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Silvia Rostianingsih
Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
Computer Science & IT

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Alarm Sensor Temperatur Untuk Ruang Pendingin Menggunakan IoT Kevin Christian; Silvia Rostianingsih; Resmana Lim
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Technological advances are so fast that all activities use computers, making it easier for users to carry out daily activities. With conditions that are increasingly advanced and developing so that the idea is created to connect hardware and software technology that helps in the daily activities of the wider community. Currently, many raw materials and food ingredients are stored in the Refrigeration Room. One example of a Cooling Room is a refrigerator freezer. Refrigeration Room is widely used to store raw food ingredients.For this reason, to make it easier to monitor the temperature in the Cooling Room, an Android Application that is connected using IoT is made with a Temperature Sensor in the Cooling Room.The test results show that the process of monitoring the Cooling Room through IoT and Android Applications can be done easily and quickly with accurate results.
Analisis Sentimen Ulasan Restoran Menggunakan Metode Support Vector Machine Yoel Julianto; Djoni Haryadi Setiabudi; Silvia Rostianingsih
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Reviews on restaurants on the internet have a huge impact on a restaurant. Reviews provided help other customers to evaluate the business or services provided from a  restaurant. Customers can leave positive or negative reviews. The large number of reviews from customers makes it difficult for restaurants to know if their restaurant has move positive or negative reviews. In this undergraduate thesis an application will be made to determine whether a restaurant has positive or negative reviews.Application that is equipped with text mining features will help restaurant in evaluate their restaurant. The steps taken are preprocessing which consist of case folding, tokenization, stopword removal, and stemming. Then the process of converting text data into vector using TF-IDF. Furthermore the data will be trained using Support Vector Machine which later will generate a model that will be used to make predictions from input data. The data which be used as training are Indonesian-language reviews from various restaurants.From this research conducted the result showed an accuracy of 93% and f1-score of 93%. To increase accuracy and f1-score values, classification model require TF-IDF parameters min_df  0.05, max_df  0.75, norm l2, n-gram (1, 2), linear SVM kernel with C 1. Besides TF-IDF and SVM parameters, the number of datasets can also increase confusion matrix and f1-score values.
Implementasi Kamera Thermal pada Raspberry pi 3 untuk Pemantauan Suhu Mahasiswa Universitas Kristen Petra Andre Cristo Singgih; Silvia Rostianingsih; Resmana Lim
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Abstract

During the COVID-19 pandemic, face-to-face learning has certain conditions so that the health of both teachers and students remains healthy. One way of prevention at Petra Christian University (PCU) is to check the temperature when entering the campus area. However, manually checking the temperature and looking at the campus access permit made several students or lecturers queue because the security had not finished seeing the campus access permit or checking the temperature of the first visitor who came.For this reason, if there is a program that is able to recognize students and check student temperatures, it will help to expedite the checking process at PCU. The author tries to implement a thermal camera and RFID scanner on a raspberry pi 3 mini computer to be used as a tool that can recognize students through the student identity card and to check student temperatures. In addition, data from checking can be stored in the database to view the records according to the checking information obtained.The results of the system test show that the implementation of the thermal camera and RFID scanner is able to read student identity card and check student temperatures. However, when checking the temperature, the camera has problems when several objects are caught on the screen. Such as the presence of other person or other object such as student’s carrying items that contain hot temperatures. This causes the system to record the temperature of the object that has the hottest temperature in front of the camera screen, this can cause information errors in the checking process.
Penerapan Segmentasi Warna Menggunakan K-Means Clustering untuk Pemilihan Template dalam Pembuatan Konten Willy Pratama Darmalim; Liliana Liliana; Silvia Rostianingsih
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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The convenience of shopping online has resulted in the development of online business trends making content and publication consistency very important to attract consumers’ attention. Color selection is an important process in content creation. However, not everyone can choose the right colors, create interesting content, and have the time to create content and organize its publication. Li's research uses Generative Adversial Networks to help design’s layout. But these elements are not provided by the application, so user still need to design themselves. To answer this problem, a content maker application was developed.K-Means Clustering is used to get the most dominant color from an image and Euclidean Distance calculates the closest color distance from the user's image with various design templates available. The additional feature of Scheduled Post addresses the problem of limited time for content publication.K-means color segmentation of 20 images with 1 or 2 dominant colors obtains 90% accuracy. Five PCU VCD lecturers rated the accuracy of selected template design color nuances 76%. Making content using thesis application is 56.18% faster than using similar application. Result of content maker design compared to other designs won 1st place with voting score of 46.66%.
