Jurnal Teknik Informatika dan Sistem Informasi

Implementation of The MinMax Method For Controlling Product Inventory at Children's Clothing Company

Arief Bagus Wicaksono — 2024-05-07

Babyje is a Micro, Small, Medium Enterprises that produces children's clothing with a variety of raw materials. Additionally, in the production process, Babyje has Bill of Material data for each of its products. Therefore, product inventory management and raw material requirement planning are important stages in the production process. However, Babyje experienced product supply problems due to unstable market demand. In July 2022 the Babyje experienced stockouts and overstocks with the same percentage, namely 50%, while in August they tended to experience overstocks with a percentage of 80% and in September tended to experience stockouts with a percentage of 70%. This phenomenon can increase storage costs and hinder buyer demand. This study uses the waterfall method for software development and the min max method for product inventory control. The purpose of this research is to produce a product inventory control application using the min max method. The results of implementing the application with the min max method can remind the business when it is the right time to restock, as well as provide recommendations for the number of orders that must be restocked. Based on the results of black box testing with a total of 120 test cases carried out by 2 user roles, all functionality goes well with a 100% success rate, and for user acceptance testing, all user roles have accepted applications with an acceptance percentage of 100%. The results of this application can calculate the difference between the number of orders that should be the number of orders suggested through the system. From the results of these calculations, there is a relative error in system calculations with a percentage of 14.02%.

Emotions Analysis of Tourist Using Lexicon Text Analysis

Dea Caesy Rahmadani — 2024-05-07

Travelers often write comments on the internet, usually about experiences, opinions, and even complaints. Comment data on the internet can provide information for stakeholders. This information can be extracted using text analysis methods such as positive and negative sentiments. Sentiments can be detailed into eight types of emotions. This study aims to extract emotions from tourists' comments on Google Map, especially on tourist-site accounts in BARLINGMASCAKEB. The dataset comments were crawled from ten tourism objects in BARLINGMASCAKEB. The method used is Lexicon Emotion Analysis. The results show that the majority of tourists have positive experiences. It is shown by the emotion "joy" and "trust." Emotions "joy" and "trust" have positive meanings, so it can be said that the majority of tourists feel positive emotions. There are sites that present highest emotions of "joy": Aquarium-Purbasari-Pancuran-Mas with 33.52%, Lembah-Asri-Serang with 30.85%, Sanggaluri-Reptile-Park by 30, 27%, Baturaden Botanical-Gardens with 27, 67 %, and Curug-Jenggala by 23.4%. At the same time, the highest types of "trust" emotions are Benteng-Pandem with 27.41%, Arjuna-Temple with 26.6%, Sikidang-Crater with 20.71%, and Menganti-Beach with 25, 74%. Only one site, the World Miniature Park, gives the highest "anticipation" emotion. Usually, caring words represent anticipation emotions, so they can still be categorized into positive emotions. The extraction of emotions is affected by the process of emotion-labeling of each comment, so further research is recommended to develop a lexicon emotion dictionary. The results of this study are expected to provide benefits for the development of the tourism industry in the BARLINGMASCAKEB area and for the academic world, especially regarding the application of text mining in the tourism sector

Evaluation Satisfaction Online Public Service Denpasar+ Application Using End User Computing Satisfaction

Ni Putu Eka Fridayanti — 2024-05-07

Denpasar is one of the cities that has implemented an online public service application (PRO Denpasar+), which is currently known as Denpasar Prama Sewaka or DPS. This application ensures that the agencies that are part of it maintain vertical integrity. However, there are still some obstacles, such as the level of implementation satisfaction that has not been identified among active users. This study emphasizes the End User Computing Satisfaction (EUCS) approach, which centers on five key factors that contribute to user satisfaction: content, accuracy, format, ease of use and timeliness in relation to user of satisfaction, which focuses on application evaluation analysis. The study included 30 and 99 respondents active application users who were residents of Denpasar City were selected through purposive sampling and using a Likert scale based on the average score of user satisfaction to measure the levell of satisfaction experienced by each variableewith 14 questions. Based on detailed data analysis, the average satisfaction score for each variable was 4.24 for content, 3.73 for accuracy, 4.23 for format, 4.19 for user-friendliness, and 4.08 for timeliness. In general, these findings indicate that application users are very satisfied with their use. However, its implementation still needs to be refined through revisions. Recommendations for improving the application were compiled using SWOT analysis and based on the achievements of each EUCS variable, respondents' scores from those who disagreed and strongly disagreed, as well as improvement input data provided by respondents through an open questionnaire for providing excellent service to the community.

