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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Proceedings of KNASTIK Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Informatika SPEKTRUM INDUSTRI Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Jurnas Nasional Teknologi dan Sistem Informasi JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Teknologi Elektro INFORMAL: Informatics Journal Proceeding SENDI_U Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Bulletin of Electrical Engineering and Informatics JOIN (Jurnal Online Informatika) Edu Komputika Journal Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika Jurnal Khatulistiwa Informatika Journal of Information Technology and Computer Science (JOINTECS) Jurnal Ilmiah FIFO INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT InComTech: Jurnal Telekomunikasi dan Komputer Insect (Informatics and Security) : Jurnal Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Applied Information System and Management ILKOM Jurnal Ilmiah Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi PHARMACY: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia) JUMANJI (Jurnal Masyarakat Informatika Unjani) JURTEKSI RESISTOR (Elektronika Kendali Telekomunikasi Tenaga Listrik Komputer) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Ilmiah Mandala Education (JIME) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Systemic: Information System and Informatics Journal Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Journal of Robotics and Control (JRC) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Cyber Security dan Forensik Digital (CSFD) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal E-Komtek JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) International Journal of Advances in Data and Information Systems International Journal of Marine Engineering Innovation and Research Edunesia : jurnal Ilmiah Pendidikan JITU : Journal Informatic Technology And Communication Journal of Innovation Information Technology and Application (JINITA) Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information Jurnal Teknologi Informatika dan Komputer Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Algoritma Techno Wahana Jurnal Pengabdian Informatika (JUPITA) Sinteza Jurnal INFOTEL Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) Scientific Journal of Informatics Control Systems and Optimization Letters Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Signal and Image Processing Letters Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Edumaspul: Jurnal Pendidikan Methods in Science and Technology Studies JOCHAC Proceeding of Informatics Collaborations and Dessimenation Meeting (Infocoding)
Abdul Fadlil
Ahmad Dahlan University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

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Evaluating IndoBERT for Fraudulent Tweet Detection on Social Media X Imroatul Khuluqi Izzah; Imam Riadi; Abdul Fadlil
Compiler Vol 15, No 1 (2026): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v15i1.3979

Abstract

The spread of fraudulent content on social media X has become an important issue because perpetrators often use persuasive, urgent, and misleading language to influence users to transfer money, share personal data, or access suspicious links. This research evaluates the performance of IndoBERT for binary classification of fraud and non-fraud Indonesian-language posts on social media X using a two-stage fine-tuning design. The dataset consists of 5,235 manually labeled posts, including 2,557 fraud and 2,678 non-fraud instances. In Stage 1, four IndoBERT variants, namely indobert-base-p1, indobert-base-p2, indobert-large-p1, and indobert-large-p2, were compared using a uniform training configuration to identify the best model. The results showed that indobert-large-p1 at epoch 5 achieved the best performance, with a validation F1-score for the fraud class of 0.8898 and a test accuracy of 0.8989. In Stage 2, the selected model was re-evaluated through a controlled grid search by varying epoch, learning rate, and batch size. Although the best Stage 2 configuration improved the validation F1-score to 0.8975, it did not surpass the best Stage 1 model on the test set. These findings indicate that IndoBERT is effective for fraud detection and that a two-stage evaluation design supports more systematic model selection.
Temporal Video Analysis for Identifying Traditional Malay Buildings Using Residual Network and Vision Transformer Sri Winiarti; Sunardi; Abdul Fadlil
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 12 No. 1 (2026): April 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The lack of digital documentation in preserving traditional Malay architecture faces serious challenges, especially with the modernization that slowly obscures the shape and authenticity of the building. Essential elements such as roof shapes, stage structures, and typical ornamental carvings are difficult to identify manually without special skills and considerable time. Malay architecture is an integral part of Indonesia's cultural heritage that needs to be documented systematically and digitally. Along with advances in Artificial Intelligence (AI) technology, traditional buildings' intense learning, identification, and classification can now be done automatically through video-based visual data processing. This study uses a video-based deep learning approach to develop and evaluate a classification system for traditional Malay buildings. Two types of architecture are used: Residual Network (ResNet) and Vision Transformer (ViT). The dataset in the form of videos of traditional buildings was collected from the Pekanbaru, Riau Province, then processed through frame extraction, spatial-temporal augmentation, and visual annotation, resulting in a total of 1,500 frames as training data. This study also presents a novel aspect by comparing the performance of five deep learning models: ResNet18, ResNet34, ResNet50, ResNet101 (CNN), and ViT based on self-attention. ViT, which is rarely used in traditional video-based architecture, shows competitive accuracy and proves its effectiveness in understanding global visual relationships. The training method is carried out using supervised learning and evaluated based on classification accuracy. The test results show that all models can accurately identify visual features of Malay architecture. ResNet50 recorded the highest accuracy (100%), followed by ResNet18 (96.0%), ResNet101 (94.9%), ResNet34 (93.9%), and ViT (93.9%). These findings strengthen the potential for utilizing deep learning in cultural preservation through a video-based automatic documentation system.
Spell Correction for the Minangkabau Language Using BERT-Based Embeddings Dewi Soyusiawaty; Abdul Fadlil; Sunardi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7182

