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PT. Kereta Api Indonesia (KAI) is a company that regulates trains in Indonesia. Railways in Indonesia still often experience delays, especially in the Cibatu Purwakarta lane which will be the object of research in this study. This research is intended as an initial stage of applying machine learning to overcome the problem of tardiness by providing the best model for predicting tardiness and what things are causing the tardiness pattern. Machine learning models considered are decision tree regression, support vector machine regression, random forest regression, ensemble learning, and gradient boosting regression. From the best machine learning techniques, a model will be made to predict the delay based on the cause of the delay. Keywords – KAI, machine learning, predict the delay, cause of delay, Cibatu Purwakarta lane

A smart city is a notion in which a city may effectively and efficiently manage its resources in order to improve the quality of life of its residents. With the existence of information technology as an enabler, the city is able to carry out sensing, understanding and acting at a certain level. Anomaly detection is a growing field of research in almost all domains, one of which is smart cities. In a smart city, anomaly detection enters into the context of understanding, where the data received, both cyber physical system sensors and user generated content are processed to obtain unusual data that can affect predictions or forecasts. Many studies have been carried out related to anomaly detection methods and techniques related to the smart city environment but have not been categorized according to the domain or framework that is in accordance with the smart city model. This study aims to present the results of a systematic literature review on anomaly detection techniques categorized according to the Garuda Smart City Model, a model designed for smart city development in Indonesia. Based on the results of a systematic literature review, many research gaps have been found for anomaly detection in a smart city environment related to performance limitations, the effect of implementation methods, and the use of anomaly detection as part of the prediction and forecasting methods.

One of the topics that have stood out lately is the revolution of the fourth industry is the technology of the Internet of Things (IoT) which emerged in this revolution providing a lot of research so that it can have an influence on every aspect of the economy. One of the technologies that are at the forefront of today’s revolution is Digital Twin, which is facilitated by advanced data analytics and IoT technology. In designing a building, especially that which is used as a workspace, it is often faced with various types of uncertainty, which are difficult to predict but may have an important effect in the future. One way to manage this uncertainty is by utilizing Digital Twin technology to identify a change and plan a good prediction so that it has an important influence on future designs. Utilizing Digital Twin technology is expected to be an opportunity to produce very well-organized predictions for creating and envisioning the future from a sensible workspace design.

Indonesia’s geographical layout makes it challenging to create a single united railroad network with many branches. Instead, the railroad operation is divided by location, mainly between large islands such as Java and Sumatra. Therefore, distributing appropriate resources for each operational area is hard to manage properly. Kereta Api Indonesia, which provides rail transportation services, needs to address and change its various business processes to achieve Digital Transformation. Among them, asset maintenance is crucial in railroad operations. This paper will focus on designing machine learning models using actual data available from railway vehicles such as generator trains to predict their condition for maintenance by utilizing classifying algorithms for machine learning, which can help automate routine checks. As a preliminary research paper, testing and evaluation of the machine learning model are not available yet.

A level crossing is an intersection where a railway line crosses a road or path. The government has made regulation that set level crossings throughout Indonesia, Provisions on traffic and road transport in Law No. 22 of 2009. However, data from PT Kereta Api Indonesia (KAI) shows that during 2020 there was 198 accidents and around 5000 trespasses occurred at level crossings. The surveillance system currently used in level crossings is still conventional, which only relies on supervisory officers and some level crossings monitored by CCTV. Research in the field of video surveillance systems with a computer vision approach has been widely carried out in the past decade. Most researches on video surveillance systems try to replace human functions or traditional systems with video processing algorithms. This paper will review the literature on the application of artificial intelligence for the surveillance system level crossings published from 2015 to 2021. The literature review process consists of analyzing the document to determine the scope of the investigation using intelligent video surveillance systems, frameworks, methods and datasets to detect level crossing violators. In the conclusion of this paper, it will be explained about the challenges and opportunities of research on the object detected violations using intelligent video surveillance system in the future.

Maintenance of assets owned by an organization or company is an activity that will never stop. From the time of implementation, maintenance will be more optimal if it is carried out before the asset is in a damaged condition or cannot operate. Pro-active repair model is proven to reduce 15-60% of operational costs. The existence of technology and computing models currently supports big data processing, both in the form of transactional data, historical data and statistical data. The asset maintenance cycle transformed into an autonomous and integrated system, will assist in the decision-making process. A machine learning approach that is supported by big data analysis is one solution that can realize the predictive maintenance process. To accurately predict the condition of critical components, it can be started with data collection, followed by detecting normal and abnormal behavior, and continued by training algorithms to make predictions. Detection of railroad anomalies is used as the initial process in the predictive maintenance of railroads. The process of detecting railroad anomalies can be done by comparing the lateral, longitudinal and vertical acceleration from the sensing results through the accelerometers on both sides of the train wheels. Differences will pay attention to the data acceleration draft rail geometry either angkatan or listringan. The results of rail anomaly detection will indicate the rail maintenance process that can be carried out immediately.

