Information Technology and Learning Methodology Amid the COVID-19 Pandemic

The impact of the COVID-19 pandemic on the education sector caused schools and universities are closed. Then, teaching and learning are delivered by an online method through information and communication technology. Some issues have emerged, especially on delivering materials and the minimum requirements of online learning. The study aims to review learning methodologies and the role of information and communication technology for future learning. Heutagogy and Computational Thinking have been selected as the learning methodology for approaching digital native generation. It is no doubt that the significant role in undertaking online education is information and communication technology. Therefore, we suggested some tools to enhance learning systems, such as gamification learning, virtual labs, and social media. We also discussed new learning media using information and communication technology in education. The study's contribution is to describe technology's role in the future learning system to be used by decision-makers in implementing e-learning better

Factors Influencing the Difficulty Level of the Subject: Machine Learning Technique Approaches

The difficulty level of a subject is needed either to understand the student acceptance of the subject and the highest level of student achievement in it. Some factors are considered, what kind of instructions, the readiness of the instructor and students in teaching and learning, evaluation and monitoring systems, and student expectations. Many factors are involved, and educators should know this. It is better if they can discern which are the prime factors and which the secondary factors. The purpose of the study is to find out the determinant factors in establishing the difficulty level of the subject from the students, teachers and infrastructure point of view using three machine learning techniques. The MSE and the variable importance measurement were used to predict between some factors such as Attendance, Instructors, and other factors as independent variables and the difficulty level of the subject as a dependent variable. The study result showed that Gradient Boosting Machine obtained the MSE value result 1.14 and 1.30 for training and validation dataset. The model generated five variable importance as an independent factor, i.e. Attendance, Instructor, The course can give a new perspective to students, The quizzes, assignments, projects and exams contributed to helping the learning, and The Instructor was committed to the course and was understandable. The Gradient Boosting Machine is superior to other methods with the lowest MSE and MAE values results. Two methods, Gradient Boosting Machine and Deep Learning, have produced the same five main factors that influenced the difficulty of the subject. It means these factors are significant and should get intention by the stakeholder

Analisis Unjuk Kerja K-Nearest Neighbour untuk Klasifikasi Citra Aksara Bali Tulis Tangan

Keterbatasan tenaga ahli filolog, dan rentannya material daun lontar yang menjadi aset warisan leluhur jaman dulu, menjadi pemicu untuk dilakukannya otomatisasi alih aksara atau transliterasi citra aksara Bali di daun lontar berbantuan komputer. Algoritma klasifikasi k-nearest neighbour atau kNN, bisa menjadi alat yang dapat digunakan untuk transliterasi tersebut. Prinsip kerja kNN yang sederhana, yaitu dengan mencocokan kemiripan data baru ke data-data uji terdekat,  mampu digunakan untuk tranlisterasi citra aksara Bali.Pendekatan yang dilakukan pada penelitian ini, selain menitik beratkan pada tahap klasifikasi, juga memperhitungkan dua tahap proses sebelum dilakukan klasifikasi. Perlu proses penyiapan citra yang terdiri dari binerisasi, pemotongan bagian kosong, penyamaan ukuran, dan penipisan, dan proses ektraksi ciri  yang  menggunakan algoritma intensity of pixels. Dengan mempergunakan 18 kelas yang mewakili 18 aksara Bali, dan jumlah data 1001 citra, diperoleh rerata prosentase akurasi 84.746%. Akurasi tersebut diperoleh dengan menerapkan prinsip uji silang 3-fold. Dari penelitian ini pula dapat disimpulkan, meskipun data citra yang digunakan adalah hasil tulisan tangan, dengan mempergunakan data latih yang cukup besar, kNN mampu digunakan untuk klasifikasi. Hal ini menunjukkan bahwa kNN dapat diterapkan sebagai metode klasifikasi citra aksara Bali di daun lontar, sehingga dapat dikembangkan lebih lanjut sebagai mesin utama untuk transliterasi citra daun lontar

Image Detection Analysis for Javanese Character Using YOLOv9 Models

The Javanese script needs to be digitized to improve access and usage, especially among younger generations. Digitizing Javanese characters is crucial for preserving Javanese culture and traditions in the long term. This study aims to detect and recognize Javanese characters using the YOLOv9 algorithm, known for its ability to detect various object types, including Latin and non-Latin scripts. The dataset used consists of 85 images of complete Javanese script arranged in a 4x5 grid of different characters. The dataset was divided into a training dataset (75 images) and a validation dataset (10 images). All data pre-processing was done using Roboflow tools. Two experiments were conducted, varying the weights of the YOLOv9 algorithm model: YOLOv9-c and YOLOv9-c-converted. The research results showed that the YOLOv9-c model outperformed YOLOv9-c-converted, achieving a confidence level of over 80% and an mAP value of 0.95 in recognizing Javanese script images. In other words, the YOLOv9 model succeeded in detecting and recognizing Javanese script

