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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal EECCIS POSITIF ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Pendas : Jurnah Ilmiah Pendidikan Dasar Antivirus : Jurnal Ilmiah Teknik Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Prima Abdika: Jurnal Pengabdian Masyarakat Journal of Community Service Jurnal Pengabdian Masyarakat Ilmu Komputer Jurnal ilmiah teknologi informasi Asia Jurnal Abdimas Mahakam
Achmad Noercholis
Institut Teknologi Dan Bisnis ASIA Malang
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Perancangan dan Pembuatan Website Majelis Ulama Indonesia Kota Batu Malang Farokhah, Lia; Noercholis, Achmad; Ahda, Fadhli Almu’iini; Sulistyo, Danang Arbian; Rofiq, Muhammad
Prima Abdika: Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2024): Volume 4 Nomor 1 Tahun 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar Universitas Flores Ende

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/abdika.v4i1.3746

Abstract

Community service is one of the activities required for lecturers at Institut Teknologi dan Bisnis ASIA Malang every semester. This activity is carried out in groups to distribute knowledge to the community. This activity is in partnership with the Batu City MUI in creating a digital website for distribution of information to the wider community. Problems arise when a partner's website is hacked or damaged by a hacker. The service team wanted to teach how to recover or mitigate after damage, but the technical team could not provide information regarding the website and suggested creating a new website. In the initial stage, this service will create a new website. The method of this service approach is to carry out discussions in group discussion forums (FGD). The results of the discussion were realized in the form of a website for the Batu City MUI. Evaluations were carried out regarding design and functionality requirements. The partners are satisfied but it must be developed further. In ongoing collaboration this website will continue to be developed. After that, training in mitigating data when exposed to hackers will be carried out in the next service.
Peningkatan Literasi Pengetahuan Kesehatan dan Teknologi untuk Pencegahan dan Deteksi Penyakit Menggunakan Digital Image processing Lia Farokhah; Achmad Noercholis; Fadhli Almuiini Ahda; Muhammad Rofiq; Danang Arbian Sulistyo
Jurnal Abdimas Mahakam Vol. 5 No. 02 (2021): Juli
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

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

Abstract

Pada era sekarang, penyakit muncul bervariasi. Alat kesehatan di Indonesia sangat bergantung dengan impor karena beberapa produk yang dibutuhkan tidak diproduksi di dalam negeri. Selain itu, harganya menjadi cukup mahal. Adapun tujuan dari pengabdian ini adalah meningkatkan literasi mengikuti model The European Health Literacy Survey: the 12 subdimensions. Adopsi model ini diharapkan akan pada tahap dimensi menilai atau mengevaluasi informasi yang relevan dengan kesehatan. Metode yang digunakan adalah edukasi masyarakat khususnya perguruan tinggi yang memiliki fokus keilmuan teknologi dan kesehatan untuk meningkatkan literasi kesehatan. Adapun hasil yang didapatkan selama pengabdian melalui kolaborasi webinar adalah cukup bagus untuk meningkatkan literasi kesehatan. Hal ini didasarkan atas fakta saat proses tanya jawab dalam penggalian informasi. kolaborasi dua keilmuan yaitu kesehatan dan teknologi bisa membuat alat deteksi maupun pencegahan penyakit yang lebih murah namun akurat menggunakan sistem cerdas.
Analisis Komparatif Arsitektur CNN untuk Klasifikasi Penyakit Daun Tebu Berbasis Transfer Learning Fakhrur Rofiq; Achmad Noercholis
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3431

Abstract

Classifying sugarcane leaf diseases is critical in modern cultivation, because symptoms are often difficult to identify accurately through visual inspection alone. This study compares the performance of three Convolutional Neural Network (CNN) architectures Xception, EfficientNetB0, and ResNet50 using transfer learning on a dataset of 2,521 sugarcane leaf images grouped into five disease classes. A preprocessing stage, including image resizing, was applied to all samples. The data were then split into 80% training, 10% validation, and 10% testing sets. Each model was trained with the same training configuration to ensure a fair comparison, with consistent hyperparameters across experiments. Testing results indicate that EfficientNetB0 achieved the most stable performance with 99.5% accuracy, followed by ResNet50 at 98.2%, whereas Xception yielded the lowest performance due to training instability. These findings suggest that CNN architectures optimized via network-scaling efficiency better handle visual variability in sugarcane leaf disease images.Keywords: Sugarcane Leaf Disease; Transfer Learning; Xception; EfficientNetB0; ResNet50. AbstrakKlasifikasi penyakit daun tebu sangat penting dalam praktik budidaya modern, karena gejala penyakit sering kali sulit diidentifikasi secara akurat hanya melalui pengamatan visual. Studi ini mengkomparasi kinerja tiga arsitektur Convolutional Neural Network (CNN), yaitu Xception, EfficientNetB0, dan ResNet50, menggunakan pendekatan transfer learning pada dataset 2.521 citra daun tebu yang dibagi ke dalam lima kelas penyakit. Tahap pra proses yang mencakup penyesuaian ukuran diterapkan pada seluruh citra. Selanjutnya, data dibagi menjadi 80% data pelatihan, 10% data validasi, dan 10% data pengujian. Setiap model dilatih menggunakan konfigurasi pelatihan yang seragam untuk memastikan perbandingan yang adil. Hasil pengujian menunjukkan bahwa EfficientNetB0 memiliki performa paling stabil dengan akurasi 99,5%, diikuti oleh ResNet50 dengan akurasi 98,2%, sedangkan Xception menunjukkan performa terendah akibat ketidakstabilan selama pelatihan. Temuan ini menunjukkan bahwa arsitektur CNN yang dioptimalkan melalui pendekatan efisiensi skala jaringan lebih mampu menangani variasi visual pada citra penyakit daun tebu. 
Implementasi MobileNet V2 untuk Klasifikasi Jenis Apel Berdasarkan Citra Digital Airlangga Marta Farizky; Achmad Noercholis
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10216