In the era of industrial revolution 4.0, artificial intelligence has became a driver in the high technology sustainability. It is important to acceleration of development planning and decision making for stakeholder based on region zone, point of society and distribution an object with system coordinates. The linkage of information and communications technology (ICT) with spatial data, located in the use of data sharing technology, architecture and data dissemination. The availability of spatial data is increasingly numerous and varied requires a high computing technology that users can access and use spatial data from various sources of spatial data providers. Therefore, the Spatial Data Infrastructure (SDI) was built to facilitate access and exchange of spatial data from various sources of spatial data providers, both government and private, which doing an important role in conducted system and platform interoperability as spatial services. The government of Indonesia has made regulation for national spatial data infrastructure (NSDI) in Law Of Presidential Number is 27 of 2014 and and strengthened by onemap policy. The advantages using geospatial artificial intelligence (GeoAI) can reduce duplication and analysis spatial processing of big data spatial. The systematic literature review process contain of regulation, method for harvesting spatial data in SDI and several GeoAI model research from 2016 to this year. The conclusion in this paper is find possibility ellaborate the artificial intelligence technology with spatial data infrastructure development from past studies in other country.

One of the most common and difficult problems to solve in distribution power systems is the location and elimination of the ground fault. For the study of an electrical distribution network in ungrounded system operating at 20 kV on the primary and 380 V on the secondary. The model uses smaller nominal voltages consisting of 380 V on the primary and the secondary. The Ground fault protection system scheme uses over voltage relays and open break delta transformers, whereas for fault location detection using voltage transformers in star star connections.The highest single line to ground fault value is used for consideration of ballast rating selection. The single line to lowest ground fault value is used for setting the threshold on the voltage relay (59N). Referring to the result of the calculation of the highest single phase to ground voltage at point 1 is 144∠1800 volts, and the lowest point at 4 is 132∠1800 volts. A 200 ohm ballast impedance with 150 watts of power capacity, and an overvoltage threshold setting of 120 volts with TDS of 40. One phase fault protection mechanism works well at each point of interference, and voltage transformers are protected from overheating and damage.

Rapid ICT development, globalization challenges, intellectual property (IP) as indicators of economic development, and the challenge are the patentability of computer-implemented inventions and artificial intelligence implications for the IP Office (IPO) to transform from the digital paradigm to the smart paradigm. Nevertheless, because of the lack of literature on the smart IPO framework, we refer to smart government and smart city framework. The aim of this paper is to propose a framework for smart IPO in developing countries in the context of smart city framework. The study found that smart IPO composed of several components such as 1) Smart IPO Model; 2) Smart IPO Enterprise Architecture; 3) Smart IPO Collaboration Model; 4) Smart IPO Development Model; 5) Smart IPO Measurement Model; 6) Smart IPO Standard; and 7) Smart IPO Services. It also found that there are four models of IP office as follows: Model 1 (basic web-based, public services); Model 2 (advanced internal administration); Model 3 (full process automation); and Model 4 (knowledge-based IP services). Moreover, we found also three stages of IP development model: Stage 1 (automation of internal processes); Stage 2 (web-based e-services); Stage 3 (e-dossier, e-filing, e-payment).

Multiple Indonesian cities currently aim to qualify as “smart cities.” Previous research on defining smart cities (e.g., the implementation-oriented maturity model) tends to focus on components over interrelationships, is challenging to apply to a specific context such as Indonesia, and offers limited support for policy-relevant questions. In this paper, we propose to address these shortcomings to support policymakers in identifying concrete action plans in Indonesia specifically. Our approach clarifies interrelationships for the context of use and supports structural (e.g., what aspects of a “smart city” are impacted by an intervention?) as well as what-if policy questions. We started with a systems' science approach to developing a cognitive map of the components and their interrelationships, as is increasingly done in participatory modeling and particularly in socio-ecological management. We transformed semi-structured interviews of 10 Indonesian experts into maps and assembled them to create the first comprehensive smart cities cognitive map for Indonesia, totaling 52 concepts and 98 relationships. While a cognitive map already provides support for decision-making (e.g., by identifying loops in the system), it is only conceptual and thus cannot form predictions. Consequently, we extended our cognitive map into a fuzzy cognitive map (FCM), whose inference abilities allow to examine the dynamic response of an indicator (e.g., “smart city”) in response to different interventions. As fuzzy cognitive maps include the strengths of interrelationships but not the notion of time, future research may refine our model using system dynamics. This refinement would support policymakers in investigating when to conduct and/or evaluate an intervention.

