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INDONESIA
Jurnal Infra
Published by Universitas Kristen Petra Surabaya
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
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Articles 1,326 Documents
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Rancang Bangun Aplikasi Panggil Tukang Berbasis Android di Daerah Surabaya Nadia Felicia Wianda; Agustinus Noertjahyana; Lily Puspa Dewi
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Nowadays, many people are using android applications in their daily lives. Many kind of applications have been made to make everyday jobs and daily routines become simpler and easier. Looking for AC Service and home repairman is one of them. In Surabaya, looking for service providers is stil use with manual search which is asking to their neighboor. But the problem is, when the people just move in the new neighborhood, and just barely know anyone around, or urgently need to repair your air conditioner problem, and they just don't know whom to ask for help. Because of the problem, Writer make an application Panggil Tukang for helping people find good technicians especially people who lives in Surabaya. The features available on this application will be help the user to find the right technician. The user can get the interesting experience to find a good technicians in this application. Features such as price negotiation, payment methods, and chat will help the user to communicate with the service provider to find the best solution for them. This application simply make life much easier for people who are looking for the right technician's help. On the other hand, this application also provide help for all the technicians' service provider who are looking for people who needs their service. The application and website were built base on PHP language code and Kotlin. While the database we are using to make this application is Firebase.
Pembuatan Aplikasi Penyimpanan Password Menggunakan Metode Honey Encryption Pada Android Nouchka Indra Dewa; Agustinus Noertjahyana; Andreas Handojo
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Currently, this data storage certainly really needs security to avoid risks such as hacker attacks, data leaks, or loss that is always there. One measure for this security is to use a password to protect the information. However, this is quite risky where passwords that have difficult combinations will be difficult to remember. To help android smartphone users in storing and securing these passwords in their smartphones, a password storage application was made. This application is made on an android smartphone using a cryptographic method, namely the Honey Encryption method. Where is an application on an Android-based smartphone that aims to store and secure passwords. Users can manually set the key that will be used in the encryption process. This encryption process is done online so that the user can make a request from the server for the key data. This online step can make it easier for users to delete and manage online backup data in the form of keys and text data for their username and password. The results of this study indicate that this system is protected by the Honey Encryption Algorithm which is able to secure the stored passwords. It also shows that this system has successfully implemented the Honey Encryption Algorithm to trick users who do not have a password by displaying the wrong password.
Deteksi Rompi dan Helm Keselamatan Menggunakan Metode YOLO dan CNN Rescky Marthen Mailoa; Leo Willyanto Santoso
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Construction workers face a high risk of injury and are prone to accidents while performing their duties. Several factors increase the chance of accidents, including open and heated environmental conditions, building heights, sharp objects, and others. The use of personal protective equipment (PPE) is essential to anticipate or reduce the risk of accidents that may occur. However, it is not uncommon for construction workers to forget or purposefully disregard personal protective equipment. To address these problems, a system capable of detecting personal protective equipment for construction workers is required. This study used the You Only Look Once (YOLO) method to detect the head and body parts of the inputted image. The detected body parts were then cut and processed using the Convolutional Neural Network (CNN) method with the ResNet50 model for classification. The training process with the ResNet50 model was modified on hyperparameters including learning rate, epoch, dense layer, dropout layer, data augmentation, and freeze layer to compare its performance with the model before modification. The results showed that the YOLO model has a very high level of detection speed with good accuracy. Meanwhile, the modified CNN model performed well with an average accuracy value of 96%
Monitoring Kadar Amonia dalam Akuarium Ikan Menggunakan Metode Verifikasi Warna RGB dengan Memanfaatkan ESP32-CAM Matius Bryant; Stephanus Antonius Ananda
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

