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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Media Sisfo The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Informatika dan Rekayasa Komputer PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science Scientific Journal of Informatics Jurnal Pengabdian Masyarakat Unama Prosiding Seminar Nasional Ilmu Teknik
Yovi Pratama
Universitas Dinamika Bangsa
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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PELATIHAN PENGENDALIAN LED MENGGUNAKAN MIKROKONTROLER ESP8266 BERBASIS WEB PADA SISWA SMA NEGERI 6 TANJUNG JABUNG BARAT Yaasin, Muhammad; Pratama, Yovi; Bustami, M. Irwan; Prananda, Trio
Jurnal Pengabdian Masyarakat UNAMA Vol 5 No 1 (2026): JPMU Volume 5 Nomor 1 April 2026
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jpmu.2026.5.1.2774

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan pemahaman dan keterampilan siswa dalam pengendalian LED menggunakan mikrokontroler ESP8266 berbasis web di SMA Negeri 6 Tanjung Jabung Barat. Metode pelaksanaan dilakukan melalui pendekatan teori dan praktik langsung, meliputi pengenalan konsep IoT, instalasi perangkat, pemrograman menggunakan Arduino IDE, serta pengujian sistem kendali berbasis web. Hasil kegiatan menunjukkan adanya peningkatan pemahaman siswa berdasarkan hasil pre-test dan post-test. Kegiatan ini diharapkan dapat menumbuhkan minat dan kreativitas siswa dalam bidang teknologi digital
Public complaint tweet data feature analysis for sentiment classification Errissya Rasywir; Yovi Pratama; Irawan Irawan; Marrylinteri Istoningtyas
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7172

Abstract

The perception of the public regarding a government's performance significantly impacts a city's advancement. This research involved analyzing complaint tweets from Jambi City residents directed at the government to gauge sentiment. In the testing phase, 500 Twitter accounts were examined to categorize sentiment as positive, negative, or neutral. Training data was prepared by extracting tokens through feature selection techniques such as information gain (IG) and mutual information (MI). For testing, all tokens are entered as data in the input layer in the recurrent neural network (RNN). From the tests carried out, the average use of feature selection can achieve a good value compared to no feature selection. But more specifically the use of IG produces better accuracy compared to the use of MI. From the research conducted, Twitter data is classified using a RNN and several tests by adding feature selection to produce differences. The results are proven to improve classification performance. With a recall value of 92.243%, it shows the system's success rate in sentiment classification and a precision of 92% indicates a level of accuracy that is sufficient to support the government's sentiment assessment.
NoduViT: An Attention-Driven DeepLab–CustomViT Hybrid Model for Segmentation-Informed Multiclass Pulmonary Patch Classification Fachruddin Fachruddin; Yovi Pratama; Errissya Rasywir; Yessi Hartiwi; Jasmir Jasmir; Hastie Audytra; Marrylinteri Istoningtyas
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7124

Abstract

Lung cancer detection using CT imaging requires accurate lung segmentation and nodule classification. This study evaluates SegFormer configurations and hybrid CNN–Transformer models using the LIDC-IDRI dataset. Initial experiments showed that pretrained and from-scratch SegFormer models failed to detect target classes, while fine-tuning achieved IoU scores of 0.6628 and 0.6244 for the right and left lungs, respectively, with corresponding F1-scores of 0.7972 and 0.7688. However, nodule segmentation remained challenging (IoU 0), with limited improvement using a Nodule Loader (IoU 0.1487, F1 0.2589). Compared with recent models reporting Dice scores above 94%, SegFormer demonstrated lower segmentation performance. For classification, standalone ViT, ResNet, and DeepLab achieved accuracies of 72%, 61%, and 82%, respectively, whereas hybrid ResNet+ViT and DeepLab+ViT achieved 98% and 99.54%. Despite these improvements, class imbalance reduced minority-class nodule recall and weighted F1-scores. Overall, fine-tuned SegFormer effectively segments major lung structures but struggles with nodules, while hybrid CNN–Transformer classifiers achieve high accuracy yet require improved class balancing for reliable clinical generalization.
Multi-Stage Feature Fusion for Breast Cancer Histopathology using EfficientNetV2 and Vision Transformers Errissya Rasywir; Yovi Pratama; Yessi Hartiwi; Fachruddin Fachruddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7769

