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Shibboleth IdP for Single Sign-On with Kubernetes and Persistent Volume Longhorn Ikhwan Alfath Nurul Fathony; Mukhammad Andri Setiawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.24272

Many organizations do not use centralized user authorization with Single Sign-On (SSO) Management to seamlessly move from one system to another. The same thing also occurred at Universitas Islam Indonesia (UII). Students were having trouble login in from one web service to another. The Board of Information Systems of UII, or Badan Sistem Informasi (BSI), implements SSO to avoid this problem. However, after BSI implemented SSO on the virtual machine, it turned out that the server load became too high. A spiking number of user logins happened in a short period. The centralized system could not handle this. The research's solution is to use a clustered service using Shibboleth IdP. The Shibboleth IdP customization can be carried out to be deployed into the Kubernetes cluster infrastructure ecosystem to meet the needs of authentication login on the business processes at UII. The Shibboleth IdP itself will be equipped with a persistent storage longhorn to support and maintain the service and avoid a single point of failure. The Kubernetes and Persistent Volume Longhorn provide a redundancy function in an application and a more flexible replication process. Inside Kubernetes, there is containerization technology. It was used to optimize the server's resources instead of replicating the application using virtual machines. With the use of centralized login by Shibboleth IdP and persistent storage longhorn, the error because of server load could be minimized. The downtime of the downed services can also be reduced. The research also proves that using Kubernetes and Persistent Volume Longhorn could help the system by preventing a Single Point of Failure using its redundancy function.

Digital literacy training for female employees at CV Gemilang Kencana Adiana, Beta Estri; Mareta, Affix; Fathony, Ikhwan Alfath Nurul; Wardhani, Olivia
Community Empowerment Vol 10 No 6 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ce.13520

CV Gemilang Kencana, a Micro, Small, and Medium-sized Enterprise (MSME) in the food and beverage processing sector in Wonosobo Regency, faces challenges in leveraging digital technology due to low digital literacy among its employees, particularly women. This community service initiative aimed to enhance the digital literacy skills of female employees through training that covered basic technology introduction, productivity application usage, and the utilization of social media for product marketing. The implementation method involved preparation, training delivery, and evaluation stages. The training results demonstrated a significant improvement in participants' digital understanding and skills, with the average pre-test score of 46.5% increasing to 81% post-training. Active participants were able to independently utilize social media for product marketing and contributed to improved operational efficiency and MSME competitiveness. This training proved effective in empowering women and supporting digital transformation within the MSME sector, while also enhancing participants' capabilities in navigating the increasingly digitalized era.

Optimisasi Whisper Speech-to-Text Bahasa Indonesia dengan Hybrid Cloud dan Multi-Engine Ikhwan Alfath Nurul Fathony; Affix Mareta; Beta Estri Adiana; Olivia Wardhani; Dimas Ardiansyah Halim
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 1 (2025): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/5n2d3s08

Automatic Speech Recognition (ASR) for the Indonesian language faces significant challenges due to high Word Error Rate (WER), especially when using pre-trained models without fine-tuning. This study develops an optimized ASR system using a hybrid cloud architecture that integrates the Faster-Whisper large-v3 engine with advanced audio preprocessing techniques. The system adopts a distributed architecture, with Google Colab (Tesla T4, 15GB VRAM) as the GPU server and Ubuntu 22.04 LTS (8 core, 32GB RAM) as the client. Evaluation was conducted on five Indonesian audio samples covering formal news, informal conversations, and long-duration recordings. The system achieved an 80% success rate in processing, with WER ranging from 27.69% (formal news) to 645.16% (informal conversations). Resource utilization was also efficient, with 21.3% GPU usage and 35.4% RAM usage. Processing time remained stable for normal-sized files but experienced timeouts on large files (>50MB). The results indicate that hybrid cloud architecture is feasible for distributed ASR processing in Indonesian, with several areas still open for optimization toward production deployment.

