Articles
30 Documents
Search results for
, issue
"Vol. 5 No. 5 (2024): September 2024"
:
30 Documents
clear
<![CDATA[Narrowband IoT in Livestock Farming: A Technological Innovation for Productivity and Sustainability ]]>
<![CDATA[Sutanto, Achmad]]>;
<![CDATA[Rakhman, Arif]]>;
<![CDATA[Afriliana, Ida]]>;
<![CDATA[Hernowo, Rudi]]>;
<![CDATA[Eko Nugroho, Wildani]]>;
<![CDATA[Fayruz, Mohammad]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1030
<![CDATA[The integration of technology in livestock farming is crucial for enhancing production efficiency and animal welfare. This study aimed to develop and evaluate the implementation of a Narrowband IoT (NB-IoT)-based automated monitoring system in poultry farming. Using an experimental design, the research involved 30,000 day-old chicks at PT. Anugerah Teknologi Ternak in Central Java, Indonesia. The NB-IoT system collected real-time data on environmental parameters and poultry activity. Time-series analysis revealed non-stationary data, while correlation analysis showed a strong negative relationship between temperature and humidity (r = -0.8521). Anomaly detection identified 13.33% of observations as anomalous, demonstrating the system's capability for early issue detection. Regression modeling (R-squared = 0.7261) indicated that temperature and humidity significantly influence poultry productivity. The study concludes that NB-IoT implementation in poultry farming has significant potential for enhancing productivity through real-time monitoring and early anomaly detection, supporting more efficient and sustainable precision farming practices. However, limitations in data stationarity and sample generalizability suggest the need for further research to improve long-term predictions and broaden applicability across diverse farming contexts.]]>
<![CDATA[The Influence Of Capital Adequacy Ratio (CAR), Non-Performing Loan (NPL), Loan To Deposit Ratio (LDR), And Operational Costs To Operating Income (BOPO) On Return On Asset (ROA) In Banks Listed On The Indonesia Stock Exchange ]]>
<![CDATA[Adhim, Chairul]]>;
<![CDATA[Mulyati, Mulyati]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1122
<![CDATA[The objective of this research is to determine the effect of the Capital Adequacy Ratio (CAR), Non-Performing Loan (NPL), Loan to Deposit Ratio (LDR), and Operational Costs to Operating Income (BOPO) on Return on Assets (ROA) in banks listed on the Indonesia Stock Exchange. The sample was selected using purposive sampling, with a total of 32 banks. The research data were tested using multiple regression analysis. The results of the study show that the Capital Adequacy Ratio (CAR) has no effect on Return on Assets (ROA), while Non-Performing Loan (NPL), Loan to Deposit Ratio (LDR), and Operational Costs to Operating Income (BOPO) have a negative and significant effect on Return on Assets (ROA). y.]]>
<![CDATA[Utilization Of Digital Platforms In Realizing SDG 8.3 Entrepreneurship In Msmes With The 9F Model Approach: A Study On The Hijab Fashion Business In Sidoarjo District, East Java. ]]>
<![CDATA[Khairinia Kusuma, Alvitariani]]>;
<![CDATA[Lindawati Lubis, Ratna]]>;
<![CDATA[Abuonji, Paul]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1137
<![CDATA[The utilization of digital platforms has become a key strategy to increase the competitiveness of MSMEs in Indonesia, particularly in the hijab fashion sector. This study aims to analyze the impact of digital platforms and the implementation of the 9F Entrepreneurship Model on MSMEs in Sidoarjo District, as well as its contribution to achieving Sustainable Development Goal (SDG) 8.3, which focuses on promoting economic productivity and entrepreneurship. This qualitative research employs a case study method involving five MSMEs in the hijab fashion industry, selected through purposive sampling. Data were collected through in-depth interviews and direct observations of business owners. The findings indicate that integrating digital platforms such as Instagram, WhatsApp, Facebook, Shopee, and TikTok with the 9F model significantly enhances innovation, visibility, and flexibility in MSMEs’ responses to market changes. Among the 169 MSMEs in the fashion sector in Sidoarjo, 26 focus on hijab fashion, with five MSMEs participating in this research. These MSMEs reported improvements in market reach and operational efficiency through digital platforms. Additionally, the integration of digital strategies supports economic growth aligned with SDG 8.3. This approach shows that the combination of digital platforms and the 9F Entrepreneurship Model can be an essential strategy for developing MSMEs in the hijab fashion sector, contributing to local economic growth and government efforts to achieve SDG 8.3.]]>
<![CDATA[The Role of Virtualization Technology to Increase Operational Cost Efficiency of Indonesian SMEs: Case Study of Internet Service Providers ]]>
<![CDATA[Iswara Sanantagraha, Arnastya]]>;
<![CDATA[Puspitaloka Mahadewi, Erlina]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1161
<![CDATA[This research investigates the role of virtualization technology in enhancing operational cost efficiency for Indonesian Small and Medium-sized Enterprises (SMEs), specifically Internet Service Providers (ISPs). A quantitative approach, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) version 2.0, was employed to analyze data collected from 68 respondents representing 216 Indonesian SME ISPs. The study focused on the relationship between virtualization technology planning, adaptation capability, server consolidation services, and operational cost effectiveness. The findings demonstrate that adaptability to virtualization technology is the most significant factor influencing operational cost efficiency. Effective implementation requires careful planning and a proactive approach to adapting to the technology's demands. Server consolidation services play a crucial role in optimizing resource utilization and reducing costs while virtualization technology offers substantial benefits, SMEs should carefully assess their specific needs and resources before implementation. Factors such as infrastructure, skills, and business objectives should be evaluated to ensure successful adoption and maximize cost savings. This research provides a foundation for further exploration into the impact of virtualization technology on SMEs. Future studies could investigate the applicability of these findings in different contexts, such as other industry sectors or regions. Additionally, examining the long-term effects of virtualization technology adoption and the potential for scalability could offer valuable insights. Further research could also focus on the challenges and opportunities associated with virtualization technology adoption in SMEs with varying sizes and resources.]]>
