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All Journal Jurnal Maritim Polimarin Riau Jurnal Teknik Informatika
Hugi Cerlyawati
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Analisa Kandungan Logam Cd, Pb, Zn Dan Cu pada Tangki Ballast Kapal Niaga di Pelabuhan Kendal dan Tanjung Mas Semarang Apriyanto Budhi Wibowo; Hugi Cerlyawati
Jurnal Maritim Polimarin Vol. 7 No. 1 (2021)
Publisher : PPPM Polimarin

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (482.818 KB) | DOI: 10.52492/jmp.v7i1.47

Abstract

Commercial ships are a means of transportation at sea. For ships that do not exchange ballast water accordingly, it can be dangerous for the port environment they enter. The purpose of this research is to determine the metal content in the ballast tank water of commercial ships that have not implemented ballast water exchange in the middle of the sea following the rules of the International Maritime Organization (IMO). Research on the content of heavy metals in the ballast water of commercial ships, both ferries and cargo ships docked at the port, has been carried out using the AAS (Atomic Absorption Spectroscopy) method on flame. The results showed that the Pb content in ballast water ranged from 0.004-1.65 mg/liter, Cd ranged from 0.0015-0.47 mg/liter, Cu ranged from 0.0035-0.362 mg/liter and Zn ranged from 0.0015-2.54 mg/liter. According to the Decree of the Minister of the Environment Number 51 of 2004, the metal content of Cd and Zn has exceeded the seawater quality standards for water port, namely 0.001 mg/liter and 0.1 mg/liter, while the content of Pb and Cu has not exceeded the port quality standards.
Model Klasifikasi Usia Kematian Neonatal (≤1 Hari dan >1 Hari) Berbasis Machine Learning Evina Widianawati; Nugraheni Kusumawati; Hugi Cerlyawati; Yanita Sri Mulyani
Riau Jurnal Teknik Informatika Vol. 5 No. 1 (2026): Maret 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i1.4302

Abstract

Periode neonatal, khususnya dalam 24 jam pertama kehidupan, merupakan fase kritis dengan risiko morbiditas dan mortalitas tinggi. Identifikasi pola klinis yang membedakan neonatus usia ≤1 hari dan >1 hari penting untuk mendukung pengambilan keputusan klinis berbasis data. Tujuan penelitian ini yaitu membandingkan performa beberapa algoritma machine learning dalam mengklasifikasikan usia neonatus serta mengidentifikasi faktor klinis yang paling berkontribusi. Penelitian analitik observasional ini menggunakan data sekunder rekam medis neonatus sebanyak 41 kasus di rumah sakit X di kota Semarang. Variabel prediktor meliputi jenis kelamin, usia gestasi, berat badan, tinggi badan, jumlah diagnosa sekunder, diagnosa utama, dan cara melahirkan. Lima algoritma diuji: Logistic Regression, Support Vector Machine (RBF), Random Forest, HistGradientBoosting, dan XGBoost. Evaluasi dilakukan menggunakan Stratified K-Fold Cross Validation dengan metrik akurasi, presisi makro, recall makro, dan F1 score. Analisis faktor terpenting dilakukan menggunakan Random Forest. Random Forest menunjukkan performa terbaik dengan akurasi 0,686 dan F1 tertimbang 0,610 ± 0,182. HistGradientBoosting dan SVM mencapai akurasi 0,657, sedangkan Regresi Logistik dan XGBoost masing-masing 0,595 dan 0,557. Usia gestasi merupakan faktor paling berpengaruh, diikuti tinggi badan, jumlah diagnosa sekunder, dan berat badan. Beberapa diagnosa utama terkait gangguan pernapasan juga termasuk dalam faktor penting. Random Forest memberikan kombinasi akurasi dan stabilitas terbaik dalam memprediksi usia kematian bayi baru lahir.
Co-Authors Apriyanto Budhi Wibowo Evina Widianawati nugraheni kusumawati Yanita Sri Mulyani