Implementasi Model Convolutional Neural Network dalam Aplikasi Android untuk Identifikasi Limbah Infeksius Mareta, Affix; Estri Adiana, Beta; Wardhani, Olivia; Alfath Nurul Fathony, Ikhwan
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 2 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i2.12693

After the COVID-19 pandemic passed, Indonesian citizens were still strict about using masks because active cases were still found. However, not all Indonesian people are aware that masks are an infectious waste, so after use, they are still disposed of carelessly. Apart from masks, other infectious waste in the form of battery waste which contains hazardous chemicals and food waste potentially to spread infectious diseases, is also dangerous for humans. These kinds of waste are major contributors to global pollution. Research on waste classification has been carried out a lot, but especially for infectious waste has not received much attention from researchers. For this reason, this research is useful to help the public distinguish infectious waste such as used food scraps, masks, and batteries so that they are more careful in disposing of waste. The research started with collecting datasets, which came from combining several infectious waste datasets available on the internet. This is done because there is no publicly available dataset that specifically contains infectious waste. Then, a classification model is created with Convolutional Neural Network (CNN) algorithm which has an accuracy of more than 90%. This algorithm has been widely used in previous studies but has never been used as a model applied to Android applications to classify infectious waste. In this study, the CNN model is applied to Android applications. From this research, an Android application with the CNN algorithm will be produced which can help Indonesians identify infectious waste with an accuracy of 94%.

Pemodelan dan Prediksi Curah Hujan Menggunakan SARIMA untuk Mendukung Perencanaan Irigasi Presisi di Kabupaten Temanggung Wardhani, Olivia; Wibowo, Rheza Ari; Fathony, Ikhwan Alfath Nurul Fathony; Adiana, Beta Estri; Natawijaya, Yasabuana Athallahaufa; Akbar, Rayfal Mayvandra Aurora
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 4 No. 02 (2025): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57255/intellect.v4i02.1642

Changes in rainfall patterns in tropical regions increase uncertainty in agricultural water management, particularly in rainfed areas such as Temanggung Regency, Indonesia. This condition highlights the need for data-driven rainfall prediction models to support precision irrigation planning and drought risk mitigation. This study aims to develop rainfall and rainday prediction models using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method based on monthly climatological data for the period 2014–2024. The analysis follows the Box–Jenkins procedure, including seasonal pattern exploration, stationarity testing, parameter identification using ACF and PACF, parameter estimation, and diagnostic and accuracy evaluation. The results indicate that the SARIMA(0,0,1)(1,0,1,12) model provides the best performance for rainfall prediction, achieving an RMSE of 99.92 mm and an MAE of 57.84 mm, while rainday prediction exhibits relatively higher errors. The model successfully captures consistent annual seasonal patterns and generates projections for 2025, indicating higher rainfall at the beginning of the year and a significant decrease during the dry season. These findings provide a quantitative basis for developing water availability risk calendars and adjusting precision irrigation strategies at the regional level, supporting sustainable water resource management and regional food security. Abstrak Perubahan pola curah hujan di wilayah tropis meningkatkan ketidakpastian dalam pengelolaan air pertanian, terutama pada wilayah tadah hujan seperti Kabupaten Temanggung. Kondisi ini menuntut pemanfaatan model prediksi berbasis data sebagai landasan perencanaan irigasi presisi dan mitigasi risiko kekeringan. Penelitian ini bertujuan untuk membangun model prediksi curah hujan dan hari hujan menggunakan metode Seasonal Autoregressive Integrated Moving Average (SARIMA) berbasis data klimatologis bulanan periode 2014–2024. Analisis dilakukan menggunakan prosedur Box–Jenkins yang mencakup eksplorasi pola musiman dan pengujian stasioneritas. Tahapan selanjutnya meliputi identifikasi parameter melalui ACF dan PACF, estimasi parameter, serta evaluasi diagnostik residual dan akurasi model. Hasil pemodelan menunjukkan bahwa model SARIMA(0,0,1)(1,0,1,12) memberikan kinerja terbaik untuk prediksi curah hujan dengan nilai RMSE sebesar 99,92 mm dan MAE sebesar 57,84 mm, sedangkan prediksi hari hujan menghasilkan tingkat kesalahan yang relatif lebih tinggi. Model mampu merepresentasikan pola musiman tahunan secara konsisten dan menghasilkan proyeksi tahun 2025 yang menunjukkan curah hujan tertinggi pada awal tahun serta penurunan signifikan pada periode kemarau. Temuan ini memberikan landasan kuantitatif untuk penyusunan kalender risiko ketersediaan air dan penyesuaian strategi irigasi presisi pada skala regional, sehingga mendukung pengelolaan sumber daya air dan ketahanan pangan daerah.

