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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 241 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.
Development of a Traffic Signal Green Time and Cycle Length Optimization Model Using NSGA-III Pasaribu, Meliana; Helmi, Helmi; Adrah, Adrah; Yaqin, Eka Nisrina
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26871

Abstract

Optimization of delay, capacity, and emissions in signalized intersections brings in conflicting goals. But most literature studies have dealt with these individual goals or used two-objective optimization methods, and such approaches are not efficient in capturing these tradeoffs. To overcome this problem, this research utilizes Non-dominated Sorting Genetic Algorithm-III in designing a model to optimize delay, capacity, and emissions for a four-leg intersection. Convergence, hypervolume indicator, and spread methods are used to examine algorithm performance, and knee point solutions are used to identify a tradeoff solution. The obtained output shows that NSGA-III gives a smooth and evenly spread Pareto front with a hypervolume and a spread of which represents excellent convergence and diversification capabilities. Following the outcome of the experiment based on knee point identification, the solution with index 38 gives an optimal control setting of q₁ = 37.7 s, q₂ = 20 s, q₃ = 20 s, q₄ = 27.8 s, with cycle length of C = 121.42 s. With this setting, the average delay is reduced by 38%, the queue length by , and the degree of saturation is improved to 0.83, while capacity is reduced moderately and total emissions increased. In summary, this research work proves the NSGA III is efficient in identifying tradeoffs among delays, capacity and emission. The highlight of this research work is that the knee point gives the most balanced operational solution without excessively increasing cycle length.
RSA-AES Cryptosystem with Auto-Key Rotation for Cloud Storage Azanuddin, Azanuddin; Nasyuha, Asyahri Hadi; Ruslianto, Ikhwan; Perangin Angin, Moch. Iswan; H. Aly, Moustafa; Agoi, Moses Adeolu
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26827

Abstract

The widespread adoption of cloud storage systems has increased the demand for cryptographic mechanisms that ensure data confidentiality while limiting security risks associated with static and long-lived encryption keys. Although hybrid RSA-AES schemes are commonly employed to balance security and computational efficiency, key management-particularly autonomous and quantitatively bounded key rotation-remains insufficiently formalized. This study proposes a hybrid RSA-AES cryptosystem equipped with an autonomous auto-key rotation mechanism defined through explicit analytical constraints. AES-256 is employed for bulk data encryption, while RSA-2048 is used for secure encapsulation of symmetric session keys. Key renewal is governed by inequality-based conditions on elapsed time (Δt ≤ 30 minutes) and encryption usage (n ≤ 10 operations), yielding a mathematically bounded key lifecycle without manual intervention or external infrastructure. System performance and operational security properties are evaluated in a simulated cloud environment using file sizes ranging from 100 KB to 10 MB. Quantitative metrics include encryption and decryption time complexity, computational overhead relative to AES-only encryption, key variability measured by Hamming distance, and data integrity verification using SHA-256. Experimental results demonstrate linear scalability and a stable average overhead of approximately 12.8%, indicating a bounded constant-factor cost independent of workload size. Successive AES-256 keys exhibit a mean Hamming distance of 127.42 bits, consistent with high key variability and effective key freshness. These findings show that analytically constrained key rotation enables controlled symmetric-key exposure while preserving practical efficiency overall.
Bayesian Nonparametric Truncated Spline Regression for Modeling Nutritional and Physical Stunting Zahra, Septi Nafisa Ulluya; Fernandes, Adji Ahmad Rinaldo; Efendi, Achmad; Nasywa, Alfiyah Hanun; Junianto, Fachira Haneinanda
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.v9i2.26759