Uji Algoritma Stacking Ensemble Classifier pada Kemampuan Adaptasi Mahasiswa Baru dalam Pembelajaran Online

Perubahan metode pembelajaran dari sistem kelas ke online membawa dampak yang sangat signifikan. Mahasiswa dituntut mampu beradaptasi pada perubahan pola belajar mengajar. Penelitian ini bertujuan untuk melakukan klasifikasi kemampuan adaptasi mahasiswa baru dalam pembelajaran online dengan pendekatan machine learning menggunakan algoritma stacking ensemble. Metode penelitian menggunakan penggabungan single classifier dengan teknik ensemble stacking atau stacked generalization menggunakan Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, dan Neural Network sebagai base learner dan Logistic Regression sebagai meta learner. Dari penelitian yang dilakukan, didapatkan f-1 score pada Random Forest sebesar 89.26%, Decision Tree 88.58%, K-NN 84.25%, SVM 88.98%, Neural Network 89.06%, Logistic Regression 89.07%, dan Stacking 88.86%. Meski dibandingkan dengan single classifier seperti Decision Tree dan K- NN, akurasi pada Stacking meningkat, akan tetapi tidak lebih optimal dari Random Forest, SVM, Neural Network, maupun Logistic Regression. Validasi keakuratan model menggunakan Cross Validation menghasilkan f-1 score konstan berada pada angka 88% untuk setiap n-fold yang menunjukkan bahwa model stacking yang diimplementasikan sudah baik dan stabil. Hal tersebut juga ditunjukkan pada hasil uji stabilitas algoritma stacking menggunakan data random yang berjumlah 10 dan 5 record masing-masing sebanyak 5 kali percobaan, hasil yang didapatkan f-1 score konsisten berada pada angka 88%

SVM-PSO Algorithm for Tweet Sentiment Analysis #BesokSenin

The hashtag #BesokSenin is a hashtag that is often trending on Indonesian Twitter on Sunday evenings. Many Indonesian Twitter users expressed their feelings about welcoming Monday using the hashtag #BesokSenin. The tweet containing #BesokSenin is known to be a motivational sentence to welcome Monday full of joy or a disappointed sentence because you have to return to your routine after taking a holiday on Saturday and Sunday. This study conducts sentiment analysis to find out the opinions of netizens on welcoming Mondays. The tweet data used is tweet data with the hashtag #BesokSenin and the keywords school, work, assignments, and college. The classification method used is the Support Vector Machine algorithm, which is optimized using the Particle Swarm Optimization method to optimize the performance of the Support Vector Machine algorithm. Results of 80% accuracy were obtained by applying the Support Vector Machine model based on Particle Swarm Optimization. This accuracy is superior to 1% compared to the results of accuracy using the usual Support Vector Machine model, which equals 79%. This shows that Particle Swarm  Optimization can optimize the accuracy of the Support Vector Machine algorithm

Student Perceptions Analysis of Online Learning: A Machine Learning Approach

The covid-19 pandemic is currently occurring affects almost all aspects of life, including education. School From Home (SFH) is one of the ways to prevent the spread of Covid-19. The face-to-face learning method in class turns into online learning using information technology facilities. Even though there are many barriers to implementing classes online, online learning provides a new perspective for students' learning process. One of the factors for the online learning process's success is the interaction between the two main actors in the learning process, i.e., lecturers and students. The study's purpose was to analyze students' perceptions of the online learning process. The research data were obtained from a student questionnaire, which included five main criteria in the learning process: 1) self-management aspects, 2) personal efforts, 3) technology utilization, 4) perceptions of self-roles, and 5) perceptions of the role of the lecturer. Students provide an assessment through a questionnaire about the online learning methods they experience during the Covid-19 pandemic. The random forest algorithm was applied to examine data. The study results were focused on three main criteria (variable importance) that affect students' perceptions of the online learning process. The results described that the students' satisfaction in online learning is influenced by 1) The relationship between students and lecturers. 2) The learning materials need to be changed and adapted to the online learning method; 3) The use of technology to access online learning. The study contributes to improving the online learning method for the student