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Journal of Science and Technology: Alpha
Published by Lembaga Publikasi Ilmiah Nusantara
ISSN : -     EISSN : 30894298     DOI : https://doi.org/10.70716/alpha
Core Subject : Economy, Science, Education, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences
ALPHA: Journal of Science and Technology is a peer-review journal that could be access to the public, published by Lembaga Publikasi Ilmiah Nusantara with registered number ISSN 3089-4298. ALPHA provides a platform for researchers, academics, professionals, practitioners and students to embed and share knowledge in the form of empirical and theoretical research papers, case studies, literature reviews and book reviews related to science and technology research. ALPHA welcomes and recognizes high quality theoretical and empirical research papers, case studies, review papers, literature reviews, book reviews, conceptual frameworks, analysis and simulation models, technical notes related to research from researchers, academics, professionals, practitioners, and students.
Arjuna Subject : Umum - Umum
Articles 30 Documents
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Klasifikasi Ulasan Mahasiswa tentang Fasilitas dan Pelayanan Perguruan Tinggi Swasta PGRI dengan BiLSTM Nururrahman, Nazdaen Akbar
Journal of Science and Technology: Alpha Vol. 2 No. 1 (2026): Journal of Science and Technology: Alpha, January 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i1.358

Abstract

Student satisfaction with campus facilities and services is an important indicator in assessing the quality of higher education. This study develops a system for classifying student reviews related to facilities and services at PGRI Private Universities using the Bidirectional Long Short-Term Memory (BiLSTM) method. The research data consist of 3,067 student reviews collected through Google Forms and Google Maps, covering five service aspects: Physical Facilities and Core Infrastructure; Academic Support Facilities and Learning Resources; Administrative Services and Staff; Facilities and Environment Supporting Non-Academic Activities; and Security and Accessibility. The BiLSTM method with 300-dimensional FastText word embeddings is employed to classify reviews into service aspect categories and satisfaction levels (Very Satisfied, Satisfied, Moderately Satisfied, Less Satisfied, and Not Satisfied). The model architecture comprises two BiLSTM layers with 128 and 64 units, respectively, along with a dropout mechanism to reduce the risk of overfitting. Model performance is evaluated using a confusion matrix, precision, recall, F1-score, and overall accuracy. The results indicate that the BiLSTM model is able to classify service aspects with good accuracy, although the performance of satisfaction level classification is still affected by data imbalance and the similarity of expressions across satisfaction categories. Overall, the proposed system can provide automated analysis of student reviews and serve as a decision-support tool for universities in understanding service quality based on review data in a more objective and structured manner.
Pengaruh Penggunaan Nitrogen Cair dalam Demonstrasi Edutainment Kimia terhadap Pemahaman Konsep Suhu dan Perubahan Wujud Zat Arnita, Baiq Dewi Aprilia
Journal of Science and Technology: Alpha Vol. 2 No. 1 (2026): Journal of Science and Technology: Alpha, January 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i1.386

Abstract

Chemical edutainment demonstrations represent an engaging instructional approach to enhance students’ understanding of chemical concepts through enjoyable experimental activities. This study aims to explain the properties of liquid nitrogen and its effects on biological materials such as chicken eggs through the experiment “Sunny-Side-Up Egg Using Liquid Nitrogen.” The method employed was a practical demonstration with a descriptive observational approach. The observations revealed that liquid nitrogen, with its extremely low temperature (–196°C), can instantly freeze eggs without inducing chemical reactions, but rather through reversible physical changes. This activity enables students to comprehend the concepts of temperature, heat, and phase transitions by directly observing extreme cooling phenomena. Therefore, the use of liquid nitrogen in chemical edutainment demonstrations proves effective in increasing students’ interest and understanding of fundamental chemical concepts.
Analisis Dampak Penggunaan Media Sosial Terhadap Indeks Prestasi Kumulatif (IPK) Mahasiswa Prodi Pendidikan Kimia FKIP Unram Arnita, Baiq Dewi Aprilia; Safitri, Baiq Antika; Ulan, Firnaning; Jannah, Riadatul
Journal of Science and Technology: Alpha Vol. 2 No. 1 (2026): Journal of Science and Technology: Alpha, January 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i1.387