<![CDATA[The Effect Addition of Soil Amandments and PGPR (Plant Growth Promoting Rhizobacteria) on the Growth and Yield of Cotton Plants Intercropped with Corn Plants in Dry Land of North Lombok Regency of Indonesia ]]>
<![CDATA[Siti Rohmaniati, Baiq]]>;
<![CDATA[Sarjan, M.]]>;
<![CDATA[Suwardji, Suwardji]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1162
<![CDATA[This research investigates the effect of soil amendments in the form of cow, form goat manures and PGPR biofertilizer on cotton plants intercropped with corn in the dry lands of North Lombok Regency. The research aims is to determine the growth and yield response of cotton plants due to the application of soil amendments and PGPR in an intercropped system with corn in dryland areas. The research was conducted from December 2023 to June 2024 in Andalan Village, Bayan District, North Lombok Regency. The method used is an experimental method with field trials. The design used was Randomized Block Design (RBD), incorporating two factors: soil amendment treatment (P) as the main plot and PGPR (K) concentration treatments as the subplot. Soil amendments consisted of three levels: P0 (no cow manure and no goat manure), P1 (20 tons/ha cow manure), P2 (20 tons/ha goat manure). PGPR concentration consisted of four levels: K0 (without PGPR), K1 (20 ml/liter PGPR application), K2 (30 ml/liter PGPR application), and K3 (40 ml/liter PGPR application). The research results indicated that the application of 20 tons/ha of goat manure (P2) produced the highest average across all observed parameters (plant height, number of leaves, branch number, stem diameter, and plant yield). Similarly, the application of 40 ml/liter PGPR produced the highest average for these observed parameters. Based on the results of the land equivalent ratio (LER) analysis, this integration system shows highly suitable and relevant to be applied in the dry lands of North Lombok.]]>
<![CDATA[Comparison of Machine Learning Algorithms in Public Sentiment Analysis of TAPERA Policy ]]>
<![CDATA[Sihombing, Eklesia]]>;
<![CDATA[Halmi Dar, Muhammad]]>;
<![CDATA[Aini Nasution, Fitri]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1164
<![CDATA[The rapid development of information technology has changed the way people interact and express their opinions on public policies, including the People's Housing Savings (Tapera) policy in Indonesia. People now primarily express their views openly on social media platforms like Twitter, generating a substantial amount of text data for analysis to understand public sentiment. However, the main challenge in this sentiment analysis is determining the most effective machine learning algorithm for classifying public opinion with high accuracy. This study aims to compare the performance of three machine learning algorithms, namely Naïve Bayes, Support Vector Machine, and Random Forest, in analyzing public sentiment towards the Tapera policy. This study analyzes public comment data obtained from Twitter. We measure the accuracy of each algorithm to determine its optimal performance in sentiment classification. The research method consists of several stages, starting with data collection, text preprocessing to clean and prepare data, and then applying the three algorithms to analyze sentiment. The results showed that Naïve Bayes had the highest accuracy of 69.17%, followed by Support Vector Machine with an accuracy of 68.42%, and Random Forest with an accuracy of 66.17%. This shows that Naïve Bayes is the most effective algorithm to use in sentiment analysis of public comments related to the Tapera policy, especially in the context of complex text data from social media. The conclusion of this study is that Naïve Bayes is superior in classifying public sentiment towards the Tapera policy compared to Support Vector Machine and Random Forest. As a result, this study makes a significant contribution to selecting the most appropriate machine learning algorithm for public sentiment analysis towards public policy, which in turn can help the government understand and respond to public perceptions more effectively.]]>
<![CDATA[The Influence of Workload and Work Environment on the Turnover Intention of Employees in PT. XYZ ]]>
<![CDATA[Rahmansyah, Ezra]]>;
<![CDATA[Indiyati, Dian]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1165
<![CDATA[This research aims to investigate the impact of workload and work environment on employee turnover intention at PT. XYZ. High turnover intentions can disrupt workforce stability and result in significant costs for the company. Two factors often associated with turnover intention are workload and work environment. Excessive workload can cause stress and fatigue, while an unsupportive work environment can reduce employee satisfaction and commitment. The study uses a quantitative approach, distributing questionnaires to 107 employees and sampling using the saturated sample method. Data analysis was conducted using SEM-PLS software. The hypothesis test tested the influence of workload and work environment on employee turnover intention. The results showed that workload variable (X1) is in the high category, work environment variable (X2) is in the bad category, and turnover intention (Y) is in the medium category. Quantitative data analysis with SEM-PLS showed that workload variables do not have a significant influence on turnover intention, while work environment variables have a significant influence. The research aims to evaluate company management's attention to workload and work environment conditions, with one variable showing a significant positive influence on employee turnover intention]]>
