Garuda - Garba Rujukan Digital

Meningkatkan Literasi Teknologi melalui Webinar Pintu Gerbang Menuju Digital Hermansyah, Masud; Andita Prasetyo , Nur; Wahid, Abdul; Afreyna Fauziah, Difari; Muliawan, Agung
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 3 No 2 (2023)
Publisher : Institut Teknologi dan Sains Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar

In the ever-evolving digital era, technology has become a major driving force in social, economic and educational change. Information and communication technologies (ICTs) have changed the way people work, communicate, and learn. As technology advances, it is important for individuals to have sufficient technological literacy to be able to participate actively in a digital society. The aim of this webinar is to provide an in-depth understanding of digital technology and teach practical skills in using it wisely. This webinar presents a series of topics related to digital technology, including digital transformation of Internet of Things (IoT) Technology in the Industrial World, Information Security Culture, and Computer and Network Security. By using the Zoom Video Communications application, webinar participants can easily participate from their respective locations, thus enabling broad participation and more flexibility for students to learn about technology. This webinar succeeded in increasing high school and vocational students' interest in the field of technology, as well as opening their insights about various career opportunities in the digital era. In addition, students also become more aware of the importance of ethics and responsibility in using technology, and are aware of its impact on society.

Pengenalan Bahasa Inggris di SDN Sugerkidul 3 Melalui Program English Time Abdul Wahid; H. P., Agustin,; Abdul Rohim, Muhamat; Ade Permana, Angga; Baharudin, Baharudin; Khairna, Almas; Avia Zulita, Nur; Labibah, Balgis; Alifandi Habi Saleh, Geo; Muqsitoh Alfatah, Aysha; Qurnia Wati , Novi; Maryam , Siti; Akbar Ramadani , Feril; Fighur Firmansyah , Moh.; Azizah , Nurdiana; Imamah Izzatul M , Nur
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 4 No 1 (2024)
Publisher : Institut Teknologi dan Sains Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31967/jpm.v4i1.987

In the current era of globalization, almost all daily activities have begun to include the use of English, so it is considered necessary to have English language skills. Seeing this condition, English learning began to be given at a low level in formal education, namely elementary school. The same is the case with the English Time activities implemented at SDN Sugerkidul 3 by the Jember Collaborative KKN Group 220. This activity was carried out with the aim of introducing basic knowledge in English to students of SDN Sugerkidul 3. This research was conducted using descriptive qualitative methods because researchers try to describe the phenomena in the field broadly and thoroughly. Based on several considerations that have been made, this English Time activity is implemented using poster media and songs to make it easier for students to absorb and understand the learning provided during the activity. The media posters and songs are also expected to be used regularly and continuously by SDN Sugerkidul 3 itself. This English Time activity was also carried out because English is a subject that has just been added to the learning curriculum at SDN Sugerkidul 3. So this activity was chosen to make it easier for SDN Sugerkidul 3 to introduce this new subject. Keywords : English Time, Poster Media, Song Media

Analisis Kinerja Algoritma Machine Learning dalam Prediksi Harga Cryptocurrency Syarif Aminul Khoiri; Abdul Wahid
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 9 No. 2 (2024): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v9i2.1965

Cryptocurrency has become an increasingly popular digital asset in recent years. However, cryptocurrency prices are highly volatile and difficult to predict due to being influenced by many factors such as market sentiment, regulations, and technological adoption. This study aims to analyze the performance of several popular machine learning algorithms in accurately predicting cryptocurrency prices. We evaluated four algorithms: Linear Regression, Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) using historical price datasets of Bitcoin, Ethereum, and Litecoin. The data were analyzed by preprocessing steps such as normalization and splitting into training and testing sets. Evaluation metrics used were Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and prediction accuracy. The experimental results showed that the LSTM algorithm had the best performance in predicting cryptocurrency prices with the highest accuracy and lowest error, followed by SVM, Random Forest, and Linear Regression. Further analysis revealed that LSTM was able to capture patterns and trends in complex time series data.