Nowadays, the railway industry has a public transport in a key position where it must be able to face the challenge of ensuring the safety and quality of service regarding health's safety in a pandemic situation. Public transport as a center of people's mobility must be safe to ensure visitors travel during the pandemic. This must be taken because the impact of COVID-19 has spread to almost all sectors and has also caused health facilities to experience the highest level of crisis. Many precautions need to be taken to reduce the spread of this disease where health care protocols must be adhered to with technology to control and manage smart railways resilience in the face of a pandemic. This paper proposes to implement CCTV analytics as a platform to process real-time data with a study case in Bandung Railway Station into knowledge displayed in a Viana Safe dashboard with accuracy 93.95% result on mask detection, social distancing to ensure the COVID-19 protocol with a real time speed of processing with NVIDIA 2080 Ti around of 25 FPS, 30 FPS of visitor counting and fever detection to screen the health status of visitor with accuracy 0.1-0.5'C of face temperature. It will send an early warning notification if the system detects the anomaly detection COVID-19 protocol violation.

The Sumatra interconnection system, which is the second-largest electricity system in Indonesia, is often experiencing rolling blackouts. That caused by a lack of supply from the power generating system of Sumatra. The Government has planned to develop new power plants through the fast track program (FTP). Until 2015, either power outages and supply shortages are still common. Therefore, an analysis of long-term electricity planning needed to support energy security in Sumatra. The Sumatra electricity system was modelled using integrated resource planning as a framework and the long-range energy alternative planning (LEAP) software as a tool to build and simulate the model. Three groups of scenarios will be compared are the fast track program (FTP) scenario, the biofuel mandatory (BM) scenario and optimization (OPT) scenario. The results show that the implementation of DSM programs through energy conservation scenario would be able to reduce the electricity demand in the future. On the other side, the alternative scenario shows that the electricity deficit could be resolved in 2022, while the optimization scenario shows that energy diversity would resolve the electricity crisis in the Sumatra interconnection system.

Face is the unique characteristic of humans that distinguishes between individuals. Every feature on the face can be analyzed to produce demographic information such as gender, age, race, etc. Any such information can be estimated through an automated process, including age. Age estimation through facial images is one of the areas that is still being pursued to deliver accurate values. To better understand facial age estimation, this paper will discuss several things regarding age estimation. First, the factors that can affect the accuracy of predictions, second, the methods used to estimate the age, and then various databases of facial images and information of age at which the picture was taken, and also the parameters that commonly used to measure the accuracy of the estimation results. This review also provides a summary regarding some researches already conducted in age estimation. In the conclusion section, there is a brief about future work that could be done, along with the reason why such research needs to do.

Nowadays a lot of literature explaining the framework of smart campus that includes terminology, definition, framework, and best practice that has been implemented in universities, therefore there needs to be a review of the paper so that it can be taken framework suitable for implementation in Indonesia. This study provides a reference for researchers in the smart campus field also for university stakeholders to gain a better understanding of smart campus terminology and framework. The paper is based on review literature from an online scientific database. This paper will contribute to the smart campus research topic with a focus on smart campus terminology and framework. This paper aims to review Smart Definition, Smart Campus Definition, and Smart Campus model.

Automatic surveillance is an effort to detect anomalies that occur in the surrounding environment such as stations, offices and other public spaces. One of the anomalies that occurs is neglected objects. Abandoned objects will become annoying or dangerous if left unattended. Abandoned object detection process begins by detecting a stationary object using Gaussian mixture models, then abandoned recognize objects using convolutional neural network. The recognition of stage objects is very helpful in determining the bounding box of objects that are left behind, thereby reducing the bias arising from shadows or lighting changes. The resulting effective method to detect abandoned objects and recognize it.

