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All Journal Jurnal Arsitektur Jurnal Matematika & Sains Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences JOURNAL ONLINE OF PHYSICS JOIV : International Journal on Informatics Visualization Jurnal Biodjati Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Journal of Computing Theories and Applications
Acep Purqon Acep Purqon
Institut Teknologi Bandung
Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Other

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Effect of Growth Space on The Productivity of Maize Using Three Sisters Cultivation with Bee Pollination Nadia Damika Putri; Acep Purqon; Ramadhani Eka Putra
Jurnal Biodjati Vol 8 No 2 (2023): November
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/biodjati.v8i2.19227

Abstract

he increasing number of food needs is one of the driving factors for increasing agricultural production, but there are constraints on the availability of land. A polyculture system with corn, beans, and pumpkins, commonly known as the three sisters, can create positive interactions that can enhance the growth and development of each plant. This system has a vast potency to be applied to urban farming inside a grow bag because it does not require ample space, the placement of plants is flexible, and it produces a variety of yields. However, it is necessary to assess the effect of growing space on the growth of maize (Zea mays) cultivated by the three-sister system. This study used a completely randomized design with three treatments and six replications. The treatment consists of three planting spaces with various growing bags (treatment A:75 L, B:100 L, and C:200 L). The results of this study showed that the highest corn productivity was in the largest growing space (treatment C), which weighed 318.40 g/cob, and without husks 246.42 g/cob, but not significantly different from treatment B (grow bag 100 L), which weights 316.20 g/cob and without pods of 240.63 g/cob. This study found that the 100 L grow bag was the optimal growing space for planting corn in containers using the three sisters technique.
Machine Learning Framework for SHM Alarm Log Triage under Alarm Flood and Limited Labeled Data: A Case Study on a Tropical Cable-Stayed Bridge Aris Haris Rismayana; Gatot Sukmara; Nana Rachmana Syambas; Drees Andriyanto Martono; Acep Purqon
Journal of Computing Theories and Applications Vol. 4 No. 2 (2026): JCTA 4(2) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17014

Abstract

Structural Health Monitoring (SHM) systems on long-span bridges routinely generate alarm volumes exceeding operators' manual review capacity, a condition known as alarm flood, so that genuine structural anomalies risk being buried among far more numerous instrumentation faults. The problem is acute in developing-country settings, where monitoring falls to small operational teams rather than dedicated staff. This study proposes an alarm-log triage framework that classifies events directly from tabular database records and static sensor metadata, requiring no raw sensor time-series, offering an alternative to the raw-signal paradigm dominating bridge SHM. The dataset comprises 63,934 alarm records logged over 2.5 years on a 641.8-m tropical cable-stayed bridge: labeling collapses as volume rises, leaving only 401 usable labeled records from the pre-flood period, a weakly-supervised task under severe label scarcity. The pipeline integrates alarm-log attributes, causally-ordered temporal window features, and as-built sensor metadata, with fold-internal resampling and recall-oriented operating-point selection. Seven classifiers were benchmarked under identical stratified 5-fold cross-validation, including TabNet alongside linear, kernel-based, tree-ensemble, and neural baselines. Gradient-boosted tree ensembles separate clearly from all other families (F1-Macro 0.7991–0.8860 outside the tree family versus 0.9354–0.9432 within it; McNemar and DeLong tests confirm the separation, p < 0.05). Differences among the tree ensembles themselves are not consistently significant, so LightGBM is reported as the selected deployment model on consistent top-tier performance (F1-Macro 0.9432, AUC-ROC 0.9885 at the default threshold) rather than as demonstrably superior. A two-part ablation shows temporal alarm-rate features supply most of the discriminative power, while explicit imbalance handling yields no measurable benefit at the observed ratio, indicating that such mechanisms should be calibrated to imbalance severity rather than applied by default. The results establish operational alarm logs as a viable, lightweight input for structural event triage and document alarm flood quantitatively in a real bridge SHMS.
Co-Authors Aprilia, Ely Aris Haris Rismayana Defianti, Hanni Dion Awfa Doddy Sutarno Drees Andriyanto Martono Faisal, Muliady Fajriharish Nur Awan Fourier D. E. Latief Gatot Sukmara Halawa Ernwati Hapsoro, Cahyo Aji IB Ilham Malik Iqbal Wira Menanza Linda Handayani M Risky Maulidya Paramitha Melisa Vira Permata Muliady Faisal Muttaqien, Fahdzi Nadia Damika Putri Nana Rachmana Syambas Nasrul Putra Ramadhani Eka Putra Rustan Rustan Rustan Suprijadi Suprijadi Tsuneomi Kagiyama Wahyu Hidayat Wahyu Srigutomo Warsa warsa Yazid Bindar Zenia F Saraswati Zikri, Arizal Akbar