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Leo Willyanto Santoso
Program Studi Informatika
Computer Science & IT

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Identifikasi Jenis Anjing Berdasarkan Gambar Menggunakan Convolutional Neural Network Berbasis Android Kevin Oktovio Lauw; Leo Willyanto Santoso; Rolly Intan
Jurnal Infra Vol 8, No 2 (2020)
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Dogs are raised by many people however, to maintain a dog, there are several factors that must be considered such as feed consumed, intensity of care, and cleanliness of the cage or the appropriate environment. Therefore, an android application is needed to identify the type of dog and provide information related to the type of dog. The method we used is You Only Look Once to detect dog objects in an image then the dog image is cropped, the results will be processed by the Convolutional Neural Network to identify the type of dog based on the image given after that displaying the results of its identification on android. The test results show that the identification results from CNN are very dependent on the results of predictions from YOLO because the input from CNN is the result of predictions from YOLO. YOLO has a disadvantage where it will detect dog dolls and dog fur as dog objects. The test results show the accuracy of YOLO to detect dogs is 94.242%, CNN accuracy of model I is 56.400%, accuracy of CNN model II is 40,000% and accuracy of CNN model III is 50.400%.
Implementasi Ethereum Claims Registry pada Ethereum Blockchain untuk Verifikasi Transaksi Konten Fotografi dengan InterPlanetary File System Reyner Reyner; Rudy Adipranata; Leo Willyanto Santoso
Jurnal Infra Vol 9, No 2 (2021)
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Nowadays, photography often receives a lot of attention from diverse backgrounds. Photography can be used to capture important moments as well as then digitally encased and stored into a storage unit. Typically, photography content is stored on servers, but there are threats in centralized storage like malware attacks, hardware failures and human errors. In the study, Ethereum Claims Registry will be implemented on smart contracts to verify certain attributes about its user which are stored in Ethereum Blockchain, so that users can either issue or retrieve existing claims. Then implementation of the interplanetary file system as a website storage and photography content. Website and Photography Content which are stored on InterPlanetary File System can be accessed globally without centralized storage and do not incur additional fees on ethereum transactions because they are stored off-chain or stored outside the blockchain. Based on the results of the tests that have been carried out, the implementation of the Ethereum Claims Registry and the InterPlanetary File System as a storage for photography content for high gas price requires 0.020518 ETH, medium gas price requires 0.010747 ETH, and low gas requires prices 0.004885 ETH. Then the implementation of the Ethereum Claims Registry obtained 1% ether efficiency on deploying smart contracts, -0.4% on make claims, and -0.4% on set claims
Implementasi Sistem Inventori pada Prodi Informatika Universitas Kristen Petra Richard Putra Sugijanto; Henry Novianus Palit; Leo Willyanto Santoso
Jurnal Infra Vol 8, No 2 (2020)
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Petra Christian University Informatics Study Program is one of the educational institutions which one of the activities to carry out an inventory of items which includes submission, purchase, recording, and distribution / delivery of items both for the purposes of supporting teaching and learning activities and for the needs of employees in carrying out their work in serving students and lecturers, and its reporting. So far there has not been an inventory system, especially in laboratories used by Informatics Study Programs. The data collection and reporting process is still done manually which requires a long time in completing each work. Errors sometimes occur in the calculation and recording of items. Items are often moved so that they forget where they are. Seeing the problems that occur, then made a computerized inventory system to simplify and speed up the data collection process, and the process of reporting inventoryThe software developed is a web-based inventory system. This system is also adjusted to the results of the analysis of the existing system with several additional features to adjust the needs of the laboratory. The web system was developed using the serveride PHP programming language as well as several supporting libraries such as clientide css, javascript in the form of bootstrap, datatables, and jQuery.The final results of software development include the movement of items that occur in laboratories, such as input data of items, lending, returning and moving items.
Penerapan IoT dan Sistem Pakar untuk Memonitoring Kualitas Air dan Mendiagnosa Penyakit pada Tambak Udang Vaname Kevin Alexander Harianto; Rudy Adipranata; Leo Willyanto Santoso
Jurnal Infra Vol 9, No 2 (2021)
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Shrimp’s death is one of the things that is avoided by Vaname shrimp farmers. Where this can occur due to poort water conditions, or the way farmer maintain their shrimp.The problem that the author wants to solve is by utilizing an application that functions to see the condition of water quality using internet of things and using an expert system with the forward chaining method to diagnose if there are symptoms that arise in shrimp.Based on the test that have been carried out, the application made is able to monitor water quality properly and the results of the method test are able eto reach an accuracy value of 100%.
