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All Journal Insect (Informatics and Security) : Jurnal Teknik Informatika EDUMATIC: Jurnal Pendidikan Informatika Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Rezki Rezki
Universitas Muhammadiyah Sorong
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Rancang Bangun Aplikasi Sistem Pakar Diagnosa Penyakit Pada Bayi Menggunakan Metode Dempster Shafer Irman Amri; Teguh Hidayat Iskandar Alam; Rezki Rezki
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 5 No. 1 (2019): Oktober 2019
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v5i1.1278

Abstract

Bayi merupakan individu yang berusia 1-12 bulan yang ditandai dengan pertumbuhan danperkembangan yang cepat disertai dengan perubahan dalam kebutuhan zat gizi (Wong, 2003).Setiap tahun diperkirakan bayi meninggal pada bulan pertama kehidupannya, dan dua pertiganya meninggal pada minggu pertama. Penyebab utama kematian pada minggu pertamakehidupan adalah komplikasi kehamilan dan persalinan seperti asfiksia, sepsis dan komplikasiberat lahir rendah. Kurang lebih 98% kematian ini terjadi di negara berkembang dan sebagianbesar kematian ini dapat dicegah dengan pengenalan dini dan pengobatan yang tepat namunkarena kurangnya waktu dan pegawai yang ada menyebabkan sosialisasi tidak dapat dilakukansecara merata.Tujuan dari penelitian ini adalah membuat sebuah aplikasi sistem pakar berbasis websiteyang dapat mempermudah kaum awam khususnya para ibu-ibu utuk mendeteksi lebih awalpenyakit yang diderita oleh anak mereka dengan menggunakan informasi yang telah diperolehdari para Pakar (Dokter Anak) dan melakukan perhitungan dengan menggunakan metodeDempster Shafer untuk menampilkan hasil yang lebih akurat.Setelah melakukan pengembangan sistem dengan metode waterfall dan pengujian sistemmenggunakan black box, dapat disimpulkan bahwa aplikasi ini dapat berjalan baik dan optimal.Aplikasi ini dinilai sangat memudahkan kaum awam khususnya ibu-ibu untuk mendeteksipenaykit yang diderita anak tanpa bertemu dokter terlebih dahulu.
Sistem Pendukung Keputusan Pemilihan Lahan Penanaman Sirih Menggunakan Metode Analytical Hirarchy Process (AHP) Rezki; Umar, Rusydi; Fadlil, Abdul
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 8 No 1 (2024): JANUARY-MARCH 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v8i1.1421

Abstract

Land is a prerequisite that must be considered in betel leaf cultivation, as it significantly influences the quality of the grown betel leaves. Poor-quality land can result in decreased betel leaf harvest, reduced shelf life, susceptibility to rot, curling, and lower selling prices. Based on this issue, a system is needed to facilitate the community in selecting the appropriate land for betel leaf cultivation, which is the Decision Support System (DSS). In this research, the SPK utilizes the Analytic Hierarchy Process (AHP) method with criteria such as soil pH, air temperature, rainfall, soil elevation, and sunlight, as well as alternatives represented by the villages of Mariat Gunung, Klaru, Klamono, and Malasom. The ranking is determined based on a consistency ratio value of <0.1, which is interpreted as being appropriate according to the CR calculation theory. The highest-ranking land is found in Klaru Village (rank 1), followed by Mariat Gunung (rank 2), Klamono (rank 3), and Malasom (rank 4). In conclusion, the AHP method can be utilized for the decision-making process in selecting land for betel leaf cultivation
Machine Learning Approaches for Export Trend Classification: Evidence from Leading Commodities in Indonesia Virasanty Muslimah; Rezki Rezki; Wildan Abdul Jabar
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.32949

Abstract

Sorong City holds a strategic position in the export economy of Papua Barat Daya; however, its export performance remains volatile due to global price fluctuations, logistical constraints, and shifts in international demand. To address these challenges, this study applies machine learning-based classification to analyze and predict export trend dynamics of Sorong’s leading commodities. Specifically, the study compares the performance of Naïve Bayes and Random Forest classifiers within a quantitative experimental framework. The dataset comprises 874 export records (2023–2025), including HS Codes, export values, destination countries, exporters, and export types. The methodological workflow encompasses data preprocessing, trend labeling, normalization, label encoding, class balancing using SMOTE, and model evaluation via 80:20 train-test split and 10-fold cross-validation. Performance metrics include accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results reveal that Random Forest outperforms Naïve Bayes, achieving 74% accuracy compared to 57%, and more effectively captures nonlinear feature relationships. Despite a reduction in ROC-AUC during cross-validation, Random Forest demonstrates greater robustness in export trend prediction. Overall, the findings highlight the potential of machine learning to enhance regional trade forecasting, inform evidence-based policy formulation, and strengthen data-driven export management in emerging regional economies.
Co-Authors Abdul Fadlil Amri, Irman Rusydi Umar Teguh Hidayat Iskandar Alam Virasanty Muslimah Wildan Abdul Jabar