Abstract

Spell checking is an essential component of natural language processing, as it directly influences applications such as sentiment analysis, text classification, and machine translation. Developing a reliable system for low-resource languages like Minangkabau is challenging due to frequent spelling variations and limited annotated data. This study proposes a contextual spell correction model using pre-trained IndoBERT and multilingual BERT (mBERT) embeddings applied without additional training. The method masks misspelled words, extracts the contextual embedding of the [MASK] token, and compares it with candidate embeddings generated through dictionary filtering and Levenshtein Distance. Evaluation was conducted on the Spell Error Corpus for Minangkabau Language (SPEML), which includes insertion errors, deletion errors, substitution errors, transposition errors, punctuation errors, real-word errors, and loanword errors. Results show that mBERT consistently outperformed IndoBERT, achieving an average F1-score of 0.83 compared to 0.75. Statistical validation using paired t-test and Wilcoxon signed-rank test further confirmed that the performance difference between the two models was significant. Both models reached perfect scores (1.0) in real-word and loanword categories, and strong results in insertion_medium (0.97 for mBERT and 0.95 for IndoBERT). The lowest performance occurred in deletion_short (0.52 for IndoBERT) and long words cases (0.57 for mBERT). In addition, a small-scale external validation using 100 Twitter/X sentences was conducted to assess the applicability of the proposed method to real-world social media text. Overall, the findings confirm the effectiveness of contextual embeddings for Minangkabau spelling correction while highlighting challenges in long misspelled words, deletion errors, and informal real-world text.
Penguatan Kompetensi Teknologi Digital Siswa Melalui Workshop Keahlian Informatika SMK PUI Majalengka Tri Ferga Prasetyo; Dedy Sumarhadi; Imroatul Khuluqi Izzah; Dian Novianti; Abdul Fadlil; Imam Riadi
Darma Abdi Karya Vol. 5 No. 1 (2026): Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM POLITEKNIK LP3I

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/darmaabdikarya.v5i1.2991

Abstract

Kegiatan Program Pemberdayaan Umat (Prodamat) sebagai bentuk pengabdian kepada masyarakat ini bertujuan untuk memperkuat kompetensi teknologi digital siswa SMK PUI Majalengka melalui workshop keahlian informatika yang berorientasi pada literasi digital, berpikir komputasional, pengenalan pemrograman, pengelolaan data sederhana, keamanan digital, dan pemanfaatan perangkat lunak produktif. Kegiatan ini dilatarbelakangi oleh kebutuhan peserta didik SMK untuk memiliki keterampilan digital yang relevan dengan dunia kerja, pendidikan lanjut, dan tuntutan transformasi digital. Metode pelaksanaan menggunakan pendekatan pelatihan partisipatif yang terdiri atas analisis kebutuhan, penyusunan modul, pre-test, penyampaian materi, praktik terbimbing, simulasi penyelesaian masalah, post-test, refleksi, dan evaluasi keberlanjutan. Peserta kegiatan adalah siswa SMK PUI Majalengka yang mengikuti sesi workshop secara langsung di laboratorium komputer sekolah. Hasil kegiatan menunjukkan bahwa workshop mampu meningkatkan pemahaman siswa terhadap konsep dasar informatika, keterampilan menggunakan perangkat digital secara produktif, kesadaran etika dan keamanan digital, serta kemampuan menyusun solusi sederhana berbasis logika komputasional. Peningkatan terlihat dari partisipasi aktif siswa, penyelesaian tugas praktik, dan perbandingan hasil evaluasi awal dan akhir. Kegiatan ini menegaskan bahwa workshop informatika yang aplikatif, kontekstual, dan berbasis praktik dapat menjadi strategi penguatan kompetensi teknologi digital siswa SMK. Program lanjutan disarankan berupa klinik proyek digital, pendampingan portofolio, dan integrasi hasil workshop ke kegiatan ekstrakurikuler atau pembelajaran produktif sekolah.
ANALISIS KINERJA MACHINE LEARNING UNTUK DETEKSI KONTEN PENIPUAN BERBAHASA INDONESIA DI TWITTER Imroatul Khuluqi Izzah; Imam Riadi; Abdul Fadlil
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7124

