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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pendidikan Vokasi JOURNAL OF EDUCATION TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Educational Science and Technology ELINVO (Electronics, Informatics, and Vocational Education) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Penelitian Pendidikan IPA (JPPIPA) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Robotics and Control (JRC) Journal of Information Engineering and Technology Journal of Information Technology and Education (JITED)
Handaru Jati
Universitas Negeri Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering Physics

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Pengembangan dan analisis kualitas aplikasi penilaian e-learning SMK berbasis ISO 19796-1 di Yogyakarta Ahmad Faiq Abror; Handaru Jati
Jurnal Pendidikan Vokasi Vol. 6 No. 1 (2016)
Publisher : ADGVI & Graduate School of Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (703.857 KB) | DOI: 10.21831/jpv.v6i1.7623

Abstract

Penelitian ini bertujuan untuk: (1) menghasilkan aplikasi penilaian e-learning Sekolah Menengah Kejuruan (SMK) berbasis ISO 19796-1 yang dapat digunakan untuk mengevaluasi e-learning SMK di Yogyakarta menggunakan teknik Analitycal Hierarchy Process (AHP) dengan metode agregasi arithmetric mean dan geometric mean, (2) menguji kualitas aplikasi dengan menggunakan strandar ISO 9126. Penelitian ini merupakan penelitian Research and Development (R&D). Proses pengembangan aplikasi menggunakan metode  Software Development Life Cycle (SDLC) dengan model Waterfall. Selanjutnya pada proses pengujian kualitas aplikasi menggunakan standar ISO 9126 yang terdiri atas aspek functionality, reliability, efficiency, maintainability, usability, dan portability. Hasil penelitian menunjukkan bahwa aplikasi penilaian e-learning SMK berdasarkan ISO 19796-1 telah berhasil dikembangkan menggunakan metode Software Development Life Cycle (SDLC) dengan model waterfall. Selanjutnya hasil dari analisis kualitas aplikasi menggunakan standar ISO 9126 menunjukkan bahwa aplikasi mempunyai hasil rata-rata sangat baik dan layak digunakan untuk penilaian kualitas e-learning SMK. DEVELOPMENT AND QUALITY ANALYSIS OF THE ASSESSMENT APPLICATION OF E-LEARNING FOR VOCATIONAL SCHOOLS BASED ON ISO 19796-1 IN YOGYAKARTAAbstractThis research aimed to: (1) produce an application to asess e-learning based on ISO 19796-1 that can be used to evaluate e-learning for vocational schools in Yogyakarta using Analitycal Hierarchy Process (AHP) with the methods of aggregation arithmetric mean and geometric mean, and (2) to assess the quality of the system using ISO 9126 standard. This research used Research and Development (R&D) method. The development of the system implemented Software Development Life Cycle (SDLC) Waterfall model. Meanwhile, for testing the quality of the system, ISO 9126 standard was used, consisting of the aspects of functionality, reliability, efficiency, maintainability, usability, and portability. The result showed that the system has been successfully developed by using Software Development Life Cycle (SDLC) with a waterfall model. Further result from quality analysis of the system using ISO 9126 standard indicates that the average result of the system was very good and worthy of use for quality assessment of e-learning system for vocational schools.
Enhancing object detection for humanoid robot soccer: comparative analysis of three models Handaru Jati; Nur Alif Ilyasa; Yuniar Indrihapsari; Ariadhie Chandra; Dhanapal Durai Dominic
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25906

Abstract

The humanoid robot soccer system encounters a notable challenge in object detection, primarily concentrating on identifying the ball and often neglecting crucial elements like opposing robots and goals, resulting in on-field collisions and imprecise ball shooting. This study comparatively evaluates three you only look once (YOLO) real-time object detection system variants: YOLOv8, YOLOv7, and YOLO-NAS. A dataset of 2104 annotated images, covering classes such as ball, goalpost, and robot, was curated from Roboflow and robot-captured images. The dataset was partitioned into training, validation, and testing sets, and each YOLO model underwent extensive fine-tuning over 100 epochs on this custom dataset, leveraging the pre-trained common objects in context (COCO) model. Evaluation metrics, including mean average precision (mAP) and inference speed, assessed performance. YOLOv8 achieved the highest accuracy with a mAP of 0.92, while YOLOv7 showed the fastest inference speed of 24 ms on the Jetson Nano platform. Balancing accuracy and speed, YOLO-NAS emerged as the optimal choice. Thus, YOLO-NAS is recommended for object detection for humanoid soccer robots, regardless of team affiliation. Future research should focus on enhancing object detection through advanced training techniques, model architectures, and sensor fusion for improved performance in dynamic environments, potentially optimizing through scenario-specific fine-tuning.
Drone-assisted deep learning weed detection for sustainable agriculture and environmental resilience Agustan Latif; Handaru Jati; Herman Dwi Surjono; Mani Yusuf
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1428-1440