Abstract

Stunting is a problem that is affected by the socioeconomic and environmental conditions of the public. The present study evaluates the impact of the financial state, environmental quality, and child feeding practices on the nutritional and physical stunting using a Bayesian nonparametric truncated spline regression model. To do this, a single knot spline structure was used a capture non-linear affects and thresholds, posterior estimation being conductied with Gibb's sampling. The results exhibit that all of the three predictors have a significance after the knot point on the right arrives, indiacting to saturation affects. As for the economic standing and the environmental quality, their effect is consistent, while feeding practices hold a more considerable impact on the nutritional stunting. From model diagnostics, the model had a good fit and predictive accuracy. The results highlight the importance of feeding practices and economic improvement and environmental sanitation, and display the benefits of the Bayesian spline technique for handling complex data.
Min-Max Fuzzy TOPSIS with Entropy Weighting for Strategic Location Multicriteria Decision Making Nurfebriyanti, Endah; Gultom, Parapat; Tulus, Tulus
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26749

Abstract

This study aimed to develop a robust, objective framework for strategic location Multicriteria Decision Making (MCDM) by effectively addressing criteria conflict and data uncertainty. The research methodology utilized a novel hybrid approach, integrating the Entropy method to determine objective criteria weights with a Min-Max Fuzzy TOPSIS model, a modification adopted specifically to improve the consistency and rationality of alternative ranking results. The model was applied to a case study concerning strategic location selection in Batubara Regency, evaluating five alternative locations based on six criteria. The major finding from the objective weighting process was that the Number of Students ( ) was the most influential criterion, receiving the highest weight of 0.294. Subsequent analysis using the modified Fuzzy TOPSIS revealed that Alternative (Madang Deras) achieved the highest performance index , securing the first rank. Numerical validation showed a significant improvement over existing approaches. When compared with the original Min-Max Fuzzy TOPSIS, the Performance Index of the best alternative ( ) increased from 0.588 to 0.673, representing a 14.46% improvement. Furthermore, when evaluated against a model using uniform weighting, the Performance Index increased from 0.449 to 0.673, reflecting a substantial 49.89% enhancement. These results demonstrate that entropy-based objective weighting meaningfully improves the discriminative power of the decision model and reduces bias. Overall, the proposed hybrid framework offers a more stable, accurate, and comprehensive approach for strategic location selection.
AMBATIG: Android Application for Generating Batik Motifs Using Frieze Symmetry Group Transformations Kartika, Dinda; Suwanto, Fevi Rahmawati; Surbakti, Nurul Maulida
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26233

Abstract

Indonesian batik is known for its diverse motifs, yet many artisans still design patterns manually, which limits variation and innovation. This study develops Ambatig, an Android application that helps artisans create batik motifs more efficiently. The application was implemented in Android Studio and follows a waterfall development model. Ambatig transforms a user drawn base cell into structured repetitions by composing distance preserving transformations, namely translation, vertical reflection, horizontal reflection, half turn rotation of 180 degrees, and glide reflection. These operations are configured according to the seven frieze types introduced in the study. Functional black box testing confirmed stable performance with all scenarios passing. Compared with previous motif generation approaches, Ambatig introduces a parameterized on device frieze transformation pipeline that produces all seven symmetry families from a single base motif. Real time preview and export features support creative exploration while maintaining mathematical coherence in digital batik design.
Uncertainty-Aware Kalman Filtering via Intrusive Polynomial Chaos for Disturbance Estimation Purnawan, Heri; Wakhid, Abdur Rohman; Fiddina, Qori Afiata; Iza, Belgis Ainatul; Sanusi, Tri Muhamad; Cahyaningtias, Sari
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (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.v9i3.26488

Abstract

Robust control under parameter uncertainty requires reliable disturbance estimation. This paper proposes an uncertainty-aware method, namely Intrusive Polynomial Chaos-based Kalman Filter (IPC-KF) for systems with probabilistic parameters and measurement noise. The method is evaluated through two numerical case studies and compared with a nominal Kalman filter (KF). Results from 100 realizations, assessed using RMSE and mean variance, show that the IPC-KF achieves estimation accuracy comparable to the nominal KF. For the spring-mass-damper system, the RMSE difference is below , with both methods yielding the same mean variance of . For the F-16 aircraft model, identical RMSE values and a mean variance of are obtained. While IPC-KF captures parameter uncertainty via polynomial chaos, augmenting the state with disturbances does not necessarily improve estimation accuracy. Further studies are needed to assess uncertainty bounds and robustness.

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