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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi International Journal of Advances in Intelligent Informatics Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Building of Informatics, Technology and Science Journal of Applied Engineering and Technological Science (JAETS) Journal of Applied Data Sciences Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Proceeding of Community Service and Engagement
Mahmud Dwi Sulistiyo
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Public Health Social Sciences Other

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Deteksi Penggunaan Masker Wajah Menggunakan YOLOv5 Dawami, Hasbi; Rachmawati, Ema; Sulistiyo, Mahmud Dwi
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak-Pandemi COVID-19 menyebabkan global krisis kesehatan. Mengenakan masker wajah menjadi salah satu protokol kesehatan yang penting dan diwajibkan oleh pemerintah. Namun, masih banyak masyarakat yang enggan mengenakan masker wajah ketika berada di ruang publik. Oleh karena itu, diperlukan sistem yang dapat mendeteksi penggunaan masker wajah pada manusia yang bertujuan untuk membantu petugas dalam menegakkan kedisiplinan masyarakat dalam rangka menerapkan salah satu protokol kesehatan tersebut. Sistem tersebut dirancang dengan model object detection yang akurat dan efisien untuk mendeteksi penggunaan masker wajah pada manusia. Tugas akhir ini membahas bagaimana membangun sistem untuk mendeteksi masker pada wajah menggunakan metode YOLOv5 menggunakan dataset face mask detection yang asli dan yang telah di augmentasi serta berbagai nilai IoU threshold mulai dari 0,1; 0,2; 0,3; 0,5 dan 0,7. YOLOv5 merupakan versi terbaru dari YOLO sehingga memiliki akurasi yang tinggi, kemampuan mendeteksi small object, serta running speed yang cepat. Hasil terbaik jika menggunakan dataset face mask detection original didapatkan dengan nilai IoU threshold sebesar 0,3 yang memilki nilai mAP pada saat testing semua kelas sebesar 0,876. Jika menggunakan dataset face mask detection yang diaugmentasi hasil terbaik didapatkan dengan nilai IoU threshold sebesar 0,5 yang memiliki nilai mAP pada saat testing untuk semua kelas sebesar 0,849.Kata kunci- object detection, you only look once, akurasi, small object, running speed,IoU threshold 
Segementasi Optik Disc dan Cup untuk Membantu Pendeteksian Glaukoma Menggunakan Segmentation Transformer Akbar, M Raehan; Rachmawati, Ema; Sulistiyo, Mahmud Dwi
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

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Abstract

Abstrak-Glaukoma kondisi di mana saraf optik yang menghubungkan mata ke otak menjadi rusak. Glaukoma dapat menyebabkan kehilangan kemampuan penglihatan jika tidak didiagnosis dan ditangani secepat mungkin. Salah satu metode yang dilibatkan dalam mendiagnosis glaukoma menghitung rasio antara optik disc dan cup citra fundus mata. Untuk menghitung rasio antara disc dan cup citra fundus mata, diperlukan sebuah proses segmentasi citra fundus mata untuk dapat mensegmentasikan bagian disc dan cup nya. Saat ini tugas segmentasi dapat dilakukan menggunakan algoritma visi komputer modern. Transformer sendiri telah menjadi salah satu state art of model yang sering diterapkan studi kasus yang menggunakan deep learning karena performanya yang mampu menandingi Convolutinal Neural Networks (CNN). Tugas akhir ini akan membahas implementasi Transformer studi kasus segmentasi disc dan cup citra fundus mata menggunakan metode Segmentation Transformer (SETR) dengan dataset REFUGE dan DRISHTI-GS1. Hasil dice coefficients score dengan menggunakan Cross Dataset Evaluation berhasil mendapatkan skor 86 persen untuk bagian disc dan 78 persen untuk bagian cup.Kata kunci - glaukoma, disc, cup, segmentasi, segmentation transformers, transformers.
PSO-Enhanced ensemble techniques for pandemic prediction and feature importance analysis Pane, Syafrial Fachri; Sulistiyo, Mahmud Dwi; Gozali, Alfian Akbar; Adiwijaya, Adiwijaya
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2091

