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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor JAIS (Journal of Applied Intelligent System) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Building of Informatics, Technology and Science International Journal of Engineering, Technology and Natural Sciences (IJETS) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informasi Cyberku
Ruri Suko Basuki
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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DEEP LEARNING JARINGAN SARAF TIRUAN UNTUK PEMECAHAN MASALAH DETEKSI PENYAKIT DAUN APEL Sutriawan, Sutriawan; Fanani, Ahmad Zainul; Alzami, Farrikh; Basuki, Ruri Suko
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 11, No 1 (2023): Jurnal TIKomSiN, Vol. 11, No. 1, April 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v11i1.729

Abstract

Diseases on apple leaves are becoming a major issue for apple growers since they can cause the crop to fail. Due to the diversity of diseases that can affect apple leaves, it can be challenging for farmers to determine the cause of leaf damage. The purpose of this research is to evaluate a convolutional neural network (CNN) method for its potential use in solving the problem of apple leaf disease identification. Four types of illness are dealt with: normal, multi-illness, rusty, and scabby. Many methods, such as data preparation and a preset VGG-16 artificial neural network (CNN) architecture, are recommended for use in the deep artificial neural network processing method. The most precise outcomes occurred when the beta parameter value was set to 2 = 0.999 at Ephoch to 85/100 with an accuracy of 0.7582, and when the epsilon parameter value was set to 1e-07 at Ephoch to 32/100 with an accuracy of 0.7582 with the best accuracy.
Dampak Penggunaan Data Augmentasi Terhadap Akurasi MobileNetV2 Dalam Deteksi Mikrosleep Berbasis Rasio Aspek Mata Maulana, Isa Iant; Riadi, Muhammad Fatah Abiyyu; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Pramunendar, Ricardus Anggi; Basuki, Ruri Suko
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8719

Abstract

Detecting microsleep is important in preventing accidents caused by decreased alertness, especially in activities that require high concentration such as driving. This study aims to develop an image-based microsleep detection model using the MediaPipe FaceMesh. The EAR value is only used for the tagging process that forms the basis for dataset creation. The main problem investigated is how to produce a classification model that can accurately distinguish between normal eye conditions and microsleep conditions using image data taken from eye area snippets. To address this issue, this study applies a series of stages, starting from dataset formation, initial processing in the form of image size adjustment, normalization, and quality improvement through data augmentation, to model training using the MobileNetV2 architecture with transfer learning and fine-tuning techniques. The results of the experiment show that the use of data augmentation strategies has a significant effect on improving model performance, with the best configuration producing a test accuracy of 87.54 percent, with other high performance metrics, namely Precision of 88.64 percent, Recall (Sensitivity) of 87.14 percent, and F1-Score of 87.34 percent. These findings prove that an eye area image-based approach combined with a convolutional neural network model is capable of providing promising performance in detecting microsleep conditions. These findings prove that an approach based on eye area images combined with a convolutional neural network model can deliver promising performance in detecting microsleep. This research is expected to form the basis for the development of a more effective microsleep detection system that can be implemented in real world environments.
LITE-BoostTrack: A Hybrid Real-Time Multi-Object Tracking Architecture for Resource-Constrained Environments Ruri Suko Basuki; Adhitya Nugraha; Ardytha Luthfiarta; Ika Novita Dewi; Allifian Ilham Febriyana; Michael Surya Adi Prasaja; Dzawil Uqul
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2478

Abstract

Multi-object tracking (MOT) is a fundamental task in computer vision that underpins applications such as intelligent surveillance, autonomous driving, and crowd analysis. The primary challenge in MOT lies in maintaining identity consistency under frequent occlusions while ensuring real-time performance on resource-constrained devices. This study proposes LITE-BoostTrack, a hybrid tracking framework that combines the confidence-based association mechanism of BoostTrack with the lightweight embedding strategy of the Lightweight Integrated Tracking and Embedding (LITE) architecture. The proposed model extracts appearance descriptors directly from the internal feature maps of the YOLOv8 detector, thereby eliminating the need for an external re-identification network. This design significantly reduces computational complexity while preserving reliable identity association. Experiments were conducted on the MOT20 benchmark using standard MOT evaluation metrics, including HOTA, MOTA, IDF1, IDSW, and FPS, to assess both tracking accuracy and runtime efficiency. The results show that LITE-BoostTrack achieves a HOTA of 27.31 and IDF1 of 37.48, outperforming LITE-BoT-SORT (HOTA 25.73, IDF1 33.88), while reducing identity switches by 37% (2,939 vs. 4,674) and maintaining real-time performance at 13.22 FPS. These outcomes demonstrate that substantial efficiency gains can be achieved through detector-level feature integration without introducing additional deep embedding modules. Although occasional failures still occur under severe occlusion, LITE-BoostTrack provides a balanced and practical solution that effectively combines accuracy and efficiency for real-time multi-object tracking in edge-computing and embedded vision systems.
Pelatihan Pemasaran Digital melalui Video Kreatif berbasis AI untuk UMKM di Kelurahan Bongsari Teguh Hartono Patriantoro; Zahrotul Umami; Tunggul Banjaransari; Ruri Suko Basuki; Ibnu Utomo W.M.; Karis Widyatmoko
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 2 (2026): MEI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i2.3298