Prototype Of An Accident Notification Device Using Node MCU Microcontroller and Crash Sensor

Sadam Muhammad Natzir — 2024-05-07

This research has successfully developed a prototype for an automatic accident notification device using the Node MCU Microcontroller and Crash Sensor. The device is designed to detect accident incidents and provide a quick response by sending notifications containing the location of the accident through Short Messages (SMS/Telegram) to the relevant units in real-time. The Crash Sensor and Node MCU work together to detect and track incidents, while the GPS marking system and cellular communication module are used to track and send the coordinates of the accident location. The prototype was tested in various accident scenarios to test the accuracy and responsiveness of the system. The results showed that this prototype is capable of detecting and sending notifications about incidents with high speed and accuracy. The SIM800l module used in this prototype has a message delivery success rate of 90%. The implementation of this technology has the potential to increase the efficiency and effectiveness of emergency response to accidents, reducing handling time and potential further losses. Furthermore, this research indicates potential for further integration with traffic control systems or emergency services, which will form a more coordinated and effective response to accident incidents.

Detection and Mitigation Distributed Denial of Service Attack in Software Defined Network

Dheni Yulia Dinda Pratiwi — 2024-05-07

Software-Defined Networking (SDN) is an approach to network management that separates the control plane from the data plane of the network. In an SDN network, the control plane is centrally controlled by software called a "controller," while the data plane consists of physical network devices such as switches and routers. However, this separation creates many security issues. Therefore, it is imperative to protect the network from various attacks. Distributed Denial of Service (DDoS) is one such attack that poses a hurdle for SDN users. Efforts to protect the SDN network from DDoS attacks require a system that can detect and prevent these attacks. In this final project, a system is created that detects DDOS attacks using Snort IDS (Intrusion Detection System) and prevents them by implementing a firewall on the server using Iptables. The implementation of Snort in the SDN system is able to detect DDoS attacks with an accuracy of 95% for slowhttptest attacks, 90% for slowloris attacks, and 100% for LOIC attacks. The average time to detect a slowhttptest attack is 0.72 seconds, a slowloris attack is 0.36 seconds, and a LOIC attack is 0.3 seconds. The implementation of iptables in the SDN system is able to block DDoS attacks with an average blocking time of 0.91 seconds against slowhttptest attacks, 1.89 seconds against slowloris attacks, and 0.77 seconds against LOIC attacks, and the system is able to manage large connection volumes to maintain the availability of the SDN system.

Indoor Position System Based on Internet of Things and Support Vector Machine

Noprianto Noprianto — 2024-05-07

Currently, the utilization of position tracking technology has extended to various aspects, primarily harnessing the Global Positioning System (GPS). However, a significant limitation arises in the accuracy of GPS when employed within indoor environments. In response to this challenge, the concept of the Indoor Positioning System (IPS) has been introduced and developed. IPS enables the tracking of positions within buildings and offers not only improved accuracy but also enhanced security, safety, and greater efficiency. The research described in this context aims to formulate solutions for tracking and identifying locations within indoor spaces, with a specific focus on the 7th floor of a civil building. The approach employed involves the implementation of access point infrastructure strategically placed within each room. The dataset utilized to support the analysis consists of 80 location points within these indoor spaces, meticulously divided into 40 points for the LIG2 room, 40 points for the LPY4 room, and the connecting corridor area. In the pursuit of achieving this objective, the study adopts the Support Vector Machine (SVM) method. Through the utilization of this technique, the SVM model is instructed utilizing Received Signal Strength Indicator (RSSI) data from microcontrollers. Through a series of trials and evaluations, the SVM method applied in this research has yielded promising outcomes. The accuracy rate reaches 79%, signifying that the proposed system is capable of accurately predicting locations based on the collected data.

Vehicle Counting System Using YOLOv5 and DeepSORT Algorithms

Putri Anggia Cahyani — 2024-05-07

Air pollution is a serious issue in big cities, such as Bandar Lampung. This is caused by high transportation activities using motorized vehicles. Data from 2021 shows a 4.30% increase in the number of motorized vehicles in Indonesia, impacting carbon emissions. In response to this problem, Greenmetric Lampung University has a green transportation work program that focuses on reducing motor vehicle emissions. To support this goal, automatic traffic monitoring is carried out by applying the field of Computer Vision, namely object tracking. The making of an object tracking system in monitoring traffic uses two combinations of the YOLOv5 and DeepSORT algorithms. The method used in this research is the Scrum method which is carried out in three sprints and divided into three stages, namely pre-game, game, and post-game. The result of this research is an object tracking system that successfully distinguishes and counts three types of vehicles (motorcycle, car, and bus) automatically, and has been tested in real-time with an average precision value of 99%, recall of 97%, F1 score of 97.2%, accuracy of 96.8%, and average accuracy of system calculation of 97.65%.