Penerapan Manajemen Risiko Teknologi Informasi Pada Perusahaan PT. X Christian Natanael Sugiharto; Alexander Setiawan; Silvia Rostianingsih
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Risk Management is one of the most crucial activities in today's business world. Along with the development of an increasingly complex era, making business people have to face many increasingly complex challenges as well. You can say that risk management must be implemented in companies that currently use IT in their dominant business processes in order to minimize risks and identify risks that will arise. The method used in this study to be applied to the company PT. X is COBIT 5 with two process domains, namely DSS01 Manage Operations and APO12 Manage Risk. The results of this study are some 24 (twenty four) company risks and risk mitigation related to what must be done in overcoming these risks.
Implementasi Locally Adaptive K-Nearest Neighbor Algorithm based on Discrimination Class (DC-LAKNN) pada Kasus Deteksi Fake Account Instagram Yosefani Kurniawan; Lily Puspa Dewi; Silvia Rostianingsih
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Instagram is one of the social media that has many users. Because of the ease of creating an account, many people create a fake account for stalking, spam attempts, fraud, photo or password theft, and even attacks another account with virus. Therefore, users need to be wary of unknown followers. Detecting account, which is real or fake can help users to be careful accepting some unknown follower. In addition, users can report to Instagram so that account can be deactivated. In this thesis, a website-based application is designed that can detect the possibility of an Instagram account being a real or fake account. The detection is carried out using the Locally Adaptive K-Nearest Neighbor algorithm classification method based on Discrimination Class (DC-LAKNN) which is an adaptive algorithm from the K-Nearest Neighbor algorithm. This algorithm pay attention at discrimination class as the basis for classification. The attributes used in the classification are user follower count, following count, biography length, media count, username digit count, username length, user has profile picture, user is private. The end result is that the Locally Adaptive K-Nearest Neighbor algorithm based on Discrimination Class (DC-LAKNN) can be used to classify Instagram accounts with an accuracy of 96.23%.
Aplikasi Sistem Pengontrolan Turtle Tub Untuk Pemeliharaan Kura-Kura Red Belly Nelsoni Dengan Arduino Kevin Pramana Pongmasak; Silvia Rostianingsih; Indar Sugiarto
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Having turtles, especially Red Belly Nelsoni turtle is very common, but usually the owner don’t really know how to properly taking care of their turtles, according to the parameters needed in turtle maintenance. The problem that the author wants to solve is by utilizing Blynk application and Internet of Things tools that has a function to control, monitor and maintain all parameters that needed in turtle maintenance, so that the owner of the turtle can more easily taking care of the turtle in the turtle tub. The test was carried out by giving 2 turtle tubs containing Red Belly Nelsoni turtles to 2 volunteers who carried out the turtle care and maintenance in different ways. From the result of the test carried out, the application has been able to help the volunteers in taking care of the turtles according to the parameter aspects that needed in turtle maintenance.