Pengenalan Intent pada Natural Language Understanding Berbahasa Indonesia dengan Menggunakan Metode Convolutional Neural Network Daniel Adi; Leo Willyanto Santoso; Alvin Nathaniel Tjondrowiguno
Jurnal Infra Vol 8, No 1 (2020)
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To keep up with technological developments and people behavior, intelligent bot has become part of the business world which help them maintain good relation with their customer. Unfortunately, resource for intelligent bot in Indonesian language is very scarce compared to High Resource Language like English. Therefore further research about Natural Language Understanding in Indonesian language is needed. We use Convolutional Neural Network method to train our model. Model consist of embedding layer, convolutional layer, max pooling, flatten, dropout, and softmax layer. In the process of making model, there are many variable that can be tested such as dropout, number of filter, size of filter, etc. This research show that the amount and quality of data for each category can affect how a model understand the feature of each category which affect the overall precision. The quality of word2vec, one of the most important resource in the model can give significant impact on precision. The size of dropout can affect how the model understand the important feature of data. From various tests, we found that the best precision is 93 %. 
Penerapan Metode Slope One dan K-Nearest Neighbor Untuk Menentukan Rekomendasi Tempat Makan di Bali Berbasis Android Lucy Rosalind; Leo Willyanto Santoso; Lily Puspa Dewi
Jurnal Infra Vol 9, No 2 (2021)
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Restaurants are an alternative if there is no food at home or whenyou are traveling somewhere. Bali is one of the popular tourismcities because of the many tourists. The number of restaurantsmakes users confused to choose a restaurant that suits theirneeds. This recommendation application is made to help users inchoosing the restaurant that suits their needs easily.The initial step taken is the scraping process, after the scrapingprocess the next step is looking for restaurants that do not putprices which will later be calculated with the price prediction withthe Slope One algorithm after the data entered into the database.The recommendation calculation is done on the backend. When onAndroid, it only needs to make API calls.Based on the results of the tests that have been carried out, theprogram has succeeded in collecting data, managing, anddisplaying restaurant data. The price prediction process with theSlope One algorithm has very good results. For the process ofscraping the data to be retrieved as the original data. The KNearest Neighbor calculation process was successfully carriedout and has sufficient accuracy with K of 35, has an accuracy of59%, and contains data of 7,890 restaurants.
Analisa Audio Features dengan Membandingkan Metode Multiple Regression dan Polynomial Regression untuk Memprediksi Popularitas Lagu Billy Faith Susanto; Silvia Rostianingsih; Leo Willyanto Santoso
Jurnal Infra Vol 9, No 2 (2021)
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Songs are artistic works that expresses ideas and emotion in the forms of rhythms, melodies, and harmonies. Songs are the source of huge profit for musicians or artists from commercial view-point. Based on the data from IFPI, the earnings from the music industry in 2019 reached US$20.2 billion, in which 56.1% of them came from streaming revenue. Spotify is one of the largest and most well-known streaming services in the world today. This research aims to make predictions of popularity from each song according to the audio feature data taken from Spotify's API. The process of prediction will use 2 regression methods, which are Linear Regression and Polynomial Regression. The model will be made using those 2 methods and will be tested with the R2, Adjusted R2, MAE, and MSE metric systems. From the analysis of the implementation to the program, the Linear Regression method had garnered the average results as follows: 0.23614 for R2, 0.23536 for Adjusted R2, and had average errors 17.38129 for MAE method, 442.31700 for MSE method. Using the Polynomial Regression method, the average results were: 0.31496 for R2, 0.25880 for Adjusted R2, and had average errors 16.47367 for MAE method, 409.76242 for MSE method.