Abstract

The development of information technology has changed the way people interact in the digital space, including through the Twitter platform, which is widely used to share information and opinions. However, this convenience has also led to the emergence of fraudulent content such as fake investments, fictitious sweepstakes, and fictitious donation requests. This study aims to analyze and compare the performance of five machine learning algorithms, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB), in detecting fraudulent Indonesian language content on Twitter. The dataset consists of 5.221 Indonesian language tweets that have been manually labeled into two classes, fraud and non-fraud. All tweets were processed through text preprocessing stages, including data cleaning, case folding, normalization, tokenization, filtering, and stemming, before being represented as numerical vectors using Word2Vec. Classification was performed using 10-fold cross-validation with evaluation metrics of accuracy, precision, recall, and F1-score. The results show that Random Forest achieved the best performance with accuracy of 85.6%, followed by SVM (84.0%), Logistic Regression (83.6%), Decision Tree (81.2%), and Naïve Bayes (78.4%). The main contribution of this study is to provide a systematic empirical comparative analysis of classification algorithms for detecting Indonesian fraudulent content on Twitter, which remains relatively underexplored. These findings show that the combination of Word2Vec and Random Forest can effectively capture the semantic context of short texts and can serve as a reference for developing automatic detection systems for fraudulent content on social media.
PREDIKSI TINGKAT KEPARAHAN KLAIM KOMPENSASI K3 MENGGUNAKAN MULTIPLE LINEAR REGRESSION DENGAN KOREKSI HETEROSKEDASTISITAS: PREDICTION OF SEVERITY LEVEL OF OCCUPATIONAL SAFETY AND HEALTH (OSH) COMPENSATION CLAIMS USING MULTIPLE LINEAR REGRESSION WITH HETEROSCEDASTICITY CORRECTION Aqid Fahri Hafin; Herman; Abdul Fadlil
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7836

Abstract

The identification of determinants influencing the severity of occupational injuries (serious claims) is crucial for formulating precise and evidence-based Occupational Health and Safety (OHS) intervention strategies. This study aims to identify and quantify the most significant ergonomic risk factors and workplace hazard exposures affecting claim severity using 200 job-type observations from the O*NET-ANZSCO dataset published by Safe Work Australia. The analytical method employed is Multiple Linear Regression (MLR) with a staged validation approach. Since the initial regression model using the original data violated the assumptions of normality (Shapiro-Wilk < 0.001) and heteroskedasticity (Breusch-Pagan = 0.025), this study applies a Log-Linear model transformation and Robust Standard Errors (HC3) estimator to produce estimates that meet the Best Linear Unbiased Estimator (BLUE) criteria. The results indicate that the final model (Log-Lin HC3) is statistically significant simultaneously (Prob(F-statistic) = 0.0003) with an Adjusted R-squared of 0.801, meaning that 80.1% of the variation in claim severity can be explained by the model. Partially, four key risk factors are identified as significant: Exposed to Disease or Infections (p = 0.001), Spend Time Making Repetitive Motions (p = 0.006), Spend Time Bending or Twisting the Body (p = 0.008), and Exposed to Radiation (p = 0.035). These findings indicate that mitigating injury severity should prioritize these specific hazard exposures and ergonomic risk factors.
ANALISIS TEKNOLOGI DAN PROSEDURAL DALAM PENANGANAN INSIDEN UNTUK MENDUKUNG FORENSIK DIGITAL Tri Rochmadi; Abdul Fadlil; Imam Riadi; Khairul Nisa Panilestari; Yanuar Wicaksono
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 2 (2026): JATI Vol. 10 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i2.17792

