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All Journal Jurnal Manajemen Bisnis Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Applied Information System and Management J-MAS (Jurnal Manajemen dan Sains) Jurnal Soshum Insentif Jurnal Doktor Manajemen (JDM) JISA (Jurnal Informatika dan Sains) Mabny : Journal of Sharia Management and Business Journal of Economic, Business and Engineering (JEBE) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Ekonomika dan Manajemen Journal of International Conference Proceedings Jurnal Pengabdian Pelitabangsa Jurnal Ilmiah Ilmu Terapan Universitas Jambi Jurnal Lentera Pengabdian Joong-Ki JEPEmas: Jurnal Pengabdian Masyarakat (Bidang Ekonomi) Strata International Journal of Social Issues Multidiscience: Journal of Multidisciplinary Science Joong-Ki DEAL: International Journal of Economics and Business
Yoga Religia
UPN Veteran Yogyakarta
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Repurchase Intention Model Development Mediated by Brand Image: A Study at Fore Coffee in Yogyakarta Nabiela, Ibriza; Wisnalmawati, Wisnalmawati; Religia, Yoga
Strata International Journal of Social Issues Vol. 2 No. 2 (2025): August
Publisher : CV. Strata Persada Academia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59631/sijosi.v2i2.417

Abstract

This study aims to develop a repurchase intention model mediated by brand image, using Fore Coffee consumers in Yogyakarta as the research context. The research is motivated by the rapid growth of social media as a digital marketing strategy and the expansion of the F&B industry, with Fore Coffee successfully integrating digital technology to reach a broader market. A quantitative approach with a descriptive research design was applied. A total of 100 respondents were selected using accidental sampling. Data were analyzed using Structural Equation Modeling (SEM) with the Partial Least Squares (SmartPLS) method. The findings indicate that Social Media Marketing has a positive and significant effect on repurchase intention and brand image. Furthermore, brand image positively and significantly influences repurchase intention. Notably, brand image mediates the relationship between Social Media Marketing and repurchase intention, implying both direct and indirect effects of digital marketing on consumer behavior. These findings highlight the strategic importance of social media in strengthening brand image to foster customer loyalty. The study also suggests that future research should incorporate additional variables such as customer satisfaction, consumer trust, or product quality to further enhance the predictive model of repurchase intention in the coffee or local beverage industry.
Edukasi dan Pelatihan Desain Kemasan Ramah Lingkungan untuk UKM Arif, Nina Fapari; Religia, Yoga; Rifani, Siti Khusnul; Rahman, Fathi Habibatur; Tasrim, Tasrim; Jayanti, Ansri
Joong-Ki : Jurnal Pengabdian Masyarakat Vol. 3 No. 3: Mei 2024
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/joongki.v3i3.3665

Abstract

Kegiatan PKM bertemakan desain kemasan yang ramah lingkungan dengan memberikan edukasi dan pelatihan terkait pengemasan. Tujuan kegiatan memberikan solusi pada permasalahan yang telah diidentifikasi dengan memberikan Edukasi dan Pelatihan kepada Pelaku UKM terkait permasalahan Kemasan (packaging). Solusi yang ditawarkan adalah 1) memberikan Edukasi tentang pentingnya Kemasan dan Material yang ramah lingkungan, 2) memberikan pelatihan membuat rancangan desain menggunakan aplikasi canva, 3) Edukasi tentang estimasi biaya material kemasan dan sistem pemasaran, dan 4) edukasi Pengolahan Limbah kemasan. Hasil kegiatan PKM: 1) Pelatihan dapat dilakukan dengan lancar dalam bentuk tatap muka (luring) dan secara daring (online), 2) Peserta pelatihan mampu berpartisipasi secara aktif dan interaktif dalam kegiatan pengabdian masyarakat, 3) Peserta pelatihan memiliki kemampuan untuk memahami materi pelatihan yang disampaikan oleh tim PKM. 4) Peserta pelatihan dapat memahami penjelasan yang diberikan dan melatih diri menggunakan materi-materi yang telah diberikan
Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications Religia, Yoga Religia; Pranoto, Gatot Tri; Suwancita, I Made
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.946