Abstract

Early detection of breast cancer is essential for improving patient survival and reducing mortality. Although histopathology-based diagnosis provides definitive clinical evidence, the complex and heterogeneous nature of breast cancer histopathology (BCH) images poses significant challenges for accurate classification. Conventional deep learning models often struggle to capture both local tissue characteristics and global contextual patterns simultaneously. To address this limitation, this study proposes a Multi-Stage Feature Fusion (MSFF) model that integrates EfficientNetV2, a CNN local texture branch, and a Vision Transformer (ViT). EfficientNetV2 extracts high-level semantic features, the CNN branch captures fine-grained local texture information, and the ViT models long-range contextual dependencies through self-attention. These complementary representations are fused through an adaptive multi-stage fusion strategy to enhance discriminative capability for binary BCH classification. An ablation study is conducted to systematically evaluate the contribution of each architectural component. Experimental results on the BreakHis dataset demonstrate that the proposed model achieves the best overall balanced classification performance, with an accuracy of 93.04%, precision of 93.08%, recall of 92.69%, F1-score of 92.89%, and an AUC of 0.9303 at the optimal 200× magnification level. The findings confirm that the proposed fusion strategy provides balanced classification performance, stable optimization behaviour, and strong generalization capability, effectively addressing BCH image heterogeneity and offering strong potential for clinical decision-support applications.
Co-Authors Abdul Haris Abdul Harris Achpal Haddid Afrizal Nehemia Toscany Agung Islamy Aryanto Agus Siswanto Akbar Ramadhan Akwan Sunoto Alvito Widianto Amroni, Amroni Angelica, Felicia Anggraini, Dila Riski Annisa putri Anton Prayitno Arahmad Taupiq asih asmarani Audytra, Hastie Bayu saputra Beni Irawan Borroek, Maria Rosario Cahyana Putra Pratama Candra Adi Rahmat Carenina, Babel Tio Chindra Saputra Defrin Azrian Desi Kisbianty, Desi Despita Meisak Dimas Pratama Dimas Yudha Prawira elvi yanti Emelia, Shinta Errissya Rasywir Evan Albert Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin Fachruddin, Fachruddin farchan akbar Feranika, Ayu Fingki Lamhot Pasaribu fiqri ansyah Hartiwi, Yessi Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hendrawan Hilda Permatasari Hussaein, Ahmad Ilham Adriansyah ilham permana Imelda Yose Indana Arum , Refi Irawan Irawan Irawan, Beni Istoningtyas, Marrylinteri Janu Hadi Susilo Jasmir Jasmir Jopi Mariyanto Julia Triani khalil gibran ahmad Kholil Ikhsan Luthfi Rifky M Fikrul Hakimi M Irwan Bustami M Reihan Al Fajri M.Rizky Wijaya Manyu, Dimas Abi Maria Rosario Borroek Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marrylinteri Istoningtyas Marshal` Koko Anand masgo Maulana Qaedi Aufar Mayang Ruza Moh. Ismail Muhammad Afif Dzaky Khairullah Muhammad Diemas Mahendra Muhammad Riza Pahlevi MUHAMMAD SURYA Muhammad Wahyu Prayogi Muhammad Zulfi Tisna Tama Mumtaz Ilham S Mumtaz Ilham Syafatullah NAIBAHO, RONALD Najmul Laila Naldi Irfan Nanda Ghina Nur Aini Nurhadi Nurhadi Pahlevi, M. Riza Pahlevi, M.Riza Pareza Alam Jusia Pareza Alam Jusia, Pareza Alam Prananda, Trio Ramadhan Saputra, Tri Reza Pahlevi Rezky Pramudia, Muhammad Riki Bayu Andhika Rio Ferdinand ROBY SETIAWAN Rohaini, Eni Rosario B, Maria Rosario, Maria Rudolf Sinaga Sandi Pramadi Santoso Saparudin, Saparudin Setiawan Assegaff SIKA, XAVERIUS Steven Ie Sudewo, Raden Tio Putra Sutoyo, Mochammad Arief Hermawan Suyanti Suyanti taupiq, Arahmad Verwin Juniansyah virginia casanova andiko andiko Warcita Warcita WILLY RIYADI Xaverius Sika Yaasin, Muhammad Yanti, Elvi Yessi Hartiwi Yessi Hartiwi Yessi Hartiwi Yoga Rizki Yuga Pramudya Zahlan Nugraha Zulia, Restutik