In our daily lives, society always live up side by side with disaster. The causes of a disaster can possibly come from either (or both) natural or human & technological factors. Several studies have stated that : improving the disaster preparedness will reduce the impact / risk of disasters. Therefore, an ICT ecosystem will be developed to increase community preparedness in facing earthquakes. Earthquake is a natural phenomenon which is pretty difficult to predict, so that one of the actions that can be taken before a disaster occurs is to increase awareness, for example by analyzing the readiness of buildings to experience earth shocks.In this paper, we will present the process of analyzing textual and image data from the condition of the building, then classifying it based on the level of readiness of the building to receive shocks caused by earthquakes. The classification process will be executed through a perceptual assessment carried out by both lay users and experts in the field of building construction and disaster. The classification results will be the input data for the process of determining the location of the building by applying the Deep Convolutional Neural Network approach. The output of this identification process is a geo-tagged photos that can be used as a reference so that disaster mitigation can be carried out by related parties.

The work of the paper aims to provide a literature review of how smart buildings operate to managing various features that can be adapt in the built environment. Based on the literature review, the focus on this paper will be dealing with a definition of a smart building, management energy, occupant comfort, heating, ventilation, and air conditioning operation planning, visualization using Building Information Modelling (BIM) as one of another way to share and exchange building information. Many features that adapt to smart buildings should fulfill to be compatible with the context of a smart city. The evaluation process always exists based on the level of human comfort and improving the quality of life in the future. Many works shows us how regulatory bodies try to replace the traditional of building management system by smart building concept with the benefit use of smart technology that can addressing information sharing, fulfill user needs, aims for the challenges of digital society, more transparency, and become openness deals to increase the value of properties.

Road quality degradation could lead to various impacts on road users, such as reduced safety during the trip, increased risk of accidents, increased potential for congestion, or increased maintenance costs. Road maintenance and monitoring by manual inspection are costly, time-consuming, complex, and prone to bias. A system capable of identifying road anomalies, such as cracks, potholes, and speedbumps, is needed. Smartphones nowadays were equipped with a variety of powerful yet low-cost sensors. It enables collecting road anomalies data in a cheap, more productive, and efficient way. This paper aims to review the development of a vibration-based system to identify road anomalies. It also tries to explain the factors that can influence the vibration-based data collection using a smartphone. This review will include features used to improve road anomalies identification results, various machine learning algorithms, and their performance in road anomalies identification.

The increasing number of four-wheeled vehicle users in urban areas is not balanced with the parking area provided in the city, especially in a crowded downtown area (usually shopping centers, offices, etc.). Lack of availability of parking area and information status in each area can make the driver pass through an area many times just to find free parking slot. In this paper we will discuss the design of on-street Smart Parking by using mobile senisng and Internet of Things(IoT), so that it can deliver information that should be known by the driver such as the amount of free parking space in each area and recommended parking area that can be addressed by considering the distance and time from the parking area to the real place to go(destination). This information is shown online and in real time to the driver.

The increase in population at the global level results in an increase in food demand and a shift towards smart agricultural practices. This is supported by the reduced availability of agricultural land, reduced natural resources, and difficulty in predicting climate, making food security a significant problem in several countries. Therefore, the technology trend of using the Internet of Things (IoT) with case studies in agriculture is a solution to increase the efficiency of the crop cultivation process and increase agricultural productivity. Currently, the use of IoT technology in the agricultural sector uses Wireless Sensor Network (WSN) technology and Data Analytics for data processing. This IoT platform combines several other technologies, such as WSN, cloud computing, middleware architecture, end-user application, and radio frequency identification (RFID). There are several benefits and challenges regarding the use of IoT technology for the agricultural sector. This paper will discuss the IoT platform and architecture and enable the application of the concept of smart agriculture.

Facial expression recognition (FER) has made much progress and is supported by many scientific studies conducted in the past decade. The technique and architecture model used in the FER are the aspects that get the most improvement from the researchers. One of the famously used techniques in conducting FER is Deep Convolutional Neural Network (DCNN). The development of DCNN architecture has a vital role in increasing the accuracy of facial expression recognition. The choice of architecture will also affect the total computational costs required to perform facial expression recognition activities. This paper compares some of the DCNN architectures in the past FER research during 2010-2020 using various FER dataset. This paper presents detailed information on several DCNN architectures in terms of the dataset preprocessing techniques and accuracy value for some accessible FER datasets such as Fer2013, CK+, AFEW, and other datasets. This research will explain other FER researchers who are new in FER research to determine the DCNN architecture used based on several FER datasets.

Smart learning is defined as an effective, efficient, and engaging learning process using information technology to improve the quality of learning which includes motivation, involvement or engagement, and performance. To build a smart learning platform, a conceptual framework is needed. Smart Learning Platform Framework is a systematic framework to build a smart learning platform, consists of specific enablers, domains, and services. Considering all stakeholders in smart learning which are learners, educators, providers, and organizers, this framework can be used by implementing a specific smart learning development process and smart learning development lifecycle.