Information System Optimation for Purchasing, Inventory, and Sales with Apriori Algorithm Implementation

Diana Juniar — 2024-05-07

The fashion retail business is a business sector that is continually growing and has many enthusiasts. Apart from companies with well-known fashion brands, this business is also often found in small and medium-scale enterprises, where business processes are implemented manually, starting from purchasing, inventory management, and selling goods. These three processes are essential in business because they are related. The recording is done manually, often allowing inaccurate data to occur. Problems that are often found are in calculating inventory, which can cause stock-outs or stock build-up. Apart from that, checking and updating data, as well as making reports, takes quite a long time and is less practical. Using a purchasing, inventory, and sales information system can help business owners in running their business because the system designed is integrated, and applies an a priori algorithm method that can help recommend which products are trending based on customer transactions, therefore business owners can optimize inventory management effectively by knowing the patterns of products frequently purchased by customers. The results of system testing using the Technology Acceptance Model (TAM), which was processed with the SmartPLS application on data from 37 respondents, gave results that the second hypothesis, namely Behavioral Intention to Use (BITU), has an effect on Actual System Use (ASU), the fourth hypothesis, namely Perceived Ease of Use (PEOU) has an impact on Perceived Usefulness (PU), and the fifth hypothesis, namely Perceived Usefulness (PU) has an effect on Attitude Towards Using (ATU), and other variables have no effect.

Focused Brand Exposure Monitoring Using Topic Modeling and Query Formulation

Iwan Santosa — 2024-05-07

Brand exposure monitoring is a specialized form of media monitoring that aims to monitor brand exposure and brand mentions in publicity media. This research proposes a framework model for monitoring brand exposure through analysis of publicity media utilizing query formulation and topic modeling, aimed at extracting important topics contained in external media or mass media, and comparing them with the institution's internal media. Topic extraction is performed using Latent Dirichlet Allocation (LDA) method on text data in the form of SERP (Search Engine Results Page) snippets. Subsequent processing utilizes Jaccard index and cosine similarity calculations. The output of the framework includes visualizations of topics representing themes of articles exposing the institution's brand, and measurement metrics that are useful for decision-making analysis related to media management and institutional communication. Testing the proposed framework using data samples resulted in expected output, namely the topic groups formed can represent dominant topics in both media categories in a given monitoring period, with the highest coverage rate reaching 82.69% and similarity of 33.97%. The use of LDA method in this study does have its limitations, specifically the formation of topic groups that do not purely contain a singular topic, but rather consist of several subtopics. However, this does not diminish the usefulness of the framework.

Clusters Analysis of Social Anxiety Disorder Criteria Based on Stages of the Treatment

Panji Yudasetya Wiwaha — 2024-05-07

This study aims to cluster the activity dataset of patients who suffer from social anxiety disorder at a Mental Healthcare Company located in the Netherlands and measure the affinity of the cluster to the identified treatment phase based on the similarity of its feature density. The methodology of data clustering is carried out in the following way: 1) data pre-processing against the anonymous patient data, communication data, tracker data of the social anxiety disorder, registration history of the daily entry, notification data, planned event completion data, questionnaires related to the relevancy of the treatment, history of the patient's treatments, and registration history of the thought record, 2) exploratory data analysis to visualize the data point distribution of the activity dataset, perform data standardization, and find the optimal number of clusters, and 3) building a clustering model using the k-Means algorithm. The effectiveness of data clustering is validated by 1) comparing the affinity of clusters to the identified treatment phase and 2) calculating the feature weights to find any features with unique characteristics (dominant) in each treatment phase. The k-Means model successfully grouped the activity dataset into 10 clusters. The clusters are analyzed based on the pattern of cluster affinity and its percentage ratio. Then, 3 clusters are selected because they are close enough to represent each treatment phase in the Mental Healthcare Company. The findings in this study show that the number of days since the patient made a registration, the number of registrations related to social anxiety disorder in the past week, the comparison of negative registrations in the past week compared to one week before, questionnaire scores related to treatment relevancies, and low scores in any questionnaire indicators are distinguished features for each treatment phase. In addition, the urgency of those features matches the therapist's top priority list when treating their clients. Nonetheless, further and comprehensive research must be conducted to understand the impact of the dominant features in each cluster so the classification model for creating a list of recommended patients based on their urgency level of treatment can be built.