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All Journal Journal of Mechanical Engineering Bulletin of Science Education INOVTEK Polbeng - Seri Informatika
Hendy Kurniawan
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Automotive Engineering Computer Science & IT Education Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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KAJIAN BEBAN PENDORONG MESIN PEMOTONG TEMPE Catur Pramono; Endang Mawarsih; Hendy Kurniawan
Journal of Mechanical Engineering Vol 2, No 1 (2018): Journal of Mechanical Engineering
Publisher : Universitas Tidar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31002/jom.v2i1.812

Abstract

One of the most important sources of vegetable protein to improve people's nutrition is soybean. Soybean is a type of food that is safe for consumption, good for maintaining health, and the price is cheap. Soybean is a type of legume plant that is often used as the basic ingredient of tempe. Tempe making through fermentation by Rhizopus sp. Processed foods tempe until now is still a culinary in Indonesia to overseas. The purpose of this study is to assess the engine driving load for tempe cutters 2kg, 3kg and 4kg for tempe chips production. The results showed that the tempe 3kg tempe load was most suitable for tempe production.
Application of Machine Learning and Deep Learning to Predict Financial Product Subscriptions Based on Customer Features Prayoga, Harditya; Ignatus Moses Setiadi, De Rosal; Hendy Kurniawan
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/cyvzwk14

Abstract

The financial industry faces challenges in predicting consumer behaviour, especially in forecasting decisions related to subscribing to financial products like term deposits. This study applies machine learning and deep learning to predict subscriptions based on demographic and behavioural data from the Bank Marketing dataset from the UCI Machine Learning Repository. The models tested include Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM). Model performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Results show that BiGRU achieves the highest accuracy of 92.52%, outperforming other models, with SVM and BiLSTM also showing strong performance. However, all models still face limitations in detecting subscribing customers, as evidenced by the high false negative rate. These findings highlight the potential of machine learning and deep learning to support data-driven decision-making in financial marketing, despite limitations such as the use of a single data source and the lack of consideration for external factors affecting customer decisions.
The Teachers' Strategies in Improving Islamic Religious Education Learning during the Pandemic at Nunggal Rejo State Elementary School Citra Ayu Anggraini; Susi Nawanti; Muhammad Amir; Ghufron Faqih; Hendy Kurniawan; Ari Purnomo; Subandi; Indah Sari; Marwiyah; Siti Munawaroh
Bulletin of Science Education Vol. 2 No. 3 (2022): Bulletin of Science Education
Publisher : CV. Creative Tugu Pena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51278/bse.v2i3.1845

Abstract

The COVID-19 pandemic has significantly disrupted the learning process, requiring teachers to adapt in order to fulfill their roles as educators and mentors. Learning during the pandemic differs greatly from pre-pandemic methods, and teachers must choose appropriate strategies to ensure effective teaching continues. This study aims to explore the strategies employed by Islamic Education (PAI) teachers to enhance PAI learning during the pandemic at SDN 1 Nunggal Rejo, and to examine how the learning process was conducted during this period. This qualitative research was conducted at SDN 1 Nunggal Rejo using data collection techniques such as observation, interviews, and documentation. The data were collected, processed, and analyzed to draw conclusions. The findings indicate that teachers implemented several strategic approaches, including goal-oriented instruction, structured procedures, and clearly defined learning activities. Teachers acted as facilitators, and although discipline and time management were challenging, learning continued through a combination of online and limited face-to-face methods. Keywords:  Teachers' Strategies, Enhance Learning Religious Education
The Implementation of AWS Cloud Technology to Enhance the Performance and Security of the Pharmacy Cashier Management System Hendy Kurniawan; L. Budi Handoko; Valentino Aldo
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/x0rctv54

Abstract

 This study examines the implementation of Amazon Web Services (AWS) in the MEKATEK pharmacy cashier management system to address the limitations of traditional systems, such as slow transaction processing, data loss risks, and challenges in handling transaction surges. The prototyping method was employed, involving user requirements analysis through interviews and observations, followed by iterative development of core features like inventory management, transactions, reporting, and data backups. Black box testing demonstrated a 100% success rate for core functionalities. Performance analysis recorded stable CPU utilisation below 5% under normal workloads and the ability to handle throughput up to 2532 packets/minute. System optimisation reduced AWS operational costs to IDR 150,000–160,000 per month. AWS implementation improved operational efficiency, strengthened data security through encryption and role-based access control, and minimised human errors. Initial user feedback indicated faster workflows, although adjustments are needed for users with limited technical backgrounds. This study recommends further development, including AI-based analytics and digital payment integration, to enhance MEKATEK’s functionality and competitiveness in the future.
Co-Authors Ari Purnomo Catur Pramono Citra Ayu Anggraini Endang Mawarsih Ghufron Faqih Ignatus Moses Setiadi, De Rosal Indah Sari L. Budi Handoko Marwiyah MUHAMMAD AMIR Prayoga, Harditya Siti Munawaroh Subandi Susi Nawanti Valentino Aldo