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Hans Juwiantho
Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
Computer Science & IT

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Meningkatkan Kesulitan Serangan Enemy Dengan Menambahkan Influence Map Pada Metode A* Pada Procedural Generated Tower Defense Game Michael Budiono; Liliana Liliana; Hans Juwiantho
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Abstract

In tower defense game, if the map dan enemy attack do not change, the strategy that will be used by the player will remain the same, this will make the game having a low replay value and will make the player stop playing the game. The existing tower defense game have used procedural generation to create different levels each time they are played, but there are still shortcoming where the map looks plain and has the same pattern every time it is played, other than that enemy attack using A* have a simple pattern and can’t  search for a profitable path for the enemy so the enemy become easy to defeat and the game become less interesting. To overcome this problem, perlin noise is used in procedural generation so that the map mode does not look plain and does not have the same pattern every time it is played, other than that enemy attack use A* by adding influence map so that enemy attack can be more challenging and interesting to the player.In this thesis, the map was created using perlin noise to determine the terrain of the tile on the map and the location of start and finish will be checked using floyd warshall algorithm to determine if the map need to be remade. For enemy attack, A* is used with the addition of influence map to make the enemy can choose a path that is more profitable for it by avoiding roads blocked by tower and roads that can be attacked by towers.The test results show that after the map is generated repeatedly for 20 times, no map has the same location and distance between start and finish. In addition, it is also found that the implementation of procedural generation and influence map made the game 25.7% more challenging when compared to games that did not use it.
Pemanfaatan text summarization dengan Support Vector Machine dan K-nearest neighbor pada analisis sentimen untuk mempermudah pengguna membaca review game STEAM Hilarius Bryan; Rolly Intan; Hans Juwiantho
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Today the development of the game is increasing and in line with the growth of the players. Usually, these players who are often called players have a special platform to see the latest game developments. One that is often targeted is Steam, where the platform provides complete information such as reviews, prices, release dates, and so on for users who want to buy games. Usually before buying a game the user will see a review first. The number of reviews on Steam makes it difficult for users to find information. From these problems, text summarization was carried out to summarize information and sentiment analysis to assess the value of the game. In order to get a good summary of the information, it is necessary to go through several data processing processes. The game review data collection process is obtained through the available Steam API. Once collected, preprocessing will be carried out to overcome the varied and inconsistent data that can affect the training process. Preprocessing includes Tokenization, Stopwords Removal, and Stemming. The text summarization process for feature to vector uses TF-IDF and Sentiment Score to get the main sentence before the training process using SVM is carried out. The classification process uses the KNN method which compares each game review data whether the data is closer to positive or negative, thus helping users when viewing game information becomes shorter and easier. Measurement of the success of this method in answering problems by testing data with the Confusion Matrix and surveying Steam users. The use of text summarization for each game review has little role in improving the results of sentiment analysis, because the method is not suitable and the game review data is in the form of an abstract. The accuracy of sentiment analysis is still better when text summarization is not carried out on the data. A total of 50 people were asked to provide statements regarding the results of sentiment analysis and text summarization. The results obtained by 40 out of 50 users said the application helped read game reviews and 10 others did not.
Sistem Pakar Diagnosa Penyakit Ikan Arwana dengan Menentukan Tingkat Kualitas Air Menggunakan Forward Chaining dan Simple Additive Weighting Kevin Christian; Djoni Haryadi Setiabudi; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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arwana fish always has its own charm for the people of Indonesia, as well as foreign countries. But every living thing must have been stricken with disease, including the arwana fish itself. Diseases in arwana are often not well identified by hobbyists and beginners because there are many parameters that must be considered. One of the problems in identifying arwana disease is the problem of the suitability of water parameters with arwana fish.            This expert system is equipped with Forward Chaining and Simple Additive Weighting methods. Forward Chaining allows the expert system to ask only the questions it needs. Simple Additive Weighting is used to determine the level of suitability of parameters in arwana fish. This method allows us to determine whether the water quality is suitable for the arwana fish by performing calculations based on the weight of the water parameters quickly.Tests were carried out by 2 experts on 20 arwana fish. The test results on the expert system for diagnosing arwana fish disease obtained an accuracy level of conformity with the expert with an accuracy value of 95%.
