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JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI
Published by Universitas Al Azhar Indonesia
ISSN : 20879725     EISSN : 23558059     DOI : -
Core Subject : Health, Science, Engineering,
Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Public Health
Jurnal AL-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI terbit 2 kali dalam setahun yaitu pada bulan Maret dan September adalah jurna; ilmiah yang mempublikasikan artikel hasil penelitan ilmiah dan ide-ide di bidang sains dan teknologi. Jurnal ini berfokus pada bidang teknik industri, teknik elektro, teknik infromatika, biologi, gizi dan teknologi pangan.
Arjuna Subject : Umum - Umum
Articles 13 Documents
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Search results for , issue "Vol 10, No 2 (2025): Mei 2025" : 13 Documents clear
Dual Sistem Keamanan Pada Pintu Dengan Pengenalan Wajah Local Binary Pattern Histogram (LBPH) Dan Sidik Jari serta Notifikasi Telegram Maulana, Abi; Ullah, Aulia; Faizal, Ahmad; Zarory, Hilman
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.3696

Abstract

Conventional door security systems, such as padlocks and manual keys, have weaknesses, including vulnerability to duplication and the risk of loss. Biometric-based systems, such as facial recognition, offer a more reliable solution through unique user identification. This study develops a door security system using the Local Binary Pattern Histogram (LBPH) method for facial recognition, complemented by fingerprint verification as an additional security layer and real-time notifications via the Telegram application. The LBPH method was chosen for its ease of implementation and processing speed, although it has limitations such as sensitivity to lighting changes and potential recognition errors due to similar facial textures. The system utilizes LBPH for initial authentication, followed by fingerprint verification. Users also receive real-time notifications via Telegram to monitor access attempts. Testing showed a facial recognition accuracy of 85% under bright lighting conditions at distances of 30–150 cm, but it decreased to 65% in dim lighting. Fingerprint verification took approximately 2 seconds, while notification delivery required 1–2 seconds on a stable internet network. This system enhances security by ensuring only registered users can unlock the door. If facial recognition fails, the door remains locked without valid fingerprint verification.Keywords - Face Recognition, Fingerprint Sensor, LBPH, Security System, Telegram Notification.
Inventory Planning and Control in Perishable Items Products Using the Single Order Quantities Method (Case Study: PT XYZ) Tanuwijaya, Amelia Tasya; Nurhasanah, Nunung; Tripiawan, Wawan
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.4102

Abstract

Effective inventory management is crucial for companies distributing perishable products. PT XYZ requires a precise control method to optimize inventory and minimize losses. This study aims to determine the top 5 priority products, identify the best forecasting method with the smallest error and analyze the implementation of Single Order Quantities (SOQ) in inventory management. Using primary and secondary data collected from 1 January 2021 to 4 April 2022, the study employed ABC Analysis to categorize products, followed by demand forecasting using Single Moving Average, Single Exponential Smoothing, and Fuzzy Time Series to select the most accurate method. Inventory planning was then carried out using SOQ to enhance efficiency. Among 196 vegetable types, 5 were identified as the highest priority: Birdseye Chili Pepper (1 kg), Red Cayenne Pepper (1 kg), Birdseye Chili Pepper (Min. 5 kg), Local Cucumber (1 kg), and Bulk Green Paprika. The SOQ method enabled optimized inventory strategies, ensuring product availability while minimizing excess stock. This research provides practical insights for effective inventory planning, particularly for businesses handling perishable goods.Keywords - Demand Planning, Fuzzy Time Series, Inventory Planning, Perishable Product, Single Order Quantity.
Prediksi Peringkat Akreditasi BAN PT Program Studi Sarjana Rumpun Ilmu Komputer Menggunakan Klasifikasi Machine Learning Aribowo, Budi; Tjahjono, Budi; Firmansyah, Gerry; Widodo, Agung Mulyo
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.3089

