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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJIET (International Journal of Indonesian Education and Teaching) Indonesian Journal of Information System International Journal of Applied Sciences and Smart Technologies Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat
Hari Suparwito
Universitas Sanata Dharma
Humanities Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Factors Influencing the Difficulty Level of the Subject: Machine Learning Technique Approaches Suparwito, Hari
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (978.99 KB) | DOI: 10.24071/ijasst.v1i1.1869

Abstract

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 stakeholders
Student Perceptions Analysis of Online Learning: A Machine Learning Approach Suparwito, Hari; Polina, Agnes Maria; Budiraharjo, Markus
Indonesian Journal of Information Systems Vol 4, No 1 (2021): August 2021
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v4i1.4594

Abstract

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.
PELATIHAN MEDIA BELAJAR BERBASIS ONLINE DI ERA PANDEMI Kartono Pinaryanto; Anastasia Rita Widiarti; Haris Sriwindono; Ridowati Gunawan; Hari Suparwito; Sri Hartati Wijono; Rosalia Arum Kumalasanti; Wiwien Widyastuti
ABDIMAS ALTRUIS: Jurnal Pengabdian Kepada Masyarakat Vol 5, No 1 (2022): April 2022
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/aa.v5i1.3916

Abstract

The education sector is one of the areas that has been most affected by the COVID-19 pandemic. Schools, which normally hold offline meetings, must now take place online. With this pandemic, the teaching process must be "forced" to be done online. The task model which is usually given in physical mode (questions on paper, done and collected) is no longer relevant to be done because of the limitations of physical meetings. On the other hand, students need an explanation from the teacher directly because they are used to the context of offline learning. Judging from the current level of smartphone ownership, whether owned by students themselves or their parents, we can use smartphone devices to help the teaching and learning process. But of course it requires technological literacy from the student side and the teacher side so that this teaching and learning process can be carried out properly. As a form of concern for the academic community of the Informatics Study Program at Sanata Dharma University to the problems that exist in the environment around the campus, we held training activities for making teaching media for State Elementary School of Timbulharjo teachers who ultimately played an important role in improving teachers' technological literacy in carrying out online learning. This activity had been carried out well offline in 2 stages, namely stage 1 on 9 and 10 June 2021 and stage 2 on 22 and 23 November 2021.
Student Perceptions Analysis of Online Learning: A Machine Learning Approach Hari Suparwito; Agnes Maria Polina; Markus Budiraharjo
Indonesian Journal of Information Systems Vol. 4 No. 1 (2021): August 2021
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v4i1.4594

Abstract

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.
Factors Influencing the Difficulty Level of the Subject: Machine Learning Technique Approaches Hari Suparwito
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v1i1.1869

Abstract

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 stakeholders
Information Technology and Learning Methodology Amid the COVID-19 Pandemic Hari Suparwito
International Journal of Applied Sciences and Smart Technologies Volume 02, Issue 02, December 2020
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v2i2.2886

Abstract

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.
Analisis Unjuk Kerja K-Nearest Neighbour untuk Klasifikasi Citra Aksara Bali Tulis Tangan Anastasia Rita Widiarti; Hari Suparwito
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 12, No 2 (2022): Oktober
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.67796

Abstract

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.
CLASTERIZATION OF LECTURER'S PROFILE IN ONLINE LEARNING DURING THE COVID-19 PANDEMIC Agnes Maria Polina; Christiyanti Aprinastuti; Hari Suparwito
IJIET (International Journal of Indonesian Education and Teaching) Vol 7, No 2 (2023): July 2023
Publisher : Sanata Dharma University Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijiet.v7i2.6495

Abstract

The learning process changed from classroom to online learning during the COVID-19 pandemic. One of the things that must be done is to analyze the readiness of lecturers in facing online learning. The purpose of this study is to cluster the profiles of lecturers dealing with online learning. The clustering method uses a Machine Learning approach with the K-means algorithm. Data were taken from 274 lecturers who returned questionnaires during April–June 2022. The questionnaire consisted of 27 questions on a Likert scale (1–4). The Boruta technique is used to determine the five most significant variables (Variable Importance) in the clustering. The results of the clustering show that the lecturers are divided into 2 large groups with the following criteria: focus on learning methods, learning materials, student independence, exploration of new knowledge, and online learning evaluation tools.
SVM-PSO Algorithm for Tweet Sentiment Analysis #BesokSenin Anggita Dewi Novia Wardhani Susanto; Hari Suparwito
Indonesian Journal of Information Systems Vol. 6 No. 1 (2023): August 2023
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v6i1.7551

Abstract

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.
Co-Authors Agnes Maria Polina Agnes Maria Polina Anastasia Rita Widiarti Anastasia Rita Widiarti Anggita Dewi Novia Wardhani Susanto Christiyanti Aprinastuti Haris Sriwindono Markus Budiraharjo Pinaryanto, Kartono Ridowati Gunawan Rosalia Arum Kumalasanti Sri Hartati Wijono Wiwien Widyastuti