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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer At-Taqaddum Management Analysis Journal Jurnal Kedokteran Gigi Universitas Padjadjaran Jurnal Ilmiah Peuradeun Jurnal STIE Semarang (Edisi Elektronik) Tunas: Jurnal Pendidikan Guru Sekolah Dasar Mudir : Jurnal Manajemen Pendidikan bit-Tech JURNAL CAPITAL : Kebijakan Ekonomi, Manajemen dan Akuntansi Darul Ilmi: Jurnal Ilmu Kependidikan dan Keislaman Journal of Applied Data Sciences Jurnal Pengabdian Masyarakat: Humanity and Medicine International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sehat Indonesia (JUSINDO) Wijaya Putra Law Review Kartika: Jurnal Studi Keislaman Scoring: Journal of Film Music Neurona Addabani: Interdisciplinary Journal of Islamic Education Jurnal Pengabdian Masyarakat Management Analysis Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Inovasi Sosial: Jurnal Pengabdian Masyarakat House of Wisdom: Journal on Library and Information Sciences Journal of Neurointervention and Stroke (JNeViS)
Achmad Junaidi, Achmad
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Pendampingan Pengembangan Soft Skills Ibu-Ibu Mekar di Era Society 5.0 di Kota Semarang Fidyah Yuli Ernawati; Hendrayanti, Silvia; Junaidi, Achmad; Sukarsono Sukarsono
Inovasi Sosial : Jurnal Pengabdian Masyarakat Vol. 2 No. 4 (2025): November : Inovasi Sosial : Jurnal Pengabdian Masyarakat
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/inovasisosial.v2i4.2521

Abstract

This community service activity aims to improve the soft skills capacity of the Ibu-Ibu Mekar community in Semarang City to face the demands of the Society 5.0 era. The program was implemented through four stages: needs assessment, participatory planning, training, and mentoring. The methods used included interactive lectures, group discussions, role-play, and contextual practice. The results of the activity showed significant improvements in participants' communication, creativity, collaboration, and problem-solving skills. In addition, several participants began to demonstrate informal leadership roles and greater involvement in community activities. These findings indicate that the experience-based participatory mentoring approach is effective in strengthening women's empowerment and preparing communities for socio-technological change. The program also helped participants develop adaptability to change, increased self-confidence, and facilitated the formation of broader social networks in their communities. Empowering women through soft skills development is expected to create a more resilient community in facing the challenges posed by digital transformation in the Society 5.0 era. This program emphasizes the importance of sustainable soft skills development as a foundation for community resilience in the Society 5.0 era.
Konstruksi Pengetahuan dalam Relasi Kuasa di Pondok Pesantren Juhairiyah; Maimun; Sa’dud Darain, A.; Fitriyah, Eliyatul; Junaidi, Achmad
Kartika: Jurnal Studi Keislaman Vol. 6 No. 1 (2026): Kartika: Jurnal Studi Keislaman (Februari)
Publisher : Lembaga Pendidikan Tinggi Nahdlatul Ulama (LPT NU) PCNU Kabupaten Nganjuk

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59240/kjsk.v6i1.451

Abstract

This study aims to examine how knowledge is constructed within the dynamics of power relations at Subulus Salam Islamic Boarding School. Using a qualitative method with a case study design, this research explores the interaction between formal and cultural authority structures and their influence on teaching practices and epistemological processes. Data were collected through in-depth interviews, participant observation, and documentation analysis, and were analyzed using an interactive model. The findings reveal that power relations in the pesantren are predominantly shaped by the central role of the kiai, whose authority determines educational policies, the selection of legitimate knowledge, and the direction of students’ intellectual development. This authoritative structure creates a learning environment in which students tend to adopt a passive stance and have limited opportunities for critical engagement. However, the study also identifies emerging initiatives within the institution to encourage intellectual participation, promote dialogical learning, and strengthen students’ critical thinking skills. These efforts indicate a shift toward more transformative and participatory educational practices. Overall, this research demonstrates that balancing traditional authority with inclusive learning strategies is essential for enhancing the quality of Islamic education in pesantren while maintaining their cultural identity.
Face Detection Based on Anti-Spoofing with FaceNet Method for Filtering Contract Cheating in Online Exam Ujianto, Erik Iman Heri; Diyasa, I Gede Susrama Mas; Junaidi, Achmad; Fatullah, Ryan Reynickha; Permanasari, Wahyu Melinda; Sari, Allan Ruhui Fatmah
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1167