<![CDATA[The Effect of E-WOM on Social Media Marketing on Purchase Intention (Case Study: Gallery Vinna) ]]>
<![CDATA[Rizka Utami, Fany]]>;
<![CDATA[Ariyanti, Maya]]>;
<![CDATA[Millanyani, Heppy]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1166
<![CDATA[This study aims to determine the effect of social media marketing through online word of mouth on purchase intention on the Tiktok Gallery Vinna platform as the object of research. The research method used is quantitative method, using an online survey by collecting 155 respondents. The data collected was analyzed with Smart PLS software to test the research hypothesis. The results showed that respondents' responses to Tiktok Gallery Vinna's social media received a good response, and respondents felt that the uploaded content could provide interesting and easy-to-understand information. Then, e-WOM has a significant effect on buying interest, and social media has the most influence on buying interest. The results also show that providing the latest information by utilizing social media can expand the network to attract buying interest in consumers. Therefore, this study examines the Tiktok platform which expands knowledge about the factors that influence attitudes and purchase intentions towards consumers through e-WOM, as well as the use of social media that have an impact on consumer purchase intentions. This research provides insight for SMEs who want to improve marketing strategies using social media.]]>
<![CDATA[Factors Analysis Of Digital Transformation Challenges In Alfamart Company ]]>
<![CDATA[Zaimah Nabila, Farah]]>;
<![CDATA[Noviaristanti, Siska]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1167
<![CDATA[Technological advancements occur continuously without our notice, requiring us to adapt to them in all aspects of life. Most firms now recognize the importance of digital transformation as a strategy for improving services and business efficiency. Alfamart, a minimarket company in Indonesia's retail industry, is facing a technological transformation. Alfamart offers a range of product categories to accommodate diverse family requirements. The acceleration of digital transformation in Indonesia necessitates the application of digital technology as a digital platform that serves as the foundation for the Industry 4.0 ecosystem by including technologies such as IoT, big data, artificial intelligence, and augmented reality. This study uses quantitative and descriptive approaches to identify the characteristics that challenge digital transformation at PT Sumber Alfaria Trijaya, Tbk (Alfamart), particularly in the International Business & Technology division. The research employs two-factor analyses: CFA (Confirmatory Factor Analysis) and EFA (Exploratory Factor Analysis). According to the research results, 4 (four) factors influencing Alfamart's digital transformation, particularly in the International Business & Technology section, shape the company's digital transformation issues. These challenges include IT, digitalization, digital business, and digital skills.]]>
<![CDATA[Comparative Analysis of K-Nearest Neighbors and Decision Tree Methods in Determining Students’ Purchase Interest in MacBook Laptops ]]>
<![CDATA[Fadilla Hasibuan, Intan]]>;
<![CDATA[Irmayani, Deci]]>;
<![CDATA[Sihombing, Volvo]]>
<![CDATA[ International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024 ]]>
Publisher : <![CDATA[Publisher Cv. Inara ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.46729/ijstm.v5i5.1169
<![CDATA[In the context of increasingly competitive technology markets, companies need to know consumer preferences accurately to optimize product offerings and increase sales. Two classification methods that are often used in data mining, namely K-Nearest Neighbors and Decision Tree, have their own advantages and disadvantages. This study proposes a solution that involves processing student data using both classification methods to identify the most accurate and effective method for identifying purchase intentions. This study aims to compare the performance of the two methods in determining student purchase intentions for MacBook laptops. The research methodology includes collecting data from 100 students covering various factors such as user experience, design and portability, technical specifications, price, and security. This data is then classified using the K-Nearest Neighbors and Decision Tree methods. Furthermore, a confusion matrix is used to provide a more detailed picture of the performance of the two methods. The results of the study show that the Decision Tree method has a higher accuracy (91%) compared to K-Nearest Neighbors (88%). In addition, Decision Tree excels in other metrics such as precision (87.18% vs. 85.71%), recall (89.47% vs. 85.71%), specificity (91.94% vs. 89.66%), and F1-Score (88.31% vs. 85.71%). The decision tree also has a higher NPV value and lower FPR and FNR rates than K-Nearest Neighbors, indicating that it is superior in avoiding misclassification. The study's conclusion is that the Decision Tree method is more effective and accurate than K-Nearest Neighbors in determining students' purchase intentions for MacBook laptops. The decision tree shows better performance in almost all evaluation metrics, making it a more reliable method to use in consumer data analysis. The results of this study are expected to help companies choose a more appropriate and effective analysis method for their marketing strategies, as well as provide a basis for further research in the field of consumer purchase intention classification.]]>