Implementasi Text Summarization pada Review Aplikasi Super di Google Play Store Menggunakan Metode Maximum Marginal Relevance Dion Alexander Louis; Silvia Rostianingsih; Leo Willyanto Santoso
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Super App is an app for reseller agents who sell and distribute basic necessities in tier 2, 3 cities and rural Indonesia. The Super app has been downloaded by around 50,000 users on the Play Store. Various reviews or reviews have also been given by users who have downloaded the Super application. Whether we realize it or not, customer opinions / reviews given on Google play, a little or a lot, will have an influence on potential customers. Based on the problems that occur, this research will implement a text summarization program on Super App reviews with the implementation of the MMR and TF-IDF methods, so that from the large number of existing reviews, only a few important sentences can be extracted, so that the conclusion making process will become easier. The results of the research using the MMR method produced an average precision value of 40.4% in 3 trials, and with the highest precision value of 60.4% in the experiment using the parameter value = 0.7
Sistem Optimalisasi Rute Model Capacitated Vehicle Routing Problem With Time Windows Menggunakan Algoritma Metaheuristic Particle Swarm Optimization pada Perusahaan Kantong Plastik HDPE PT XYZ Jason Jason; Silvia Rostianingsih; Andreas Handojo
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Technology has been one of the key factors behind industrial revolution. Companies are now required to use technological assistance and data processing to produce faster and more efficient business processes. This is also the case with Company XYZ. Company XYZ is an HDPE plastic manufacturer domiciled in Surabaya. Currently, the company is trying to handle the increasing frequency of shipments that exist in the company. Due to the increasing frequency of shipments, the company is often overwhelmed in handling its shipments because there is no system that can quickly determine the shipping route for the company. Moreover, there are other route determining factors such as shipment weight, truck capacity, and special delivery hour requests that add to the complexity of the route to be calculated manually. So a system is needed that is able to provide route recommendations quickly. This route optimization system is designed using the PHP programming language and the Bootstrap frontend framework to support the system UI Design. The database used is mySQL database. The system will be created in 2 modules, namely a module for the admin and a module for the driver. For this system to work, firstly the system will run the KMeans Cluster function from the database to cluster all customers in the company. This cluster is one of the factors determining the fitness value in the Particle Swarm Optimization algorithm. After the order data is obtained, the system will use the PSO algorithm to determine the delivery agenda for each truck. The determining factors of PSO include customer location, priority hours of customer requests, order weight, and loading capacity of different types trucks. After obtaining the delivery table of each truck, the system will use the help of Google Waypoints API to determine the routing order from each truck. The final result of this system is a delivery route optimization system that is able to provide route selection recommendations for each truck in the company. The system is also able to sort shipments with various shipping priority restrictions. From the test results, the PSO algorithm in the system is able to produce routes with less total distance traveled and less travel duration than the routes generated manually by the employees in the company.
Sistem Pendukung Keputusan Pemberian Kredit berdasarkan Klasifikasi Kelancaran Pembayaran Kredit Menggunakan Metode VIKOR pada Bank XYZ Daniel Hartono; Leo Willyanto Santoso; Silvia Rostianingsih
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Banks must carry out complex assessments before being able to determine who is the most eligible prospective debtor who can be given a loan. This is due to limited funds and the risk of bad credit cases. The limited manpower and manual processes cause the whole process of lending at XYZ Bank to be prone to human error and become inefficient. As a solution for XYZ Bank to overcome existing problems, a credit decision support system is needed that can assist XYZ Bank in selecting and determining prospective debtors who can be given loans. Therefore, in this study, the KNearest Neighbor method was used to assist XYZ Bank in predicting the smoothness of credit payments of a prospective debtor. Then, this research continues with ranking using the VIKOR method to determine who is the most ideal debtor candidate to be given a loan. Based on the results of the classification test using both training data and new data, the highest accuracy is obtained at 100% for each type of loan. Based on the results of the ranking test, the accuracy of the business loans ranking is 83.33%, the accuracy of the consumer loans ranking is 80.33%, and the accuracy of the various-purpose loans ranking is 70%. The results of the questionnaire evaluation in system testing conducted by 6 respondents assessed that the application design was 76.67% good, the application functionality was 86.67% good, the ease of use of the application was 83.33% good, the application answered the needs was 86.67% good, and the overall application was 90% good.
Co-Authors Alexander Setiawan Andre Cristo Singgih Andreas Handojo Calvin Christopher Kurniawan Christian Natanael Sugiharto Daniel Hartono Dion Alexander Louis Djoni Haryadi Setiabudi Feronica Natalia Rivaldi Henry Novianus Palit Indar Sugiarto Jason Jason Josia Christian Kevin Christian Kevin Pramana Pongmasak Kevin Shaquille Limanuel Leo Willyanto Santoso Liliana Liliana Lily Puspa Dewi Melvin Soeharto Michael Jonathan Resmana Lim Resmana Lim Stephen Cornelius Hertanto Willy Pratama Darmalim Yansen Tri Utomo Yoel Julianto Yosefani Kurniawan Yoshua Refo Yulia Yulia