Artificial Intelligence-Based Human Resource Performance Assessment for Good University Governance: Meta-Analysis and Systematic Literature Review Shinta Ratnawati; Miftachul Mujib; Ikhwan Alfath Nurul Fathony; Khairul Ikhwan; Dewi Anggraeni; Bagus Fauzan Azhari
Annals of Human Resource Management Research Vol. 6 No. 1 (2026): March
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ahrmr.v6i1.3640

Purpose: This study examines the role of Artificial Intelligence (AI)-based Human Resource (HR) performance evaluation in enhancing Good University Governance (GUG), particularly in improving accountability, transparency, efficiency, and responsiveness in higher education institutions. Research Methodology: A Systematic Literature Review (SLR) and meta-analysis were conducted on 65 peer-reviewed articles published between 2015 and 2025, sourced from Scopus, Web of Science, and ScienceDirect. The effect sizes were calculated, and heterogeneity tests were performed to ensure the robustness of the findings. Results: The results reveal that AI-based HR performance evaluation has a moderate to strong positive relationship with governance effectiveness (r = 0.45) and a moderate positive relationship with governance transparency (r = 0.33). These findings indicate that AI enhances data accuracy, reduces subjective bias, and supports more efficient and consistent decision-making in higher education governance. Conclusions: This study concludes that AI integration in HR performance evaluation significantly contributes to the implementation of GUG principles. It offers both theoretical contributions to digital governance literature and practical implications for university leaders and policymakers. Limitations: This study is limited by the scope of the 65 selected articles, which may not fully represent all existing research on AI-based HR evaluation in higher education contexts.

Kontrol Pembatasan Konten Anak dan Penggunaan Platform Digital UMKM di Lingkungan Ibu Rumah Tangga Banyubiru Damar Wicaksono; Ikhwan Alfath Nurul Fathony; Alifia Revan Prananda; Rheza Ari Wibowo; Sunny Alodia Widyadhana; Naufal Miftakhul Siddiq
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3156

Program pengabdian masyarakat di Desa Banyubiru ini bertujuan untuk meningkatkan literasi digital ibu rumah tangga dalam dua aspek utama, yaitu pengendalian konten digital bagi anak-anak dan pemanfaatan platform digital untuk mendukung usaha mikro kecil dan menengah (UMKM). Kegiatan dilatarbelakangi oleh rendahnya pemahaman masyarakat terhadap keamanan konten digital, privasi data pribadi, serta potensi ekonomi dari media digital. Metode yang diterapkan meliputi sosialisasi, pelatihan teknis, simulasi studi kasus, dan pendampingan langsung. Peserta dilatih untuk menggunakan fitur parental control, manajemen waktu layar (screen time management), serta praktik keamanan digital dalam penggunaan media sosial dan aplikasi UMKM. Selain itu, dilakukan edukasi mengenai strategi pemasaran digital dan pengelolaan akun usaha secara aman. Pendekatan partisipatif diterapkan dengan melibatkan ibu rumah tangga sebagai peserta aktif dalam diskusi, praktik, dan pembentukan kelompok belajar digital di tingkat desa. Evaluasi dilakukan melalui survei dan wawancara yang menunjukkan peningkatan signifikan dalam pemahaman peserta terhadap keamanan digital dan penggunaan platform UMKM, di mana lebih dari 80% peserta mampu menerapkan praktik pembatasan konten anak dan pengelolaan akun digital secara mandiri. Hasil kegiatan tidak hanya meningkatkan kesadaran terhadap pentingnya kontrol konten digital dalam keluarga, tetapi juga mendorong pemberdayaan ekonomi rumah tangga melalui literasi digital yang aman dan produktif.