Abstract

During the pandemic crisis that hit after 2020, Indonesia, like many other countries, faced tremendous challenges in areas such as health, economy, and mobility. An in-depth understanding of the dynamics and changes in these areas is essential to address the impacts of the pandemic. This research is an attempt to deeply analyze the impact of the pandemic and the most effective forecasting methods based on data and phenomena. Indonesia, with its growing economy and constantly adapting health system, faces conventional economic impacts, while its health system response tries to keep up with urgent needs driven by the spread of the virus. In the context of mobility, changes in how people move and interact significantly affect virus transmission. Modeling a pandemic event with all its complexities is not an easy task. Even more so, in finding the right method for prediction, ensemble techniques such as stacking and regression voting are emerging as promising approaches. However, deep learning and particle swarm optimization (PSO) techniques offer new innovations. The results of this study show that the ensemble vote provides the best performance in predicting confirmed positive cases and mortality based on factors of health, economic and population mobility in Indonesia. Through feature importance analysis using MDI and Tree SHAP, we conclude that factors such as active cases, the number of vaccinations, and economic indicators, such as close IDR and close IHSG, have a significant influence on the growth of confirmed positive cases. Meanwhile, recovery factors and vaccination number play an important role in the growth of the number of death cases. This study confirms that a multivariate approach that considers health, economy and mobility is the key to understanding and responding more effectively to the pandemic in Indonesia.
Menjembatani Teknologi dan Spiritualitas: Pengenalan Artificial Intelligence di Pondok Pesantren melalui Workshop Kolaboratif Mahmud Dwi Sulistiyo; Didit Adytia; Z.K. Abdurahman Baizal; Raihani Mohamed; Nabila Wardah Zamani; Nurfadhlina Mohd Sharef
I-Com: Indonesian Community Journal Vol 5 No 3 (2025): I-Com: Indonesian Community Journal (September 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i3.7799

Abstract

Pesantren sebagai lembaga pendidikan berbasis keagamaan memiliki tantangan dalam mengakses perkembangan teknologi terbaru seperti Artificial Intelligence (AI). Menjawab kebutuhan ini, tim pengabdian dari Telkom University menyelenggarakan workshop pengenalan AI dan mengundang sivitas Pondok Pesantren Modern Assuruur. Kegiatan ini bertujuan untuk meningkatkan literasi digital para santri melalui pengenalan konsep dasar AI dan pemanfaatannya dalam pembelajaran Al-Qur’an. Workshop dilakukan secara hybrid bersama narasumber dari Telkom University dan Universiti Putra Malaysia (UPM), mencakup pretest, materi inti, praktik aplikasi Tarteel, dan posttest. Hasil evaluasi menunjukkan peningkatan skor pemahaman peserta dari rata-rata 93,97 menjadi 98,10, disertai penurunan standar deviasi dari 14,50 menjadi 5,88. Kegiatan ini membuktikan bahwa pendekatan yang kontekstual dan aplikatif mampu meningkatkan pemahaman santri terhadap teknologi modern serta membuka peluang integrasi AI dalam pendidikan Islam.
Investigating Shallow Learning Methods for Optical Character Recognition of Indonesia’s Nusantara Scripts Sulistiyo, Mahmud Dwi; Putrada, Aji Gautama; Ihsan, Aditya Firman; Yunanto, Prasti Eko; Richasdy, Donny; Sailellah, Hassan Rizky Putra; Sabrina Adinda Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6648