Abstract

Social media represents a form of technological advancement that is unavoidable among students. There are various types of social media platforms that are currently popular, including Facebook, Instagram, WhatsApp, TikTok, YouTube, Twitter, and Blogger. Each platform has specific advantages that attract its users, making them spend extended periods navigating the digital world. The use of social media depends on individual behavior as well as environmental influences. This study aims to determine the effect of social media usage on students’ GPA. The research employs a quantitative descriptive and inferential approach, with data analyzed using one-way ANOVA, normality testing, and homogeneity testing. The sample consisted of 31 fourth-semester students from the Chemistry Education study program. The findings indicate that students most commonly use WhatsApp (24%) and Instagram (28%), followed by YouTube (9%), Facebook (10%), TikTok (16%), Line (4%), Snapchat (4%), and X (4%). The survey also revealed a striking result regarding the intensity of social media use: 74% of students reported using social media for more than 4 hours per day, while 26% used it for less than 4 hours. Students who used social media for less than 4 hours had an average GPA of 3.23, whereas those who used it for 4 or more hours had an average GPA of 3.21. The one-way ANOVA analysis resulted in a significance value of 0.908 > 0.05, indicating that H₀ is accepted, meaning there is no significant effect of social media usage duration on students’ GPA. The normality test yielded a significance value of 0.200 > 0.05, indicating that the data are normally distributed. Meanwhile, the homogeneity test produced a significance value of 0.770 > 0.05, showing that the data are homogeneous.
Peran Pengelolaan Hama Terpadu (PHT) dalam Pertanian Berkelanjutan Manfaat Ekonomi dan Kepatuhan Hukum Yassin, Haykal; Aditia, Rian; Natasya, Heni Alia
Journal of Science and Technology: Alpha Vol. 2 No. 1 (2026): Journal of Science and Technology: Alpha, January 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i1.388

Abstract

Integrated Pest Management (IPM) represents a cornerstone of sustainable agriculture, combining ecological balance, economic efficiency, and legal compliance into a unified framework. This study explores the multifaceted role of IPM in promoting environmentally responsible farming practices while ensuring long-term productivity. By reducing dependence on synthetic pesticides, IPM mitigates soil and water contamination, preserves biodiversity, and supports climate resilience. Economically, IPM enhances farmers’ competitiveness by lowering input costs, stabilizing yields, and improving market access through compliance with safety standards. Legally, IPM aligns with national agricultural regulations and international conventions, reinforcing institutional accountability and farmer protection against sanctions. The integration of ecological, economic, and legal dimensions demonstrates that IPM is not merely a pest control strategy but a comprehensive system for advancing food security and sustainable development. The findings emphasize that IPM implementation strengthens agricultural innovation, supports educational objectives in Indonesia, and contributes to global sustainability agendas.
Integrasi Proses Elektrokimia dan Adsorpsi Karbon Aktif untuk Pengolahan Lindi Tempat Pemrosesan Akhir Sampah Muhsinun, Muhsinun; Littaqwa, Lalu Auliya Akraboe
Journal of Science and Technology: Alpha Vol. 2 No. 1 (2026): Journal of Science and Technology: Alpha, January 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i1.389

Abstract

This study evaluated the performance of an integrated electrochemical and activated carbon adsorption process for the treatment of landfill leachate. The initial leachate characteristics showed a COD of 4,850 mg/L, color intensity of 6,200 Pt Co, and ammonia concentration of 780 mg/L, with a low BOD/COD ratio indicating poor biodegradability. The electrochemical process operated at an optimum current density of 20 mA/cm² achieved 46.3 percent COD removal and 62.5 percent color reduction within 120 minutes, with an energy consumption of 18.4 kWh/kg COD removed. Standalone adsorption using 20 g/L of activated carbon reduced COD by 38.7 percent and color by 71.2 percent within 150 minutes; however, adsorption capacity declined by 35 percent after two cycles without regeneration. The integrated system operated at pH 6, current density of 20 mA/cm², and 120 minutes hydraulic retention time achieved 78.9 percent COD removal, 91.4 percent color removal, and 64.8 percent ammonia reduction. The specific energy consumption decreased to 12.6 kWh/kg COD removed, demonstrating improved energy efficiency compared to the electrochemical process alone. In situ electrochemical regeneration conducted for 30 minutes at 30 mA/cm² restored up to 87 percent of the initial adsorption capacity after three operational cycles. These results confirm a significant synergistic effect between electrochemical oxidation and activated carbon adsorption, leading to enhanced treatment efficiency and operational stability for complex landfill leachate.
Perbandingan Metode Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) dalam Memprediksi Harga Saham Telkom Gede Yogi Pratama; Onis Alamsyah; Hanif Aljauziah; Muh Sohibul Ihsania; Mohammad Mirza; Lathifah Laili Andita
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.475