Shibboleth IdP for Single Sign-On with Kubernetes and Persistent Volume Longhorn Ikhwan Alfath Nurul Fathony; Mukhammad Andri Setiawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.24272

Many organizations do not use centralized user authorization with Single Sign-On (SSO) Management to seamlessly move from one system to another. The same thing also occurred at Universitas Islam Indonesia (UII). Students were having trouble login in from one web service to another. The Board of Information Systems of UII, or Badan Sistem Informasi (BSI), implements SSO to avoid this problem. However, after BSI implemented SSO on the virtual machine, it turned out that the server load became too high. A spiking number of user logins happened in a short period. The centralized system could not handle this. The research's solution is to use a clustered service using Shibboleth IdP. The Shibboleth IdP customization can be carried out to be deployed into the Kubernetes cluster infrastructure ecosystem to meet the needs of authentication login on the business processes at UII. The Shibboleth IdP itself will be equipped with a persistent storage longhorn to support and maintain the service and avoid a single point of failure. The Kubernetes and Persistent Volume Longhorn provide a redundancy function in an application and a more flexible replication process. Inside Kubernetes, there is containerization technology. It was used to optimize the server's resources instead of replicating the application using virtual machines. With the use of centralized login by Shibboleth IdP and persistent storage longhorn, the error because of server load could be minimized. The downtime of the downed services can also be reduced. The research also proves that using Kubernetes and Persistent Volume Longhorn could help the system by preventing a Single Point of Failure using its redundancy function.

Cloud Infrastructure Security: Detecting and Analyzing Attacks on Windows Server 2019 Fathony, Ikhwan Alfath Nurul; Mareta, Affix; Wardhani, Olivia; Suseno, Hakkan Azrul; Ghifari, Galang Ahmad
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 2 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i2.48759

Cloud infrastructure security represents a critical challenge in addressing cybersecurity threats, particularly for internet-facing services such as Remote Desktop Protocol (RDP) and SQL Server. This research investigates cloud infrastructure security based on Windows Server 2019 through the development of a proactive and responsive attack detection and analysis framework using the Wazuh platform as Security Information and Event Management (SIEM) integrated with the MITRE ATT&CK framework. The research method employs an experimental approach with continuous monitoring for 30 days of two Windows Server 2019 units running RDP and SQL Server services. Attack simulations were conducted using eight different scenarios including RDP brute force, SQL Server authentication brute force, port scanning, privilege escalation, lateral movement, data exfiltration, persistence mechanisms, and defense evasion. Monitoring results revealed 110,492 total security events, dominated by 109,057 authentication failures (98.7%) and only 171 successful authentications, with the remainder consisting of other activities such as port scanning and process execution. The Wazuh-based detection system with MITRE ATT&CK integration successfully mapped 15 attack techniques, 10 of which were actively observed during the 30-day monitoring period, with a detection rate of 93.2%, false positive rate of 6.8%, and average response time of 2.4 seconds. Compliance analysis showed 87% compliance with PCI DSS, 91% with NIST 800-53, 85% with HIPAA, and 89% with GDPR. The research concludes that the integration of Wazuh SIEM with the MITRE ATT&CK framework is effective in detecting and analyzing cyber attacks on Windows Server 2019, with practical contributions in the form of implementation guidelines for rule-based detection and correlation rules for multi-stage attack detection.

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