Abstract

Keterampilan dalam memasarkan produk pada era digital saat ini wajib dimiliki setiap pelaku UMKM tanpa terkecuali. Naiknya jumlah penjualan produk barang/jasa yang mayoritas didominasi oleh penggunaan media konten, membutuhkan strategi dalam pengemasan, baik dari perencanaan produksi, produksi, dan distribusi.  Pengabdian masyarakat ini bertujuan untuk meningkatkan kemampuan pemasaran UMKM di Kelurahan Bongsari, Kecamatan Semarang Barat melalui sosialisasi penggunaan video kreatif sebagai alat promosi digital. Hadirnya teknologi AI disignalir mempermudah setiap orang untuk berkolaborasi dan menghasilkan ide kreatif yang akan meningkatkan penjualan. Dengan metode workshop interaktif, kegiatan melibatkan 30 peserta UMKM dan menghasilkan peningkatan pengetahuan dari 45% menjadi 78%. Hasil menunjukkan bahwa video kreatif memiliki jangkauan yang luas untuk mendorong inovasi pemasaran, meskipun tantangan akses teknologi masih perlu diatasi. Kegiatan ini berkontribusi pada pengembangan ekonomi lokal dan merekomendasikan perluasan program serupa. Kata Kunci: UMKM Bongsari, Pemasaran Digital, Video Kreatif, Konten Digital, Inovasi
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization Heru Agus Santoso; Eko Hari Rachmawanto; Adhitya Nugraha; Akbar Aji Nugroho; De Rosal Ignatius Moses Setiadi; Ruri Suko Basuki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14744

Abstract

Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
A Hybrid VADER–IndoBERT Framework for Robust Sentiment Analysis of Long and Ambiguous Indonesian Texts: Margareta Valencia Suci Handayani; Ruri Suko Basuki; Muljono; Raden Arief Nugroho; Dhendra Marutho; Yo Ceng Giap; Deshinta Arrova Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7377

Abstract

The rapid expansion of digital learning platforms has increased the reliance on user-generated reviews for service evaluation and quality monitoring. However, sentiment analysis of Indonesian reviews remains challenging due to the prevalence of long sentences, mixed sentiments, and ambiguous linguistic expressions. This study introduces a Hybrid VADER–IndoBERT framework designed to improve sentiment classification robustness on complex Indonesian texts. A dataset of 4,904 Ruangguru application reviews was collected through web scraping and processed using a hybrid pipeline consisting of preprocessing, translation-based silver-standard sentiment labeling with VADER, and class balancing via Random Oversampling (ROS). The IndoBERT classifier was evaluated against a Bidirectional Long Short-Term Memory (BiLSTM) baseline. Experimental results show that IndoBERT achieved 90.9% accuracy, outperforming BiLSTM at 86.4%, demonstrating the superiority of Transformer-based architectures in capturing long-range dependencies and handling ambiguous sentiment cues. These findings highlight the effectiveness of integrating lexicon-based and Transformer-based approaches to achieve more robust sentiment analysis on linguistically complex Indonesian texts.
Co-Authors Adhitya Nugraha Adhitya Nugraha Agung Nugroho Ahmad Zainul Fanani Ahmad Zaniul Fanani Akbar Aji Nugroho Allifian Ilham Febriyana Alzami, Farrikh Ardytha Luthfiarta Astrid Novita Putri, Astrid Banjaransari, Tunggul De Rosal Ignatius Moses Setiadi Deshinta Arrova Dewi Dhendra Marutho Dzawil Uqul Edi Faisal Eko Hari Rachmawanto Fikri Budiman Harun Al Azies Heru Agus Santoso Ibnu Utomo W.M. Ika Novita Dewi Jehad A.H Hammad Latius Hermawan Linda Marlinda Margareta Valencia Suci Handayani Maulana, Isa Iant Mauridhi Hery Purnomo Michael Surya Adi Prasaja Mochamad Hariadi Muhammad Naufal Muljono, - Pulung Nurtantio Andono Raden Arief Nugroho Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Setiawan, Dicky Suharnawi Suharnawi Suharnawi Sutriawan Teguh Hartono Patriantoro Tengku Riza Zarzani N Wicaksono, Duta Firdaus Widyatmoko Karis Yo Ceng Giap Zahrotul Umami