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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan JIEET (Journal of Information Engineering and Educational Technology) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information System, Applied, Management, Accounting and Research Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech ILKOMNIKA: Journal of Computer Science and Applied Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Bertuah : Jurnal Syariah dan Ekonomi Islam Journal of Applied Data Sciences International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Vocational Education and Information Technology (JVEIT) Jurnal Penelitian Sistem Informasi ILTEK : Jurnal Teknologi Jurnal Informatika Polinema (JIP) Horizon: Indonesian Journal of Multidisciplinary Repeater: Publikasi Teknik Informatika dan Jaringan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika Jurnal Informatika Dan Tekonologi Komputer Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Achmad Junaidi
Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Agriculture, Biological Sciences & Forestry 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Satellite Image Super Resolution using Gradient-Prior Achmad Junaidi
IJCONSIST JOURNALS Vol 4 No 2 (2023): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v4i2.107

Abstract

Super-resolution (SR) has been used in the realm of remote sensing to improve the resolution of an image and get more detailed spatial information than the original image captured by the sensor on the acquisition device. Several SR methods with different approaches, only focusing on sharpening the edges and forgetting non-edge areas. One of the SR methods that utilize prior gradients, can produce high resolution (HR) images in a short time and produce sharp images for non-homogeneous areas. But for areas that tend to be homogeneous, a lot of noise appears. This problem will affect the remote sensing process due to the amount of noise that arises. This paper offers to use dynamic weighting on the gradient prior that will reduce the noise on the homogeneous area, while still able to maintains to produce the sharp edges in non-homogeneous areas. An experimental comparison is conducted on both homogeneous and non-homogeneous area using the previous method and the proposed method.
Intent-Based Networking-Driven Network QoS Management Automation Using Qwen2.5-Coder-7B LLM and n8n Orchestration: Otomatisasi Manajemen QoS Jaringan Berbasis Intent-Based Networking Menggunakan LLM Qwen2.5-Coder-7B dan Orkestrasi n8n Muhammad Rafi Muhtaddin Noor; Achmad Junaidi; Eva Yulia Puspaningrum
Journal of Vocational Education and Information Technology (JVEIT) Vol. 7 No. 1 (2026): Jurnal JVEIT : Vol 7 No 1 2026
Publisher : Lembaga Pengembangan dan Inovasi Universitas Dharmas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56667/jveit.v7i1.2279

Abstract

Quality of Service (QoS) management using Simple Queue on MikroTik routers often faces allocation inefficiencies due to manual, static, and reactive configurations. This study proposes a closed-loop autonomous automation architecture based on Intent-Based Networking (IBN) by integrating the Qwen2.5-Coder-7B Large Language Model (LLM) and the n8n orchestration engine. Procedurally, the system workflow encompasses a pre-execution monitoring phase via SSH speedtest, text denoising using structured prompt engineering, and automated configuration injection through the MikroTik RouterOS v7 REST API. Experimental results across 10 test cases demonstrated that the model's cognitive component achieved a 100% Exact Match Rate in translating unstructured natural language commands into deterministic JSON objects. The state-aware logic within n8n effectively acted as a network safety valve by autonomously capping the child bandwidth allocation to a maximum threshold of 70% of the actual ISP capacity to prevent bufferbloat. Furthermore, the system successfully isolated command executions using HTTP PATCH to minimize the control-plane overhead on the resource-constrained MikroTik hAP Lite router. The measured end-to-end operational latency ranged from 27.66 seconds to 43.98 seconds, which was predominantly driven by the active probing sampling time on the physical ISP link. This integration successfully delivered an adaptive, precise, and secure autonomous network quality management system for SOHO environments while eliminating human configuration errors.
IMPLEMENTASI ARSITEKTUR EFFICIENTNETB2 UNTUK KLASIFIKASI CITRA DAUN HERBAL Dinda Friska Oktaviana; Achmad Junaidi; Muhammad Muharrom Al Haromainy
ILTEK : Jurnal Teknologi Vol. 20 No. 02 (2025): ILTEK : Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Islam Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47398/iltek.v20i02.257

