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Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods Santiko, Irfan; Soeprobowati, Tri Retnaningsih; Surarso, Bayu; Tahyudin, Imam; Hasibuan, Zainal Arifin; Che Pee, Ahmad Naim
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.598

Learning achievement is undoubtedly impacted by each person's unique learning style. The assessment pattern is less focused due to the intricacy of the current components. In fact, general elements like VARK are thought to create complexity that can impair focus when combined with elements like environmental conditions, teacher effectiveness, and stakeholder policies. Although it is only ideal in specific areas, the application of supported information technology has so far yielded positive results. This essay attempts to be creative in evaluating how well students learn in higher education settings. An assessment framework that uses multidimensionality and simplifies features is the innovation that is being offered. Method, Material, and Media (3M) are the three categories into which simplification of aspects is separated. However, the Dimensions are categorized into five groups: Traditional, Enhance, Mobile, Ubiquitous, and Smart (TEMUS). Approximately 1200 respondents consisting of students and lecturers formed into a dataset in 2 types of data, namely test data and training data. The trial was conducted using 4 models, namely Random Forest, SVM, Decision Tree, and K-Nearest. The test results were interpreted in MSE, R-Square, Accuracy, Recall, Precision, and F1-Score. Based on the comparison of test results, it states that Random Forest has the most optimal results with MSE values of 0.46, R Square 0.99, Accuracy 0.86, Recall 0.86, Precision 0.87, F1 Score 0.84. Based on the results obtained, it proves that in addition to being able to carry out the classification process, the TEMUS Dimensional Framework can form a pattern of compatibility with each other, between the learning styles of Lecturers and Students. According to this TEMUS framework, teacher and student performance will be deemed suitable and effective when the 3M components are assessed from both perspectives in the same way. If not, a review will be conducted.

Measuring interest and talent in determining learning using the quadrant model in the learning process in a smart classroom Santiko, Irfan; Soeprobowati, Tri Retnaningsih; Surarso, Bayu; Tahyudin, Imam
Jurnal Inovasi Teknologi Pendidikan Vol. 12 No. 1 (2025): March
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v12i1.73585

Naturally, the learning process in smart classrooms is greatly impacted by the trend of individual learning, now known as personalized learning. Several studies have demonstrated that, because of the problem of technological advances, the effectiveness of the anticipated results has not been fully achieved. While there are technology benefits, some scholars link them to issues. This study aims to demonstrate it by evaluating learning interests and talents. A sample of at least 1000 students from 419 universities participated in the questionnaire experiment. Each of the three questionnaire domains, affective, cognitive, and psychomotor, was examined using ANOVA. The coefficient test uses two variables: interest and talent. With an ANOVA P-value of 0.021 for psychomotor and 0.031 for affective and cognitive, the three domains demonstrated a statistically significant connection. The coefficients of interest and talent, which average between 1 and 0.05 for P emotional and cognitive interest (0.054) and P talent (0.023) and between 0.027 and 0.055 for P psychomotor interest and P talent, demonstrate the significant values of both factors. The developed interest and talent measuring model can be used to forecast learning outcomes based on these findings. In addition to information technology, the results of this interest and talent-measuring design can be utilized to define and evaluate the learning process, including its appropriateness. Further research recommendations include a framework to measure interests and talents early, aiding admissions, curriculum, resources, methods, and learning media development.

KONSEP DAN MODEL FITUR E-CRM PADA LAYANAN TIKET TRANSPORTASI ONLINE DI WILAYAH PURWOKERTO JAWA TENGAH Santiko, Irfan; Suryani, Ade Irma
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 4 No. 5 (2019)
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar

Internet network users are increasing and everyone has their own busy lives. This can happen to people who usually use transportation services to travel outside the city and the internet network can be used as a liaison between the transportation and also customers. To help customers make online bookings, both door-to-door and point-to-point transportation, choosing a schedule and destination location as needed, a study is carried out, which is making a ticket reservation feature for web-based transportation agents. E-Ticketing is one of the results of the development of Customer Relationship Management (CRM) features where digital areas in the business are the basis of service products. However, not all transportation service companies know the basis of CRM and how to implement the actual or minimum standards that can be applied in accordance with the business strategies used in customer service.