Abstract

Apple variety classification based on digital images is a challenging problem because some varieties have similar visual characteristics, such as color, shape, and surface texture of the fruit. This similarity can lead to misidentification when performed manually. This study aims to develop an apple variety classification model using the MobileNetV2 architecture with a transfer learning and fine-tuning approach. The dataset used consists of 1,237 apple images representing four varieties: Anna, Manalagi, Rome Beauty, and Granny Smith. After data cleaning to remove duplicate images, 1,050 images were obtained for use in the study. The pre-processing stage includes image resizing to 224 × 224 pixels, normalization, and texture enhancement using a combination of Unsharp Masking and the Sobel operator. To improve the model's generalization capability, data augmentation and class weighting were applied during the training process. The model was then evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The test results showed that the MobileNetV2 model achieved an accuracy of 99.37%, a precision of 99.48%, a recall of 99.07%, and an F1-score of 99.27%. Confusion matrix analysis showed that out of 158 test data, there was only one misclassification, namely the Rome Beauty image predicted as Anna. In addition, the model was successfully implemented into a web-based application using Streamlit so that it can be used to directly identify apple varieties. The results showed that MobileNetV2 is effective for classifying apple varieties with a high level of accuracy and good computational efficiency, so it has the potential to be applied to image-based fruit identification systems in agriculture.
MODEL PELATIHAN CODING DAN ARTIFICIAL INTELLIGENCE BERBASIS PROYEK UNTUK MENINGKATKAN INOVASI PEMBELAJARAN GURU Abd Hadi; Achmad Noercholis; Tri Wahyuni
Jurnal Pengabdian Masyarakat Ilmu Komputer Vol. 3 No. 2 (2026): Mei
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jpmik.v3i2.3827

Abstract

Teknologi digital seperti coding dan Artificial Intelligence (AI) kini mengubah lanskap pendidikan vokasi. Sekolah menengah kejuruan dituntut mencetak lulusan yang gesit menghadapi industri modern. Namun, kenyataan di SMKN 1 Pamekasan menunjukkan sebaliknya. Para guru masih kesulitan memahami dasar coding, minim pelatihan AI praktis, dan jarang memanfaatkan teknologi ini untuk media ajar kreatif. Akibatnya, integrasi teknologi di kelas berjalan lambat. Program pengabdian ini hadir untuk menjembatani celah tersebut melalui pelatihan dan pendampingan praktis berbasis project-based learning. Guru diajak mempraktikkan langsung pengenalan wajah, klasifikasi gambar dengan Teachable Machine, hingga membuat chatbot pendidikan sendiri. Evaluasi menunjukkan kemajuan nyata. Skor pemahaman guru melesat dari rata-rata pre-test 52% menjadi 85% pada post-test. Bahkan, 90% peserta sukses menuntaskan proyek chatbot mereka. Program ini terbukti memicu kolaborasi erat antar-guru dalam melahirkan inovasi pembelajaran di sekolah vokasi.
IMPLEMENTASI SNORT INLINE MODE DAN ELK STACK UNTUK DETEKSI DAN MITIGASI SERANGAN PADA INFRASTRUKTUR LAYANAN DIGITAL PENDIDIKAN BERBASIS DOCKER Vian Maulana Fatah; Achmad Noercholis; Fransiska Sisilia Mukti
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 No. 02 Juni 2026 Press
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.54016