The main problem that is often found is poor aquarium water quality during the maintenance period, which can result in an increase of ammonia levels in the aquarium. In this thesis, the specimen that will be used for testing is predatory fish, where the food of this fish is raw meat or live fish whose size is smaller than them. The leftover of this food can increase the production of ammonia in the aquarium water. An increase in ammonia production will result in an increase in the nitrogen cycle as well, where the cycle will produce more nitrogen which results in reduced oxygen in the water. The effect of ammonia on fish can vary from difficulty of breathing, loss of appetite, and over time it will cause death in fish. In this thesis, an IoT(Internet of Things)- based monitoring system for aquarium ammonia levels will be used. This problem has actually been handled in several previous studies, one of which was researched by Talanta, D. E. entitled "Arduino-based Design and Build of Arduino-based Ammonia & PH Water Control in Fish Cultivation", but was considered less successful because Talanta only used an MQ-155 sensor to detect ammonia gas. While in this thesis the automation system is used to monitor & control ammonia levels in the aquarium by using the Camera function of ESP32-CAM to take pictures of the test kit and will then be processed with Python by utilizing the OpenCV library to verify RGB color in order to determine the ammonia level in the water. Based on the results of the system testing that has been carried out, it can be concluded that the ammonia detection accuracy of this system is 66.7%, this is because the measurement range of the water test strips being used is quite large, ranging from 0-6 PPM. So, it cannot produce small results such as 0.25 PPM, because 0.25 PPM levels will be directly classified to 0.5 PPM levels. In addition, it can also be concluded from the experiment conducted for 4 days that the automatic water change system in this thesis has an accuracy rate of 87.5% (8 trials with 1 failure) in maintaining water parameters safe for fish.
Aplikasi Channel Management dan Point of Sales pada perusahaan retail PT. XYZ dengan menggunakan metode Cross-channel dan Market Basket Analysis Kenny Nugraha; Andreas Handojo; Alexander Setiawan
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

PT XYZ is a company engaged in the retail and distribution of baby equipment located in Surabaya. Generally, the company has many stores ranging from physical stores and online stores. The problem faced at this time is that there is no system that can connect the company's internal systems including the cashier system, warehouse system, sales calculation system with the existing system in online stores such as in this case the Shopee system. To answer these problems, PT XYZ requires a website-based channel management system so that it can be integrated both on mobile and desktop. It is hoped that by using this technology, it can help companies improve the performance and sales performance of stores both physical stores and online stores. An integrated Multichannel Management System and Point of Sales Application can record every sale, stock management practically and efficiently so that when a stock transaction occurs it will always be synchronized along with transaction data from between online stores and offline stores.
Sales Forecasting pada Dealer Motor X Dengan LSTM, ARIMA dan Holt-Winters Exponential Smoothing Jennifer Soeryawinata; Henry Novianus Palit; Leo Willyanto Santoso
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

In the world of commerce, inventory is an important issue. Occasionally, motorcycle dealer X experience lost revenue due to a lack of motorcycle inventory as well as lost storage space due to under-selling motorcycles being stocked in large quantities. If the restock process is easy to do, it will answer the problem. Inventory of motorcycles at the motorcycle dealer X was sent from Jakarta to Central Sulawesi. If the motorcycle dealer X wants to do a restock, it will take a long time and expensive shipping costs. To overcome the problems at the motorcycle dealer X, a prediction or forecasting of motorcycle sales is needed. With this forecast, it is hoped that the owner of the motorcycle dealer X can determine the number and type of motorbikes that must be sent from Jakarta each month. In this study, we will use Long-Short Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and HoltWinters Exponential Smoothing to forecast motorcycle sales and then compare their performance using evaluation metrics, such as the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From this third model, the best model for forecasting is ARIMA with the lowest RMSE (1.1339-5.8936) value for all types of motors and has the lowest MAPE values for three types of motors. If the LSTM model is compared with the HoltWinters model, the LSTM model is better at forecasting with smaller RMSE and MAPE values for most types of motors.
Klasifikasi Benda Organik dan Anorganik Dengan Metode YOLOv3 dan ResNet50 Kevin Reynaldi Tanjung; Liliana Liliana; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