Abstract

This study develops a reliable face-based verification system for online examinations by integrating a face recognition model with a blink detection mechanism to minimize the risk of identity fraud, also known as "contract cheating," and static image manipulation. "Contract cheating" refers to the practice where students hire others to complete their exams or assignments, compromising academic integrity. The growing reliance on online exams has raised concerns about the credibility of facial verification, as conventional methods are often vulnerable to spoofing attempts. To address this issue, the proposed system combines FaceNet, a deep learning model for identity recognition, with Dlib’s eye blink detection to provide a stronger layer of protection. The system was evaluated using 5-fold and 10-fold K-fold cross-validation, and additional testing assessed the impact of different video frame rates on performance. The results show that the system performs effectively in identifying legitimate users and detecting spoofing. FaceNet achieved an accuracy of 96.67 percent, outperforming DeepFace, which showed poorer results in precision, recall, and F1 score for some participants. Both models were evaluated on the same dataset, consisting of 150 images. The preprocessing pipeline, including face detection using MTCNN, cropping, and resizing, was applied consistently to both models to ensure a fair comparison of their performance. The system also demonstrated adaptability, achieving correct classifications at both 15 and 30 frames per second. Anti-spoofing tests based on the eye blink detection system detected all real faces, while static images were classified as spoofing. These results confirm that combining face recognition with liveness detection enhances the security of online examination platforms. The findings demonstrate the system's potential to reduce contract cheating and impersonation fraud, making online examinations more credible. Future work may focus on implementing adaptive thresholding for blink detection and integrating multimodal verification techniques to improve robustness across diverse real-world environments.
Model Hibrida Deret Waktu Adaptif untuk Peramalan Dinamis Permintaan Penumpang Kereta Api Menggunakan Kalman Filter Isworo, Muhamad Raihan Ramadhani; Tri Anggraeny, Fetty; Junaidi, Achmad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Peramalan permintaan penumpang kereta api yang akurat sangat penting untuk optimasi operasional dan perencanaan layanan transportasi PT KAI. Model peramalan konvensional sering menghadapi tantangan dalam menangani dinamika permintaan yang kompleks, terutama saat terjadi perubahan pola mendadak dan kejadian khusus seperti hari libur atau kejadian luar biasa. Penelitian ini mengusulkan model hibrida adaptif yang menggabungkan SARIMAX dan Prophet dengan optimasi bobot menggunakan Kalman Filter. Data yang digunakan adalah jumlah penumpang bulanan PT KAI DAOP 8 periode 2016-2023 yang mencakup fluktuasi musiman dan kejadian khusus yang mempengaruhi permintaan penumpang. Hasil menunjukkan bahwa model hibrida adaptif mencapai MAPE 4.32%, lebih baik dibandingkan dengan SARIMAX (7.72%) dan Prophet (6.06%). Kalman Filter berhasil mengoptimalkan bobot secara dinamis, meningkatkan kemampuan adaptasi model terhadap perubahan pola permintaan. Model ini menunjukkan performa akurasi dan stabilitas yang tinggi, serta dapat digunakan untuk meramalkan permintaan penumpang di masa depan dan memberikan rekomendasi untuk perencanaan kapasitas PT KAI yang lebih efektif.   Abstract Accurate forecasting of rail passenger demand is essential for operational optimization and planning of transportation services. Conventional forecasting models often face challenges in handling complex demand dynamics, especially when sudden pattern changes and special events occur. This study proposes an adaptive hybrid model combining SARIMAX and Prophet with weight optimization using the Kalman Filter. The data used is the monthly passenger number of PT KAI DAOP 8 from 2016 to 2023, which includes seasonal fluctuations and special events affecting passenger demand. The results show that the adaptive hybrid model achieved a MAPE of 4.32%, better than SARIMAX (7.72%) and Prophet (6.06%). The Kalman Filter successfully optimized the weights dynamically, improving the model's adaptability to changing demand patterns. This model demonstrates high accuracy and stability, and can be used to forecast future passenger demand and provide recommendations for more effective capacity planning for PT KAI.
Costs of Stroke Treatment Under National Health Insurance at Dr. Mohammad Hoesin General Hospital Ramadhoni, Pinto Desti; Junaidi, Achmad; Octavinawaty, Lenny; Apriyono, Apriyono; Oktaviandi, Ardy
Journal of Neurointervention and Stroke Vol. 1 No. 1: MAY 2025
Publisher : Neurointervention Working Group of Indonesian Neurological Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63937/jnevis-2025.11.1