The Institute for Innovation and Entrepreneurship (LPIK) is a hub to facilitate and bridge between ITB inventor and Entrepreneurial to interact with the Industry to co-create the value and to commercialize the technology. This institute is under Vice-rector of innovation and partnership of ITB. The study aims to analyze the current situation and propose a web-based platform to facilitate the interaction among the stakeholders in LPIK effectively and efficient. Service science perspective will be used in this study, start from the problem identification until solution development. The methodology was used is the qualitative approach, i.e., focus group discussion (FGD), survey and strategic assumption surfacing and testing (SAST). The finding is the assumption based on the importance, and the certain level of IES development is relevance (absolutely importance-very certain). The other assumption is considered as a factor which influence IES performance is accuracy, customization, content, timeliness, quality of interaction and also format. Keywords: IES (Innovation Entrepreneurship System), platform, service science, strategic assumption surfacing and testing (SAST), value co-creation

The use of smart mobile devices in the learning process is one of the factors that can influence the success of the learning process. Lecturers are a key factor in the success of this implementation. How lecturers can be ready to accept the use of a smart mobile device in the middle of the learning process that is still considered a nuisance. The purpose of this study is to analyze what factors affect lecturers’ readiness to use smart mobile devices into learning activities and also provide an overview of how IT Governance implementation can help the implementation of smart mobile devices. The researcher took a sample of lecturers who actively taught at the Information Systems Department, BINUS University in the even semester 2018 period. Authors used PLS-SEM to analyze data. The results of this study show whether computer self-efficacy, subjective norms and relative benefits have a significant impact on the willingness of teachers to integrate smart mobile devices into the learning process.

The Internet of Things has a big influence on the appearance of new technologies in learning. Smart education is a learning concept that is developed by intelligent technology development. Therefore smart education must be able to provide an update of learning methods. Smart education must make it easier for users to interact and collaborate to make, organize, and share knowledge from anywhere and at any time accessible. In this paper, we conduct a Systematic Literature Review (SLR) research method to find out the features that exist in MOOCs in the era of intelligent education. This paper presents a comprehensive view of the development of MOOC features in the era of smart education in higher education. Our contribution is comprehensive information about MOOC features in the era of intelligent education in higher education, to improve the quality of learning, and possible use in practice.

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit / Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical.

The size, especially the growth rate, of blockchain has been a major problem since its introduction in 2008. A common blockchain scheme will have exponential growth rate that affect the scalability of blockchain. To overcome this problem, complementary block is proposed. The blockchain is split into multiple chain where each block in a chain is a complementary of a block in another chain. The result shows that complementary block have smaller growth rate compared to original blockchain scheme. Despite its small data duplication, the complementary block still retains the same integrity level as the original blockchain.

This paper presents the implementation on smart system for rapid transit using CCTV surveillance. Researchers proposed deep learning algorithms to detect objects with Convolutional Neural Network (CNN) and monitoring passengers’ behavior like flow analytics, avoiding dangerous areas, and preventing intruder visitor[1][2].Research of this paper also implemented in Railway Station in Bandung with multiple CCTV sources. The system aims to make station better and able to improve quality of service in many scope areas (safe, secure and convenient)[3].Objective of this research is to develop smart surveillance with CCTV in smart station. The system consists of deep learning algorithm and big data technologies such as Hadoop, Apache Kafka and Apache Spark.

Nowadays the population in the city grow rapidly [1]. It may cause many problems and those must be tackled with smart city solution because manual policy cannot handle that situation due to limited physical supporting tools [1] [2]. One of the problems is traffic congestion and its management. Moreover, other smart city initiatives such as traffic control and violence detection are applied to solve that issue in order to improve quality of life [3]. In this paper, a cloud-based with big data and video analytics with deep learning are proposed in the context of efficiency where accuracy and speed of processing and transmissions play a critical role into access policy and control with our integration platform based on Garuda Smart City Framework (GSCF) [1] [4].

