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All Journal LOGIK@ CAUCHY: Jurnal Matematika Murni dan Aplikasi Phenomenon : Jurnal Pendidikan MIPA Mimbar Agribisnis: Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis Jurnal Matematika: MANTIK Risâlah, Jurnal Pendidikan dan Studi Islam Desimal: Jurnal Matematika Juring (Journal for Research in Mathematics Learning) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Matematika UNAND Jurnal Mantik Vygotsky: Jurnal Pendidikan Matematika dan Matematika Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) InPrime: Indonesian Journal Of Pure And Applied Mathematics Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jambura Journal of Biomathematics (JJBM) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Tarbiyatuna: Jurnal Pendidikan Ilmiah PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Matematika Integratif International Journal of Applied Mathematics, Sciences, and Technology for National Defense Journal Of Agribusiness, Social and Economic
Irma Fauziah
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Identifikasi Komoditas Unggulan Pertanian Kecamatan Bandar, Kabupaten Batang, Jawa Tengah Simamora, Liska; Fauziah, Irma; Novitaningrum, Restie; Fadhilah, Muhammad Luthfie
Journal of Agribusiness, Social and Economic Vol. 5 No. 2 (2025)
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/jase.v5i2.7132

Abstract

This study aims to identify the leading agricultural commodities in Bandar Subdistrict, Batang Regency, Central Java, using the Location Quotient (LQ) method. The background of this research is based on the absence of clear specialization in agricultural commodities in Bandar, despite its agroecological potential. The data used include harvested area statistics for major food crops such as rice, maize, cassava, sweet potatoes, and peanuts. The LQ method measures the comparative advantage of a commodity by comparing its share of harvested area at the subdistrict level with that at the regency level. The results reveal that four commodities namely maize (LQ=1.2), cassava (LQ=3.2), sweet potatoes (LQ=8.8), and peanuts (LQ=5.6) are categorized as leading commodities. Each of these demonstrates regional specialization and is supported by factors such as agroclimatic suitability, farming experience, input availability, and market demand. Conversely, rice has an LQ value of 0.7, indicating it is not a leading commodity in the area due to limited water infrastructure. This research provides valuable insights for policymakers in designing development strategies that are focused and sustainable. The findings contribute to improving the effectiveness of agricultural planning and optimizing resource allocation, ultimately supporting farmer welfare and local economic resilience through the strengthening of agribusiness potential.
LITERASI DIGITAL PETANI DI DESA SEKITAR KAWASAN INDUSTRI: STUDI KASUS DI KAWASAN INDUSTRI BATANG, JAWA TENGAH Fadhilah, Muhammad Luthfie; Fauziah, Irma; Rahmaningtyas, Avivah
Journal of Agribusiness, Social and Economic Vol. 5 No. 2 (2025)
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/jase.v5i2.7430

Abstract

The rapid development of industrial areas in Indonesia presents unique challenges for the community, particularly in adapting to technology and information. In this context, digital literacy is a crucial aspect that determines the ability of farmers to maintain and develop their agricultural practices amidst the flow of modernization and digitalization that accompanies the growth of industrial areas. The objectives of this study are: 1) to analyze the factors that determine the digital literacy of farmers around the Batang Industrial Park area, and 2) to examine the differences in digital literacy levels between farmers located in areas directly adjacent to the industrial area and those located in areas that are not directly adjacent. The research method used a survey. The analysis used multiple linear regression analysis and the Wilcoxon-Mann-Whitney difference test. The study's results indicate that age and education significantly influence farmers' digital literacy level. The older the farmer, the lower the digital literacy. Meanwhile, about education, the higher the level of education, the higher the level of digital literacy of farmers. Furthermore, there are differences in digital literacy levels between farmers located in areas directly adjacent to the Batang Industrial Park area and in areas not directly adjacent. Stakeholders need to collaborate to improve digital farmer literacy, such as by providing access to digital devices and regular training on the use of digital agricultural technology, so that farmers can keep up with current developments and improve the efficiency of their businesses.
Pengetahuan dan Sikap Petani terhadap Keberadaan Industri di Kabupaten Batang serta Dampaknya pada Praktik Keberlanjutan Pertanian Rahmaningtyas, Avivah; Fauziah, Irma; Fadhilah, Muhammad Luthfie
Journal of Agribusiness, Social and Economic Vol. 5 No. 2 (2025)
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/jase.v5i2.7434

