooking oil. The method used is the Delphi method that will integrated with fussy method. The method is called Fuzzy-Delphi method.

Determination of risk sources and risk indicators is carried out by conducting literature studies and expert assessments. The results of the expert assessment are processed using the Delphi fuzzy and AHP fuzzy methods. The Delphi fuzzy method is used to make decisions regarding risk sources and risk indicators based on expert opinion. The AHP fuzzy method is used to determine the weight and priority order of risk sources and risk indicators for palm cooking oil logistics. The risk sources obtained based on the study of the literature are as many as 10 risk sources which are then selected 8 risks that are approved by experts. While the risk indicators obtained based on the study of the literature are as many as 34 indicators, then as many as 23 indicators are considered important by experts. Based on the risk weight assessment, the order of risk with the highest weight is supply risk with a weight of 0.2803, partnership risk with a weight of 0.1619, production risk with a weight of 0.1576, storage risk with a weight of 0.1262, technology risk with weight of 0.93, transportation risk with a weight of 0.0811, infrastructure risk with a weight of 0.0603 and market risk with a weight of 0.0395.