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All Journal HAYATI Journal of Biosciences Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Oceanography Casuarina: Environmental Engineering Journal
Sembiring, Agustinus
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Energy Engineering Environmental Science Immunology & microbiology

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Studi Dasar Kesehatan Lingkungan di Kecamatan Hu’u, Sumbawa Barat, NTB, Indonesia: Bahasa Indonesia Kharistanto, Robertus Tegar Kurnia; Aprilia, Hesti Dwi; Fahlevy, Karizma; Sembiring, Agustinus; Suroso, Adi
Casuarina: Jurnal Teknik Lingkungan Vol 3 No 1 (2025): CEEJ OCT 2025
Publisher : LRI Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/ceej.v3i1.4937

Abstract

Good environmental quality is essential to support the optimal life and development of communities. Hu’u District, Dompu Regency, possesses abundant natural resource potential; however, human activities, especially agriculture, can affect the local environmental conditions. This study aims to evaluate the environmental health status in Hu’u District, focusing on groundwater quality, soil, and air parameters based on the standards outlined in the Regulation of the Minister of Health of the Republic of Indonesia Number 2 of 2023. Sampling conducted during the wet season (February–March 2023) at nine groundwater locations, ten soil locations, and five air sampling sites around villages in Hu’u District. Physical, chemical, and microbiological analyses were performed to assess water and soil quality. The results showed that several groundwater parameters, such as color, turbidity, and total dissolved solids (TDS), exceeded the established thresholds, likely due to natural organic matter, mineral dissolution, and agricultural activities. Total coliform bacteria were detected at several points, indicating potential domestic waste contamination, although Escherichia coli was not found. Concentrations of heavy metals include Barium (Ba), Copper (Cu), and Zinc (Zn), in some soil locations exceeded quality standards, suspected to be associated with agricultural activities and active geological characteristics. Additionally, air quality for chemical parameters is classified as good, but CO concentrations need to be prioritized for monitoring as its potential to increase due to the burning of agricultural waste activities. These findings provide an important basis for developing adaptive environmental management policies in Hu’u to maintain ecosystem sustainability and community quality of life.
Ocean Wave Modelling in Cempi Bay, West Nusa Tenggara during Northwest and Southeast Monsoon Hidayat, Esa Fajar; Havis, Muchammad Iqbal; Sembiring, Agustinus; Fahlevy, Karizma; Abdullah, Faizal Ade Rahmahuddin
Indonesian Journal of Oceanography Vol 7, No 4 (2025): Indonesian Journal of Oceanography
Publisher : University of Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijoce.v7i4.29242

Abstract

Coastal regions in Indonesia, such as Cempi Bay in West Nusa Tenggara, are strongly influenced by seasonal monsoon systems, which significantly affect wave dynamics and coastal processes. As the Northwest Monsoon (NWM) and Southeast Monsoon (SEM) exert non-uniform effects along Indonesia’s coastline, accurate wave modeling is essential to support coastal management, fisheries, and disaster mitigation in this area. This study applies a numerical wave model driven by monsoonal wind data, supplemented with bathymetry and open-boundary conditions, to simulate and analyze ocean wave characteristics in Cempi Bay during the NWM and SEM periods, focusing on significant wave height ( ) and dominant wave period ( ). Model performance was evaluated using Mean Absolute Percentage Error (MAPE), correlation coefficient ( ), and Root Mean Square Error (RMSE). During NWM,  achieved a MAPE of 15.49%,  = 0.55, and RMSE = 0.19 m, indicating good agreement, while the initial SEM run showed a high MAPE of 42.83% for  that improved to 20.16% after scaling the  input by 79%, reducing the RMSE from 0.48 m to 0.26 m and increasing  from 0.67 to 0.68. Spatial analysis revealed distinct wave propagation patterns between the monsoons and confirmed lower wave energy in the inner part of Cempi Bay, highlighting the importance of capturing seasonal wave variability for effective coastal infrastructure planning and monsoon-adapted management strategies.
Assessment of Macrobenthos Assemblages Along the Seabed Characteristic in the Cempi Bay, West Nusa Tenggara, Indonesia: A Case Study in the Dry Season Fahlevy, Karizma; Sembiring, Agustinus; Kaessari Magenta, Rias; Iqbal Havis, Muchammad; Suroso, Adi; Nauval, Rahmat; P. H. Simanjuntak, Charles
HAYATI Journal of Biosciences Vol. 33 No. 2 (2026): March 2026
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.33.2.540-555