Abstract

Accurate stock price prediction remains a challenging task due to the highly volatile nature of financial markets and the influence of various macroeconomic factors and market sentiment. PT Telkom Indonesia Tbk (TLKM), one of the largest publicly listed companies in Indonesia, has attracted significant attention from investors because of its substantial market capitalization and active stock trading. This study aims to compare the performance of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in predicting TLKM stock prices using time series data. The dataset consists of historical TLKM stock data, including the Open, High, Low, Close, Adjusted Close, and Volume variables. Data preprocessing involved data cleaning, normalization using the Min-Max Scaling technique, and time series sequence generation through the sliding window approach. Both LSTM and GRU models were developed using comparable network architectures and trained with the Adam optimizer and the Mean Squared Error (MSE) loss function. Model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results demonstrate that both models effectively capture historical stock price patterns. However, the GRU model consistently outperformed the LSTM model by achieving lower prediction errors while requiring lower computational complexity and training time. These findings suggest that GRU is a more effective and computationally efficient approach for predicting TLKM stock prices based on time series data.
Prediksi Produktivitas Padi di Provinsi Nusa Tenggara Barat Tahun 2025 Menggunakan Metode Regresi Linear Berdasarkan Data Tahun 2019–2024 Alya Yayan Apriyandi; Baiq Fiky Renita
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.477

Abstract

This study examines fluctuations in rice productivity in West Nusa Tenggara Province during the period 2019–2024. The issue is important because rice productivity is directly related to production planning, food availability, and regional food security strategies. This study aims to analyze the effects of harvested area and rice production on rice productivity and to develop a prediction model using Multiple Linear Regression. The data used in this research are secondary data obtained from official publications of Statistics Indonesia (BPS), covering three main variables: harvested area, rice production, and rice productivity. The research stages include data collection, data cleaning, productivity calculation, descriptive statistical analysis, regression modeling, model evaluation, and interpretation of results. The analysis indicates that harvested area and rice production simultaneously explain variations in rice productivity with a very high coefficient of determination. The production coefficient is positive, while the harvested area coefficient is negative in the model, indicating that increased production without proportional expansion of harvested area tends to improve productivity. The resulting model can serve as an initial data-driven approach to support agricultural planning, although future research should incorporate climate, irrigation, fertilizer application, seed variety, and cultivation technology variables to improve prediction accuracy.
Analisis Pengaruh Jumlah Penduduk terhadap Insidensi Demam Berdarah Dengue di Provinsi Nusa Tenggara Barat: Pendekatan Regresi Linear Sederhana dan Implikasinya terhadap Kebijakan Pembangunan Daerah Rian Aditia; Hisbullah; Hanif Al Jauziah
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.487

