Pengembangan Game Augmented Reality Pembelajaran Bahasa Pemrograman Dasar Menggunakan Agile Scrum

The agile scrum methodology for augmented reality development increases project team efficiency. Private campus are frequently confronted with the dilemma of new students with various backgrounds that come not only from vocational high schools but also from high schools. First year students in the informatics study programme come not only from vocational informatics high schools, but also from high schools that specialize in social studies and languages. This is a difficult task in terms of imparting a comprehension of the fundamentals of programming. This study develops augmented reality in order to teach HTML and Javascript. By combining basic principles with gaming, the proposed augmented reality (AR) makes programming interesting. Players must comprehend their programming logic in order to be immersed in a virtual environment by answering coding bug questions. During usability testing, the System Usability Scale (SUS) assesses user happiness and AR knowledge. Participants from various programming backgrounds were tested on their knowledge of programming languages. According to usability research, 59% of people found AR programming languages useful for learning and understanding basic programming languages. AR and Agile Scrum make programming more enjoyable. This study demonstrates how augmented reality can be used to teach programming languages. These findings imply that Agile Scrum and AR methods can improve learning and programming foundations. More research and development could lead to more complete and complicated AR learning environments for programming instruction.Game instruksional sangat cocok untuk Agile Scrum, yang berhasil dalam proyek pengembangan perangkat lunak yang bergerak cepat. Studi ini membuat game augmented reality untuk mengajarkan HTML dan Javascript. Game augmented reality (AR) yang disarankan membuat pembelajaran pemrograman menjadi menyenangkan dengan menggabungkan konsep dasar ke dalam gameplay. Membenamkan pemain di lingkungan virtual dengan menjawab pertanyaan bug pengkodean mengharuskan mereka menggunakan bakat pemrograman mereka. Skala Kegunaan Sistem (SUS) mengukur kepuasan pengguna dan pengetahuan game AR selama pengujian kegunaan. Peserta dengan latar belakang pemrograman berbeda diuji pemahaman mereka tentang bahasa pemrograman inti game AR. Sebagian besar gamer memahami bahasa pemrograman dasar, menurut pengujian kegunaan. Game AR dan Agile Scrum menjadikan belajar pemrograman menyenangkan dan mudah. Penelitian ini menunjukkan bagaimana game augmented reality dapat mengajarkan bahasa pemrograman. Temuan menunjukkan bahwa prosedur Agile Scrum dan game AR interaktif dapat meningkatkan dasar-dasar pembelajaran dan pemrograman. Lebih banyak penelitian dan pengembangan dapat mengarah pada lingkungan pembelajaran AR yang lebih lengkap dan kompleks untuk pengajaran pemrograman.   &nbsp

Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent

Cancer is a disease induced by the abnormal growth of cells in body tissues. This disease is commonly treated by chemotherapy. However, at first, cancer cells can respond to the activity of chemotherapy over time, but over time, resistance to cancer cells appears. Therefore, it is required to develop new anti-cancer drugs. Indenopyrazole and its derivative have been investigated to be a potential drug to treat cancer. This study aims to predict indenopyrazole derivative compounds as anti-cancer drugs by using Ant Colony Optimization (ACO) and Artificial Neural Network (ANN) methods. We used 93 compounds of indenopyrazole derivative with a total of 1876 descriptors. Then, the descriptors were reduced by using the Pearson Correlation Coefficient (PCC) and followed by the ACO algorithm to get the most relevant features. We found that the best number of descriptors obtained from ACO is ten descriptors. The ANN prediction model was developed with three architectures, which are different in hidden layer number, i.e., 1, 2, and 3 hidden layers. Based on the results, we found that the model with three hidden layers gives the best performance, with the value of the R2 test, R2 train, and Q2 train being 0.8822, 0.8495, and 0.8472, respectively.    &nbsp

XGBoost and Convolutional Neural Network Classification Models on Pronunciation of Hijaiyah Letters According to Sanad