Abstract

Daun herbal telah lama dimanfaatkan sebagai bahan pengobatan tradisional di Indonesia, namun proses identifikasi jenisnya sering menjadi tantangan akibat keterbatasan pengetahuan masyarakat dan kemiripan visual antar daun herbal. Penelitian ini bertujuan menguji performa model klasifikasi daun herbal untuk memperoleh akurasi optimal. Metode yang digunakan adalah CNN dengan arsitektur EfficientNetB2 untuk mengklasifikasikan citra sepuluh jenis daun herbal Indonesia. Dataset merupakan gabungan data primer dan sekunder, yang kemudian dibagi menjadi 80:20 untuk pelatihan dan pengujian. Tiga skenario jumlah epoch diterapkan, yaitu 10, 20, dan 30, dengan konfigurasi tiga hidden layer yang masing-masing berisi 128 neuron. Hasil pengujian menunjukkan bahwa konfigurasi terbaik diperoleh pada skenario 30 epoch dengan akurasi rata-rata mencapai 99,09%. Nilai presisi, recall, dan f1-score pada skenario ini masing-masing sebesar 99%, menunjukkan kinerja yang sangat tinggi dan konsisten. Selisih performa antar skenario pengujian tergolong tipis, sehingga setiap konfigurasi mampu memberikan hasil yang kompetitif. Selain itu, model berhasil membedakan jenis daun dengan kemiripan visual tinggi secara akurat. Dengan demikian, EfficientNetB2 berhasil mencapai akurasi optimal untuk klasifikasi citra daun herbal.
Klasifikasi Citra Tulisan Tangan Aksara Sunda Berbasis Inception-ResNetV2 dengan Transfer Learning Clara Diva Paramitha; Achmad Junaidi; Muhammad Muharrom Al Haromainy
ILTEK : Jurnal Teknologi Vol. 20 No. 02 (2025): ILTEK : Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Islam Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47398/iltek.v20i02.258

Abstract

Aksara Sunda adalah aksara yang semakin jarang digunakan sehingga masyarakat awam sering tidak familiar dengan bentuknya, terutama saat membaca tulisan tangan yang selalu memiliki variasi tergantung penulisnya sehingga ada keterbatasan dalam mengenali bentuknya. Penelitian ini berfungsi untuk menguji performa model deep learning untuk tugas klasifikasi 23 kelas Aksara Sunda serta menguji kombinasi model terhadap berbagai parameter agar dapat memberikan hasil optimal. Penelitian ini menggunakan Inception-ResNetV2 yang dikombinasikan dengan metode fine-tuning transfer learning untuk diuji terhadap tiga optimizer dan learning rate sebanyak 20 epoch. Data pada penelitian ini gabungan dari data GitHub dan data pengumpulan mandiri. Pengujian ini menggunakan rasio 80:20 untuk data latih dan data uji. Optimizer yang diuji adalah SGD, Adam, dan RMSProp. Hasil pengujian menunjukkan bahwa tiap-tiap optimizer mampu memberikan hasil teroptimalnya pada parameter tertentu. Melihat skor performa, RMSProp 0.0001 berhasil mencapai nilai akurasi data uji tertinggi pada 99.15%, diikuti oleh SGD 0.01 dengan akurasi data uji 98.66%, lalu disusul Adam 0.0001 dengan akurasi data uji 96.61%. Akan tetapi, melihat grafik kurva, optimizer SGD lebih stabil dibandingkan RMSProp—yang mengalami guncangan di awal—ataupun Adam—yang mengalami gejala overfitting ringan. Hasil kontradiktif ini dapat menjadi pembelajaran untuk penelitian selanjutnya.
Face Detection Based on Anti-Spoofing with FaceNet Method for Filtering Contract Cheating in Online Exam Erik Iman Heri Ujianto; I Gede Susrama Mas Diyasa; Achmad Junaidi; Ryan Reynickha Fatullah; Wahyu Melinda Permanasari; Allan Ruhui Fatmah Sari
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.
IMPLEMENTASI HYBRID MODEL CEEMDAN-ARIMA-LSTM PREDIKSI HARGA SAHAM PENUTUP Dafauzan Bilal Syaifulloh; Fetty Tri Anggraeny; Achmad Junaidi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.6938

Abstract

Pergerakan harga saham yang bersifat non-linear dan non-stasioner menjadi tantangan utama dalam proses peramalan deret waktu. Penelitian ini mengusulkan model hybrid CEEMDAN–ARIMA–LSTM untuk meningkatkan akurasi prediksi harga penutupan saham PT Industri Jamu dan Farmasi Sido Muncul Tbk (SIDO). Metode CEEMDAN digunakan untuk mendekomposisi data saham menjadi beberapa Intrinsic Mode Functions (IMF), yang selanjutnya dianalisis menggunakan Sample Entropy (SampEn) guna mengidentifikasi tingkat kompleksitas dan menentukan model yang paling sesuai. Komponen dengan karakteristik linier diprediksi menggunakan ARIMA, sedangkan komponen non-linier dimodelkan menggunakan LSTM. Hasil prediksi dari seluruh IMF kemudian direkonstruksi menjadi nilai akhir. Evaluasi kinerja menggunakan MAPE, MAE, RMSE, dan R² menunjukkan bahwa model hybrid memberikan akurasi yang lebih tinggi dibandingkan model tunggal, dengan nilai MAPE yang termasuk dalam kategori sangat akurat. Temuan ini menegaskan bahwa integrasi CEEMDAN dengan pendekatan statistik dan deep learning mampu menangani dinamika kompleks pada data saham serta meningkatkan kualitas prediksi secara signifikan.
Pengembangan FS-CPSM (Feedback System-Based Creative Problem Solving Metaverse) Menggunakan Game Edukasi Mitigasi Banjir Berbasis Roblox Studio Mochammad Afdal Susilo Aji; Achmad Junaidi
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 3 (2026): Agustus : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i3.4469