There are still many Indonesian people throw waste in the wrong place. One of the reasons is that there are still many Indonesian people who still find it difficult to sort organic and inorganic objects. Therefore, the introduction of organic and inorganic objects is very important and we need something that can help in sorting organic and inorganic objects. By knowing the difference between organic and inorganic objects, people can sort out organic and inorganic waste. The methods used are You Only Look Once to get waste objects from an images or videos. The detected object will be cut and the results will be processed by the Convolutional Neural Network with the ResNet50 architectural model for classification. In the YOLOv3 and ResNet50 training process, adjustments are made to find parameters to get best accuracy This research will classify objects on waste objects in images or videos. The Mean Average Precision obtained by YOLOv3 is 45% and the average loss is 91%. For ResNet50 there is rule of thumb where when using input size 416x416 and the lower the number of learning rates can increase accuracy. When combined, ResNet50 is able to increase the accuracy of the detected object types by YOLOv3.
Analisis Sentimen Mahasiswa di Surabaya Terhadap Pelayanan Vaksinasi COVID-19 Menggunakan Beberapa Classifier Meliana Kusuma Pangkasidhi; Henry Novianus Palit; Andre Gunawan
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Indonesia is one of the countries that are currently struggling to deal with the COVID-19 virus pandemic by providing vaccination. The government is currently trying to persuade the public to do vaccination by maximizing COVID-19 vaccination services. In reality, vaccination services still have problems with some aspects. To see various insights on vaccination services that have been implemented, therefore a research was conducted in the field of sentiment analysis to analyze public opinion. In this research, classifiers that will be used are Naïve Bayes, Support Vector Machine (SVM), Random Forest, and Light Gradient Boosting Machine (LGBM) to perform text classification and their performances will be compared with evaluation metrics. There are two types of datasets used, namely questionnaire dataset and social media dataset. The questionnaire model will be tested using a social media dataset, while the social media model will use social media dataset that will be split. The testing results show that the model trained with the social media dataset produces better performance than the questionnaire model. Of these four classifiers, the best model for aspect and sentiment classification is Random Forest
Sistem Pakar untuk Mendiagnosa Kerusakan pada Sepeda Motor Kawasaki KLX 150 Menggunakan Metode Forward Chaining dan Certainty Factor Maria Eve Angeline; Djoni Haryadi Setiabudi; Kartika Gunadi
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

The Kawasaki KLX 150 is an all-road or dual sport motorcycle, which means it can be used on the road or off-road. Nowadayas dirt bike riders are not just crossers, ordinary people are starting to like dirt bikes to be used as daily vehicles. Dirt bikes have engines and various kinds of devices or parts that can be damaged or be problematic. The damage that often occurs on a dirt bike is considered trivial and not understood. Therefore, an expert system was created that can detect damage to the Kawasaki KLX 150 motorcycle, with the hope that this research can help replace the role of mechanics to diagnose damage based on the symptoms experienced. The expert system to diagnose damage to the Kawasaki KLX 150 will use the Forward Chaining method and the Certainty Factor method. The use of forward chaining method in this expert system is to collect facts obtained from users so that the system will produce conclusions. The use of Certainty Factor in this study is to provide a level of confidence from the results of system diagnosis in the form of metrics. From this expert system, it can provide information about the name of the damage, how to handle it and the level of confidence in the diagnosis. Application testing for the diagnosis of damage to the Kawasaki KLX 150, using real data with experts, resulted in a system accuracy of 90%. The application for the diagnosis of damage to the Kawasaki KLX 150 is also considered complete, accurate, appropriate and easy to use (user friendly) by the user.
Penerapan Artificial Neural Network dan Rule Based Classifier untuk Mengklasifikasikan Pendonor Darah Potensial pada Sistem Broadcast Pendonor Widya Arditanti; Andreas Handojo; Tanti Octavia
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

One of UTD PMI Surabaya’s task is to provide safe and quality blood when blood is needed in an emergency. The availability of blood at UTD PMI Surabaya can be erratic, because it depends on the number of donors that fluctuates and the storage time of blood is not long. Therefore, UTD PMI Surabaya needs a system to invite potential donors to meet blood needs when needed in an emergency, by minimizing blood wasted. The classification model and the creation of a recommendation system will produce a list containing donors who have been sorted by priority. Testing was carried out by dividing the data according to the conditions of the data collection environment (before the pandemic, during the pandemic and a combination of before and during the COVID-19 pandemic). The highest MRR value was obtained from the ANN model made from a combined data of 90% classification results using RBC and fake data. The accuracy value obtained from the model is 91.13% for training and 91.83% for testing. The resulting MRR value is 8.07 x 10-4 .

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