Abstract

The selection of prospective customers who apply for credit in the banking world is a very important thing to be considered by the marketing department in order to avoid non-performing loans. The website www.kaggle.com currently provides South German Credit data in the form of supervised learning data. The use of data mining techniques makes it possible to find hidden patterns contained in large data sets, one of which is using classification modeling. This study aims to compare the classification of South German Credit data using the Naïve Bayes algorithm and compare the classification of South German Credit data using the Naïve Bayes algorithm with particle swarm optimization (PSO). The test was carried out using a confusion matrix to determine the accuracy, precision and recall values of the research model. Based on the test, it is known that PSO is able to increase the accuracy and recall of Nave Bayes, but PSO has not been able to increase the precision value of Nave Bayes. The test results show that PSO optimization gives Naïve Bayes an increase in the value of accuracy by 0.46%, and gives Naïve Bayes an increase in recall value by 3.02%. 
Grouping of Village Status in West Java Province Using the Manhattan, Euclidean and Chebyshev Methods on the K-Mean Algorithm Pranoto, Gatot Tri; Hadikristanto, Wahyu; Religia, Yoga
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1097

Abstract

The Ministry of Villages, Development of Disadvantaged Areas and Transmigration (Ministry of Village PDTT) is a ministry within the Indonesian Government in charge of rural and rural development, empowerment of rural communities, accelerated development of disadvantaged areas, and transmigration. Village Potential Data for 2014 (Podes 2014) in West Java Province is data issued by the Central Statistics Agency in collaboration with the Ministry of Village PDTT which is in unsupervised data format, consists of 5319 village data. The Podes 2014 data in West Java Province were made based on the level of village development (village specific) in Indonesia, by making the village as the unit of analysis. Base on the Regulation of the Minister of Villages, Disadvantaged Areas and Transmigration of the Republic of Indonesia number 2 of 2016 concerning the village development index, the Village is classified into 5 village status, namely Very Disadvantaged Village, Disadvantaged Village, Developing Village, Advanced Village and Independent Village based on the ability to manage and increase the potential of social, economic and ecological resources. Village status is in fact inseparable from village development that is under government funding support. However, village development funds have not been distributed effectively and accurately according to the conditions and potential of the village due to the lack of clear information about the status of the village. Therefore, the information regarding the villages priority in term of which villages needs more funding and attention from the government is still lacking. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Data grouping using k-means is done by calculating the closest distance from data to a centroid point. In this study, different types of distance calculation in the K-means algorithm are compared. Those types are Manhattan, Euclidean and Chebyshev. Validation tests have been carried out using the execution time and Davies Bouldin index. From this test, the data Village Potential 2014 in West Java province have grouped all the 5 status of the village with the obtained number of villages for each cluster is a cluster village Extremely Backward many as 694 villages, cluster Villages 567 villages, cluster village Evolving as much as 1440 villages, the cluster with Desa Maju1557 villages and the cluster Independent Village for 1061 villages. For distance calculation, Chebyshev has the most efficient accumulation time of 1 second compared to Euclidean 1.6 seconds and Manhattan 2.4 seconds. Meanwhile, the Euclidean method has the value, Davies Index most optimal which is 0.886 compared to the Manhattan method 0.926 and Chebyshev 0.990.
South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts Religia, Yoga; Pranoto, Gatot Tri; Santosa, Egar Dika
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.837

Abstract

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.
Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification Religia, Yoga; Maulana, Donny
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.925

Abstract

Airline companies need to provide satisfactory service quality so that people do not switch to using other airlines. The way that can be used to determine customer satisfaction is to use data mining techniques. Currently, the website www.kaggle.com has provided Airline Passenger Satisfaction data consisting of 22 attributes, 1 label and 25976 instances which are included in the supervised learning data category. Based on several previous studies, the Naïve Bayes algorithm can provide better classification performance than other classification algorithms. Several studies also state that the use of Naive Bayes can be optimized using Genetic Algorithm (GA) to obtain better performance. The use of Genetic Algorithm for Nave Bayes optimization in classifying Airline Passenger Satisfaction data requires further research to ensure the performance of the given classification. This study aims to compare the use of the Naive Bayes algorithm for the classification of Airline Passenger Satisfaction with and without GA optimization. The data validation process used in this study is to use split validation to divide the dataset into 95% training data and 5% testing data. The test results show that the use of GA on Naive Bayes can improve the classification performance of Airline Passenger Satisfaction data in terms of accuracy and recall with an accuracy value of 85.99% and a recall of 87.91%.
Seven Tools as Quality Control to Reduce Defective Products in the Honeycomb Board Machine Process Wiji Safitri; Ahmad Sutrimo; Miftakul Huda; Yoga Religia
DEAL: International Journal of Economics and Business Vol. 1 No. 01 (2023): October 2023
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/deal.v1i01.2681