Abstract

Indonesia has numerous regional scripts—or so-called Nusantara scripts—and recognizing them is important to preserve Indonesia's cultural heritage. The advances of AI and computer vision technologies make it possible for a machine to optically read the handwritten scripts through the Optical Character Recognition (OCR) technique. However, collecting some of the top OCR solutions and comprehensively investigating their performances on the Nusantara scripts is currently lacking. This study investigates and evaluates some shallow learning-based methods on our newly introduced datasets, consisting of more than 38,000-character images across 80 letter classes in total; here, we focus on three regional scripts: Javanese, Sundanese, and Balinese. The methods include Random Forest, SVM, Logistic Regression, and Gaussian Naïve Bayes, as well as boosting techniques such as XGBoost, Light GBM, and CatBoost. A 5-fold cross-validation approach assessed model performance based on accuracy, precision, recall, and F1-score. Based on the experimental results, the methods demonstrated their competitiveness in reaching the best models for scripts; in particular, XGBoost, Light GBM, and Random Forest-Gini were the winners for Javanese, Sundanese, and Balinese scripts, respectively. These findings demonstrate the effectiveness of ensemble learning methods for diverse handwritten scripts. Comparative analysis to prior deep learning studies is also discussed in this paper. In addition, this research also contributes to preserving Indonesian traditional scripts, as well as offers insights for future regional OCR in other countries.
Pengembangan Media Komunikasi SD Ar-Rafi’ Bandung Dalam Bentuk Jurnal Digital dan Chatbot Informasi Layanan Febryanti Sthevanie; Gia Septiana Wulandari; Mahmud Dwi Sulistiyo
The Proceeding of Community Service and Engagement (COSECANT) Seminar Vol. 4 No. 2 (2024): The Proceeding of Community Service and Engagement (COSECANT) Seminar
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cosecant.v4i2.8502

Abstract

Pada jenjang pendidikan dasar, media komunikasi antara sekolah dengan stakeholder memegang peranan. Dua stakeholder yang memilki peran penting pada sebuah lembaga pendidikan sekolah dasar adalah masyarakat umum dan orang tua siswa, sehingga media komunikasi yang digunakan oleh sekolah dengan kedua stakeholder tersebut memegang peranan penting. Saat ini, media komunikasi yang dimiliki oleh SD Ar-Rafi’ untuk berkomunikasi dengan orang tua adalah berupa buku jurnal. Buku ini digunakan sebagai panduan siswa dan orang tua dalam melaksanakan program sekolah. Buku jurnal yang digunakan berbentuk hardcopy dan memiliki kekurangan ketika buku tersebut tertinggal atau hilang, sehingga proses komunikasi dan pencatatan tertunda atau terputus, sehingga dibutuhkan digitalisasi buku jurnal menjadi berbentuk website. Tim abdimas telah mengembangkan website yang dapat memberikan kemudahan user dalam mengakses informasi dan data secara langsung melalui internet. Website yang dibangun memberikan akses kepada guru untuk memberikan feedback ataupun panduan berupa tugas program sekolah untuk dilaksanakan oleh siswa dengan pendampingan oleh orang tua di rumah. User orang tua dapat memantau tugas-tugas yang diberikan serta memberikan laporan hasil pengerjaan anak di rumah. Kemudian dalam berkomunikasi dengan masyarakat luas, SD Ar-Rafi’ masih menggunakan website yang bersifat serta kontak admin melalui aplikasi WhatsApp. Akan tetapi, komunikasi dengan admin terbatas pada waktu dalam menjawab pertanyaan-pertanyaan masyarakat. Dalam rangka meningkatkan kecepatan respon kendala tersebut, dibutuhkan media komunikasi yang dapat menjawab pertanyaan masyarakat yang umum ditanyakan dengan lebih cepat. Oleh karenanya, tim abdimas mengembangkan teknologi Chatbot yang dapat menjawab pertanyaan masyarakat yang umum ditanyakan kepada admin, sehingga masyarakat bisa mendapatkan respon yang lebih cepat terkait pertanyaan yang dimilikinya.
MNetNCR: MobileNet model for efficient traditional Nusantara script character recognition Wisesty, Untari Novia; Ihsan, Aditya Firman; Sulistiyo, Mahmud Dwi; Richasdy, Donni; Yunanto, Prasti Eko; Kosala, Gamma; Gandhi, Arfive; Sthevanie, Febryanti
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.pp1513-1528