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major vector-borne public health problem in Indonesia, including West Nusa Tenggara Province (NTB). Beyond its epidemiological consequences, dengue also affects household economic burden, community productivity, and regional health expenditure. This study aims to analyze the effect of population size on dengue incidence in NTB and to formulate its implications for regional development policy. A quantitative approach was applied using simple linear regression. The study used secondary data on population size and dengue cases from ten districts/cities in NTB during the 2018-2023 period, resulting in 60 observations. Population size was treated as the independent variable, while the number of dengue cases was used as the dependent variable. The analysis showed a positive and statistically significant linear relationship between population size and dengue incidence, with the regression equation Y = -66.384 + 0.0037X. The ANOVA test produced an F-value of 199.284 with p < 0.001, while the coefficient of determination calculated from the model sum of squares yielded an R² of 0.775. These findings indicate that population size explains approximately 77.5% of the variation in dengue cases within the simple model. The results highlight the importance of integrating demographic information into health planning, environmental control, sanitation improvement, and evidence-based regional development policy. Nevertheless, further studies should incorporate additional variables such as population density, rainfall, temperature, humidity, sanitation quality, and socioeconomic factors to obtain a more comprehensive epidemiological understanding.
Prediksi Kunjungan Wisatawan Nusantara dan Mancanegara di Provinsi Nusa Tenggara Barat Menggunakan Long Short-Term Memory Berbasis Adam Optimizer dan Gradient Clipping Muhammad Habibi; Muhammad Zaenul Hari; Rian Aditia
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.488

Abstract

Tourism is one of the strategic sectors that significantly contributes to regional economic growth, particularly in West Nusa Tenggara (NTB), Indonesia. Accurate forecasting of tourist arrivals is essential to support tourism planning, destination management, and evidence-based policy making. However, conventional forecasting methods often experience limitations in capturing nonlinear and long-term temporal patterns in tourism time-series data. This study proposes a Long Short-Term Memory (LSTM)-based forecasting model optimized using the Adam Optimizer and Gradient Clipping techniques to improve prediction accuracy and training stability. Monthly tourist arrival data consisting of domestic and international visitors during the period of 2014-2023 were obtained from the Tourism Office of West Nusa Tenggara Province. Data preprocessing was performed using Min-Max Scaling before dividing the dataset into training and testing sets with ratios of 70:30 and 80:20. The proposed model was evaluated using the Root Mean Squared Error (RMSE) metric under two training scenarios of 100 and 200 epochs. Experimental results demonstrate that the best forecasting performance was achieved using a 70:30 training-testing ratio with 200 epochs, resulting in the lowest RMSE value of 66.70. The integration of Adam Optimizer and Gradient Clipping improves model convergence stability while reducing prediction errors. Furthermore, the proposed model effectively captures seasonal patterns and long-term trends in tourist arrivals, making it suitable for supporting smart tourism development and intelligent decision-support systems for tourism management in West Nusa Tenggara.
Analisis Klaster Populasi Ternak di Provinsi Nusa Tenggara Barat Tahun 2015–2024 Menggunakan Algoritma K-Means sebagai Pendukung Sistem Pengambilan Keputusan Berbasis Data Onis Alamsyah; Ardha Haulani
Journal of Science and Technology: Alpha Vol. 2 No. 2 (2026): Journal of Science and Technology: Alpha, April 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/alpha.v2i2.489

Abstract

The livestock sector is one of the strategic contributors to regional economic development in West Nusa Tenggara (NTB), Indonesia. However, the unequal distribution of livestock populations across districts and municipalities presents significant challenges for formulating equitable and data-driven livestock development policies. Therefore, an objective grouping of regions based on livestock population characteristics is required to support effective decision-making. This study aims to analyze the clustering of livestock populations in West Nusa Tenggara Province using the K-Means clustering algorithm as a data mining approach to support data-driven decision-making. The study utilizes secondary data on livestock populations consisting of large livestock, small livestock, and poultry collected from all districts and municipalities in West Nusa Tenggara during the 2015–2024 period. Prior to the clustering process, the dataset was preprocessed through data cleaning, normalization, and attribute selection to improve clustering performance. The K-Means algorithm was then implemented by iteratively calculating Euclidean distance until the cluster centroids converged. The experimental results successfully classified the livestock population into three clusters representing low, medium, and high population categories. The clustering results reveal considerable disparities in livestock population distribution among regions, indicating different development priorities and resource allocation needs. Furthermore, the proposed clustering model provides valuable information for supporting regional livestock planning, livestock assistance distribution, infrastructure development, and strategic policy formulation. From an Informatics perspective, this study demonstrates the applicability of K-Means clustering as an effective data mining technique for regional classification and highlights its potential integration into Decision Support Systems (DSS) to facilitate evidence-based policy making in the livestock sector.

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