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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) snimed Jurnal Ilmiah Farmasi Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah PREPOTIF : Jurnal Kesehatan Masyarakat JURNAL PENDIDIKAN, SAINS DAN TEKNOLOGI Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal Research of Social Science, Economics, and Management Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Fudholi, Dhomas Hatta
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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YOLO-based Small-scaled Model for On-Shelf Availability in Retail Fudholi, Dhomas Hatta; Kurniawardhani, Arrie; Andaru, Gabriel Imam; Alhanafi, Ahmad Azzam; Najmudin, Nabil
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5600

Abstract

The availability of the shelf (OSA) in the retail industry plays a very crucial role in continuous sales. Unavailability of products can make a bad impression on customers and reduce sales. The retail industry may continue to develop through the rapidly advancing technology era to thrive in a market where competition is increasingly tough. Along with technological advances in recent decades, artificial intelligence has begun to be applied to support OSA, particularly by using object detection technology. In this research, we develop a small-scale object detection model based on four versions of the You Only Look Once (YOLO) algorithm, namely YOLOv5-nano, YOLOv6-nano, YOLOv7-tiny, and YOLOv8-nano. The developed model can be used to support automatic detection of OSA. A small-scale model has developed in the sense of postpractical implementation through low-cost mobile applications. We also use the quantization method to reduce the model size, INT8 and FP16. This small-scale model implementation also offers flexibility in implementation. With a total of 7697 milk-based retail product images and 125 different product classes, the experiment results show that the developed YOLOv8-nano model, with a mAP50 score of 0.933 and an inference time of 13.4 ms, achieved the best performance.
Lightweight Models for Real-Time Steganalysis: A Comparison of MobileNet, ShuffleNet, and EfficientNet Bauravindah, Achmad; Fudholi, Dhomas Hatta
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6091

Abstract

In the digital age, the security of communication technologies is paramount, with cybercrime projected to reach $10.5 trillion annually by 2025. While encryption is vital, decrypted data remains vulnerable, prompting the exploration of steganography as an additional security layer. Steganography conceals data within digital media, but its misuse for cyberattacks—such as embedding malware—has highlighted the need for steganalysis, the detection of hidden data. Despite extensive research, few studies have explored lightweight deep learning models for real-time steganalysis in resource-constrained environments like mobile devices. This research evaluates MobileNet, ShuffleNet, and EfficientNet for such tasks, using the BOSSbase-1.01 dataset. Models were assessed based on accuracy, computational efficiency, and resource usage. MobileNet achieved the highest computational speed but with only 63.8% accuracy, falling short of practical application. ShuffleNet and EfficientNet performed at random-guessing levels with 50% accuracy, reflecting the challenges of steganalysis on mobile platforms. Future work aims to improve accuracy by integrating advanced preprocessing techniques, attention mechanisms, and hybrid architectures, as well as leveraging ensemble methods for improved detection. Data augmentation, transfer learning, and hyperparameter tuning will also be explored to optimize model performance. This study contributes by identifying these challenges and offering insights for future research, focusing on optimizing models and preprocessing techniques to enhance detection accuracy in resource-constrained environments.
ENHANCING CUSTOMER INSIGHT THROUGH ASPECT-BASED SENTIMENT ANALYSIS OF SMART DEVICE REVIEWS : CROSS-BRAND INSIGHTS FROM APPLE, SAMSUNG, AND XIAOMI Al-Sabahi, Abdullah Yahya Moqbel; Fudholi, Dhomas Hatta
PENDIDIKAN SAINS DAN TEKNOLOGI Vol 12 No 3 (2025)
Publisher : STKIP PGRI Situbondo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47668/edusaintek.v12i3.1796

Abstract

In today’s highly competitive smart device market, understanding customer sentiment is essential for driving product innovation and brand loyalty. This study applies Aspect-Based Sentiment Analysis (ABSA) using transformer-based models BERT, RoBERTa, and DistilBERT on over 20,000 reviews collected from Amazon, Flipkart, Walmart, and Best Buy between 2021 and 2024. The analysis focuses on customer feedback regarding Apple, Samsung, and Xiaomi smartphones and smartwatches. Key product aspects such as Battery, Camera, and Performance were extracted, and sentiment trends were compared by brand and device type. Findings reveal Samsung devices received the highest engagement, with its watches praised but phones criticized. Xiaomi’s reviews showed strong polarization, while Apple maintained consistent but lower review volumes. Temporal trends showed a significant rise in positive sentiment in 2024, indicating improving product satisfaction. This research offers actionable insights for original equipment manufacturers (OEMs) and marketers, highlighting which features drive satisfaction and how sentiment evolves over time.
Pengembangan Chatbot Informasi Hukum Layanan Publik Berbasis Retrieval-Augmented Generation Menggunakan LangChain dan OpenAI di Ombudsman DIY Yasmin, Saarah Muthiah; Fudholi, Dhomas Hatta
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.995