Abstract

Effective weed detection plays a crucial role in sustainable agriculture, boosting crop productivity and supporting environmental conservation. This study compares three deep learning models—YOLOv5, YOLO-NAS, and mask region-based convolutional neural network (Mask R-CNN)-against traditional methods in terms of accuracy, processing speed, and adaptability in tropical agricultural conditions, with Merauke, Indonesia, as the case study. The results show that YOLO-NAS delivers the highest accuracy at 96% with a processing time of 25 ms per image, making it suitable for high precision applications. YOLOv5 balances strong accuracy (94%) with faster processing at 12 ms per image, establishing it as the most effective for real time scenarios. Mask R-CNN also achieves 94% accuracy and provides advanced segmentation capabilities, but its slower processing speed of 31 ms limits large-scale implementation. Traditional methods perform poorly in comparison, with only 85% accuracy and processing time above 50 ms per image. These findings highlight the transformative potential of artificial intelligence (AI)-based weed detection for precision agriculture, particularly in tropical regions like Merauke. Adoption of models such as YOLOv5 reduces manual labor dependence while advancing efficient, eco-friendly weed management. Future research should expand datasets and explore newer models like YOLOv8, YOLO-NAS, vision transformers (ViTs), and hybrid approaches.
IndoBERT for educational assessment: comparative analysis of transformer models in Indonesian question generation Handaru Jati; Yuniar Indrihapsari; Pradana Setialana; Danang Wijaya; Satya Adhiyaksa Ardy; Dhista Dwi Nur Ardiansyah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1804-1813

Abstract

This study asks whether a monolingual encoder can realistically outperform multilingual and larger transformer models for Indonesian automatic question generation (AQG) when all models share the same training budget. We compare Indonesian bidirectional encoder representations from transformers (IndoBERT), multilingual BERT (mBERT), and BERT-large using a single fine-tuning pipeline with answer highlighting, applied to an Indonesian version of TyDiQA-GoldP and a 20,000 translated subset of SQuAD 2.0. We evaluate model quality using bilingual evaluation understudy score n-gram 4 (BLEU-4), metric for evaluation of translation with explicit ordering (METEOR), and ROUGE-Lincoln (ROUGE-L). IndoBERT consistently achieves the best scores on both datasets (e.g., BLEU-4 of 19.69 on TyDiQA-GoldP and 3.79 on the SQuAD 2.0 subset) while requiring less computation than mBERT and BERT-large. Our results show that language-specific pretraining gives clear advantages for Indonesian AQG, yielding higher accuracy at lower computational cost than multilingual or larger encoders. The work closes a gap in Indonesian AQG benchmarking by providing the first head-to-head comparison of IndoBERT, mBERT, and BERT-large under a shared fine-tuning and evaluation protocol. For educational assessment, the findings offer a practical recipe for building deployable AQG systems on mid-range GPUs that generate higher quality questions without prohibitive training or inference budgets.
Pengembangan Sistem Manajemen Dokumentasi Rest Api di PT Appfuxion Indonesia Ananda Mukhammad Ikhsan; Handaru Jati
Journal of Information Technology and Education (JITED) Vol. 3 No. 2 (2025): September 2025
Publisher : Department of Electronics and Informatics Engineering Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jited.v3i2.2509