Classification of Hate Speech in TikTok Social Media Comments Using Naive Bayes Algorithm and TF-IDF Weighting Utami , Putri Febi; Krisbiantoro, Dwi; Santiko, Irfan; Riyanto, Andi Dwi
Journal of Multimedia Trend and Technology Vol. 4 No. 3 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i3.102

This research focuses on the classification of hate speech in Indonesian Tik Tok comments. Tik Tok, as a social media platform with high interaction intensity, generates a large volume of comments with diverse linguistic characteristics, including the use of formal and informal language. This linguistic variation poses challenges in the content moderation process, particularly in automatically identifying hate speech. The research dataset is secondary data obtained by combining public datasets and scraped Tik Tok comments, with an initial total of 5,698 comments. The collected data represent general user comments with variations in formal and informal language. To improve data quality, pre-processing stages were carried out including text cleaning, tokenization, normalization, stop-word removal, and stemming. After pre-processing, 4,542 comments were obtained that were suitable for use in the modeling process. Experimental results show that the Multinomial Naïve Bayes model with TF-IDF weighting is able to classify hate speech with high performance. Model accuracy reached 93% before parameter optimization and increased to 95% after hyperparameter tuning with an alpha value of 0.5. The confusion matrix results show a relatively low misclassification rate, although the class distribution in the dataset still shows imbalance. The findings of this study indicate that the Multinomial Naïve Bayes approach is effective in recognizing linguistic patterns of hate speech in Indonesian TikTok comments, including text with informal language characteristics.

Sentiment Analysis in User Reviews of Tourist Attractions in East Nusa Tenggara Using Machine Learning Classification Aulia Dian Agustina; Primandani Arsi; Pungkas Subarkah; Irfan Santiko
Journal of Multimedia Trend and Technology Vol. 5 No. 1 (2026): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v5i1.82

This study aims to analyze user review sentiments for six tourist attractions in East Nusa Tenggara Province by utilizing a large amount of review data obtained from Google Maps. Data was collected through a scraping process using Serp API, followed by cleaning and text pre-processing to improve data quality. Sentiment labeling was performed automatically using the Indo-BERT model to obtain three sentiment classes: positive, negative, and neutral. Text feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, then classified using the baseline Support Vector Machine (SVM) model and the optimized SVM model with Grid-Search CV. The evaluation results showed that the baseline SVM model produced an accuracy of 83.87%, but showed an imbalance in performance between classes with a Macro F1-score of 0.4287. After parameter optimization using Grid-Search CV, the optimized SVM model produced an accuracy of 78.27% with an increase in the Macro F1-score value to 0.4818. This increase indicates an improvement in the model's ability to recognize minority sentiment classes despite a decrease in overall accuracy. Overall, the optimized SVM model provides more balanced and representative classification results in describing tourists' perceptions based on online reviews, so it can be used as a basis for sentiment analysis in the tourism sector.

Peningkatan Kapasitas Pengelola BUMDes Melalui Pelatihan Akuntansi Dasar dan Implementasi Aplikasi Keuangan Berbasis Web Irfan Santiko; Zanuar Rifai; Muhamad Bintang Cakra Kencana; Lili Dwi Rakhmawaty
Jurnal Pengabdian Masyarakat (ABDIRA) Vol 6, No 4 (2026): Abdira, Oktober
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/abdira.v6i4.1352

This community service program aims to address the manual financial record-keeping problem faced by BUMDes Berkah Sentosa in Pangebatan Village, which has frequently caused data discrepancies and conflicts among its managers, while also improving the effectiveness, accountability, and cost efficiency of financial management in its food-sector business unit, and preparing financial reports that meet the requirements for funding applications, whether government subsidies, financial institution credit, or external investment. The method applied was Direct Action, through direct mentoring in the implementation of the BUMDes Financial Application (Intellectual Property Rights No. 000385898) developed by the implementation team, to address limitations in human resources for financial management. The results show an improved understanding among partners regarding financial reporting and accounting, as well as the establishment of a recording system that produces more transparent, structured, accurate, efficient, and real-time accessible financial and managerial reports, thereby supporting BUMDes in achieving better financial management.

Implementing an AI Agent Chatbot to Improve Service Effectiveness in Mobile Phone Spare Parts and Service Stores Futikhat Salsabila; Irfan Santiko
Journal of Multimedia Trend and Technology Vol. 4 No. 1 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i1.88

The development of digital technology encourages MSMEs to shift to automated service systems to increase efficiency. This study aims to develop a WhatsApp-based AI agent chatbot to improve the effectiveness of customer service at the Citra Karya Gemilang Store, especially in handling spare part stock information and cellphone service consultations. This study uses the Software Development Life Cycle (SDLC) Waterfall model with the stages of requirements analysis, system design, implementation, testing, and maintenance. The system was developed by integrating the WAHA API Gateway as a WhatsApp connector, n8n Workflow Automation as a conversation flow manager, Gemini AI as an artificial intelligence engine that understands message context, and Google Sheets as a dynamic database that stores product and service information in real-time. The implementation results show that the chatbot is able to provide a fast response 24 hours a day. Based on User Acceptance Testing (UAT) of 20 respondents, the system obtained a satisfaction level of 92% (Very Satisfied).