Abstract

Hubungan antara masyarakat dan lembaga layanan publik kerap menghadapi berbagai tantangan, khususnya dalam praktik maladministrasi, penyalahgunaan wewenang, dan kurangnya transparansi. Untuk menjawab permasalahan tersebut, penelitian ini mengusulkan pengembangan chatbot berbasis kecerdasan buatan generatif dengan pendekatan Retrieval-Augmented Generation (RAG) menggunakan LangChain dan OpenAI. Sistem ini dirancang untuk menyajikan informasi hukum yang akurat, kontekstual, dan mudah dipahami oleh masyarakat. Metode yang digunakan dalam penelitian ini meliputi perancangan sistem chatbot dengan arsitektur RAG menggunakan LangChain dan OpenAI. Dokumen hukum diolah menjadi embeddings, disimpan dalam basis data vektor Chroma, dan digunakan dalam proses prompt engineering untuk menghasilkan jawaban yang kontekstual. Evaluasi sistem dilakukan melalui penyebaran kuesioner kepada lima ahli dari Lembaga Ombudsman DIY, dengan analisis data menggunakan pendekatan deskriptif. Evaluasi sistem dilakukan dengan memberikan kuesioner kepada lima ahli dari LO DIY dan menggunakan analisis deskrpitif. Hasil evaluasi menunjukkan bahwa sistem memperoleh skor tinggi dalam indikator Perceived Usefulness (rata – rata = 12) dan  Relevansi (rata – rata = 8), serta skor sangat tinggi dalam indikator Akurasi (rata – rata = 18,6)  dan indikator Clarity (rata – rata = 8,4). Dengan demikian, penerapan teknologi RAG dalam pengembangan chatbot berpotensi meningkatkan pemahaman masyarakat terhadap hukum layanan publik serta memperkuat transparansi dan akuntabilitas dalam penyelenggaraan pelayanan publik. Hal ini menunjukkan potensi strategis pemanfaatan AI dalam mendorong tata kelola pelayanan publik yang lebih responsif, akuntabel, dan inklusif.
Efficient Thoracic Abnormalities Detection Using Mobile Deep Learning Models Bauravindah, Achmad; Fudholi, Dhomas Hatta; Wahyuningrum, Rima Tri
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2268

Abstract

Indonesia faces a critical shortage of radiologists, with only 1.2 radiologists per 100,000 individuals. This shortage leads to delays in diagnosing thoracic abnormalities such as pneumothorax, cardiomegaly, nodule/mass, consolidation, and infiltration. Chest X-ray (CXR) interpretation remains challenging due to overlapping radiological features, necessitating AI-assisted solutions. This study evaluates three lightweight deep learning models—MobileNetV2, ShuffleNetV2, and EfficientNetB0—for automated thoracic abnormality detection using the ChestX-ray8 dataset. We assessed model performance using accuracy, precision, recall, F1-score, and AUC-ROC, selecting the best model based on the highest per-fold F1-score. EfficientNetB0 emerged as the top-performing model, achieving a macro-average F1-score of 0.556 and AUC-ROC of 0.765, outperforming MobileNetV2 (0.494, 0.719) and ShuffleNetV2 (0.481, 0.713). Grad-CAM analysis revealed strong localization for pneumothorax and consolidation but misclassifications in cardiomegaly and nodule/mass detection due to poor feature differentiation. The findings highlight EfficientNetB0’s potential as an AI-assisted diagnostic tool for low-resource settings while also underscoring the need for segmentation-based pretraining and multi-scale feature extraction to enhance detection accuracy. Future work should focus on optimizing sensitivity to subtle abnormalities and ensuring clinical trust through improved interpretability techniques.
Mi-Botway: a Deep Learning-based Intelligent University Enquiries Chatbot Windiatmoko, Yurio; Hidayatullah, Ahmad Fathan; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.614 KB) | DOI: 10.29099/ijair.v6i1.247

Abstract

Intelligent systems for universities that are powered by artificial intelligence have been developed on a large scale to help people with various tasks. The chatbot concept is nothing new in today's society, which is developing with the latest technology. Students or prospective students often need actual information, such as asking customer service about the university, especially during the current pandemic, when it is difficult to hold a personal meeting in person. Chatbots utilized functionally as lecture schedule information, student grades information, also with some additional features for Muslim prayer schedules and weather forecast information. This conversation bot was developed with a deep learning model adopted by an artificial intelligence model that replicates human intelligence with a specific training scheme. The deep learning implemented is based on RNN which has a special memory storage scheme for deep learning models, in particular in this conversation bot using GRU which is integrated into RASA chatbot framework. GRU is also known as Gated Recurrent Unit, which effectively stores a portion of the memory that is needed, but removes the part that is not necessary. This chatbot is represented by a web application platform created by React JavaScript, and has 0.99 Average Precision Score.
A Mobile Deep Learning Model on Covid-19 CT-Scan Classification Susanto, Prastyo Eko; Kurniawardhan, Arrie; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (386.607 KB) | DOI: 10.29099/ijair.v6i1.257

