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All Journal Journal of Computer and Information CommIT (Communication & Information Technology) Teknik dan Ilmu Komputer JURNAL PENDIDIKAN TAMBUSAI REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Intelligent Decision Support System (IDSS) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Hoga Saragih
Jurusan Teknik Elektro, Fakultas Teknik, Universitas 17 Agustus 1945 Jakarta Jln Sunter Permai Raya, Sunter Agung Podomoro, Jakarta Utara 14350
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences

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Efisiensi Rantai Pasok Berbasis Artificial Intelligence Melalui Transformasi Digital ERP–AI (Studi Kasus PT. Akila Kitchen) Hindrawan, Dimas; Saragih, Hoga
Jurnal Pendidikan Tambusai Vol. 9 No. 3 (2025): Desember
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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Abstract

Transformasi digital berbasis Artificial Intelligence (AI) telah merekonstruksi lanskap manajemen rantai pasok modern dengan menggeser proses pengambilan keputusan dari sistem administratif reaktif menuju optimasi prediktif berbasis data. Namun, bukti empiris mengenai dampak integrasi Enterprise Resource Planning (ERP) berbasis AI pada industri dengan karakteristik mudah rusak dan permintaan berbasis acara, seperti katering, masih terbatas—terutama di negara berkembang. Penelitian ini menganalisis bagaimana integrasi ERP–AI meningkatkan kinerja rantai pasok end-to-end pada PT. Akila Kitchen, perusahaan katering menengah di Jakarta. Metode yang digunakan adalah mixed methods, dengan mengombinasikan data kuantitatif dari log ERP–AI, pemantauan rantai dingin berbasis IoT, serta dashboard logistik real-time, yang kemudian ditriangulasi dengan evaluasi kualitatif terhadap perubahan proses bisnis. Hasil penelitian menunjukkan peningkatan kinerja yang signifikan setelah implementasi ERP–AI: akurasi peramalan meningkat dari 82% menjadi 95%, food waste turun menjadi 0,9%, lead time produksi berkurang 33%, dan ketepatan pengiriman naik dari 87% menjadi 98%. Temuan tersebut memberikan validasi empiris bahwa arsitektur ERP yang diperkaya AI mampu meningkatkan adaptivitas rantai pasok, mengurangi variabilitas operasional, serta memperkuat ketahanan organisasi di sektor layanan makanan katering. Penelitian ini memberikan kontribusi akademik melalui model empiris integratif untuk transformasi rantai pasok berbasis AI, sekaligus menawarkan implikasi praktis bagi pelaku industri yang mengadopsi teknologi prediktif dan otomatis.
Distributed cyber defense framework based on federated learning for attack detection in defense infrastructure Saragih, Hondor; Saragih, Hoga; Manurung, Jonson; Adha, Rochedi Idul; Naibaho, Frainskoy Rio
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.346

Abstract

Cyber threats targeting defense infrastructure have escalated in complexity, rendering centralized intrusion detection systems insufficient due to their inability to guarantee data privacy across distributed military nodes. This study proposes a distributed cyber defense framework that employs federated learning to enable collaborative model training without transmitting raw network traffic beyond individual nodes. The framework integrates an adaptive aggregation strategy combining FedAvg and FedProx, a hybrid deep learning architecture consisting of convolutional neural networks and long short term memory networks, an autoencoder module for unsupervised anomaly detection, a Byzantine robust aggregation mechanism, and post hoc explainability through SHAP and LIME. Experiments were conducted on CIC IDS 2017, CIC IDS 2018, UNSW NB15, and a synthetically generated military network traffic dataset. The proposed framework attained a peak accuracy of 98.74% and an F1 score of 98.12% on CIC IDS 2017, consistently outperforming five baseline methods by up to 5.29 percentage points in F1 score. Future work will investigate differential privacy integration and model compression for deployment on resource constrained tactical edge devices.
Co-Authors ., Waisen Adha, Rochedi Idul Agus Suharto, Agus Andreas Winata, Andreas Bambang Meiraharjo Beny Beny Bobby Reza Bramasto Wiryawan Yudanto Budianto, Alfian David Chandra Didik Setiyadi Ferdiansyah, Gusti Reza Fitroni, Magit Frainskoy Rio Naibaho Francis M. Sarimolle Genrawan Hoendarto Gusvita, Gusvita Harta Pangestu, Harta Hindrawan, Dimas Hondor Saragih Indra Sulistyo Wibowo Indri Neforawati, Indri Irwan Prasetya Gunawan Iwan Adhicandra Luky Adrianto Lydia Theodore, Lydia Manurung, Jonson Mohamad Ali Murni Marbun Ramdhany, Rizky Rezky, Syarifah Fadillah Rudi Wicaksono Rufman Iman Akbar E Safarina, Siti Surya Hendra Putra T. Husain Tony Darmanto Violitta Yesmaya Wisnu Darjono Tulodo Utomo