Abstract

Perkembangan teknologi informasi di lingkungan perguruan tinggi mendorong peningkatan pemanfaatan sistem digital dalam berbagai aktivitas akademik dan administratif. Namun, digitalisasi tersebut juga diikuti oleh meningkatnya risiko insiden keamanan siber yang dapat mengancam integritas, kerahasiaan, dan ketersediaan data. Oleh karena itu, kesiapan teknologi dan prosedural dalam penanganan insiden menjadi aspek penting untuk mendukung proses investigasi forensik digital secara efektif. Penelitian ini bertujuan untuk menganalisis tingkat kesiapan teknologi dan prosedural dalam penanganan insiden keamanan siber pada 10 perguruan tinggi swasta di Daerah Istimewa Yogyakarta. Penelitian menggunakan pendekatan mixed-method melalui kuesioner, wawancara, analisis capability level, dan gap analysis dengan mengacu pada kerangka kerja COBIT 2019 pada domain Align, Plan and Organize (APO), Build, Acquire and Implement (BAI), serta Deliver, Service and Support (DSS). Hasil penelitian menunjukkan adanya variasi tingkat kapabilitas antar wilayah dan universitas. Wilayah Sleman mencapai tingkat predictable process dengan indeks pengelolaan insiden 3,61 dan teknologi infrastruktur 3,55, sedangkan wilayah Bantul dan Kota Yogyakarta berada pada tingkat managed process dengan nilai rata-rata sekitar 2,29. Temuan juga menunjukkan adanya kesenjangan menuju target optimising process (level 5), terutama pada aspek standardisasi prosedur, dokumentasi kebijakan, serta dukungan infrastruktur teknologi. Oleh karena itu, peningkatan tata kelola keamanan informasi, penguatan dokumentasi SOP, dan pengembangan kapasitas sumber daya manusia menjadi rekomendasi utama untuk meningkatkan kesiapan forensik digital di perguruan tinggi.
Model Prediktif Penyakit Tanaman Tomat Menggunakan Arsitektur EfficientNetB0 Tri Ferga Prasetyo; Sunardi; Abdul Fadlil
TEMATIK Vol. 12 No. 2 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i2.2722

Abstract

Tomato (Solanum lycopersicum) is highly vulnerable to a range of foliar diseases that can reduce yield and hinder crop quality, particularly when early symptoms are difficult to distinguish in field conditions. To address this challenge, this study develops a predictive model for automatic tomato leaf disease classification using a lightweight EfficientNetB0 architecture. The dataset consists of 5,967 images from nine categories, combining 70% publicly available Kaggle PlantVillage data and 30% real-field images captured under natural outdoor illumination. The methodological pipeline includes preprocessing, data augmentation, and transfer learning, followed by fine-tuning of the upper layers of EfficientNetB0 to improve its ability to generalize toward field-specific variations such as uneven lighting and complex backgrounds. Evaluation results show that the model achieves an accuracy of 89%, with macro-average and weighted-average scores of 90%. These findings demonstrate that EfficientNetB0 provides an effective balance between predictive accuracy and computational efficiency, supporting its potential deployment in early detection systems and edge-based agricultural applications for real-time tomato disease monitoring.
Audit Kredit Digital Berbasis Explainable Artificial Intelligence (XAI): Tinjauan Pustaka Sistematis Muhammad Arief Sutisna; Imam Riadi; Abdul Fadlil
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2614