Abstract

Accreditation ranking is one of the causes and indicators chosen by prospective students when choosing a study program in higher education. From the data collected, only 5% of study programs in the Computer Science group have a Superior accreditation rating and an A accreditation rating in LLDikti Region III Jakarta. So it is necessary to know the factors that influence the accreditation ranking. The machine learning methodology used in this approach is K-Nearest Neighbors (KNN) and from the data obtained there are 6 factors that can be strongly suspected to influence the study program accreditation value. The four machine learning models, namely KNN, Gaussian Naïve Bayes Decision Tree and Logistic Regression, it was found that the KNN machine learning model with 2 input variables had the highest AUC value, namely 84.38%. Meanwhile, from the model simulation run by KNN machine learning, 2 input variables can produce relatively accurate prediction results. And the results of cross validation with 10 folds support the selected machine learning with an accuracy level of 80%. In general, the KNN machine learning model with 2 input variables was able to predict the accreditation rating of Study Programs, especially from the Computer Science Cluster.Keywords – Accreditation, Area Under Curve (AUC), Department of School, Kfold Cross Validation, Machine Learning.
Design of Ready-to-Drink Coffee Product Packaging Using Kansei Engineering Method and Eye Tracking Purwandari, Aprilia Tri; Yasmin, Moza Aisyah; Aribowo, Budi
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.4124

Abstract

Packaging plays an important role in enhancing a product's competitiveness. Therefore, the aim of this research is to design attractive packaging for ready-to-drink coffee that will boost the company's competitiveness in selling such products, especially for Micro, Small, and Medium Enterprises (MSME), which face tough competition from both local and international companies. The objective of this research is to design ready-to-drink coffee packaging that caters to consumer preferences using Kansei Engineering and Eye Tracking. Based on the analysis using Kansei Engineering and Eye Tracking methods, two packaging recommendations for ready-to-drink coffee were obtained. If the company wants to focus on environmentally friendly packaging design, use bulb-shaped packaging made of glass, with a medium size of 300-350 ml, a monochrome label that provides detailed information and attached with pictures, based on the results of the Kansei Engineering analysis. On the other hand, if the focus is on aesthetics, the company should use bulb-shaped packaging made of plastic, with a medium size of 300-350 ml, a monochrome label that provides detailed information and attached with pictures, based on the results of the Eye-tracking analysis. Keywords - Conjoint Analysis, Eye Tracking, Kansei Engineering, Packaging, Ready-to-Drink Coffee.
Design of Economic Order Quantity on Polyester Yarn Raw Material Based on Artificial Neural Network Forecasting Fathurrohman, Muhammad Salman; Nurhasanah, Nunung; Tripiawan, Wawan
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.4105

Abstract

Polyester yarn is a key raw material in textile manufacturing due to its durability and affordability. PT ABC relies on external suppliers for polyester yarn, making inventory management crucial for production efficiency. However, the company's current ordering approach has led to occasional stock shortages, impacting operations. This study develops an inventory control model using the Economic Order Quantity (EOQ) method, incorporating safety stock and reorder point calculations to minimize stockouts and reduce inventory costs. Additionally, Artificial Neural Networks (ANN) are used to forecast demand for 2022, improving estimation accuracy. Based on historical demand data from 2019 to 2021, the EOQ method lowers inventory costs compared to the company’s approach, achieving efficiency gains of 19%, 12%, and 29%, saving IDR45,745,000, IDR23,735,000, and IDR98,020,000, for each respective year. The ANN model utilizing the TrainLM training function achieves the lowest Mean Squared Error (MSE) of 0.063528 and forecasts a total raw material requirement of 2,510,628 kg for 2022. The EOQ value for 2022 is set at 44,817 kg, with safety stock and reorder point levels of 8,438 kg and 29,360 kg, respectively.Keywords – Artificial Neural Network, Economic Order Quantity, Inventory, Reorder Point, Safety Stock.
Mutu Fisikokimia & Organoleptik Es Krim Buah Kundur (Benincasa hispida) Jaya, Bill Loren; Hartati, Fadjar Kurnia; Prayudanti, Adhania Andika
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.3677

Abstract

Ice cream is a popular food product, but innovation using local ingredients remains limited. This study evaluates the effect of winter melon (Benincasa hispida) addition on the physicochemical and organoleptic qualities of ice cream to support product diversification and enhance nutritional value. The research employed a Randomized Block Design (RBD) with variations: 10% and 25% winter melon in diced or puréed forms. Parameters analyzed included melting rate, Overrun, crude fiber content, vitamin C levels, and organoleptic tests (taste and mouthfeel) using a hedonic scale (25 panelists). Results showed that P1 (10% diced winter melon) yielded the best performance with the highest organoleptic scores (taste: 4.08; mouthfeel: 4.29) and the highest total effectiveness (0.720). P2 (25% diced winter melon) recorded the highest vitamin C and crude fiber content but had lower organoleptic acceptance. Formulations P3 and P4 (puréed winter melon) showed balanced results but did not surpass P1. The P1 formulation, combining whipped cream, low-calorie sugar, and 10% diced winter melon, was the best combination, meeting physical, chemical, and organoleptic quality standards. The use of winter melon not only enhances the nutritional value of ice cream but also supports diversification based on local resources.Keywords - Ice Cream, Wax Gourd, Benincasa hispida, Physicochemical Quality, Organoleptic.
Product Price Optimization in Microeconomics: Exploring the Role of Artificial Intelligence Algorithms – A Literature Review Hidayanti, Nur Fitri; Syaharuddin, Syaharuddin; Ariani, Zaenafi
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.3770

