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INDONESIA
Zero : Jurnal Sains, Matematika, dan Terapan
Published by Universitas Islam Negeri Sumatera Utara
ISSN : 2580569X     EISSN : 25805754     DOI : 10.30829
Core Subject : Science, Education, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics
Arjuna Subject : -
Articles 184 Documents
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Optimization of Clove Oil Blending Ratio to Gasoline Engine Performance Abdullah, Nasruddin A.; Prayoga, Bagas; Amir, Fazri; Rizal, Teuku Azuar; Amin, Muhammad; Umar, Hamdani
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26659

Abstract

The increasing need for cleaner and more efficient fuels encourages the use of bio-additives to improve the combustion quality of gasoline engines. However, research on the direct effect of variations in the ratio of clove oil in Pertalite gasoline on engine performance and emissions is still limited. This study examined the effect of mixing clove oil in four fuel compositions, namely pure Pertalite (P0), Pertalite + clove oil 0.5% (P0.5), 1% (P1), and 2% (P2), on engine performance parameters and exhaust emission characteristics. The results showed that the P2 blend provided the most significant improvement in engine performance, characterized by a 4.6% increase in torque and 6.3% power,  a decrease in specific fuel consumption of up to 7.1%, and an increase in calorific value of up to 7.8%. Thermal efficiency also increased at high rounds of 1 %, indicating better energy conversion. In terms of emissions, a decrease in CO of 0.006% and a decrease in CO₂ of 0.1% indicate more complete combustion, although HC increases by 34 ppm (parts per million) due to the  volatile characteristics of clove oil. Overall, the addition of 2% clove oil has been shown to improve combustion quality without engine modification. These findings confirm the potential of clove oil as a viable and relevant renewable bio-additive to support energy transition efforts towards a cleaner and more sustainable transportation system.
Embedded TinyML for Predicting Soil Moisture Conditions in Rice Fields Using Weather Data Surbakti, Nurul Maulida; Kartika, Dinda; Amry, Zu; Ashari, Muhammad; Pahlawan, Riza
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26551

Abstract

This study implements a lightweight TinyML model to classify soil moisture conditions and support irrigation decisions in rice cultivation, chosen over conventional cloud-based ML because it enables low-power, low-latency, fully offline inference on microcontrollers—critical for rural areas with limited connectivity. Trained on 3,021 localized microclimate records from Denai Lama Village (temperature, humidity, rainfall, cloud cover) using logistic regression for its simplicity and interpretability under resource constraints, the model was deployed on an ESP32 for real-time predictions into three classes (underwatered, optimal, overwatered). Experimental results show accuracy = 0.982 and weighted F1 = 0.982 on the validation set (ROC–AUC = 0.997), and on the held-out test set (N = 194) the model achieved 93.4% accuracy, 0.927 weighted F1 (precision 0.914; recall 0.942), and ROC–AUC = 0.988. These findings indicate that TinyML provides a practical, low-cost, and scalable edge-AI pathway for reliable, energy-efficient decision support in precision irrigation without network dependence, offering a deployable template for smallholder farming contexts.
Regional Clustering of CO₂ Emissions in Indonesia for Emission Policy Targeting Habibah, Sayyidah Ummi; Sofro, A'yunin
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26449

Abstract

Regional disparities in Indonesia’s CO2 emissions highlight the need for emissions policies tailored to regional conditions rather than uniform national policies. This study addresses this issue by applying clustering analysis to identify emission patterns across five sectors: Energy, IPPU, Agriculture, Forestry, and Waste. K-Medoids and Fuzzy K-Medoids were selected for their robustness to outliers and their ability to capture complex, cross-sectoral emission characteristics more effectively than conventional methods. The results show that the K-Medoids method produced the most reliable clustering, with a Silhouette Coefficient of 0.5981 and a Dunn Index of 0.0310, indicating a moderate cluster structure. Two clusters were identified: provinces with low emissions dominated by the forestry sector, and provinces with high emissions driven by non-forestry activities. These cluster-based patterns provide a practical basis for directing emission policy interventions according to regional characteristics.
Data-Efficient LSTM Modeling for Climate-based Dengue Early Warning in Lampung, Indonesia Fauzi, Rifky; Sinaga, Mia Syntia Br; Rizka, Nela; Noor, Dear Michiko Mutiara; Pribadi, Aswan Anggun; Edriani, Tiara Shofi
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26192

Abstract

We present a data-efficient recurrent framework for climate-informed dengue early warning in Lampung Province. Monthly incidence and climate records are transformed into supervised sequences with 2–3-month lags, consistent with the observed lead–lag structure. Three architectures i.e. single-layer LSTM, stacked LSTM, and Temporal-Attention LSTM (TA-LSTM) are tuned via a compact genetic search under a time-ordered split. Performance improves with longer history; the TA-LSTM (37 units) attains the best accuracy. Permutation feature importance reveals a clear hierarchy: relative humidity and maximum temperature dominate, autoregressive incidence contributes moderately, while rainfall, sunshine, and minimum temperature are secondary; average temperature is largely redundant. The findings indicate that adding meaningful historical context and selective temporal weighting yields robust early-warning capability from coarse, time-limited data, and that humidity–temperature dynamics, together with short-term incidence persistence, are the principal drivers in this provincial setting.

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