Abstract

Digital transformation in the financial sector encourages the application of Artificial Intelligence (AI) in the credit audit process. While AI is capable of improving the speed and accuracy of risk assessments, modern models such as deep learning are black box, raising issues of transparency and accountability—two things that are critical in credit audits that must comply with regulations and build stakeholder trust. This study uses the Systematic Literature Review (SLR) approach to synthesize the scientific literature that discusses the application of Explainable Artificial Intelligence (XAI) in digital credit audits. The SLR process includes: Query formulation (e.g. "explainable AI", "credit audit", "interpretability model"), Screening of studies based on relevance, methodological quality, and year range of publication, Extraction of key data (XAI method, dataset type, prediction model, tools used, and key findings), and Comparative synthesis analysis. Based on the Systematic Literature Review, it was found that the main XAI methods are SHAP and LIME, the most commonly used prediction models are Random Forest and XGBoost and several repeated weaknesses were found, including limitations in the representativeness of the dataset, the risk of overfitting, and the trade-off between the level of accuracy and the level of interpretability. Thus, the proper integration of  XAI is expected to increase transparency, fairness, and trust in AI-based digital credit audits, while paving the way for more ethical audit practices and in line with regulatory demands.
Comparative Evaluation of Thresholding Methods for Optimized Digital Document Parsing Accuracy Muhammad Noko Darpito; Kartika Firdausy; Abdul Fadlil
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6982