Abstract

In today’s data-driven economy, pricing strategies have become increasingly critical amid rapidly evolving market conditions. The integration of artificial intelligence (AI) offers new opportunities to optimize pricing decisions and strengthen competitive advantage. This study investigates the use of AI algorithms in optimizing product pricing within microeconomic contexts. Using a qualitative method and systematic literature review, it draws on publications from the past decade indexed in Scopus, DOAJ, and Google Scholar. The findings highlight that AI-based price optimization is shaped by several key factors: data availability, algorithm complexity, and the alignment of AI systems with existing business models. However, major challenges such as data bias, limited computational resources, and insufficient organizational readiness often hinder successful implementation. Despite these barriers, AI shows great promise in enhancing pricing accuracy, efficiency, and adaptability to market fluctuations. This research offers a comprehensive overview of the limitations and potential of AI in price optimization, emphasizing the importance of addressing technical and organizational challenges. It contributes to a deeper understanding of how AI can transform traditional pricing strategies and encourages further empirical research to explore its real-world applications within dynamic microeconomic settings.Keywords - Artificial Intelligence, Microeconomics, Pricing Algorithms, Price Optimization.
Front Cover Jurnal Al Azhar Indonesia Cover, Front
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.4335

Abstract

Design of Working Facilities Based on Posture Risk Assessment and Anthropometry Nofierni, Nofierni; Patmawati, Debby; Aunurrofiq, Fadli
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.3610

Abstract

The operators on assembly lines are complaining about pain in their muscle and joints when carrying out work. This study aimed to assess posture risk on assembly line with manual work, and propose appropriate design for work station facilities. The research was conducted  to operators in the n transformer assembly lines. The method used to evaluate the working posture effort and risk was the combined Nordic Body Map and  Rapid Upper Limb Assessment (RULA). Based on the RULA method, the operator's body position is at high risk with a score of seven (7), so immediate working corrections are needed. The current faulty design of work stations that are not in accordance with ergonomic principles causes work musculoskeletal disorder in wound core assembly line operators. Based on the lengthy analysis of the faulty operator postures in the wound core assembly, a new table and chair design was proposed. Keywords – Anthropometry, Ergonomics, Manual Work, Posture, Risk Assessment.
Pengaruh Variasi Jumlah Neuron dalam Hidden Layer Algoritma Pelatihan Levenberg-Marquardt Jaringan Backpropagation: A Systematic Literature Review Irianto, Ade Gilang Hendra; Sudarmilah, Endah
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 10, No 2 (2025): Mei 2025
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v10i2.3788

Abstract

This analysis is done to determine and is a consideration for future research related to different types of problem solving by using the training algorithm of Backpropagation network. This study uses 4 steps selections in filter articles that will be used in literary studies, namely 1) Identification 2) Screening 3) Eligibility and 4) Included. The number of items filtered in this study is 73 articles. The article was filtered through the identification phase with a total of 205 articles, then in the screening process by assimilating the title and summary, then the eligible process with many articles filtered by 132 articles did not meet the requirements to get the final results of 73 articles for the analysis process. The number of nerve cells indicates that there is no rules that are determined related to the exact quantity of nerve cells in the hidden layer depending on all research needs and parameters applied in research. Although in some articles, the accuracy value is not briefly mentioned that the Levenberg-Marquardt training algorithm is effective in solving problems, in 21 articles filtered that the Levenberg- Marquardt training algorithm has an accuracy rate of over 90%, indicating that this algorithm can be an alternative choice as a problem-solving tool due to its effectiveness and optimal accuracy of results.Keywords - Accuracy, Backpropagation, , Effectiveness, Hidden Layer, Levenberg-Marquardt Algorithm

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