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All Journal JADECS (Journal of Art, Design, Art Education and Culture Studies) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Abdimasku : Jurnal Pengabdian Masyarakat
Al zami, Farrikh
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Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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GUIDELINES OF MOOD – THINKING – LOGIC PROFILING & ANTI-HOAX FRAMEWORK: DETECTING SOMEONE'S MOTIVES ON SOCIAL MEDIA Gamayanto, Indra; wibowo, sasono; Novianto, Sendi; Al zami, Farrikh; Sirait, Tamsir Hasudungan; sani, Ramadhan rakhmat
JADECS (Journal of Art, Design, Art Education & Cultural Studies) Vol 6, No 2 (2021)
Publisher : Jurusan Seni dan Desain, Fakultas Sastra, Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um037v6i22021p80-102

Abstract

Abstract—Social media is a lifestyle, starting from how to think and behave towards something. Understanding what is on social media requires a systematic guide to distinguish between true and false information. Therefore, this article will answer it. Two important parts of this article are discussing mood-thinking-logic which is the basis of every human's thinking, which then results in two attitudes, namely doing the right or wrong thing. This article complements the two articles that have been published. Because the problem regarding hoaxes is still an unfinished debate and still has problems finding the right formula or guide, in this article we create two concepts to solve this problem. the first concept produces guidelines of mood-thinking-logic profiling, which are concepts for understanding the layers of feelings, thoughts and logic of a person and the motives he does in social media, then the second concept is anti-hoax framework which discusses seven levels of hoaxes and solutions to overcome hoaxes. Both of these concepts will be accompanied by examples of case studies that discuss these matters, so that readers will understand the two concepts. Furthermore, this research is still being developed because it still needs a lot of refinement, and this research is part of the text mining research that we are currently doing.Keywords—Mood, Thinking, Logic, Profiling, Anti Hoax
RETRACTED : Pelatihan Game Design Untuk Siswa SD Pada Pusat Kegiatan Belajar Masyarakat (PKBM) Semarang Gamayanto, Indra; Wibowo, Sasono; Novianto, Sendi; Al Zami, Farrikh; Sundjaja, Arta Moro; Sirait, Tamsir Hasudungan
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2251

Abstract

Artikel dengan judul Pelatihan Game Design Untuk Siswa SD Pada Pusat Kegiatan Belajar Masyarakat (PKBM) Semarang telah dilakukan pencabutan dari jurnal Abdimasku Vol. 7 No. 2 Mei 2024, pada tautan daring https://abdimasku.lppm.dinus.ac.id/index.php/jurnalabdimasku/article/view/2251. Hal ini dikarenakan kesamaan judul artikel pada https://pubmas.umus.ac.id/index.php/devozione/article/view/935.
Enhancing Vision Transformer Performance with Rotation Based Augmentation for Classifying Images of Colon Cancer Pathology Prasetya, Rudy Eko; Soeleman, M. Arief; Al Zami, Farrikh; Affandy, Affandy; Marjuni, Aris; Assaqty, Mohammad Iqbal Saryuddin
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24918

Abstract

Background: In medical imaging, classifying images of colon cancer pathology is still an essential challenge, especially for facilitating early diagnosis and successful intervention. Recently, Vision Transformer (ViT) models have demonstrated great promise for a variety of computer vision tasks, including the classification of medical images. However, the lack of annotated medical datasets and the intrinsic unpredictability of histopathology pictures sometimes restrict their performance. Objective: This study aims to enhance the performance of ViT models in colon cancer pathology classification by introducing a targeted data augmentation strategy, with a particular focus on rotation-based augmentation. Methods: We proposed a data augmentation pipeline that uses controlled changes to improve the number and diversity of training data. Like Rotation, Flip and Geometry are emphasized to replicate the real-world tissue orientation variations that are frequently seen in colon pathology slides. 10,000 JPEG pictures of colon cancer pathology, each with a resolution of 768 x 768 pixels, are used to train the models. We use models trained with and without the suggested augmentation pipeline to compare ViT performance across accuracy, sensitivity, and specificity in order to assess the impact of augmentation. Results: According to study results, rotation-based augmentation enhances ViT performance, achieving up to 99.30% accuracy and 99.50% sensitivity while preserving training times. In real-world pathology settings, where slide orientation varies greatly and can affect categorization consistency, these enhancements are especially pertinent. Conclusion: The proposed rotation-centric data augmentation technique enhances the performance of the ViT model in the classification of images showing colon cancer pathology.
Optimizing Chronic Kidney Disease Diagnosis Using the C4.5 Algorithm and Missing Value Imputation Strategies Riyanto, Ahmad; Purwanto, Purwanto; Al Zami, Farrikh; Andreuw Meda, Ridodio
Jurnal Penelitian Pendidikan IPA Vol 11 No 9 (2025): September: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i9.12456

Abstract

The occurrence of missing values in data mining is a significant challenge that can hinder the knowledge extraction process. Incomplete data not only reduces efficiency in data management and analysis, but also has the potential to bias decision-making. This study aims to improve the performance of the C4.5 algorithm in dealing with missing value problems through the application of imputation techniques and GridSearchCV optimization. In this study, we propose an approach to handling missing values by combining several imputation methods, including minimum, maximum, mean-mode, median, and k-Nearest Neighbors (k-NN). These methods are applied to the Chronic Kidney Disease dataset obtained from the UCI Repository. After the imputation process, we performed hyperparameter optimization using GridSearchCV to find the best parameter combination for the C4.5 algorithm. Experimental results show that the application of imputation techniques and GridSearchCV optimization significantly improves the classification accuracy of the C4.5 algorithm. The comparison results show that the application of missing value handling, combined with GridSearchCV optimization, successfully improves the accuracy of the model by 2.25% compared to without using missing values. This proves that handling missing values along with proper GridSearchCV optimization can improve the prediction quality of the model.
Co-Authors Affandy Affandy Andreuw Meda, Ridodio Arta Moro Sundjaja, Arta Moro Assaqty, Mohammad Iqbal Saryuddin Indra Gamayanto Marjuni, Aris Prasetya, Rudy Eko Purwanto Purwanto Ramadhan Rakhmat Sani Riyanto, Ahmad Sasono Wibowo Sendi Novianto Sirait, Tamsir Hasudungan Soeleman, M. Arief