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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Karajata Engineering Journal of Green Complex Engineering
Indriyanti Azis
Universitas Muhammadiyah Makassar
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Energy Engineering

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Analisis Debit Dengan Metode F.J. Mock Dan NRECA Serta Perbandingannya Terhadap Debit Pos Duga Air M. Agussalim; Indriyanti Azis; Sukmasari Antaria; Riswal K; Annisa Febrianti; Ayu Rosdiana
Jurnal Karajata Engineering Vol. 5 No. 1 (2025): 2025
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/karajata.v5i1.3624

Abstract

The Makawa river basin located in the Rongkong watershed has an increase in surface flow which affects the discharge flow which can cause flooding. Therefore, discharge data is needed to manage existing water resources. This study aims to analyze the water discharge using the F.J. Mock and NRECA methods, which are then compared to the water estimation post discharge. The results of this study showed that the F.J. Mock method has a relative error of 48% and a correlation coefficient of 0,690 with a strong interpretation, while NRECA showed a relative error of 50% and a correlation coefficient of 0,669 with a strong interpretation. Therefore, it is concluded that the F.J. Mock method is more appropriate than the NRECA method with lower relative error results, and a higher correlation coefficient than NRECA. The result of the simulation discharge between F.J. Mock, NRACA and water estimation post discharge are quite far apart.
Irrigation Serviceability under Seasonal Scarcity: Digital Water Balances and Canal Deterioration in Pamukkulu Nur Hikmah Amelia; Farida Gaffar; Indriyanti Azis
Journal of Green Complex Engineering Vol. 4 No. 1 (2026): August
Publisher : Gio Architect

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59810/greenplexresearch.v4i1.291

Abstract

This study evaluates whether Irrigation Service Agreement (ISA) commitments remain physically deliverable under seasonal water scarcity and canal deterioration in the Pamukkulu Irrigation Area, South Sulawesi, Indonesia. A descriptive quantitative design combined farmer questionnaires, field observations, and a 72-period five-day SIPASI water-balance series across six SCADA-supported secondary-canal service units covering 2,885 ha; the September–October 2026 periods were treated as planning projections rather than realized observations. Service performance was assessed through equity, adequacy, and reliability, while principal survey proportions were summarized with Wilson 95% confidence intervals. Mean intake requirement was 4,796.8 L s−1, whereas mean available discharge was 2,380.6 L s−1, equivalent to 49.6% of requirement. The mean K factor was 0.47; full-service conditions were identified in 19 of 72 periods, while severe-deficit conditions were identified in 31 periods. For the projected 1–11 September 2026 periods, available flow covered only 1.4–2.0% of intake demand. Farmers identified leakage as the dominant loss mechanism (72.7%), secondary canals as the highest-loss segment (60.6%), and downstream shortage as the main consequence (51.5%). The results support K-triggered scarcity rules, targeted secondary-canal maintenance, and tighter operational linkage among ISA, SIPASI, SCADA, and P3A.
Detection of Pulmonary Tuberculosis in Chest X-Ray Images Using CNN-VGG19: Evidence from a Local Indonesian Clinical Dataset Emil Agusalim Habi Talib; Mar'atul Azizah; Muhammad Faisal; Abd Rakhim Nanda; Nizirwan Anwar; Indriyanti Azis; Rizki Yusliana Bakti; M. Agusalim; Desi Anggreani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7607

Abstract

Pulmonary tuberculosis (TB) remains a major public health challenge, especially in developing countries where early and accurate diagnosis is critical. Although deep learning models have shown promising performance in TB detection using chest X-ray (CXR) images, most studies rely on large-scale public datasets that may not adequately represent the variability and limitations of real-world clinical data. This study aimed to evaluate the robustness and generalization capabilities of the CNN-VGG19 architecture when applied to small-scale clinical datasets. The dataset consisted of 434 CXR images collected from Haji Makassar Hospital, Indonesia, and categorized into tuberculosis, pleural effusion, and normal classes. A transfer learning approach was applied, and model performance was evaluated using analyses of accuracy, precision, recall, F1 score, and confusion matrices, with comparisons to ResNet50 and InceptionV3 under identical experimental conditions. The results showed that CNN-VGG19 achieved an accuracy of 0.8807 and a macro F1 score of 0.8800, outperforming the comparative models. These findings suggest that CNN-VGG19 provides stable performance in a data-constrained environment and highlight the impact of class overlap on classification performance.
Co-Authors Abd Rakhim Nanda Annisa Febrianti Ayu Rosdiana Bakti, Rizki Yusliana Desi Anggreani Emil Agusalim Habi Talib Gaffar, Farida M Agusalim M. Agussalim Mar'atul Azizah Muhammad Faisal Nizirwan Anwar Nur Hikmah Amelia Riswal Karamma Sukmasari Antaria