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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Analit: Analytical And Environmental Chemistry
Tia Haryanti
Universitas Gunadarma
Computer Science & IT Engineering

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EVALUASI SUSU SEGAR MENGGUNAKAN LIFE CYCLE ASSESSMENT (LCA) DAN ANALYTICAL HIERARCHY PROCESS (AHP) UNTUK PENERAPAN GREEN SUPPLY CHAIN €‹ €‹MANAGEMENT Tia Haryanti; Rossi Septy Wahyuni
Analit : Analytical and Environmental Chemistry Vol. 9, No. 01 April (2024) Analit : Analytical and Environmental Chemistry
Publisher : Jurusan Kimia FMIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/analit.v9i01.189

Abstract

This study aims to evaluate the impact of waste on the environment in fresh milk production activities. Fresh milk supply chain activities start from dairy farming activities to the distribution process of fresh milk to the fresh milk processing industry. This study aims to identify the most significant potential of fresh milk production activities on the environment, as well as provide improvement alternatives based on the most significant environmental impacts caused by fresh milk production activities. This research was conducted in a dairy farming cooperative which is an organization that produces fresh milk, namely Karya Nugraha Jaya Producer Cooperative Kuningan. Evaluation of the environmental impact of fresh milk production activities using the Life Cycle Assessment (LCA) method. The analysis was conducted using SimaPro 9.5 software. Based on the results of the LCA, it is known that the fresh milk extraction activities that occur on the farm have the highest impact contribution of 3.57E3 Pt. Proposed improvements are given based on fresh milk extraction activities using the Analytical Hierarchy Process approach. The proposed improvement alternatives were analyzed using the pairwise comparison method to determine the highest weight. The Installation of Wastewater Treatment (IPAL) was recommended as a prioritized improvement with a total value of 0.48963.  
Pre-driving fatigue screening from short-term heart rate variability with subject-independent validation Tia Haryanti; Eri Prasetyo Wibowo; Wahyu Kusuma Raharja; Rossi Septy Wahyuni; Imliyati Sari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2885-2895

Abstract

This study evaluates fatigue screening from 30-second electrocardiogram (ECG) recordings using short-term heart rate variability (HRV) features in a pre-driving context. The dataset comprises 99 participants (one session each) with fatigue labels derived from the Karolinska sleepiness scale (KSS), where the primary label (K1) defines non-fit as KSS ≥ 7. A subject-independent logistic-regression model was trained under a leave-one-subject-out (LOSO) scheme. Probabilities were calibrated using Platt scaling and evaluated through threshold-free metrics (receiver operating characteristic (ROC)-area under the curve (AUC), precision-recall (PR)-AUC) as well as calibration performance using the Brier score. The model achieved ROC-AUC =0.687 (95% confidence interval: 0.591–0.776), PR-AUC =0.621, and a Brier score of 0.200. At the operating threshold t = 0.255, the model achieved sensitivity of 1.000 with no false negatives, while specificity remained 0.091 (95% confidence interval: 0.030–0.140). Reliability analysis indicated reasonable calibration in the operational probability range. These findings support short-term HRV derived from ECG as a screening tool that prioritizes avoiding missed non-fit cases, paired with a triage scheme (fit/review/non-fit) to manage uncertainty near the decision threshold. Future work should incorporate ECG morphology and signal quality cues and aim to improve specificity without sacrificing sensitivity.
Co-Authors Eri Prasetyo Wibowo, Eri Prasetyo Imliyati Sari Rossi Septy Wahyuni Rossi Septy Wahyuni Wahyu Kusuma Raharja