In this study, we considered crypto-governance as a solution for critical problems in private university management: fraud avoidance diplomas, transcripts and diploma supplement. To avoid fraud, it can be done by blockchain technology by the use of University, Company, Traceability, Profit and Policy-making of crypto-governance. Crypto-governance is able to use IBM using hyperledger fabric protocol and private blockchain network. Muhammadiyah Tangerang University (UMT) saved the graduate documents storage manually by human resources. Moreover, it would be wasting time for many tasks, for example when staffs searched database, they would consume a lot of time and the graduate documents were vulnerable to damage in the disaster. Based on the problems above it would face the research questions; how did we keep the graduate documents storage secure and how it could be authentic guaranteed? Blockchain technology with distributed ledger and consensus is considered to overcome this problem. There were 5 steps taken in this research starting from discussing, literature review, analyzing, learning blockchain technology, creating system model and making a conclusion. The conclusions of this study were expected that the data of graduate students could find faster, easily and authentic guaranteed.

Tacit Knowledge is one of the potential assets in developing community-based learning. A problem often encountered is the difficulty in formalizing and articulating the knowledge, and for this reason, a media is required to extract into an easily translated form. This study focuses on explicit forms of knowledge, and as a result, the method employed is the collection of research findings related to tacit knowledge and community learning. Also, these findings were surveyed based on predetermined keywords and analysed into a complete report which can be used as material for decision making especially in the matter of sharing this knowledge in community-based learning. The purpose of this study was to determine the understanding of measuring this knowledge in terms of existing dimensions, such as technical and cognitive. However, in this case, the conditions and processes help inform about tacit with reference to the Community. The survey activity is still limited to its conceptual measurement and has not yet led to implementation in the form of community learning where members can interact directly using text, images, videos and other forms. In addition to using two dimensions of measurement, knowledge can be shared through socialization, externalization, combination and internalization that refer to the Nonaka and Ba models for learning to provide a sense of fun for the community group.

Despite the popularity of the Massive Open Online Courses, smallscale research has been done to understand the factors that influence the teaching-learning process through the massive online platform. Using topic modeling approach, our results show terms with prior knowledge to understand e.g.: Chuck as the instructor name. So, we proposed the topic modeling approach on helpful subjective reviews. The results show five influential factors: “learn easy excellent class program”, “python learn class easy lot”, “Program learn easy python time game”, and “learn class python time game”. Also, research results showed that the proposed method improved the perplexity score on the LDA model.

Security evaluations of electronic cash (e-cash) schemes usually produce an abstract result in the form of a logical proof. This paper proposes a new method of security evaluation that produces a quantitative result. The evaluation is done by analyzing the protocol in the scheme using the Markov chain technique. This method calculates the probability of an attack that could be executed perfectly in the scheme’s protocol. As proof of the effectiveness of our evaluation method, we evaluated the security of Chaum’s untraceable electronic cash scheme. The result of our evaluation was compared to the evaluation result from the pi-calculus method. Both methods produced comparable results; and thus, both could be used as alternative methods for evaluating e-cash security.

CCTV is a sophisticated tool for monitoring and surveillance. It was chosen because of the low price and ease of use. CCTV creates issues in monitoring because officers must always supervise every CCTV monitor to look for abnormal conditions. This research proposes the implementation of smart CCTV for surveillance in Bandung Station. It applied deep learning algorithm to create smart CCTV for sensing and understanding station environment such as passengers’ flow, crowded area, prohibited location and others. Big data technologies are also carried out to solve the complexity of data. The objective of this research is to improve the quality of services in Bandung Station and make the station safer, more secure and more convenient.

Currently, higher education is institutions that should be managed as a global business institution. Higher education should improve continually its business processes according to the demands of increasing its stakeholders. of the problems faced in the development of educational, activities if related to the use of information technology for each service in higher education for stakeholders is the issue of available funds. Cloud computing also has advantages related to infrastructure and maintenance and increases operating efficiency. Most institutions are currently moving towards cloud computing technology to reduce operational costs. For achieving these objectives, the institutions need to be supported by reliable technology. This study aim to propose cloud model for education and IT Governance conceptual framework for Cloud Computing model adoption, Tarumanagara University as a study case.

Twitter is social media that has become a source of information for real-time traffic condition in Indonesia. Generally, information contained on Twitter is used for shortterm needs, such as to find out the point of congestion at that time. Whereas, the information can be collected and processed for long-term needs, such as congestion-prone points mapping at certain times. This processed information is very useful for city stakeholders. Information extraction is needed to process information in the form of text from social media that is originally unstructured to be structured. NER technique can be applied to obtain entities that represent traffic condition. This study classifies entities into 11 classes with the BIO encoding scheme, namely B_TIME, I_TIME, B_LOCT, I_LOCT, B_COND, I_COND, B_CAUS, I_CAUS, B_WEAT, I_WEAT, B_MISC, I_MISC, and O. The defined classes represent entities of time, location, condition, cause, weather, miscellaneous, and others. This study proposes a solution in the form of model architecture design using deep learning approach. Bidirectional LSTM approach is used to handle word level. While the CNN approach is used to handle character level. The combination of these two deep learning methods accompanied by word embedding produces the value of F-measure 0.789.