Abstract

Highlight: Discrepancy in costs hospitalization Prevalence and risk factors ABSTRACT Introduction: Indonesia’s National Health Insurance/Jaminan Kesehatan Nasional (JKN) consider stroke a catastrophic disease due to its high treatment costs and healthcare system burden. Stroke patients need extended hospitalization, advanced procedures, and long-term rehabilitation, making it financially and socially burdensome. Endovascular procedures like mechanical thrombectomy and coiling improve clinical outcomes but are expensive. Objective: To outline the characteristics and hospitalization costs of stroke patients—both ischemic and hemorrhagic—covered by JKN at Dr. Mohammad Hoesin General Hospital, focusing on cost differences among conservative therapy, thrombolysis, mechanical thrombectomy, and coiling.  Method: A descriptive study with retrospective data collection was performed at a Type A hospital in South Sumatra, using patient records from January to April 2024. Result: Ischemic stroke was the most common type, with most patients aged 46-65 and male. Most patients stayed less than ten days on second-class wards. Conservative therapy was the most frequently used treatment. Hypertension and kidney disorders were the biggest risk factors and comorbidities. Hospital charges for mechanical thrombectomy and coiling exceeded INA-CBG (Indonesian Case Based Groups) reimbursement rates, highlighting a substantial gap between actual hospital costs and insurance coverage. For both stroke types, medication costs dominated total expenses. Conclusion: The significant gap actual hospital costs and INA-CBG reimbursement  rates for stroke treatments, especially for mechanical thrombectomy and coiling, may affect hospital policies on these interventions. To ensure long-term stroke management, revisions to reimbursement schemes should take into account the high costs associated with endovascular therapy.
Single-Visit Root Canal Treatment on a Mandibular First Premolar with Three Root Canals Junaidi, Achmad; Sari, Annisa Fitria
Jurnal Sehat Indonesia (JUSINDO) Vol. 8 No. 1 (2026): Jurnal Sehat Indonesia (JUSINDO)
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/-.v8i1.500

Abstract

Background: In root canal treatment, accurate diagnosis of root canal morphology is crucial for treatment success. Vertucci reported that in mandibular premolars, 74% have one canal, 25.5% have two canals, and 0.5% have three canals. Therefore, pre-treatment radiography, careful examination, and appropriate techniques are essential to identify root canal orifices, especially in cases with canal variations. The purpose of this case report is to demonstrate the importance of knowledge about the morphology of the root canal system and how to manage root canal care in the first premolar tooth of the lower jaw with three root canals. Case: A 57-year-old female patient with a complaint of a large cavity of the left lower jaw premolar tooth, no pain and wants to be treated. The radiographic results showed that the tooth pulp was open and the morphology of the root canal showed that there were three root canals. Management: Access opening and search of three root canals is carried out using ultrasonic tips and magnification assistance with a dental microscope. The root canal is prepared with rotary NiTi fileby irrigation using 5.25% sodium hypochlorite, 17% EDTA. Obturation using thermal fill and the use of dental crowns as final restoration will be done at the next visit. Conclusion: An understanding of the morphology of the root canal system and the proper use of tools and materials will influence success in the treatment of complex root canals in premolar teeth with three root canals.
Klasifikasi Penyakit Mata Menggunakan ResNet-50 Berdasarkan Citra Fundus Kurniawan, Muh. Irsyad Dwi; Sari, Anggraini Puspita; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3306