Implementasi Web Service Integrasi Data Penelitian dan Pengabdian Masyarakat dengan SISTER Ristekdikti dengan Metode REST Lettisia Apriolita Harjono; Leo Willyanto Santoso; Justinus Andjarwirawan; Resmana Lim
Jurnal Infra Vol 8, No 2 (2020)
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Every universities is obliged to apply research and community service. Based on data frm the official website of Ristekdikti, the number of lecturers at universities in Indonesia is very large, so we need a system to collect data and manage lecturer data. With so many data held by the lecturer, the data collection becomes ineffective when having to retype it. Therefore, a program was made to help lecturers to collect research and community service data through data that was already in the university’s information system. This research uses a web service that has been provided by SISTER Ristekdikti. The method used is Representational State Transfer (REST). Data from the university is about research and community service. Input data must be mapped first to fit the required data. The program of this research will provide the success of adding data to SISTER Ristekdikti. The test result of the research program show that the data has been successfully entered into the SISTER Ristekdikti mirror database. From the results of the questionnaire obtained the results that the overall application is good with an average value of 2.7. In addition, the application helps lecturers to complete P2M data and the data displayed in accordance with needs with an average value of 3.
Klasifikasi Artikel Berita Bahasa Indonesia Dengan Naive Bayes Classifier Anthony Setiawan; Leo Willyanto Santoso; Rudy Adipranata
Jurnal Infra Vol 8, No 1 (2020)
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Human access to latest news now becoming more easier and much more, caused by advanced technological development in latest years. But, the article categorization is still manually inserted by the writer, so sometimes by human error, some mistake can be happening, like inserting wrong category or sometimes the writer purposely insert wrong category just because that category is so popular just to boost his viewer count. That’s why there is an application in the form of website to automatically categorizing the article that fit mostly to their its category.This application is using N-Gram feature and Naïve Bayes Classifier method to classifying news content. N-Gram feature is a feature that group words based on the amount of N, like unigram or bigram. Naïve Bayes Classifier is a method that using probability to solve some problem.According to the test using Naïve Bayes Classifier, in dataset training and test with ratio of 50 : 50, at unigram section the correct accuracy result are 0.901,  and the bigram result are 0.508. In dataset ratio of 60 : 40, at unigram section the correct accuracy result are 0.904, and the bigram result are 0.498. In dataset ratio of 70 : 30, at unigram section the correct accuracy result are 0.947, and the bigram result are 0.519. In dataset ratio of 80 : 20, at unigram section the correct accuracy result are 0.887, and the bigram result are 0.507. So, the conclusion is dataset training and test with ratio of 70 : 30 yield highest accuracy, in unigram (0.947) and also bigram (0.519).
Sistem Informasi Administrasi Merchandise pada Perusahaan Tour and Travel X Hennyta Sutanto; Yulia Yulia; Leo Willyanto Santoso
Jurnal Infra Vol 7, No 2 (2019)
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Inventory data which has been stored so far is still manual and has not been used to generate information. Inventory data is prone to error as result for manual process. General Affair division needs a software to control inventory flow in a structured manner and generate information from the data. The software is expected to help company to control inventory flow.Before starting the application development, analysis and design of the company’s stock and the needs of General Affair were done. The analysis and design stage produce a number of diagrams that will be used to create the application.The final result of the development is an Administrative Information System. The application includes purchase, sales, delivery and receive order, inventory, customizable pivot table of purchase, sales, and inventory, income statement, purchase forecast, and merchandise, vendor, user maintenance features. Based on the research, it concluded that 75% of application features are good.
Co-Authors Adam Putra Sulaiman Adrian Evan Alexander Setiawan Alexander William Sutjiadi Alvin Nathaniel Tjondrowiguno Anthony Setiawan Billy Faith Susanto Daniel Adi Daniel Valentino Hidayat Djoni Haryadi Setiabudi Doenny Auddy Lionovan Erriv Septianfan Budi Handry Khoswanto Hendry Gozali Hendyk Cendana Santoso Hennyta Sutanto Henry Novianus Palit Ichwanto Hadi Isak Imanuel Leong Justinus Andjarwirawan Kartika Gunadi Kevin Alexander Harianto Kevin Oktovio Lauw Lettisia Apriolita Harjono Liliana Liliana Lily Puspa Dewi Lucy Rosalind Michael Christianto Wujaya Michael Christiawan Resmana Lim Reyner Reyner Richard Putra Sugijanto Ricky Handoyo Rolly Intan Rudy Adipranata Silvia Rostianingsih Valerie Caroline Alverina Yulia Yulia Zachary Osborn