Urban environmental monitoring is an important thing in the implementation of Smart city because it provides a sense of security for its citizens. Closed Circuit Television (CCTV) is used to conduct surveillance in urban areas carried out by the government, company or community, whether indoors or outdoors. Data generated from monitoring results are converted in text form. In this paper researchers analyze previous studies that discuss the development of a model of moving image interpretation. The result is an integration model of the results of the interpretation of moving images from the results of CCTV monitoring that are converted in text form to be able to store information from monitoring so that it can be used to support city analysis.

The development of smartphones in terms of technology and its usage led to a development of new applications and new paradigms called mobile crowdsensing (MCS). MCS is a term used to turn a smartphone into a data sensor, data collector and sharing center by utilizing various sensors such as cameras, microphones, accelerometers, barometers, and more. In addition to the user mobility, it is possible to capture an interesting phenomenon for the user. The captured data and phenomenon can then be processed to become useful information. Various studies have made various applications and systems to capture various phenomena in the environment by utilizing the MCS paradigm. MCS is used by various cities around the world in making applications to get urban problems reports from the citizens to the government such as roads, floods, fallen trees, crime and other problems. The purpose of this application is to speed up the settlement of city problems. This paper will conduct analysis of citizen participation in providing data to the SOROT (MCS application for citizen reporting) platform. This paper will also discuss the result of SOROT implementation in Bekasi city to see whether there is already perceived benefits and to see the various improvements that can be done in the future.

There are many ways for the company to improve its performance, one of them is optimizing the internal control of the company's activities. Internal control is intended to evaluate company activities and operations. This study took a case study at PT. XYZ related to the evaluation of internal controls in warehouse management using the COSO framework approach. From 5 elements and 17 Principle, study found, there are 2 principles that have not been applied in PT. XYZ; enforced accountability and control over technology. The recommendation given is system improvement as intended the inventory system to be more accurate and reliable to enable smart warehouse systems inside organizations.

Now people who use angkot is smaller than motorcycle or car. Angkot also continues to be unifit for operation due to its low occupancy rate. Long travel time, traffic conditions, unfair price, and driver's attitude make people who use angkot is decreased. Operational cost also is greater than income. Angkot can carry on people, but it also has advantage to carry on goods ot packages with cheaper price than online motorcycle taxi. Unlike online motorcycle taxi, angkot has fixed and reversible closed loop routes. It means, angkot has to fulfill their routes then back again. They can't go back in the middle of their route. We propose cooperation model for delivery using angkot. Based on the simulation, angkot still have potential opportunity to compete with online motorcycle taxi because the price is cheaper than online motorcycle taxi even it needs to exchange angkot.

The education system continues to evolve from Education 1.0 to Education 4.0. Currently, education has reached the Education 4.0 stage. Education 4.0 requires technological commitment. Learning processes focus on students, not the teacher as in the previous educational system. Learning processes involve other stakeholders such as the industries, the communities, the experts, or the government. Smart Hybrid Learning Method is a learning method that responds to the Education 4.0 system, which is possible to create collaboration among stakeholders. The purpose of this research is to produce a model of Smart Hybrid Learning Method and architectural framework design of Smart Hybrid Learning System using three-layer architecture approach. The Smart Hybrid Learning Method is the evolution of the flipped or inverted classroom method combined with several other learning method approaches, such as Challenge Based Learning and Case Based Learning. Learning can be done inside and outside the classroom. To apply the Smart Hybrid Learning Method, it needs system called Smart Hybrid Learning System, which can be accessed by several stakeholders. The use of three-layer architecture will create flexibility to function and layer in the system.

Data is new gold which became one of important property because data can give information, knowledge and can be used as a strategic guide that helps executives to know patterns they may not have noticed before. The amount of data on local government income (PAD) generated by each transaction creates a large amount of data, so that appropriate mechanisms and technologies are needed to obtain appropriate information and knowledge. Therefore, descriptive analytics is conducted to give exposure to decision makers about phenomena that occur on PAD in order to become new information for decision makers and as a material consideration in the decision-making process to increase PAD. In this research descriptive analytics is done using data visualization to conduct historical analysis, and to know and understand the current condition. The result of descriptive analytics are able to provide a knowledge of PAD, so it can be used by decision-makers to increase PAD and know the potential PAD.