Abstract

Preservation of traditional Nusantara scripts is very important because these traditional scripts are part of the cultural heritage that reflects the identity and history of the nation. This research proposed MobileNet for Nusantara character recognition (MNetNCR) model based on MobileNetV3 architecture to recognize traditional Nusantara scripts with lightweight, efficient architecture, and accurate recognition. The novel and comprehensive datasets for traditional Nusantara scripts have been curated in this research, that will later be stored digitally and can be used in further research. This novel dataset includes handwritten Balinese, Batak, Javanese, Lontara, and Sundanese scripts, each with unique visual characteristics. The proposed MNetNCR model is highly effective in recognizing characters, achieving F1-scores of 0.9934 for Balinese, 0.9450 for Batak, 0.9788 for Javanese, 0.9936 for Lontara, and 0.9961 for Sundanese scripts, according to the experimental results. The MNetNCR model built in this research has been proven to be effective and efficient in recognizing traditional scripts accurately. It also supports the preservation and promotion of the nation's cultural and historical heritage.
YOLOv11-LCA: YOLOv11 Enhanced with the Low-Complexity Attention Mechanisms for a Robust Waterway-Floating Trash Detection Muhammad Rafly Arjasubrata; Mahmud Dwi Sulistiyo
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/dpqnng18

Abstract

Accurate trash detection in aquatic environments remains a significant challenge for detection models, which exhibit persistent limitations in identifying small and partially submerged objects. Additionally, a notable gap exists in methodologies for fine-tuning the detection model to optimize performance for a specific waterways. To address these limitations, the first objective is to develop a detection model designed to enhance performance on small and partially submerged trash, and the second is to establish a framework for efficiently adapting the model to achieve high accuracy within local waterways. First, the YOLOv11 architecture is enhanced by integrating LCAM and LCBHAM attention mechanisms and pre-trained on various combinations of public datasets to establish a robust, baseline model. For the second objective, this baseline model is adapted using a data-efficient framework. This study process introduces the BojongTrash dataset, captured from a specific waterway, and involves systematically fine-tuning the model on incremental subsets of this data to determine the minimum quantity of images and training epochs required to achieve high accuracy in the target environment. The proposed YOLOv11s-LCA architecture demonstrated a statistically validated improvement over its baseline, increasing the mAP50 score from 0.779 to 0.836 on the FloW-Img dataset with only a 0.1% parameter increase. Furthermore, the research establishes a highly efficient fine-tuning framework, demonstrating peak mAP50 performance of 0.908 that achieved by fine-tuning on 1,000 images for only 3-5 epochs. Therefore, this research validates lightweight attention mechanisms as an efficient strategy for enhancing detection in complex environments and provides a practical framework that enables the rapid deployment of tailored, high-accuracy monitoring systems.
Co-Authors Ade Romadhony Aditya Firman Ihsan Adiwijaya Agung Toto Wibowo Aji Gautama Putrada Akbar, M Raehan Alfian Akbar Gozali Andre Sitompul Arfive Gandhi Azzam, Muhammad Abdullah Bayu Prabawa Bedy Purnama Bimmo Satryo Wicaksono Dawami, Hasbi Deny Haryadi Dewi Nasien Didit Adytia Donni Richasdy Ema Rachmawati Ema Rachmawati Fachri Pane, Syafrial Faturahman, Farhan Febryanti Sthevanie Firdauz, Salma Salsabila Gamma Kosala Ganjar Gingin Tahyudin Gia Septiana Wulandari Hamdy Nur Saidy Hifzi Priabdi Jondri Jondri Marliani Harahap Muhammad Arzaki Muhammad Helmy Faishal Muhammad Rafly Arjasubrata Muhammad Rakha Muhammad Ridha Nabila Wardah Zamani Nurfadhlina Mohd Sharef Prasti Eko Yunanto Pratama, Alif Adwitiya Raihani Mohamed Rasyid Kurniawan Rasyid Kurniawan Richasdy, Donny Rifqi Luthfil Hadi Rita Rismala Sabrina Adinda Sari Sailellah, Hassan Rizky Putra Sugondo Hadiyoso Syafrial Fachri Pane, Syafrial Fachri Tahyudin, Ganjar Gingin Tjokorda Agung Budi Wirayudha Tri Kusnandi Fazarudin Tri Kusnandi Fazarudin Tri Kusnandi Fazarudin, Tri Kusnandi Ubaidillah, Rifky Fahrizal Untari Novia Wisesty Wahyu Hauzan Rafi Wahyu Rismawan ZK Abdurahman Baizal