Abstract

The ecosystem in Cempi Bay, Indonesia, is known for its diverse coastal ecosystems. Gathering baseline data to understand the habitat and its fauna is essential. The study aims to update macrobenthos data and identify natural patterns across sediment gradients, specifically in the dry season. Cempi Bay has three distinct areas: the lower reach (open sea), the middle reach (mixing water), and the upper reach (influenced by freshwater). The most dominant classes in the macrobenthic assemblages were Gastropoda, Bivalvia, and Polychaeta, especially in the lower and middle reaches with sand and silty sand seabed textures. However, the upper reach area with sandy silt textures showed a different pattern of macrobenthic assemblages, with fewer classes, leaving only Gastropoda and Bivalvia. The feeding habits of macrobenthos also varied, with carnivores, deposit feeders, and suspension feeders dominating the lower and middle reach areas. It is important to continue monitoring macrobenthos to understand if these patterns are natural or caused by environmental changes.
Contextual Smart School Architecture Integrating SERI and TIER for Digital Transformation Sembiring, Agustinus; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15910

Abstract

The digital transformation of elementary education has become an inevitable demand in the era of the Fourth Industrial Revolution. Nevertheless, schools in non-metropolitan regions continue to face persistent challenges, including limited infrastructure, low technology penetration, and insufficient policy support. This study aims to design a contextual smart school architecture by integrating the Smart Education Readiness Index (SERI) and the Transformation Impact and Essential Readiness (TIER) framework. A descriptive–qualitative approach, supported by quantitative survey data from 40 educators and education personnel, was employed to assess institutional readiness and formulate strategic priorities. The SERI assessment revealed an average digital readiness score of 3.12 (scale 0–4), with four dominant dimensions: Teaching and Learning Process (3.45), Assessment (3.28), Innovative Analysis (3.21), and IR 4.0 Policy (3.30). These dimensions were further validated through a Prioritisation Matrix weighted at 60% for cost factors, 20% for key performance indicators, and 20% for contextual proximity. The findings emphasize that effective digital transformation must leverage local strengths, be aligned with global frameworks, and be implemented through structured strategies. The key contribution of this research lies in the proposal of a modular, integrated, and sustainable smart school architecture model that can be replicated nationally to bridge global standards with local realities. This study provides both theoretical insights and practical implications for policymakers and educational leaders seeking to advance equitable digital transformation in non-metropolitan schools.
Comparative Academic Performance Prediction in Primary Schools Using Linear Regression and Random Forest Sembiring, Agustinus; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15953

Abstract

Predicting academic performance is an important aspect of data-driven decision making in education, particularly in primary schools where early identification of learning difficulties is crucial. This study compares the performance of Linear Regression and Random Forest Regression models for predicting students’ academic performance using an Educational Data Mining approach. The experiment uses the Students Performance Dataset from Kaggle, consisting of 1000 student records with eight predictor variables, including demographic and learning-related attributes. The target variable is the average score derived from mathematics, reading, and writing results. Model development and evaluation are conducted using Python in Google Colaboratory. Performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), while Random Forest is further optimized using GridSearchCV with 5-fold cross-validation. The results show that Linear Regression achieves the best performance (R² = 0.162, RMSE = 13.40, MAE = 10.49), outperforming both the default Random Forest (R² ≈ 0.000) and the tuned Random Forest (R² ≈ 0.112). Although the explained variance is relatively low, this finding indicates that simple demographic features provide limited predictive power for academic performance. A case study using a local dataset from a private primary school involving 132 sixth-grade students further confirms that Linear Regression performs more effectively than Random Forest for small and simple educational datasets. These results highlight the importance of aligning model selection with dataset characteristics in educational data mining.
Co-Authors Aprilia, Hesti Dwi Fahlevy, Karizma Faizal Ade Rahmahuddin Abdullah Hidayat, Esa Fajar Iqbal Havis, Muchammad Kaessari Magenta, Rias Kharistanto, Robertus Tegar Kurnia Muchammad Iqbal Havis Nauval, Rahmat P. H. Simanjuntak, Charles Santoso, Handri Suroso, Adi