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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Pendidikan Vokasi JOURNAL OF EDUCATION 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) Journal of Soft Computing Exploration
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 Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering Physics

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Pengembangan Sistem Manajemen Dokumentasi Rest Api di PT Appfuxion Indonesia Ananda Mukhammad Ikhsan; Jati, Handaru
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.
IndoBERT for educational assessment: comparative analysis of transformer models in Indonesian question generation Jati, Handaru; Indrihapsari, Yuniar; Setialana, Pradana; Wijaya, Danang; Ardy, Satya Adhiyaksa; Dwi Nur Ardiansyah, Dhista
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.
Drone-assisted deep learning weed detection for sustainable agriculture and environmental resilience Latif, Agustan; Jati, Handaru; Surjono, Herman Dwi; Yusuf, Mani
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.
A comparative analysis of five textual similarity methods for automatic short answer grading Bakti, Imam Rangga; Jati, Handaru; Nurkhamid, Nurkhamid; Bunda, Yola Permata
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.11

Abstract

This study investigates the application of text mining techniques in Automatic Short Answer Grading (ASAG) by comparing five textual similarity methods: Cosine Similarity, Jaccard Similarity, Dice’s Coefficient, Overlap Coefficient, and Matching Coefficient. The dataset consists of five definition-based questions answered by 25 students in a Human–Computer Interaction course. The data were preprocessed using case folding, tokenization, stop word removal, and stemming. The results show that Cosine Similarity achieved the highest similarity score of 67.00%, followed by Overlap Coefficient (66.67%) and Dice’s Coefficient (63.16%), while Jaccard Similarity and Matching Coefficient produced lower scores of 46.15%. These findings indicate that vector-based similarity methods are more effective in handling variations in sentence structure and keyword usage compared to set-based approaches, particularly for definition-based short answers. This study provides a comparative evaluation of multiple lexical similarity methods within a unified experimental setting, offering practical insights for selecting appropriate techniques in ASAG applications.
Co-Authors Agustan Latif Ahmad Faiq Abror, Ahmad Faiq Al-Khawarizmi, Andi Hakim Ananda Mukhammad Ikhsan Andri Yuwanto Annurdien Rasyid Ardiansyah, Dhista Dwi Nur Ardiyanto, Widya Ardy, Satya Adhiyaksa Bakti, Imam Rangga Bonita Destiana Bunda, Yola Permata Bunga Anindita Pramono Cahyana, Cecep Wahyu Dhista Dwi Nur Ardiansyah Dominic, Dhanapal Durai Dwi Kurniawan, Prabowo Dwi Nur Ardiansyah, Dhista Eko Marpanaji Eswantoro, Bayu Nur Rahman Fadly, Hawangga Dhiyaul 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 Nurkhamid Nurkhamid Nurkhamid, N. Nurkhamid, Nurkhamid Nurlansa, Osiany Pahtoni, Tri Yuli Pawitra, Mahendra Astu Sanggha Qotrunnada, Dafa Zenita Rachelita Embun Safira Ratna Wardani Reny Kartika Ningtyas Saputra, Apry Aditya Sarah Bibi 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