Abstract

Visual impairment from diabetic retinopathy, glaucoma, and cataracts remains a critical global health issue, emphasizing the need for early and accurate diagnosis to prevent permanent vision loss. This research presents an automated detection system utilizing ResNet-50, a deep learning architecture, to classify fundus images into multiple retinal disease categories. Unlike conventional convolutional neural networks used in prior studies, this approach leverages ResNet-50's residual learning mechanism to better identify complex retinal patterns. The study employed 4,184 fundus photographs from Kaggle, divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. Images were preprocessed through resizing to 224×224 pixels, normalized with ImageNet parameters, and augmented using random rotation and flipping techniques to enhance model generalization. Dataset splitting followed stratified sampling with an 80-20 train-test ratio, maintaining balanced class representation. Model training spanned 20 epochs using the Adam optimizer across three learning rates: 0.1, 0.01, and 0.001. The 0.001 learning rate produced optimal results with 90.35% accuracy, 90.28% precision, 90.18% recall, and 90.21% F1-score. The confusion matrix indicated strong performance in detecting diabetic retinopathy (219 correct predictions) and normal cases (189 correct predictions), though minor misclassifications occurred between glaucoma and normal categories. These findings validate ResNet-50's residual architecture as an effective tool for extracting discriminative retinal features, offering a computationally efficient solution for automated eye disease screening. Future work should incorporate explainability methods like Grad-CAM to enhance clinical interpretability and build trust among healthcare professionals in AI-assisted diagnostic systems.
Optimizing Gaussian Mixture Model Using Principal Component Analysis for Welfare Clustering Wahyu Gunawan, Rafif Ilafi; Al Haromainy, Muhammad Muharrom; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3310

Abstract

Welfare inequality among regions remains a fundamental challenge in achieving balanced development across East Java Province. The complexity of social, economic, and development indicators often obscures the true patterns of regional welfare. To address this issue, this study proposes a more efficient analytical approach by integrating Principal Component Analysis (PCA) and the Gaussian Mixture Model (GMM) to cluster regions based on welfare levels. The dataset, obtained from the Central Bureau of Statistics (BPS) of East Java for the 2010–2024 period, includes diverse social and economic indicators. PCA was employed to reduce dimensionality and eliminate variable correlations, preserving the essential information within the data. The resulting principal components were then analyzed using GMM to uncover welfare clustering patterns. Based on the evaluation using the Bayesian Information Criterion (BIC) and silhouette score, the optimal configuration was achieved with two clusters, a tolerance of 1e-2, a maximum iteration of 200, and a silhouette score of 0.3403. The first cluster represented regions with higher welfare conditions, while the second indicated relatively lower welfare. These findings demonstrate that the PCA–GMM integration not only improves clustering accuracy but also enhances interpretability of welfare distribution across regions. Future studies may combine PCA with non-linear dimensionality reduction techniques such as Uniform Manifold Approximation and Projection (UMAP) to preserve local structures within complex datasets. Such integration is expected to reveal subtler and more dynamic welfare patterns, offering deeper insights into regional development disparities.
Development of Blockchain-Based Escrow System with IPFS Protocol for Secure Digital Transactions Sitompul, Pelean Alexander Jonas; Wahanani, Henni Endah; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3337

Abstract

Digital transactions are essential to modern economic activities, yet challenges related to trust, transparency, and security persist. This research develops a blockchain-based escrow system integrated with the InterPlanetary File System (IPFS) to address these issues through a decentralized, tamper-resistant architecture. The primary aim is to create an escrow platform that minimizes human intervention while ensuring data integrity, thereby overcoming limitations found in traditional escrow mechanisms that rely heavily on legality and banking institutions. This study demonstrates the feasibility of blockchain technology enhancement to existing escrow models, especially for traders conducting high-value digital transactions. The system enables secure interactions between buyers, sellers, and viewers through a decentralized application (dApp) that assigns user roles and executes transaction logic. Funds are securely locked within the smart contract, while digital assets are stored in IPFS. In cases of dispute, the viewer can cancel the transaction, triggering an automated refund to the buyer and deletion of associated asset data to maintain fairness and security. Smart contract development and testing are carried out using the Hardhat framework before deployment to networks such as the Ethereum-based Sepolia Testnet. The results show that the proposed system reduces transaction risks, increases user trust, and enhances transparency throughout the digital transaction process. This research introduces a practical framework for decentralized escrow systems and provides valuable insights for industries seeking secure, blockchain-driven transaction solutions. The system developed in this study serves as a reference model for integrating traditional transaction with blockchain technology, encouraging broader adoption and future exploration of decentralized systems.
Comparative Analysis of LSTM and GRU Algorithms for Inflation Rate Forecasting Ardiyansyah, Moh. Angga; Al Haromainy, Muhammad Muharrom; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3370