Analisis Kinerja Algoritma Machine Learning dalam Prediksi Harga Cryptocurrency Syarif Aminul Khoiri; Abdul Wahid
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 9 No. 2 (2024): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v9i2.1965

Cryptocurrency has become an increasingly popular digital asset in recent years. However, cryptocurrency prices are highly volatile and difficult to predict due to being influenced by many factors such as market sentiment, regulations, and technological adoption. This study aims to analyze the performance of several popular machine learning algorithms in accurately predicting cryptocurrency prices. We evaluated four algorithms: Linear Regression, Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) using historical price datasets of Bitcoin, Ethereum, and Litecoin. The data were analyzed by preprocessing steps such as normalization and splitting into training and testing sets. Evaluation metrics used were Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and prediction accuracy. The experimental results showed that the LSTM algorithm had the best performance in predicting cryptocurrency prices with the highest accuracy and lowest error, followed by SVM, Random Forest, and Linear Regression. Further analysis revealed that LSTM was able to capture patterns and trends in complex time series data.

Purchasing Decision Model, Product Quality And Service Quality On Consumer Satisfaction On The Shopee Application Marketplace In Jember Regency Angga Ade Permana Angga; Wasana Sinrungtam; Ihrom Caesar Ananta Putra; Abdul Wahid; Zainul Hasan
ABM: International Journal of Administration, Business and Management Vol 6 No 1 (2024)
Publisher : LPPM Institut Teknologi dan Sains Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31967/abm.v6i1.1070

Previous research is the most important thing in a research or scientific article. Previous research is useful for strengthening theories and phenomena that influence variables. This article reviews the factors that influence Product Quality and Service Quality on Purchasing Decisions and Consumer Satisfaction, a Marketing Management Literature Study. The purpose of writing this marketing management literature review article is to build a hypothesis that can be used in further research. The results of this library research are: 1) The Influence of Product Quality on Consumer Satisfaction; 2) The Influence of Service Quality on Consumer Satisfaction; 3) The Influence of Product Quality on Consumer Satisfaction; 4) The Influence of Service Quality on Consumer Satisfaction; 5) The Influence of Consumer Satisfaction on Purchasing Decisions;

The effect of product quality, brand image, promotion, and price on consumer purchase decisions in umkm on car-free day kab. Jember Angga Ade Permana; Dedy Wijaya Kusuma; Wasana Sinrungtam; Ihrom Caesar Ananta Putra; Abdul Wahid
ABM: International Journal of Administration, Business and Management Vol 7 No 2 (2025): December 2024 - May 2025
Publisher : LPPM Institut Teknologi dan Sains Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Previous research is the most important thing in a research or scientific article. Previous research is useful for strengthening theories and phenomena that affect variables. This article reviews the Influence of Product Quality, Brand Image, Promotion and Price on Consumer Purchasing Decisions at UMKM Car Free Day Kab. Jember. The purpose of writing this marketing management literature review article is to build a hypothesis that can be used in further research. The results of this study are: 1) Product Quality partially does not affect consumer Purchasing Decisions; 2) Brand Image partially does not affect consumer Purchasing Decisions; 3) Promotion partially affects consumer Purchasing Decisions; 4) Price partially does not affect Consumer Purchasing Decisions; 5) Product Quality, Brand Image, Promotion and Price simultaneously affect Consumer Purchasing Decisions at UMKM Car Free Day, Jember regency.

Strategi Retensi Pelanggan Berbasis Historis: Optimalisasi Model Prediksi Churn Menggunakan Machine Learning Abdul Wahid; Agung Muliawan
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.237