Abstract

The rapid development of digital education services has increased educational institutions' dependence on information technology infrastructure. Various cyber threats such as brute force attacks, port scanning, ping sweep, and denial-of-service attacks can disrupt service availability and interfere with academic and administrative activities. Therefore, an effective security mechanism is required to detect and mitigate cyberattacks in real time. This study aims to implement a Snort Inline Mode-based Intrusion Prevention System integrated with the ELK Stack (Elasticsearch, Logstash, Kibana) within a Docker-based digital education services infrastructure environment. The research employed an experimental method by developing a containerized architecture consisting of attacker, digital education service server, Snort IPS, Filebeat, Logstash, Elasticsearch, and Kibana containers. Active mitigation was implemented using Netfilter Queue (NFQUEUE), enabling Snort to inspect and block malicious traffic before reaching the target server. Four attack scenarios were tested, including SSH Brute Force, ICMP Ping Sweep, TCP Port Scan, and TCP SYN Flood. The results indicate that the system successfully detected and mitigated all attack scenarios with a mitigation success rate of 100% and response times below one second. Furthermore, ELK Stack integration provided centralized and real-time security monitoring through Kibana dashboards, facilitating threat analysis and security management. The findings demonstrate that the combination of Snort Inline Mode and ELK Stack in a Docker environment can serve as an effective security solution to support the reliability, security, and availability of digital education services infrastructure
Implementasi Fail2Ban dan ELK Stack untuk Mitigasi Serangan Brute Force pada REST API Login E-Learning Berbasis Docker Widi laksono, Anggi; Achmad Noercholis; Fransiska Sisilia Mukti
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 03 (2026): Volume 11 Nomor 03, September 2026 Public
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i03.55491

Abstract

The increasing adoption of digital learning systems (E-Learning) has raised the need for security mechanisms capable of protecting authentication services from cyber threats. One of the most common threats is a brute force attack, which exploits repeated login attempts using automated username and password combinations. This study aims to implement Fail2Ban and the ELK Stack to mitigate brute force attacks on a Docker-based E-Learning Login REST API. The research employed an experimental method by developing a containerized environment consisting of an E-Learning Login REST API, Fail2Ban, Filebeat, Logstash, Elasticsearch, Kibana, and attacker containers. Brute force attack simulations were conducted using Hydra against the authentication service. Data were collected from authentication logs, Fail2Ban logs, and security monitoring results visualized through Kibana dashboards. The results show that Fail2Ban successfully detected 180 failed login attempts originating from six attacker IP addresses and automatically blocked all attacker IPs, achieving a mitigation success rate of 100%. The average mitigation response time ranged from 0.314 to 0.443 seconds with a bantime of 500 seconds. Furthermore, the ELK Stack successfully provided real-time visualization of failed login activities, attacker IP addresses, and blocking actions. The findings indicate that the integration of Fail2Ban and the ELK Stack in a Docker environment is effective in improving the security of the E-Learning Login REST API against brute force attacks through centralized detection, mitigation, and security monitoring mechanisms.  
ANALISIS KOMPARATIF EFISIENSI KOMPUTASI MODEL XGBOOST PADA INFRASTRUKTUR DOCKER CONTAINER DAN NATIVE OS UNTUK MENDUKUNG KEANDALAN LAYANAN PEMBELAJARAN DIGITAL BERBASIS KECERDASAN BUATAN Fadilah, A; NoerCholis, Achmad; Sisilia Mukti, Fransiska
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 03 (2026): Volume 11 Nomor 03, September 2026 Publish
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i03.56080

Abstract

The reliability of AI-based digital learning services depends heavily on the appropriate selection of machine learning model deployment infrastructure. Educational institutions commonly face computational resource constraints, choosing between a native OS and Docker Container, a critical factor affecting the responsiveness and availability of digital services that underpin academic activities. This study empirically compares the computational efficiency (CPU and RAM) and accuracy of the Extreme Gradient Boosting (XGBoost) algorithm in these two execution environments, using the CIC-IDS 2017 dataset from the Canadian Institute for Cybersecurity, comprising approximately 2.8 million records across 15 classes as a representative large-scale computational workload. A quantitative experimental method with real-time resource monitoring was applied to identical hardware. Results show that the Native OS environment achieved an average end-to-end (E2E) execution time of 209.72 seconds, approximately 9.4 times faster than Docker Container, which required an average of 1,965.83 seconds due to a 2-core CPU restriction. Native OS averaged 90.60% CPU and 403.01 MB RAM, while Docker averaged 24.41% CPU and 683.76 MB RAM with a +280.75 MB memory overhead. Critically, all model quality metrics Accuracy, Precision, Recall, and F1-Score were identical at 99.89% in both environments, confirming that the execution environment does not affect AI model quality. This study concludes that to support the reliability of AI-based digital learning services requiring real-time responses, using a native OS or providing unrestricted resource allocation in a Docker Container is the recommended deployment architecture for educational institutions
Co-Authors Abd Hadi Agus Rahman Alamsyah Airlangga Marta Farizky Akbar, Dzikrullah Asmoro, Zain Bagus Dwi Didik Suprayitno Fadhli Almu’iini Ahda Fadilah, A Fakhrur Rofiq Farokhah, Lia Fransiska Sisilia Mukti Ike Kusdyah Rachmawati M. Aziz Muslim Maftuch Maftuch Mohammad Zainuddin, Mohammad Muhammad Luqman Hakim Muhammad Rofiq Muhammad Rofiq Sisilia Mukti, Fransiska Suastika Yulia Riska Sulistyo, Danang Arbian Titania Dwi Andini Tri Wahyuni Vian Maulana Fatah Widi laksono, Anggi