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All Journal ILKOM Jurnal Ilmiah Science in Information Technology Letters Bulletin of Social Informatics Theory and Application
Ming Foey Teng, Ming Foey
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Computer Science & IT Social Sciences

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How much does distance learning affect social life and psychology of growing adolescent Teng, Ming Foey; Putra, Yudha Kusuma; Ilham, Muhammad; Pratama, Wahyu Arbianda Yudha
Bulletin of Social Informatics Theory and Application Vol. 4 No. 2 (2020)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v4i2.289

Abstract

Distance learning is one of the most important innovations in the education field. It provides flexibility when considering time and place in education while enhancing the efficiency of gathering knowledge. While distance learning improves the quality of education, distance learning diminishes the interaction between students and teachers or among students themselves. The lack of interaction that occurs in distance learning affects adolescent’s social life and psychology. In this research, we would like to study all the impacts that are going to affect students, especially in their social life and psychology. In this research, the method used is a document study or document analysis. The purpose of this research is to observe and analyze the impact of distance learning to improve distance learning in the future not only in educational but also in social and psychological side. With this research, we find that students that use distance learning without any interaction with the others tend to induce an antisocial behavior which leads to loneliness and suicidal thought.
Comparison Analysis of Digital Forensic Tools on Instagram Messenger using The National Institute of Standards and Technology (NIST) Method Harno Supardin; Satra, Ramdan; Asis, Muh. Arfah; Teng, Ming Foey
Bulletin of Social Informatics Theory and Application Vol. 6 No. 1 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i1.534

Abstract

Technological developments from time to time are very rapid, one of which is the development of smartphones which are always evolving in terms of operating systems, features, specifications, and applications. Today's increasingly sophisticated technology has become an important part of people's lives, some activities of people's lives can be carried out by utilizing technology, including committing crimes in cyberspace. One of the most widely used social media applications is Instagram. Instagram messenger causes cybercrime, pornography, fraud and cyberbullying. This study aims to compare the performance of digital forensic tools in obtaining digital evidence on Instagram messenger using the NIST Method. The results of this study indicate that MOBILedit Forensic and Magnet Axiom have the following accuracy results in restoring deleted data on Instagram messenger, MOBILedit Forensic 69.23% and Magnet Axiom 76.92%.
Water quality identification based on remote sensing image in industrial waste disposal using convolutional neural networks Widiharso, Prasetya; Handoko, Wahyu Tri; Wibawa, Aji Prasetya; Handayani, Anik Nur; Teng, Ming Foey
Science in Information Technology Letters Vol 2, No 2: November 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i2.638

Abstract

Measuring the quality of river water used as industrial wastewater disposal is needed to maintain water quality from pollution. The chemical industry produces hazardous waste containing toxic materials and heavy metals. At specific concentrations, industrial waste can result in bacteriological contamination and excessive nutrient load (eutrophication). Using the Convolutional Neural Network (CNN), the method for measuring water quality processes remote sensing images taken via an RGB camera on an Unmanned Aerial Vehicle (UAV). The parameter measured is the change in the color of the river water image caused by the chemical reaction of the heavy metal content of industrial waste disposal. The test results of the Convolutional Neural Network (CNN) method in 2.01s/step obtained the value of training loss mode 17.86%, training accuracy 90.62%, validation loss 23.43%, validation accuracy 83.33%.
Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widyaningtyas, Triyanna; Haviluddin, Haviluddin; Hasihi, Cholisah Erman; Teng, Ming Foey; Darwis, Herdianti
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1759.382-389

Abstract

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.
Co-Authors Aji Prasetya Wibawa Anik Nur Handayani Darwis, Herdianti Harno Supardin Hasihi, Cholisah Erman Haviluddin Haviluddin Muhammad Arfah Asis Muhammad Ilham Pratama, Wahyu Arbianda Yudha Purnawansyah Purnawansyah Putra, Yudha Kusuma Ramdan Satra Triyanna Widyaningtyas, Triyanna Wahyu Tri Handoko Widiharso, Prasetya