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All Journal Jurnal Teknologi Informasi dan Komunikasi COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Sari, Wulan Inda
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Social Sciences

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Analisis Klaster Produksi Cabai Besar dan Cabai Rawit Antar Provinsi di Indonesia Menggunakan Algoritma K-Means. Budiman, M. Hafiz; Ardiansyah, Ferdy; Rahmi, Eriski Aulia; Nasution, Mauludimas; Sari, Wulan Inda; Sarah, Siti
COMSERVA : Jurnal Penelitian dan Pengabdian Masyarakat Vol. 5 No. 10 (2026): COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/comserva.v5i10.3528

Abstract

Produksi cabai merupakan salah satu komponen penting dalam stabilitas pasokan komoditas hortikultura di Indonesia. Ketimpangan produksi cabai antarprovinsi menyebabkan fluktuasi harga dan ketidakstabilan pasokan, sehingga pemetaan wilayah berdasarkan kapasitas produksi menjadi sangat relevan. Penelitian ini bertujuan mengklasterkan provinsi di Indonesia berdasarkan produksi cabai besar dan cabai rawit menggunakan algoritma K-Means. Data diperoleh dari Badan Pusat Statistik (BPS) tahun 2023 dan dianalisis menggunakan pendekatan unsupervised learning. Proses penelitian meliputi pembersihan data, normalisasi, pemilihan parameter jumlah klaster, penerapan algoritma K-Means, dan evaluasi menggunakan Silhouette Coefficient. Hasil penelitian menunjukkan terbentuknya tiga klaster, namun hanya dua yang stabil yaitu klaster produksi rendah dan klaster produksi tinggi. Klaster produksi tinggi dihuni oleh provinsi Jawa Timur, Jawa Tengah, Jawa Barat, serta Sumatera Utara sebagai sentra utama. Nilai silhouette untuk klaster produksi rendah mencapai 0.50–0.75, menunjukkan pemisahan klaster yang kuat. Temuan ini dapat menjadi dasar perencanaan distribusi dan pengembangan wilayah produksi cabai nasional.
KERANGKA FORENSIK JARINGAN BERBASIS NEURAL NETWORK UNTUK DETEKSI DAN ANALISIS SERANGAN SIBER Budiman, Hafidz; Ardiansyah, Ferdy; Sitorus, Sahat Parulian; Rahmi, Eriski Aulia; Sarah, Siti; Sari, Wulan Inda
Jurnal Teknologi Informasi dan Komunikasi Vol 19 No 1 (2026): April
Publisher : STMIK Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47561/jtik.v19i1.350

Abstract

The increasing complexity of cyberattacks requires network forensic methods capable of reconstructing, detecting, and interpreting malicious activity with high accuracy. Existing forensic approaches still face limitations when analyzing large scale network traffic, particularly when attack patterns resemble normal user behavior, which complicates the identification of incidents and the reconstruction of attack timelines. This study proposes a neural network based network forensic framework that integrates attack identification, network traffic classification, and activity reconstruction to support digital investigations. The research employs an experimental design with a mixed traffic dataset comprising normal and malicious activities, including network scanning, SSH brute-force attempts, denial-of-service attacks, and malware distribution. The neural network model performs the detection phase by classifying network traffic, while a structured forensic pipeline guides the extraction of digital artifacts and the correlation of network metadata. The results indicate that the proposed model achieves 97.82 percent accuracy, a low false-positive rate, and faster processing time compared with conventional network forensic approaches. Forensic analysis of network logs reveals attack patterns characterized by intensive scanning on common service ports, repeated authentication attempts on SSH services, anomalous packet inter arrival intervals during denial of service attacks, and increased payload entropy associated with malware communication. These findings demonstrate the effectiveness of integrating neural network techniques into network forensic investigations, supporting improved detection capabilities and the reconstruction of digital evidence during cyber incident analysis.
Co-Authors Budiman, Hafidz Budiman, M. Hafiz Ferdy Ardiansyah, Ferdy Nasution, Mauludimas Rahmi, Eriski Aulia Siti Sarah, Siti Sitorus, Sahat Parulian