Abstract

COVID-19 pandemic is currently happening in the world. Previous studies have been done to diagnose COVID-19 by identifying CT-scan images through the development of the novel Joint Classification and Segmentation System models that work in real-time. In this study, the author focuses on a different motivation and innovation focused on the development of mobile deep learning. Mobile Net, a deep learning model as a method for classifying the disease COVID-19, is used as the base model. It has a good level of efficiency and reliability to be implemented on devices that have small memory and CPU specifications, such as mobile phones. The used data in this study is a CT-scan image of the lungs with a horizontal slice that has been classified as positive or negative for COVID-19. To give a broader analysis, the author compares and evaluates the model against other architectures, such as MobileNetV3 Large, MobileNetV3 Small, MobilenetV2, ResNet101, and EfficientNetB0. In terms of the developed mobile architecture model, the classification of COVID-19 using MobileNetV2 obtained the best result with 0.81 accuracy.
TEMPORAL SPATIAL PROPERTY PROFILING AND IDENTIFICATION OF EARTHQUAKE PRONE AREAS USING ST-DBSCAN AND K-MEANS CLUSTERING Samsudin, Angga Radlisa; Fudholi, Dhomas Hatta; Iswari, Lizda
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1293

Abstract

Indonesia is a country located at the confluence of three major tectonic plates, namely Indo-Australia, Eurasia, and the Pacific so that earthquakes often occur, one of which is in West Nusa Tenggara Province. One way to accelerate the disaster mitigation process is to analyze earthquake occurrence based on spatial temporal aspects. This study uses data from BMKG NTB Province during 2018 with a total of 3,699 earthquake events which are then analyzed using ST-DBSCAN and K-Means. ST-DBSCAN analysis was used to determine earthquake prone areas based on the date and location of the event, while k-means used the depth and magnitude of the earthquake. The results show that the distribution pattern of earthquakes in the NTB region has a stationary pattern and there are similar prone areas based on the location and time of occurrence as well as the strength and depth of the earthquake. The ST-DBSCAN method using latitude and longitude attributes produces one cluster that covers 96.33% of the total data. Meanwhile, K-Means using the depth and magnitude attributes produced four clusters. The four clusters were obtained from the cluster density using the silhouette score value between -1 and 1. The K-means analysis used a silhouette score result of 18.527 which was found in cluster 1. Earthquake prone areas in the distribution of earthquakes or types of earthquakes are located in Gangga and Bayan sub-districts of North Lombok and in Sambelia and Sembalun sub-districts of East Lombok. The sub-district with the most frequent earthquakes is Sambelia sub-district with 112 earthquakes. Then the strength of the largest earthquakes on average occurred in Gangga sub-district with magnitudes of 4 to 6.2 SR with shallow earthquake types. The prone area is located at the foot of the mountain and directly adjacent to the ocean.ith shallow earthquake types. The Prone area is at the foot of a mountain and directly adjacent to the ocean.
Implementation Of Deep Learning For Fake News Classification In Bahasa Indonesia Widhi, Eko Prasetio; Fudholi, Dhomas Hatta; Hidayat, Syarif
Journal Research of Social Science, Economics, and Management Vol. 3 No. 2 (2023): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v3i02.546

Abstract

Fake news has become a serious threat in the digital information era. This research aims to develop a model for detecting fake news in Bahasa Indonesia using a deep learning approach, combining the Long Short-Term Memory (LSTM) method with word representations from Word2vec Continuous Bag of Words (CBOW) to achieve optimal results. Our main model is LSTM, optimized through hyperparameter tuning. This model can process information sequentially from both directions, allowing for a better understanding of the news context. The integration of Word2vec CBOW enriches the model's understanding of word relationships in news text, enabling the identification of important patterns for news classification. The evaluation results show that our model performs very well in detecting fake news. After the tuning process, we achieved an F1-Score of 97.30% and an Accuracy of 98.38%. 10-fold cross-validation yielded even better results, with an F1-Score and Accuracy reaching 99%.
Co-Authors Ahmad Fathan Hidayatullah, Ahmad Fathan Al-Faruq, Umar Abdul Aziz Al-Sabahi, Abdullah Yahya Moqbel Alhanafi, Ahmad Azzam Andaru, Gabriel Imam Arifin, Dimastyo Muhaimin Arrie Kurniawardhani Arrochman, Ibnu Fajar Bagus W, Alan Tusa Basbeth, Faishal Bauravindah, Achmad Chairullah Naury Dewa, Chandra Kusuma Eka Handayani Eko Prasetio Widhi Fahmi, Yunizar Khairul Fikri Kurniawardhan, Arrie Lizda Iswari Manoppo, Tommy Nugraha Mustofa, M Najmudin, Nabil Naury, Chairullah Nayoan, Royan Abida N Nayoan, Royan Abida N. Nur Iman Pailus, Muhammad Ridho Rahmadi Rima Tri Wahyuningrum Saefudin Saefudin Satyatama, Mochamad Rezky Septia Rani Siti Mutmainah Suci Hanifah Suryatin, Suryatin Susanto, Prastyo Eko Suyuti, Maghfirah Syarif Hidayat Wijayanti, Shofia Yasmin, Saarah Muthiah Yoga Sahria Yudi prayudi Yurio Windiatmoko, Yurio Zahra, Annisa