The earthquake disaster has occurred in Indonesia. Earthquake disaster resulted in heavy casualties, property losses, and loss of municipal infrastructure. Disaster mitigation efforts after earthquake events are needed to reduce the number of fatalities. This paper aims to design the distribution emergency information application in earthquake post-disaster in Meteorological, Climatological, and Geophysical Agency (BMKG). This application is useful to minimize the number of earthquake disaster victims, which makes it easier for the rescue squads to track the whereabouts of victims. Utilizes Internet of Things (IoT) technology in post-disaster management can solve that problems, where the smartphone can be used as a coordinate sensor for the victim. Therefore Service Computing System Engineering (SCSE) is used in the design. The proposed design is then measured based on SOA principles, coupling, cohesion, complexity, and reusability. After measuring the evaluation of the design, it has been verified that the design could pursue the next step of the engineering process.

Document management system makes user to access information anytime and anywhere. The purpose of this research is to analyze what variables have impact on the intention to use of document management system. To achieve the objectives conducted by identification problem on the research model developed from the model of Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology to the use of document management system. Furthermore, the data obtained from the respondents tested outer model, inner model test, hypothesis test and to produce a comprehensive analysis will be conducted Importance Performance Analysis. This research will result in a comprehensive analysis and able to provide recommendations and solutions for improvement from the implementation DMS has been done earlier.

The information system is one brand for educational institutions to face various challenges from competitors in the same field. The information system is also the main media for consumers to assess and measure the achievements of an educational institution. The purpose of this study was to help the School of Computer Science (STIKOM) Dinamika Bangsa Jambi conduct an audit of the academic information system and information technology governance using the COBIT 5 standard. Data were collected using a questionnaire that refers to the scale. Based on the calculation of capability level, the current capability level of STIKOM Dinamika Bangsa Jambi in managing Academic Information Systems focuses on Evaluate, Direct, and Monitor (EDM) domains. The current capability level generally leads to level 2 managed process with a value of 1.80, which means that IT processes have been carried out, achieved goals, and are well managed. This result is obtained based on the average value of governance practice.

The recent development of e-learning platform has provided the learning environment with exciting experiences to teach a various and massive learners population using MOOC (Massive Open Online Courses) platforms such as edX, Coursera, etc. A large user uses these platforms to enhance their competency and improve their skills. Numerous platform of MOOC has been implemented in higher education institution, and the public debate about MOOC is growing; is the platform relevant to be formal education or not. Therefore, this research objective is to explore the MOOC's features, particularly, to identify the difference platform of edX and Coursera. Those platforms have the most significant number of learners; in learner's perspectives through the Resource Activity Support Evaluation (RASE) pedagogical method and learning activity components, which consists of Absorb, Do, Connect activity. The study conducted at a private university in Indonesia with 60 students as respondents and they were also involved in experiments activity to use MOOC's platform. This study used quantitative approach to identify the difference platform between edX and Coursera. The significant results from this study can open mind the researcher to understand the uniqueness of MOOC's platform particularly in edX and Coursera, and it also can be considered as a starting finding toward advanced research in education technology area for the higher education institution.

The research aimed at providing an outcome summary of extraordinary events information for public health surveillance systems based on the extraction of online medical articles. The data set used is 7,346 pieces. Characteristics possessed by online medical articles include paragraphs that comprise more than one and the core location of the story or important sentences scattered at the beginning, middle and end of a paragraph. Therefore, this study conducted a summary by maintaining important phrases related to the information of extraordinary events scattered in every paragraph in the medical article online. The summary method used is maximal marginal relevance with an n-best value of 0.7. While the multi feature selection in question is the use of features to improve the performance of the summary system. The first feature selection is the use of title and statistic number of word and noun occurrence, and weighting tf-idf. In addition, other features are word level category in medical content patterns to identify important sentences of each paragraph in the online medical article. The important sentences defined in this study are classified into three categories: core sentence, explanatory sentence, and supporting sentence. The system test in this study was divided into two categories, such as extrinsic and intrinsic test. Extrinsic test is comparing the summary results of the decisions made by the experts with the output resulting from the system. While intrinsic test compared three n-Best weighting value method, feature selection combination, and combined feature selection combination with word level category in medical content. The extrinsic evaluation result was 72%. While intrinsic evaluation result of feature selection combination merger method with word category in medical content was 91,6% for precision, 92,6% for recall and f-measure was 92,2%.