Sistem Informasi dan Rekomendasi Kegiatan Kemahasiswaan Universitas Menggunakan Content-Based Filtering pada Web App RE*ACH sebagai Pusat Informasi Kegiatan Kemahasiswaan Universitas untuk Mahasiswa Misael Rithe Setio; Henry Novianus Palit; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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In living the college life in Petra Christian University, each college students have his own problems. One of them is the problem of meeting the requirement for Student Activity Credit Unit (SKKK). There are also various reasons for each student to have this kind of problem. However, getting the opportunity to pass the selection as a committee member or participate in some student activities is one of the reason that the students complain most. Therefore, an information system and recommendation system for student activities must be created in a centralized platform that can be accessed by all Petra Christian University students. In helping the information system to be more precise in providing information, recommended system is added to the system so that the information related to the users can be addressed correctly to the users who really need it. In making the recommendation system, the ContentBased method with cosine similarity is used because the method tends to recommend products based to each user’s individual preferences. Users of RE*ACH application are all Petra Christian University students, so the dataset will consists of personal data from all Petra Christian University registered in the application.
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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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.
Sistem Suggestion dengan Metode TOPSIS untuk Meningkatkan Keberhasilan Serious Game Greenlife Town Edward Manhattan Prasetio; Gregorius Satia Budhi; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Indonesia has a huge potential for renewable energy sources. However, this potential has not been fully utilized. One of the reasons is that the Indonesian people still do not understand renewable energy sources properly. Due to a lack of understanding and education, very few people are motivated to advance renewable energy sources in Indonesia. The solution to this problem is to create a platform for education to encourage. The platform is in the form of Serious Game. However, in practice, players often find it difficult to complete the game, so that the player's focus is more on solving problems than receiving information. Therefore, this thesis also utilizes the suggestion system to provide assistance options to players using the TOPSIS method. The test results show that the suggestion system is not able to increase the success of players in completing the game. The resulting effect is the opposite, where players increasingly have a worse final score even touching the number 88%. This failure is not solely caused by the method used, but also the way the method is implemented, the user convenience in playing, and the form of testing that is less flexible.
Prediksi Harga Saham Yang Bersifat Siklikal Di Indonesia Menggunakan Metode LSTM dan SVM Gabriel Adisurya Harsono; Alexander Setiawan; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Stock Market prediction is not an easy task, eventhou using technical analysis. Technical analysis is a technique that can predict stock market price. But using this technique is not an easy task, hence there is a change of mistake in analyzing the stoks that create losses for investors and traders. This is where machine learning and deep learning are able to predict a stock that have cyclical behavior. Cyclical stocks have patterns that other stocks do not have, where machine learning and deep learning model are able to catch and learn the pattern on cyclical stocks and predict the stock prices. The prediction are used to give recommandation for the users when to but or when to sell stocks, but it also able to give recommandation and show stock prices a year in the future. In this Undergraduatae Thesis will be used five methode and two type of data, this include LSTM, Bi-LSTM, GRU, SARIMAX, and SVR, the data contains multivariate and univariate type of data. Multivariate data will contain the stock prices, Jakarta composite index, and the support data close prices. The result of prediction will be compared with the existing data using RMSE then compare the result with all existing model. In the proses of the undergraduate thesis, every parameters in the model will be searched using GridsearchCV and the best parameter for each models. From this research found 1 best metode for multivariate prediction and one for univariate. for multivariate the best parameter for the prediction is LSTM where the RMSE value is 63.67 training and 74.82 for testing. For univariate prediction the best metode is SVR where the RMSE value is 58.04 for training and 75.29 for testing.