Abstract

This study aims to analyze the effect of farmers’ knowledge on attitude and the effect of attitude on farming sustainability practices in Batang Regency. The research is motivated by the establishment of the Batang Special Economic Zone (SEZ) in 2025, which influences farmers’ behavior near SEZ. The research sample consisted of 200 farmers from Gringsing, Limpung, and Tulis sub-districts, which are closest to Batang SEZ This number met the minimum requirement of 170 respondents (the number of indicators multiplied by 10). Primary data were collected through interviews using questionnaire in July 2025. The study employed a quantitative approach using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The results show that the Knowledge variable has a significant positive effect on Attitude (T-statistic = 11.333 > 1.960; path coefficient = 0.570). Furthermore, Attitude significantly influences Farming Sustainability (T-statistic = 4.669 > 1.960; path coefficient = 0.270).
PENGARUH LITERASI DIGITAL DAN SOCIAL INFLUENCE TERHADAP ADOPSI TEKNOLOGI DIGITAL OLEH PETANI KOPI PEREMPUAN DI KABUPATEN TEMANGGUNG Fauziah, Irma; Rahmaningtyas, Avivah; Luthfie Fadhilah, Muhammad
Journal of Agribusiness, Social and Economic Vol. 5 No. 2 (2025)
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/jase.v5i2.7452

Abstract

This study aims to analyze the influence of digital literacy and social support on the adoption of digital technology among female coffee farmers in Temanggung Regency. Primary data were obtained through structured interviews using a questionnaire. The research location and respondents were determined through purposive sampling, with criteria including female farmers who are actively engaged in coffee farming activities, reside in Temanggung Regency, and have utilized digital technology to support their farming operations. The total number of respondents in this study was 70. Data were analyzed using multiple linear regression. The results show that digital literacy has a positive and significant effect on the ability of female farmers to utilize digital technology, both in accessing cultivation information and in marketing agricultural products. Meanwhile, social support has a more dominant influence in encouraging the adoption of digital technology, particularly through the influence of family members, farmer groups, agricultural extension workers, and agricultural input suppliers. These findings highlight that improving digital literacy capacity needs to be complemented by strengthening social networks to accelerate an inclusive and sustainable digital transformation in coffee farming managed by women.
Analisis Faktor-faktor yang Memengaruhi Keputusan Pembelian Makanan Khas Daerah Secara Online oleh Konsumen Wanita di Indonesia Fauziah, Irma; Rahmaningtyas, Avivah; Cahyaningtyas, Nur Indah; Novitaningrum, Restie; Putri, Levana Masitajasmin; Fadhilah, Muhammad Luthfie; Simamora, Liska
Mimbar Agribisnis : Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis Vol 12, No 1 (2026): Januari 2026
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ma.v12i1.19852

Abstract

The rapid growth of e-commerce has transformed consumer behavior, particularly in the process of making online purchase decisions. This study aims to analyze the influence of price perception, trust, ease of use, and perceived risk on online purchase decisions among female consumers. The focus on female consumers is essential, given their high participation in online shopping activities and distinct psychological characteristics compared to men, such as heightened sensitivity to price and risk. The sampling technique used in this study was non-probability sampling with a purposive sampling approach, involving 105 respondents. The analytical method employed was quantitative using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of the study show that all four independent variables—price perception, trust, ease of use, and perceived risk—have a significant influence on online purchase decisions. Price perception emerges as the most dominant factor, indicating that female consumers are highly responsive to discounts and promotional strategies. Trust also plays a crucial role, especially for those who have no prior experience shopping on the platform. Perceived risk has a negative influence, reflecting concerns about transaction security and uncertainty regarding product quality. Meanwhile, ease of use contributes positively to purchase decisions through smooth navigation and system efficiency.
Value Orientation and Ecoliteracy as Key Factors in Green Product Purchase Decisions Rahmaningtyas, Avivah; Cahyaningtyas, Nur Indah; Putri, Levana Masitajasmin; Fadhilah, Muhammad Luthfie; Novitaningrum, Restie; Fauziah, Irma; Simamora, Liska
Mimbar Agribisnis : Jurnal Pemikiran Masyarakat Ilmiah Berwawasan Agribisnis Vol 12, No 1 (2026): Januari 2026
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ma.v12i1.22272