Abstract

Quality is a requirement for a product that will be distributed to consumers. Quality is also a competitive advantage for the company. PT NKR Industri as a company that produces paper still has not met the target to reduce the number of product defects. Product defects set by the company are a maximum of 3%, but currently product defects are up to 5%. Seven tools are tools used to control quality. This research is quantitative research. Data was collected through interviews and direct observation. The population and sample in this research is defect data on honeycomb board machines for the period July to December 2022. The data analysis technique uses seven tools. After mapping with seven tools, one of which is through a fishbone diagram, product defects that occur are caused by environmental factors consisting of room temperature, material factors consisting of damp paper and expired glue, method factors consisting of the dandori method is not suitable, machine factors consisting of less maintenance and the equipment has entered a maintenance period, the measurement factor consists of less carefull measurement process, and finally the man factor consists of lack of knowledge and not carefull.
Investigating Green Purchase Decission of SMEs in Development Counties: A Literature Review Yoga Religia; Budi Purnomo Saputro; Al Virizky Arjunitha Al Munawir
Journal of International Conference Proceedings Vol 7, No 4 (2024): 2024 Wimaya Yogyakarta Proceeding
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v7i4.3551

Abstract

This study investigates factors influencing green product purchasing by Small and Medium Enterprises (SMEs) in developing countries, focusing on the Theory of Consumption Values (TCV) and the Theory of Planned Behavior (TPB). Using a systematic literature review, articles published between 2020 and 2024 were analyzed. Findings show that TCV is crucial for understanding green purchasing behavior through functional, social, emotional, and epistemic values, significantly impacting consumer intentions. Consumers prefer products that meet functional needs and align with their values. TPB highlights attitudes, subjective norms, and perceived behavioral control in shaping purchase intentions. Positive environmental attitudes, social support, and perceived control encourage green product purchasing. Integrating TCV and TPB offers a comprehensive understanding of the dynamics influencing these decisions. Practically, this study guides marketers in developing effective green marketing strategies by highlighting product benefits and leveraging community support. Although focused on recent literature and TCV and TPB, further research could explore other factors influencing green purchasing in different contexts. This study enriches the literature on green purchasing and supports sustainable economic growth for SMEs in developing countries.
EXPLAINABLE MACHINE LEARNING FRAMEWORK FOR HOTEL CUSTOMER LOYALTY PREDICTION USING TRANSACTIONAL BEHAVIORAL DATA Pranoto, Gatot Tri; Pebrianti, Dwi; Religia, Yoga; Agusalim, Lestari
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 3 (2026): Volume 10, Nomor 3, June 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i3.56180