Abstract

Flooding in Indonesia is becoming increasingly frequent. Data from the National Agency for Disaster Countermeasure (BNPB) records an average of over 3,000 flood events annually over the past decade (BNPB, 2023). Meanwhile, classroom instruction on flood mitigation often relies on lectures and lacks direct student engagement. This study developed FS-CPSM (Feedback System-Based Creative Problem Solving Metaverse), an educational game created in Roblox Studio that integrates the six-stage Creative Problem Solving (CPS) framework into a fictional post-flood village environment. Players are guided through an interactive interface spanning stages from "Mess Finding" to "Acceptance Finding" supported by an automated feedback system that provides validation and hints when answers are incomplete. The study employed a Research and Development (R&D) methodology with functional testing. Results showed that all 12 key features ranging from the CPS interface and the Sumatra flood story panel to the feedback system operated without errors, achieving a 100% system success rate. This research demonstrates that Roblox Studio is a viable platform for educational games based on structured problem-solving.
Klasifikasi Penyakit Mata Menggunakan ResNet-50 Berdasarkan Citra Fundus Muh. Irsyad Dwi Kurniawan; Anggraini Puspita Sari; Achmad Junaidi
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 Rafif Ilafi Wahyu Gunawan; Muhammad Muharrom Al Haromainy; Achmad Junaidi
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 Pelean Alexander Jonas Sitompul; Henni Endah Wahanani; Achmad Junaidi
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.
Co-Authors Achmad Rozy Priambodo Afifudin, Muhammad Agung Mustika Rizki, Agung Mustika Akbar, Refansya Rachmad Akmal, Mohammad Faizal Al Fathoni, Hanif Allan Ruhui Fatmah Sari Andreas Nugroho Sihananto Andreas Nugroho Sihananto Anggraini Puspita Sari Anggraini Puspita Sari Anggraini Puspita Sari Ar Romandhon, Mitzaqon Gholizhan Arif Saifudin, Muhamad Ariq Musyaffah Ghufron, Althaf Bachtiar Riza Pratama Basuki Rahmat Basuki Rahmat Masdi Siduppa Belia Putri Salsabila beni tiyas kristanti Ciptaagung Firjat Ardine Clara Diva Paramitha Dafauzan Bilal Syaifulloh Darmawan, Marcellinus Aditya Vitro Dinda Friska Oktaviana Dunuroi Assuryani Dwi Arman Prasetya Efendi, Ridwan Eka Prakarsa Mandyartha Ekamartha, Ken Narendra Erik evranata Pardede Erik Iman Heri Ujianto Eva Yulia Puspaningrum Eva Yulia Puspaningrum Farrel Tiuraka Vierino Fauzan Novriandy, Muhammad Fetty Tri Anggraeny Firza Prima Aditiawan Galan Ahmad Defanka Galan Ahmad Defanka Hafiyan Fazagi Adnanto Hakim, Albi Akhsanul Henni Endah Wahanani Henni Endah Wahanani I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa Isworo, Muhamad Raihan Ramadhani Izzatul Fithriyah Kartini Kartini kristanti, beni tiyas Kus Dwi Prastyo Lesmana, Benedictus Rafael Mandyartha, Eka Prakarsa Maulana, Hendra Mochammad Afdal Susilo Aji Mochammad Afdal Susilo Aji Mochammad Yoga Firnanda Moh. Angga Ardiyansyah Mohammad Haydir Awaludin Waskito Mohammad Syarifuz Zaim Muh. Irsyad Dwi Kurniawan Muhammad Azka Zaki Muhammad Baihaqi Arrisalah Muhammad Muharrom Al Haromainy Muhammad Rafi Muhtaddin Noor Mustika Rizki, Agung Mutiq Anisa Tanjung Muttaqin, Faisal Nugroho Sihananto, Andreas Nurlaili, Afina Lina Pelean Alexander Jonas Sitompul Pratama, Novandi Kevin Prinafsika PW, Benar Setya Rachmadhany Iman Rafie Ishaq Maulana Rafif Ilafi Wahyu Gunawan Rahmanda Putri, Endin Ratantja Kusumajati, Fatwa Rayya Ruwa'im Nafie Ridwan Efendi Riza Satria Putra Rizki, Agung Mustika Royan Fajar Sultoni Ryan Reynickha Fatullah Sajiwo, Achmad Fauzihan Bagus Sebrina, Aida Fitriya Shahab, Muhammad Syaugi Syahbagus Radithya Haryo Santoso Thalita Syahlani Putri Tinambunan, Fernanda Wahyu Melinda Permanasari Wardah Gracillaria Suharyono, Farra William Lijaya Therry, Renaldy