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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
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 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 Maulana, Isa Iant Mauridhi Hery Purnomo Michael Surya Adi Prasaja Mochamad Hariadi Muhammad Naufal Pulung Nurtantio Andono 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 Zahrotul Umami