Abstract

Inflation is a critical economic indicator that directly affects price stability, purchasing power, and the formulation of fiscal and monetary policies. In East Java, inflation has demonstrated considerable year-to-year volatility, creating significant challenges for policymakers in maintaining regional economic stability. This situation highlights the need for forecasting models that are both accurate and capable of adapting to complex economic data patterns. This study presents a comparative analysis of two deep learning algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting year-on-year (YoY) inflation in East Java using data from January 2005 to December 2024. The dataset was processed using Min–Max normalization and a 12-month sliding window to capture long-term dependencies and seasonal variations. Model performance was evaluated using RMSE, MAE, and MAPE. The findings demonstrate that no single model performs best across all metrics. The LSTM4 model with a [128,128] architecture achieved the lowest MAE and MAPE values, indicating superior average predictive accuracy and stronger capability in learning complex long-term inflation patterns. In contrast, the GRU1 [64,64] model produced the lowest RMSE and the shortest training time, highlighting its efficiency in minimizing extreme prediction errors and reducing computational cost. These results offer valuable insights for policymakers in East Java: LSTM is more suitable for applications requiring high prediction accuracy, whereas GRU is preferable for real-time or resource-efficient forecasting systems, especially in fast-changing economic environments.
Co-Authors Abadi , Totok Wahyu Abda Abda Adwirianny, Ashita Hulwah Agung Prasetyawan AHMADI Amar, Muhammad Ana Afida Anggraeni, Dya Anggraini Puspita Sari Apriyono, Apriyono Ardiyansyah, Moh. Angga Arrisalah, Muhammad Baihaqi Aryanto, Tossy Awerman Awerman Buhori, A Ary Firman Chotib, Moc Dimas Satria Prayoga Diyasa, I Gede Susrama Mas Elsa Adelia, Febri Enggardipta, Raras Ajeng Erik Iman Heri Ujianto Fatullah, Ryan Reynickha Febrianti, Sania Fetty Tri Anggraeny Fidyah Yuli Ernawati, Fidyah Yuli Firza Prima Aditiawan Fitriyah, Eliyatul Hartarini, Yovita Mumpuni Hendrayati, Selvia Henni Endah Wahanani Ibnu Elmi AS Pelu Isworo, Muhamad Raihan Ramadhani Juhairiyah Khairil Anwar Kurniawan, Muh. Irsyad Dwi Lesmana, Dimas Bayu Putra Maimun marisdina, selly Marsha Ayunita Irawati Mohamad Rifqy Roosdhani Muhammad Muharrom Al Haromainy Muhammad Noor Muhlisa, Safitri Nainggolan, Putri Drani Nindhita, Yoga Octavinawaty, Lenny Okparasta, Andika Oktaviandi, Ardy Permanasari, Wahyu Melinda Permani, Dyah Pradana, Adi Pramesti, Adella Anggia Prastyo, Kus Dwi Pribadi Santosa Rafiqi, Ilham Dwi Rahmat Fauzi Ramadhoni, Pinto Desti Rini Nindela, Rini Robita, Achmad Rosa, Marta Salsabila, Belia Putri Samsul Arifin Sari, Allan Ruhui Fatmah Sari, Annisa Fitria Sari, Oki hardiyanti Rukmana Sa’dud Darain, A. Silvia Hendrayanti Sitompul, Pelean Alexander Jonas Sopi, Sopi Subekti, Heny Sukarsono Sukarsono Tri Endra Untara, Tri Endra Tsanie, Maria Latifa Tunjung Nugraheni Vierino, Farrel Tiuraka Wahyu Gunawan, Rafif Ilafi Wahyudi Wahyudi Yulita Kristanti Yusril Yusril Zumrotun Nafiah