Retensi pelanggan merupakan aspek strategis yang sangat penting dalam menjaga keberlanjutan bisnis, terutama di era kompetisi yang semakin ketat. Penelitian ini berfokus pada upaya optimalisasi model prediksi customer churn berbasis riwayat historis pelanggan dengan memanfaatkan pendekatan machine learning. Dua algoritma utama yang digunakan adalah Support Vector Machine (SVM) dan Jaringan Saraf Tiruan (Artificial Neural Network/ANN) sebagai representasi dari metode ANN. Untuk meningkatkan performa prediksi, diterapkan pula teknik ensemble classifier berupa bagging dan boosting. Guna mengatasi kompleksitas data dan mengurangi risiko overfitting, digunakan teknik dimensionality reduction melalui Principal Component Analysis (PCA). Dataset yang digunakan mencakup berbagai variabel penting seperti data demografis, perilaku pembelian, serta interaksi pelanggan dengan perusahaan. Hasil penelitian menunjukkan bahwa penerapan PCA mampu meningkatkan akurasi model, di mana ANN mencapai 92,37% dan SVM 85,13%. Penerapan metode boosting meningkatkan performa menjadi 93,34% untuk ANN dan 92,73% untuk SVM, sedangkan hasil terbaik diperoleh melalui bagging dengan akurasi 94,38% dan 94,15%. Temuan ini membuktikan bahwa kombinasi antara reduksi dimensi dan ensemble classifier dapat secara signifikan meningkatkan ketepatan prediksi customer churn, sehingga mendukung pengambilan keputusan strategis dan penyusunan strategi retensi pelanggan yang lebih proaktif, terukur, dan efektif. Customer retention is a very important strategic aspect in maintaining business sustainability, especially in an era of increasingly fierce competition. This study focuses on optimizing the customer churn prediction model based on customer historical data by utilizing a machine learning approach. The two main algorithms used are Support Vector Machine (SVM) and Artificial Neural Network (ANN) as representations of ANN methods. To improve prediction performance, ensemble classifier techniques such as bagging and boosting were also applied. To overcome data complexity and reduce the risk of overfitting, dimensionality reduction techniques were used through Principal Component Analysis (PCA). The dataset used included various important variables such as demographic data, purchasing behavior, and customer interactions with the company. The results show that the application of PCA improves model accuracy, with ANN reaching 92.37% and SVM 85.13%. The application of the boosting method improves performance to 93.34% for ANN and 92.73% for SVM, while the best results are obtained through bagging with an accuracy of 94.38% and 94.15%. These findings prove that the combination of dimension reduction and ensemble classifiers can significantly improve the accuracy of churn prediction, thereby supporting strategic decision-making and the development of more proactive, measurable, and effective customer retention strategies.

Digital Literacy and Data Security Training for Digital Era Village Cooperative Management Abdul Wahid; Syarif Aminul Khoiri
TGO Journal of Community Development Vol. 3 No. 2 (2025): July - December
Publisher : Trescode Green Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56070/jcd.2025.007

The Merah Putih Village Cooperative is a village cooperative that has a strategic role in strengthening the people's economy, especially through savings and loan services and trading agricultural products. However, in the digital era, cooperatives still face challenges in the form of low digital literacy of administrators and weak management of member data security. Administrative processes that are still manual and have minimal understanding of cybersecurity risks have the potential to hinder the operational effectiveness and sustainability of cooperatives. This community service activity aims to increase digital literacy and data security awareness for the management of the Merah Putih Village Cooperative through a participatory and collaborative approach. Implementation methods include problem identification, programme dissemination, digital literacy and data security training, application of cloud-based technologies, and post-training mentoring and evaluation. Training includes the use of digital productivity applications, cloud-based document management, as well as the implementation of data security practices such as the use of strong passwords, two-factor authentication and data backup. The results of the activity show a significant increase in the digital competence of the management, the efficiency of cooperative administration, as well as increasing awareness of the importance of member data protection. This activity is the first step in the digital transformation of village cooperatives towards more effective, safe, transparent and sustainable governance, while strengthening synergies between universities, village governments and the community.

STRATEGI MANAJEMEN PEMASARAN DIGITAL UNTUK MENINGKATKAN DAYA SAING UMKM DI ERA EKONOMI DIGITAL Permana Angga Ade; Abdul Wahid; Dedy Wijaya Kusuma; Ahmad Sauqi; Mustofa; Wasana Sinrungtam
Jurnal Riset Bisnis Vol. 9 No. 2 (2026): April
Publisher : Fakultas Ekonomi dan Bisnis Universitas Pancasila Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65179/jrb.2026.9820

This research aims to examine effective digital marketing management strategies to enhance the competitiveness of Micro, Small, and Medium Enterprises (MSMEs) in the digital economy. Using a mixed-methods approach (qualitative and quantitative), it identifies best practices and obstacles MSMEs face in implementing digital marketing. The findings reveal that digital marketing, especially social media, significantly boosts MSME competitiveness. However, constraints like limited budgets, low digital literacy, and inadequate human resources hinder its optimization. Practical training, gradual budget allocation, and government support are needed to improve the effectiveness of MSMEs' digital marketing strategies.

Title

Found 9 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 9 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 9 Documents
Search