Comparison of Support Vector Machine and XGBoost Algorithms in Sentiment Analysis of Visitor Reviews of Baturraden Tourism Forest Catur Risma Utami; Irfan Santiko
Journal of Multimedia Trend and Technology Vol. 4 No. 2 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i2.91

The Google Maps platform provides a platform for visitors to express their opinions through reviews. This study aims to compare the performance of the Support Vector Machine and XGBoost algorithms in sentiment analysis of Baturraden Tourism Park visitor reviews. Data were collected using scraping techniques and obtained 4,096 reviews. After going through preprocessing stages including cleaning, tokenization, normalization, stopword removal, and stemming, the data used in the analysis process amounted to 2,912 reviews. Word weighting was carried out using the TF-IDF method, and the SMOTE technique was applied to address class imbalance. The results showed that the Support Vector Machine algorithm performed better than XGBoost with an accuracy rate of 94.52% before SMOTE and 94.86% after SMOTE, while XGBoost obtained an accuracy of 92.80% before SMOTE and 93.15% after SMOTE. These findings indicate that the Support Vector Machine is more effective in classifying positive and negative sentiments. This study is expected to contribute to the application of machine learning methods to understand visitor opinions on the Google Maps platform. Especially in the context of tourist attractions.

Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods Irfan Santiko; Tri Retnaningsih Soeprobowati; Bayu Surarso; Imam Tahyudin; Zainal Arifin Hasibuan; Ahmad Naim Che Pee
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.598

Learning achievement is undoubtedly impacted by each person's unique learning style. The assessment pattern is less focused due to the intricacy of the current components. In fact, general elements like VARK are thought to create complexity that can impair focus when combined with elements like environmental conditions, teacher effectiveness, and stakeholder policies. Although it is only ideal in specific areas, the application of supported information technology has so far yielded positive results. This essay attempts to be creative in evaluating how well students learn in higher education settings. An assessment framework that uses multidimensionality and simplifies features is the innovation that is being offered. Method, Material, and Media (3M) are the three categories into which simplification of aspects is separated. However, the Dimensions are categorized into five groups: Traditional, Enhance, Mobile, Ubiquitous, and Smart (TEMUS). Approximately 1200 respondents consisting of students and lecturers formed into a dataset in 2 types of data, namely test data and training data. The trial was conducted using 4 models, namely Random Forest, SVM, Decision Tree, and K-Nearest. The test results were interpreted in MSE, R-Square, Accuracy, Recall, Precision, and F1-Score. Based on the comparison of test results, it states that Random Forest has the most optimal results with MSE values of 0.46, R Square 0.99, Accuracy 0.86, Recall 0.86, Precision 0.87, F1 Score 0.84. Based on the results obtained, it proves that in addition to being able to carry out the classification process, the TEMUS Dimensional Framework can form a pattern of compatibility with each other, between the learning styles of Lecturers and Students. According to this TEMUS framework, teacher and student performance will be deemed suitable and effective when the 3M components are assessed from both perspectives in the same way. If not, a review will be conducted.

Assessing the Accuracy Level of University-Based Website-Based Search Engines Using F-Measure and Hellinger Irfan Santiko; Gerry Andriana
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.716

Websites are an information medium that is becoming something that is needed in this era. Including the media within the campus environment. The problem is that the campus as a forum or place for student learning is considered less than optimal in presenting information on student learning activities. For example, library reference information, administration, important announcements, and other similar information. The current solution is that universities use social media platform communication media which are considered accurate, which actually adds to problems when the media is used not in accordance with its function, such as promotions, hoax information and irrelevant information. This causes the information to become too massive so that the level of accuracy and relevance is reduced. The author's solution is to optimize the search engine on the campus website platform to be used as an absolute information medium. So the information obtained will be more targeted and accurate. Starting from measuring the level of accuracy to the impact of the results will be discussed in this article. The technique used to measure accuracy is a quantitative technique consisting of the F-Measure and the Hellinger Method. As a result, the campus will know that to distribute related news, the campus can find out keywords that are considered strategic in every report on the media website.

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