Abstract

Consumer awareness regarding health and environmental sustainability has increased interest in green products. Companies now recognize the importance of understanding consumer behavior in purchasing eco-friendly products, as trust and perceptions of safety significantly influence market success. This study aims to analyze how consumer ecoliteracy and value orientation affect purchasing decisions for Chocolate Monggo’s green products, providing insights to support effective marketing strategies for sustainable food brands. This study was conducted using ecoliteracy and value orientation as independent variables and purchase decision as the dependent variable. Data were collected via a Google Form and analyzed through Structural Equation Modelling (SEM) using Partial Least Squares (PLS) in SmartPLS 4.0. The findings reveal that ecoliteracy and value orientation are significant to consumers’ purchasing decisions for Chocolate Monggo. These results suggest that Chocolate Monggo should focus on strategies to elevate public ecoliteracy and promote the personal and social benefits associated with green product consumption. 
Lee-Carter–ARIMA hybrid approach and machine learning for mortality rate forecasting in the United States: Implications for national defense and population risk assessment Vita Nuarini; Mahmudi; Nina Fitriyati; Madona Yunita Wijaya; Irma Fauziah
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 2 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/rhy4zg18

Abstract

Background: The Accuracy of mortality rate forecasting plays an important role in various decision-making processes in the life insurance sector, including determining premium amounts. In addition, it also contributes to assessing the readiness of human resources to support national defense, as well as to conducting risk assessments aimed at maintaining demographic stability. The United States mortality data was selected as the study object due to the availability of comprehensive and high-quality. Aims: This study explores five hybrid approaches that combine stochastic models and machine learning, along with one non-hybrid approach to assess their potential to improve forecasting accuracy. Method: In this study, the Lee-Carter–ARIMA, Lee-Carter–Random Forest, Lee-Carter–ANN, Lee-Carter–ARIMA–Random Forest, Lee-Carter–ARIMA–ANN, and ANN models were evaluated. These models were applied to mortality rate data from nine divisions in the United States (US), stratified by gender, using training data from 1966 to 2005 and test data from 2006 to 2015. The best model is determined based on the smallest Mean Absolute Percentage Error (MAPE) value while also considering the interpretability of the model. Result: The study's results show that, across the number of divisions, the Lee-Carter–ARIMA–Random Forest model produces the smallest MAPE values most often. However, in terms of average MAPE, the Lee-Carter–ARIMA–ANN model performs better, with MAPEs of 9.66% for females and 9.28% for males. Furthermore, neither of these models yields a substantial improvement in predictive accuracy compared with the Lee-Carter–ARIMA model. Conclusion: Considering the relatively small decrease in MAPE and the difficulty of interpreting machine learning models due to their black box nature, the Lee-Carter–ARIMA model demonstrates the best overall performance relative to the other models. Nevertheless, the Lee-Carter–ARIMA–Random Forest and Lee-Carter–ARIMA–ANN models show potential as alternative approaches that merit further investigation and may contribute to national defense planning and support the maintenance of demographic stability.
Co-Authors Achmad Reza Fahcruroji Alfina Nur Safitri Ana Nurhasanah Aniswita Ayu Amalia Azizah, Maghvirotul Bagus Fajar Apriyanto Cahyaningtyas, Nur Indah Fadhilah, Muhammad Luthfie Fahcruroji, Achmad Reza Firmanti, Pipit Handarbeni, Zalfa Talitha Hartati, Eti Inna, Suma Isnaniah, Isnaniah Jefry Apriyanto Kurnia, Hafsah Laili Nahlul Farih Liska Simamora Madona Yunita Wijaya Mahmudi Mahmudi Mahmudi Muhammad Manaqib, Muhammad Mujiyanti Mujiyanti Nadilia, Nindita Nina Fitriyati Nindita Nadilia Novitaningrum, Restie Nur Indah Cahyaningtyas NURLIANA NURLIANA Priska Maya Putri Putri, Levana Masitajasmin Rahmaningtyas, Avivah Safitri, Nur Izzati Santoso, Ary Suma Inna Taufik Edy Sutanto Vita Nuarini Wahyu Triwulan Asih Wahyuningrum, Dinar Wijaya, Madona Yunita Wulandari, Fitria Sisca Zhafirah, Elisda Mieldhania