Jurnal Ilmiah Teknik Komputer

SECURITY REPORTING AND AUDIT INFORMATION SYSTEM WEBSITE-BASED INFORMATION SYSTEMS AT UNIVERSITAS JENDERAL SOEDIRMAN

Azmi Taqiyudin — 2026-02-24

Information systems have an important role for every university, including Universitas Jenderal Soedirman. The Information Technology and Systems Development Institute (LPTSI) of Universitas Jenderal Soedirman is responsible for various information systems at Universitas Jenderal Soedirman to support various academic and non- academic activities. Currently, there is no security reporting and audit system for information systems at Universitas Jenderal Soedirman. Reporting is done manually, causing various obstacles such as time efficiency, process effectiveness, and lack of adequate documentation. In addition, the lack of structured information system management is a challenge in managing the system. In order to increase the effectiveness and efficiency of security reporting activities and information system audits so that they are more structured, research was conducted that aims to develop a website-based Information System for Security Reporting and Information System Audits (SIPEKA). SIPEKA is structured and is expected to assist auditors in the audit process to improve the security and management of existing information systems. This research was conducted using the waterfall method, PHP programming language, laravel framework, mysql database, and produced a system that has various features, namely registering only for work unit users, login, submission and reporting can be managed according to each user who can upload files on the add and change form, and view and print the results of both routine and incidental information system audits managed by Universitas Jenderal Soedirman. SIPEKA was tested using the black box testing method and of the 12 features tested all showed valid results.

DETECTION SYSTEM OF MOTORCYCLE USER VIOLATIONS WITHOUT HELMETS USING THE YOLO ARCHITECTURE CNN METHOD BASED ON EMBEDDED SYSTEMS

Bariq Baharudin — 2026-02-12

Safety Driving is a critical aspect of daily life that cannot be ignored. Riding a motorized vehicle, such as a motorcycle or car, carries a number of risks, and it is important to adopt appropriate safety measures. The use of helmets is one of the measures to improve safety in driving, this is regulated in Law number 22 of 2009 concerning Road Traffic and Transportation (LLAJ). However, in everyday life there are still many motorcycle users who violate these regulations. Security officers often find it difficult to identify violations that occur due to the disproportionate number of violators and security officers. The application of Machine Learning CNN method YOLO architecture is believed to be able to help security officers in identifying violations that occur. This program is made in the form of an embedded system in the form of a Raspberry Pi 4. From the results of training the model using 1056 images with 30 epochs , the results of the accuracy of the model itself is at 74.9%. Testing the system itself using the Blackbox method of 5 features tested shows valid results, but the FPS measurement only gets an average of 1.76 frames per second when the system is run. This shows that the system has met the functionality but has not met the performance.

GEOGRAPHIC INFORMATION SYSTEM OF EARTHQUAKE RISK ASSESSMENT USING K-MEANS ALGORITHM AND MEVN STACK CASE STUDY OF INDONESIA REGION

Bagas Prasetya — 2026-02-12

Indonesia is located in a very active seismic region, known as the ring of fire. Earthquake risk assessment serves to categorize the level of hazard from earthquakes based on their effects. Earthquake risk assessment can be done using the clustering method. Clustering is the process of grouping a pattern that does not yet have a label and is done without supervision into a group that has certain characteristics. The algorithm used for clustering in this research is the K-Means algorithm. This algorithm allows data to be grouped into clusters so that data that have similarities are in the same cluster. The K-Means clustering model was developed using the magnitude and depth attributes of the earthquake. The model was successfully implemented and produced 15 clusters as the best number of clusters. The clustering results obtained from the model were then implemented in the Geographic Information System using MEVN Stack. MEVN Stack is a combination of framework and database consisting of MongoDB, Express.js, Vue.js and Node.js. This system can identify earthquake-prone areas by presenting information in the form of heatmaps displayed on a map. Each heatmap can show statistics complemented by historical information on earthquakes that have affected the area. In addition, the system allows users to measure earthquake risk through the Risk Map feature, where users can select a point on the map to see the level of earthquake risk at the point selected by the user.

WEB-BASED INVENTORY SYSTEM FOR STATE PROPERTY AT JENDERAL SOEDIRMAN UNIVERSITY USING AGILE DEVELOPMENT METHOD

Muhammad Firmansyah — 2026-02-12

Inventory of State Property includes activities for registering, recording and managing state-owned assets, one of which is within the university environment. This process must be reported at least once every 5 (five) years based on Government Regulation Number 28 of 2020 concerning Amendments to Government Regulation Number 27 of 2014 concerning Management of State/Regional Property. The aim of developing the Inventory System for State Property is to support the transformation process of Jenderal Soedirman University into a Legal Entity State University (PTN-BH). This system was built using the Agile Development method with PHP language and the Laravel framework. The stages of the Agile Method in system development are requirements, design, development, testing, and deployment. In the Agile method, the requirements stage involves collecting and managing user and system requirements, which are then processed into a flow diagram at the design stage. Next, system development is carried out at the development stage according to the analysis results, followed by feature function testing at the testing stage. The results of the research are a web-based inventory system for state property that can record inventory needs, print labels, print reports and monitor goods. The system testing process using the black-box method produces data that all features are valid and can run as planned in the system requirements analysis process.

SENTIMENT ANALYSIS OF APEX LEGENDS GAME REVIEWS ON STEAM USING NAÏVE BAYES CLASSIFIER

Bariq Jauhar Rizqullah — 2026-02-12

The rapid development of video games, which can now be played online through several platforms, is remarkable. One of the commonly used platforms is Steam, where each game has reviews, but some reviews are biased and ambiguous. In this case, sentiment analysis is useful to determine whether the reviews contain positive aspects like improving strategic skills or negative aspects like the potential for addiction. It is also used to evaluate the model's performance in identifying these reviews. The sentiment analysis model was created using Word2Vec and Naive Bayes methods. The research resulted in a sentiment analysis model with an accuracy of 75%, precision of 83%, and recall of 82% from 4332 review data, consisting of 3087 positive and 1245 negative data. Therefore, it can be concluded that Apex Legends game is positively received by users.