According to Sanad, the pronunciation of Hijaiyah letters can serve as a benchmark for correct or valid reading based on the makhraj and properties of the letters. However, the limited number of Qur'anic Sanad teachers remains one of the obstacles to learning the Qur'an. This study aims to identify the most practical combination of classification models in constructing a voice recognition system that facilitates learning without requiring direct interaction with a teacher. The methods employed include the XGBoost algorithm and CNN. As a result, out of the 12 letter trait labels, the CNN model was utilized for 10 of them, specifically for traits S1, S2, S4, S5, T1, T2, T3, T4, T5, and T6, on trait labels S3 and T7 applying the XGBoost model. Furthermore, the inclusion of additional data yielded performance results for each property, with an average accuracy of 78.14% for property S (letters with opposing properties), 70.69% for property T (letters without opposing properties), and an overall average of 73.79% per letter.Huruf Hijaiyah adalah huruf yang terdapat dalam susunan Al-Qur'an. Karakter huruf hijaiyah adalah kenampakan karakter yang keluar dari makhrajnya, sedangkan huruf makharijul adalah tempat keluarnya huruf saat melafalkan huruf hijaiyah. Huruf Hijaiyah yang ada dalam Sanad dapat dijadikan sebagai patokan bacaan yang benar atau sahih karena sudah memenuhi ciri dan makna huruf tersebut. Terbatasnya jumlah pengajar Al-Qur'an masih menjadi salah satu kendala dalam mempelajari Al-Qur'an dengan baik. Hal ini ditunjukkan dengan sedikitnya pengajaran Al-Qur'an pada Sanad yang membuka pembelajaran tahsin, padahal pembelajaran tahsin pada Sanad merupakan salah satu pelajaran yang memiliki standar dalam pengucapan kaidah huruf sesuai dengan sifat hurufnya. Sistem pengenalan suara dapat mengenali suara sehingga dengan teknologi ini diharapkan dapat mendukung pembelajaran tanpa harus bertemu dengan guru. Pada penelitian ini dibangun model klasifikasi huruf hijaiyah berdasarkan karakteristik huruf menggunakan algoritma pembelajaran dangkal XGBoost dan algoritma pembelajaran mendalam CNN. CNN cenderung menghasilkan kinerja yang lebih baik daripada Extreme Gradient Boosting (XGBoost). Model algoritme XGBoost memiliki akurasi superior pada properti S2 dan T7. Namun, memorinya rendah. Penambahan data memberikan keseimbangan terhadap hasil kinerja sehingga nilai akurasi, presisi, memori, dan skor F-1 memiliki tingkat yang cukup. Akurasi untuk sifat S rata-rata 78,14%, properti T 70,69%, dan rata-rata per huruf 73,79%

Identifikasi Kesamaan Pertanyaan pada Soal Bahasa Indonesia Menggunakan Metode Recurrent Neural Network (RNN)

In a question-and-answer forum, the identification of question similarity is used to determine how similar two questions are. This procedure makes sure that user-submitted questions are compared to the questions in a database for matches to improve system performance on the online Q&A platform. Currently, question similarity is mostly done in foreign languages. The purpose of this research is to identify question similarities and evaluate the effectiveness of the methods used in Indonesian language questions. The data used is a public dataset with labeled pairs of questions as 0 and 1 where label 0 for different pairs of questions and label 1 for the same pairs of questions. The method used is a Recurrent Neural Network (RNN) with the Manhattan Distance approach to calculate the similarity distance between two questions. The question pairs are taken as two inputs with a reference label to identify the similarity distance between the two question inputs. We evaluated the model using three different optimizers namely RMSprop, Adam, and Adagrad. The best results were obtained using the Adam optimizer with 80:20 ratio split-data and overall accuracy is 76%, precision is 74%, recall is 98.8%, and F1-score is 85.1%.Identifikasi kesamaan pertanyaan merupakan bagian penting dalam Question Answering System. Identifikasi kesamaan pertanyaan dilakukan dengan tujuan untuk membuat sebuah sistem menjadi lebih efisien dalam memberikan jawaban secara cepat dan akurat. Fokus yang dikerjakan pada penelitian ini adalah mengidentifikasi kesamaan pertanyaan pada soal bahasa Indonesia serta mengevaluasi efektivitas penggunaan metode pada bahasa Indonesia. Data yang digunakan adalah dataset dengan pasangan pertanyaan berlabel 0 dan 1 dimana label 0 untuk pasangan pertanyaan yang berbeda dan label 1 untuk pasangan pertanyaan yang sama. Kesamaan pertanyaan tersebut diidetifikasi dengan menggunakan model Recurrent Neural Network (RNN) dengan pendeketakan Manhattan Distance. Pasangan pertanyaan dijadikan sebagai dua inputan dengan acuan label untuk mengidentifikasi jarak kesamaan antara kedua inputan tersebut menggunakan pendekatan Manhattan distance. Model dievaluasi dan menghasilkan skor akurasi sebesar 76%, presisi 74%, recall 98,8% dan f1-score 85,1%. Hasil tersebut diperoleh melalui penambahan stopword_removal pada data. Analisis kami terhadap hasil yang didapatkan adalah dengan penambahan fungsi stopword_removal pada proses preprocessing dapat meningkatkan hasil identifikasi kesamaan pertanyaan pada soal bahasa Indonesia

Analisis Komparatif Karakteristik Kebakaran Hutan Berbasis Machine Learning di Sumatera dan Kalimantan

Sumatra and Borneo are areas consisting of rainforests with a high vulnerability to fire. Both areas are in the tropics which experience rainy and dry seasons annually. The long dry season such as in 2019 triggered forest and land fires in Borneo and Sumatra, causing haze disasters in the exposed areas. This indicates that climate variables play a role in burning forests and land in Borneo and Sumatra, but how climate affects the fires in both areas is still questionable. This study investigates the climate variables: temperature, humidity, precipitation, and wind speed in relation to the fire’s characteristics in Borneo and Sumatra. We use the Random Forest model to determine the characteristics of forest fires in Sumatra and Borneo based on the climate variables and carbon emission levels. According to the model, the fire event in Sumatra is slightly better predicted than in Borneo, indicating a climate-fire dependence is more prominent in Sumatra. Nevertheless, a maximum temperature variable is seemingly an important indicator for forest and land fire in both domains as it gives the largest contribution to the carbon emission

Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Annotation and analysis of online customer reviews were identified as significant problems in various domains, including business intelligence, marketing, and e-governance. In the last decade, various approaches based on topic modeling have been developed to solve this problem. The known solutions, however, often only work well on content with static topics. As a result, it is challenging to analyze customer reviews that include dynamic and constantly expanding collections of short and noisy texts. A method was proposed to handle such dynamic content. The proposed system applied a dynamic topic model using BERTopic to monitor topics and word evolution over time. It would help decide when the topic model needs to be retrained to capture emerging topics. Several experiments were conducted to test the practicality and effectiveness of the proposed framework. It demonstrated how a dynamic topic model could handle the emergence of new and over-time-correlated topics in customer review data. As a result, improved performance was achieved compared to the baseline static topic model, with 25% of new segmented texts discovered using the dynamic topic model. Experimental results have, therefore, convincingly demonstrated that the proposed framework can be used in practice to develop automatic review annotation tools

Regression Analysis for Crop Production Using CLARANS Algorithm

Crop production rate relies on rainfall over Rejang Lebong district. Data showed a discrepancy between increased crop production and rainfall in Rejang Lebong District. However, the spatiotemporal distribution of the crop variable's dependencies remains unclear. This study analyses the relationship between rainfall and crop production rate in the Rejang Lebong district based on the performance of the machine learning method. In addition, this research also performed regression analysis to carry out rainfall clusters and crop production. This order provides information in the form of cluster results to determine how much the rainfall variable influences the crop production rate  in each cluster. Harnessing the Elbow, CLARANS, Simple Linear Regression, and Silhouette Coefficient methods, this study used 231 rainfall data sourced from the Bengkulu BMKG and 110 data for plant production obtained from BPS Bengkulu Province from 2000-2022. This research found that the optimal clusters were 3 clusters. C1 contains 106 data with the largest regression value for chili = 0.127, C2 contains 15 data with the largest regression value for mustard greens = 0.135, and C3 contains 110 data with the largest regression value for cabbage = 0.408, eggplant = 0.197, and carrots = 0.201. Furthermore, this research also found that the biggest correlation of crops with highly significant improvement would be cabbage commodity (Y=0.4114X+0.2013) and chili plantation with high RSME (0.9897)

The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students

Choosing a specialization was not an easy task for some students, especially for those who lacked confidence in their skill and ability. Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide the learning outcomes record, which showed the specialization classification for the Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showed imbalanced class distribution because the number of each field differed greatly. Therefore, SMOTE was added to classify the imbalanced class. The accuracy obtained from the combination of RBM and SMOTE was 70% with a 0.4 mean squared error

Retweet Prediction Using Multi-Layer Perceptron Optimized by The Swarm Intelligence Algorithm

Retweets are a way to spread information on Twitter. A tweet is affected by several features which determine whether a tweet will be retweeted or not. In this research, we discuss the features that influence the spread of a tweet. These features are user-based, time-based and content-based. User-based features are related to the user who tweeted, time-based features are related to when the tweet was uploaded, while content-based features are features related to the content of the tweet. The classifier used to predict whether a tweet will be retweeted is Multi Layer Perceptron (MLP) and MLP which is optimized by the swarm intelligence algorithm. In this research, data from Indonesian Twitter users with the hashtag FIFA U-20 was used. The results of this research show that the most influential feature in determining whether a tweet will be retweeted or not is the content-based feature. Furthermore, it was found that the MLP optimized with the swarm intelligence algorithm had better performance compared to the MLP

Scalability Testing of Land Forest Fire Patrol Information Systems

The Patrol Information System for the Prevention of Forest Land Fires (SIPP Karhutla) in Indonesia is a tool for assisting patrol activities for controlling forest and land fires in Indonesia. The addition of Karhutla SIPP users causes the need for system scalability testing. This study aims to perform non-functional testing that focuses on scalability testing. The steps in scalability testing include creating schemas, conducting tests, and analyzing results. There are five schemes with a total sample of 700 samples. Testing was carried out using the JMeter automation testing tool assisted by Blazemeter in creating scripts. The scalability test parameter has three parameters: average CPU usage, memory usage, and network usage. The test results show that the CPU capacity used can handle up to 700 users, while with a memory capacity of 8GB it can handle up to 420 users. All users is the user menu that has the highest value for each test parameter The average value of CPU usage is 44.8%, the average memory usage is 69.48% and the average network usage is 2.8 Mb/s. In minimizing server performance, the tile cache map method can be applied to the system and can increase the memory capacity used