Abstract

Automated parsing of semi-structured documents has become increasingly important, particularly in standardized formats like SATS-LN, which contain fixed-layout fields such as permit number, addresses, validity period, and item types. This study investigates the impact of two thresholding methods Otsu and Sauvola on object detection accuracy using Faster R-CNN with Detectron2. A dataset of 200 SATS-LN documents, captured via scanner and camera, was augmented into 3,600 images and labeled for seven key fields. Image quality was evaluated using PSNR, SSIM, and MSE, while detection performance was measured through mAP, AP50, AP75, AR@100, precision, recall, and F1-score. Results showed that Sauvola preserved structural layout more effectively (SSIM: 0.76 for scanner, 0.47 for camera), although Otsu achieved higher PSNR on scanned images. Sauvola attained the highest macro and weighted F1-score (0.998), with near-perfect label detection and consistent performance across augmentations. Overall, Sauvola is more reliable for enhancing segmentation and detection in layout-based document processing.
Co-Authors Aang Anwarudin Abdul Azis Abdul Azis Achmad Nugrahantoro Aditiya Dwi Candra Adnan, Adnan Ahmad Naufal Ahmat Taufik Aji Pamungkas Akrom, Akrom Alfiansyah Imanda Putra Alfiansyah Imanda Putra Alfian Amiruddin, Nanda Fahmi Andi Zulherry Andrianto, Fiki Anggit Pamungkas Annisa, Putri Anton Yudhana Anton Yudhana Anwar Siswanto ANWAR, FAHMI Anwarudin, Aang Aqid Fahri Hafin ardi, Ardi Pujiyanta Arief Setyo Nugroho Arief Setyo Nugroho Arif Budi Setianto Arif Budiman Arif Budiman Arif Wirawan Muhammad Asep Ririh Riswaya Asno Azzawagama Firdaus Atmojo, Dimas Murtia Aulia, Aulia Az-Zahra, Rifqi Rahmatika Aznar Abdillah, Muhamad Bagus Primantoro Bambang Priambodo Bashor Fauzan Muthohirin Basir, Azhar Candra, Aditiya Dwi Darajat, Muhammad Nashiruddin Davito Rasendriya Rizqullah Putra Dedy Sumarhadi Dewi Soyusiawaty Dewi Soyusiawaty Dheni Apriantsani Budiman Dhimas Dwiki Sanjaya Dian Novianti Dian Permata Sari Dianda Rifaldi Dikky Praseptian M Dimas Murtia Atmojo Doddy Teguh Yuwono Dwi Susanto Dwi Susanto Edy Fathurrozaq Egi Dio Bagus Sudewo Eko Budi Cahyono Eko Prianto Eko Prianto, Eko Elvina, Ade Endang Darmawan Endang Darmawan Ermin Al Munawar Ermin Ermin Esthi Dyah Rikhiana Fahmi Anwar Fahmi Auliya Tsani Fahmi Auliya Tsani Fahmi Fachri Fanani, Galih Faqihuddin Al-anshori Faqihuddin Al-Anshori, Faqihuddin Fathurrahman, Haris Imam Karim Fauzi Hermawan Fiki Andrianto Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Yasin Fitri Muwardi Furizal Gusrin, Muhaimin Gustina, Sapriani Hafizh, Muhammad Nasir Haksono, Muhammad Rizky Hanif, Abdullah Hanif, Kharis Hudaiby Harman, Rika Helmiyah, Siti Hendril Satrian Purnama Herdiyanto, Erik Herman Herman Herman Herman Herman Yuliansyah Herman Yuliansyah, Herman Herman, - Ibnu Rifajar Ibrahim Mohd Alsofyani Ihyak Ulumuddin Ikhsan hidayat Ilhamsyah Muhammad Nurdin Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imam Riadi Imroatul Khuluqi Izzah Indah Purnama Sari Irjayana, Rizky Caesar Irwansyah Irwansyah Itsnaini Irvina Khoirunnisa Izzan Julda D.E Purwadi Putra januari audrey Jayawarsa, A.A. Ketut Jody Yuantoro Jogo Samodro, Maulana Muhamammad Joko Supriyanto Joko Supriyanto Kamilah, Farhah Kartika Firdausy Khairul Nisa Panilestari Khoirunnisa, Itsnaini Irvina Kusuma, Nur Makkie Perdana Laura Sari Lestari, Yuniarti Lin, Yu-Hao Luh Putu Ratna Sundari M. Nasir Hafizh Maftukhah, Ainin Maulana Muhammad Jogo Samudro Mhd. Basri Miftahul Rozak Mini, Ros Mohd Hatta Jopri Muammar Mudinillah, Adam Mufaddal Al Baqir Muh. Fadli Hasa Muhaimin Gusrin Muhajir Yunus Muhamad Daffa Al Fitra Muhamad Rosidin Muhammad Arief Sutisna Muhammad Faqih Dzulqarnain Muhammad Faqih Dzulqarnain, Muhammad Faqih Muhammad Johan Wahyudi Muhammad Kunta Biddinika Muhammad Ma’ruf Muhammad Nasir Hafizh Muhammad Noko Darpito Muhammad Nuh Muhammad Nur Faiz Muhammad Nurdin, Ilhamsyah Muhammad Rizki Setyawan Mukti, Sindhu Hari Munawaroh Munawaroh Muntiari, Novita Ranti Murinto Murinto - Murinto Murinto Murni Musliman, Anwar Siswanto Mustofa Mustofa Muthorihin, Bashor Fauzan Mutiara Titani Mutiara Titani Muwardi, Fitri Nasution, Dewi Sahara Nasution, Musri Iskandar Nilam Tri Astuti Nopi Hidayat Nurwijayanti Nuryasin, Ilyas Pahlevi, Ryan Fitrian Ponco Sukaswanto Poni Wijayanti Prabowo Soetadji Prabowo, Basit Adhi Prayogi, Denis Putra, Fajar R. B Putri Annisa Putri Annisa Putri Purnamasari Ramadhani, Muhammad Ramdhani, Rezki Razak, Farhan Radhiansyah Rezki Rezki Rifqi Rahmatika Az-Zahra Rizky Andhika Surya Rochmadi, Tri Rohmat Ibrahim Roni Anggara Putra Rusydi Umar Rusydi Umar S Sunardi S, Sunardi Saad, Saleh Khalifah Safiq Rosad Saifudin Saifudin Saifullah, Shoffan Saleh khalifa saad Santi Purwaningrum Sarmini Sarmini Sarmini Sarmini Septa, Frandika Setyaputri, Khairina Eka Setyaputri, Khairina Eka Setyaputri, Khairina Eka Shinta Nur Desmia Sari Sri Winiarti Sri Winiarti Subandi, Rio Sugiyarto Surono Sugiyarto Surono Sukaswanto, Ponco Sukma Aji Sulis Triyanto Sunardi Sunardi Sunardi Sunardi Sunardi, Sunardi Surya Yeki Surya Yeki Syamsiar, Syamsiar Syarifudin, Arma Tole Sutikno Tresna Yudha Prawira Tri Ferga Prasetyo Tri Ferga Prasetyo Tri Rochmadi Tristanti, Novi Tuswanto Tuswanto Virdiana Sriviana Fatmawaty Wahju Tjahjo Saputro Wahyusari, Retno Winoto, Sakti Wulandari, Cisi Fitri Yana Mulyana Yana Mulyana Yanuar Wicaksono Yasidah Nur Istiqomah Yeki, Surya Yohanni Syahra Yossi Octavina Yulianto, Dinan Yulianto, Muhammad Anas Yuminah yuminah yuminah, Yuminah Yuminah, Yuminah Yuwono Fitri Widodo Zein, Wahid Alfaridsi Achmad Zulhijayanto -