Aplikasi Analisa Sentimen Bilingual dan Emoji pada Komentar Media Sosial Instagram Menggunakan Metode Support Vector Machine Satria Adi Nugraha; Henry Novianus Palit; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Indonesia is ranked 4th as the most Instagram user in the world. This makes business people triggered to promote their products and services to content creators to make reviews and upload them on Instagram. Business people need to evaluate uploads to assess whether the promotions carried out get a positive or negative response from netizens. Evaluation can be done by checking the comments column. Instagram comments not only contain comments in Indonesian but in English along with emojis. However, checking manually will certainly take a lot of time. Therefore, it is necessary to build an application system that can detect bilingual sentiments and emojis in Instagram comments. This system was built using the Support Vector Machine method to classify language, Indonesian sentiment, and English sentiment and then evaluated using the accuracy value. The data used is a sample of uploaded comments in the form of posts, reels, and IGTV. The combination of preprocessing cleansing, normalization, stopwords removal, and stemming as well as parameter tuning using GridSearchCV was also tested to find the best model. The model is divided into language classification models with Indonesia, Inggris, and Campuran labels, Indonesian sentiment classifications, and English sentiment classifications with positive, neutral, and negative labels. The best accuracy obtained by the model for language classification, Indonesian sentiment, and English sentiment is 88.77%, 73.10%, and 71.56%, respectively. In addition, emojis need to be analyzed because the model that analyzes emojis has 3.875% better accuracy than the model that ignores emoji.
Prediksi Penjualan Pada Data Penjualan Perusahaan X Dengan Membandingkan Metode GRU, SVR, DAN SARIMAX Jordan Nagakusuma; Henry Novianus Palit; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Sales forecasting is an attempt to predict sales using several methods, such as statistical methods, machine learning, and others. Sales forecasts are considered important because poor forecasts can have an impact on the company's inventory so that it can cause storage of too much or too little goods, causing the company to lose. Therefore, we need a model that can predict sales so that companies can plan before filling stock. However, forecasts cannot be done directly, because a company's sales data is definitely influenced by various factors and sales last month are not always the same as in the future, so external data is needed in predicting future sales. Therefore, in this thesis a prediction will be made using 3 models, namely the GRU, SVR and SARIMAX models with the help of external data in the form of CPI data and inflation data. In addition, this thesis also conducted a correlation test to determine whether the sales data to be predicted has significance/relationship with external data so that it helps in predicting sales data. The results obtained from this study are that pot data is more suitable for using univariate data with the GRU model, with RMSE Train 3.22, RMSE Test 2.93. For hanger and sealware data, the best model for prediction is the SARIMAX model with univariate data type (RMSE 30.43) and multivariate data type (RMSE 8.07).
Sistem Rekomendasi Content Based Filtering Pekerjaan dan Tenaga Kerja Potensial menggunakan Cosine Similarity Philips Nogo Raharjo; Andreas Handojo; Hans Juwiantho
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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During the pandemic, there was an economic problem that forced companies to do something to avoid any loss. One of the action is to terminate the employment with their workforces. In the conventional way, the workforce and the company will waste a lot of time looking for the right fit for them. So, the recommendation system for jobs and workforce plays an important role in these conditions. Because with the existence of recommendation system that can help from both sides, it will speed up the meeting between companies that need workers and workers who need jobs. Based on the test have been carried out, the recommendation system using the TF-IDF model can provide good recommendations based on the calculation of the Mean Reciprocal Rank getting 0.857 and Mean Average Precision of 0.833, where these results are quite good.
Co-Authors Alexander Setiawan Andreas Handojo Djoni Haryadi Setiabudi Edward Manhattan Prasetio Gabriel Adisurya Harsono Gregorius Satia Budhi Henry Novianus Palit Hilarius Bryan Jordan Nagakusuma Kevin Christian Kevin Reynaldi Tanjung Liliana Liliana Michael Budiono Misael Rithe Setio Philips Nogo Raharjo Rolly Intan Satria Adi Nugraha