Abstract

Tujuan penelitian ini adalah: (1) Mengembangkan sistem manajemen dokumentasi REST API di PT Appfuxion Indonesia. (2) Menguji sistem manajemen dokumentasi REST API di PT Appfuxion Indonesia menggunakan pengujian functional suitability untuk uji kelayakan fungsi perangkat lunak dan pengujian usability untuk menguji kemudahan penggunaan sistem perangkat lunak oleh pengguna. Metode yang digunakan adalah penelitian dan pengembangan atau Research and Development (R&D) dengan prosedur pengembangan perangkat lunak yaitu model waterfall. Tahapan prosedur waterfall adalah komunikasi, perencanaan, pemodelan, konstruksi, dan penyerahan perangkat lunak kepada pelanggan/pengguna. Terdapat 2 responden ahli di bidang pengembangan perangkat lunak untuk pengujian functional suitability dan 9 responden dari karyawan PT Appfuxion Indonesia untuk pengujian functional usability. Metode pengumpulan data menggunakan observasi, wawancara dan kuesioner. Hasil dari penelitian ini adalah: (1) Perangkat lunak sistem manajemen dokumentasi REST API di PT Appfuxion Indoneasia dikembangkan dengan model waterfall dan menggunakan framework Spring boot. (2) Hasil pengujian pada aspek functional suitability mendapatkan nilai 100% yang artinya semua fungsi yang dirancang berjalan baik dan “Sangat Layak” digunakan. Untuk hasil pengujian aspek usability yaitu aplikasi web acceptable atau diterima oleh pengguna, grade scale C, adjective rating good, dan SUS score percentile rank mendapatkan grade B artinya aplikasi web mudah digunakan.
Pengembangan Sistem Informasi Salon Kecantikan berbasis Website menggunakan Teknologi Mern Stack (Studi Kasus Nesya Salon Berastagi) Yosep R. Silaban; Handaru Jati
Journal of Information Technology and Education (JITED) Vol. 3 No. 2 (2025): September 2025
Publisher : Department of Electronics and Informatics Engineering Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jited.v3i2.2515

Abstract

Penelitian ini bertujuan: (1) Mengembangkan dan menyediakan sistem informasi salon kecantikan berbasis website di Nesya Salon Berastagi untuk mengatasi keterbatasan informasi layanan dan produk serta memfasilitasi pemesanan layanan online oleh pelanggan dan manajemen data reservasi, layanan, produk, dan karyawan oleh pemilik dan karyawan salon. (2) Mengetahui tingkat kelayakan sistem informasi salon kecantikan berbasis website yang dirancang berdasarkan aspek functional suitability dan usability. Metode penelitian menggunakan Research and Development (R&D) dengan model waterfall, meliputi 5 tahap yaitu komunikasi, perencanaan, pemodelan, konstruksi, dan pendistribusian. Subjek penelitian untuk pengujian functional suitability dua ahli sistem informasi, sementara untuk usability melibatkan 22 responden, termasuk pemilik, karyawan, dan pelanggan salon, dengan metode pengumpulan data berupa observasi, wawancara, dan kuesioner. Hasil penelitian menunjukkan bahwa sistem informasi berbasis website di Nesya Salon Berastagi, yang dirancang dengan model waterfall dan teknologi MERN stack, memperoleh nilai 100% pada functional suitability, hal ini menunjutkkan bahwa semua fungsi berjalan baik dan “Sangat Layak” digunakan. Usability, sistem acceptable dengan grade scale C, adjective rating good, dan SUS persentile B, menunjukkan kemudahan penggunaan sistem.
Co-Authors Agustan Latif Ahmad Faiq Abror Al-Khawarizmi, Andi Hakim Ananda Mukhammad Ikhsan Andri Yuwanto Annurdien Rasyid Ardiansyah, Dhista Dwi Nur Ardiyanto, Widya Ardy, Satya Adhiyaksa Ariadhie Chandra Bonita Destiana Bunga Anindita Pramono Cahyana, Cecep Wahyu Danang Wijaya Dhanapal Durai Dominic Dhista Dwi Nur Ardiansyah Dhista Dwi Nur Ardiansyah Dominic, Dhanapal Durai Dwi Kurniawan, Prabowo Eko Marpanaji Eswantoro, Bayu Nur Rahman Fadly, Hawangga Dhiyaul Faridah, Linda Gagas Akar Ilalang Herman Dwi Surjono Heru Setiawan Hung, Hui-Chun Ilyasa, Nur Alif Jayanto, Rohmad Dwi Mahali, Muhamad Izzudin Mani Yusuf Marwan Efendi Muhamad Ali Kasri Muhammad Arisandy Rizky Muhammad Yarjullah Hanif Nur Alif Ilyasa Nurkhamid Nurkhamid Nurkhamid, N. Nurlansa, Osiany Nurwijayanti Kusuma Pahtoni, Tri Yuli Pawitra, Mahendra Astu Sanggha Qotrunnada, Dafa Zenita Rachelita Embun Safira Ratna Wardani Reny Kartika Ningtyas Rustam Asnawi Saputra, Apry Aditya Sarah Bibi Satya Adhiyaksa Ardy Sayud, Sunthon Setialana, Pradana Subhan, Agung Rizki Sukarni, Jaka Tiala, Maria Bernadetha Charlotta Wonda Wajar Novianto Wicaksono, Alim Tegar Wijaya, Danang Wulandari, Catur Nur Agustina Yosep R. Silaban Yuniar Indrihapsari