Abstract

Customer loyalty has become a critical factor for sustaining competitiveness in the hotel industry, particularly in increasingly digital and data-driven business environments. Although hotels continuously generate large volumes of transactional customer data, transforming this data into actionable insights for customer retention and marketing decision-making remains a significant challenge. This study proposes an Explainable Machine Learning Framework for hotel customer loyalty prediction using transactional behavioral data. The study used a publicly available hotel customer transaction dataset from the Mendeley Data repository, comprising 2,000 customer records. A supervised machine learning approach was employed using Logistic Regression, Decision Tree, Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. Model performance was evaluated using Stratified K-Fold Cross-Validation and classification metrics, including Accuracy, Precision, Recall, F1-Score, and ROC-AUC. Experimental results demonstrated that all evaluated models achieved perfect classification performance, with Accuracy, Precision, Recall, F1-Score, and ROC-AUC values reaching 1.000. SHAP analysis revealed that frequency_of_bookings, days_since_last_booking, total_meal_charges, and total_revenue_generated were the primary drivers of customer loyalty prediction, while average_stay_duration showed minimal influence. The findings indicate that booking frequency and customer recency are the most influential behavioral factors affecting loyalty outcomes. From a managerial perspective, the proposed framework provides actionable insights for customer retention, customer segmentation, loyalty programs, and personalized marketing strategies. This study contributes to predictive customer analytics and Explainable Artificial Intelligence by integrating predictive accuracy with transparent interpretation of customer behavior in hotel loyalty management.
TOE Framework for E-Commerce Adoption by MSMEs during The COVID-19 Pandemic: Can Trust Moderate? Yoga Religia; Muhamad Ekhsan; Miftakul Huda; Anton Dwi Fitriyanto
Applied Information System and Management (AISM) Vol. 6 No. 1 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i1.30954

Abstract

Currently, there are still many MSMEs in the regions that have not been connected to the digital ecosystem. This results in limited market reach, a lack of visibility, a lack of operational efficiency, and difficulty competing in the digital market. The purpose of this study is to review the adoption of e-commerce among MSMEs during the COVID-19 pandemic within the scope of the organization. Integrating the TOE framework (technology, organization, environment) with trust is carried out to explain the key parameters behind the adoption of e-commerce by MSMEs. This study collected samples using a saturated sample technique from 181 people who were members of the population. There were 153 questionnaires that were returned in full for further analysis using SEM-PLS modeling. The test results showed that technology did not have a significant influence on the adoption of e-commerce. Organizations, the environment during the pandemic, and trust have had a significant influence on the adoption of e-commerce. In addition, organizations that are moderated by trust have no significant effect on e-commerce adoption. The role of trust is as a moderation predictor. This research shows that the TOE framework is still strong enough to be used in explaining the adoption of e-commerce by MSMEs. This research also expands the TOE framework, where trust can also influence MSMEs to adopt e-commerce. Researchers and managers can use the set of variables that have been identified to strategize the adoption of e-commerce by MSMEs. This study presents a series of variables that can be used to study the adoption of e-commerce by MSMEs in the future.
Co-Authors Abdul Mazid Adi Rusdi Agung Nugroho Agus Sriyanto Ahmad Nurhakim Ahmad Nurhakim2 Ahmad Sutrimo Al Virizky Arjunitha Al Munawir Alvian Alvin Mubarok Amali Amali Ambarwati, Sri Dwi Ari Anastasya, Ellyn Andi Saputra Anton Dwi Fitriyanto Ari Susanto Arif, Nina Fapari Arvita Emarilis Intani Astuti, Sri Endhri Bella Yoviana Hardini Budi Purnomo Saputro Dani Hamdani Dian Sulistyorini Wulandari Donny Maulana Effendi, Irhas Erni Ratnasari2 Fathi Habibatur Rahman Fathi Habibatur Rahman Firdausy, Almer Al Gatot Tri Pranoto Heri Purwanto Heri Susanto Heriyanto2 Heriyanto2 Hidayat, Ravindra Safitra Ikhsan Romli Imam Prayogo Pujiono Jamaliah Said Jayanti, Ansri Jayanto, Nur Dwi Lestari Agusalim Maulana, Donny Miftakul Huda Muhamad Ekhsan Muhamad Ridwan Muhammad Asrori Muhammad Irhas Effendi, Muhammad Irhas Nabiela, Ibriza Nanang Tedi Nunu Nurhasanah Nurrohariah Nurrohariah Pamuji, Windy Anindya Pebrian Pebrian Pebrianti, Dwi Ramawati, Yussi Said, Jamaliah Santosa, Egar Dika Siti Suharyani Subagio, Hani Suganda, Intan Rizkiawati Suwancita, I Made Tasrim, Tasrim Tiara, Luh Putu Tri Mardiana, Tri Triyono Triyono Wahyu Hadikristanto wiji safitri Wisnalmawati Wisnalmawati Yugi Setyarko Yussi Ramawati Zulfikar Muhamad Hasan