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Richki Hardi
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Multica Science and Technology
Published by Universitas Mulia
ISSN : -     EISSN : 27762386     DOI : https://doi.org/10.47002/mst.v1i1
Core Subject : Science,
Computer Science & IT Energy Library & Information Science Physics
Focus and Scope The journal covers all aspects of science and technology, that is: Science: Bioscience & Biotechnology; Chemistry; Food Technology; Applied Biosciences and Bioengineering; Environmental; Health Science; Mathematics; Statistics; Applied Physics; Biology; Pharmaceutical Science; etc. Technology: Artificial Intelligence; Computer Science; Computer Network; Data Mining; Web; Language Programming; E-Learning & Multimedia; Information System; Internet & Mobile Computing; Database; Data Warehouse; Big Data; Machine Learning; Operating System; Algorithm; Computer Architecture; Computer Security; Embedded system; Cloud Computing; Internet of Thing; Robotics; Computer Hardware; Geographical Information System; Virtual Reality; Augmented Reality; Multimedia; Computer Vision; Computer Graphics; Pattern & Speech Recognition; Image processing; ICT interaction with society; ICT application in social science; ICT as a social research tool; ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 57 Documents
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Naïve Bayes Classification Algorithm Application on Nutritional Status of Pregnant Women in Lhokseumawe City Ilham Sahputra; Difa Angelina; Mutammimul Ula
Multica Science and Technology Vol 4 No 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.851

Abstract

The nutritional status of pregnant women is a measure of success in fulfilling nutrition for pregnant women. Poor nutritional status of pregnant women will cause an imbalance of nutrients which can cause nutritional problems in pregnant women. Therefore, we need a system that can predict the nutritional status of pregnant women. This can be implemented by utilizing the naïve Bayes classification algorithm. This research was carried out with the aim of further studying how to apply the Naïve Bayes algorithm to predict the nutritional status of pregnant women, and how the success of this application is based on the accuracy value of the resulting calculations. Based on data on the prevalence and condition of pregnant women in Lhokseumawe and calculations using a series of formulas for mean, standard deviation, probability, and gaussian values, it was found that 50 pregnant women were predicted to have normal nutritional status, while 19 others had nutritional status. not enough. From the results of the accuracy carried out, it was found that the error value (error) of the application used was 48% while the accuracy value of the application was 53% or low. That way, the calculation formula developed in this study needs to be further developed to encourage the accuracy of the application made so that the application results are reliable in real life.
Analysis of Measuring Student Satisfaction with Teacher Performance Assessment Using the Naive Bayes Model Irma Yurni; Arief Rahman; Zahratul Fitri
Multica Science and Technology Vol 4 No 1 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i1.852

Abstract

Naive Bayes model analysis to evaluate teacher quality based on the grades given by students with the variables of communication, mastery of material, teacher involvement, and teaching methods have an important role in determining student satisfaction assessments. The analysis results from the naive Bayes model show that student satisfaction tends to be higher for teachers who have good communication skills, strong mastery of the material, active involvement in the teaching and learning process, and the use of effective and innovative teaching methods. Therefore, to improve the quality of education, it is necessary to increase teacher competence in these four variables. In addition, the application of Naive Bayes model analysis can be an effective tool for identifying students who need improvement and development in analyzing student satisfaction. Naïve Bayes model analysis is used to predict the probability of student satisfaction based on the attributes involved. The results of the research show that analyzing and classifying student satisfaction assessments with good accuracy using the Naive Bayes model makes it easy to estimate the probability of satisfaction based on the attributes given. The results of research using the naive Bayes model with a probability of yes 0.0576 with a likelihood of yes and no getting a value of 0.6 while the probability of no is 0.0384 with a value of 0.4. for Normalization results 1 and Probability Value YES > Probability Value NO, Then Student Satisfaction with Teacher Performance is Satisfied with Teacher Performance.
Implementation of Ward AHC for Material Clustering Based on Mechanical Parameters Yusuf, Edy; Bakhtiar; Syukriah; Burhanuddin; Riyadhul Fajri
Multica Science and Technology Vol 4 No 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.977

Abstract

This study aims to implement the Ward Agglomerative Hierarchical Clustering (Ward AHC) algorithm to classify materials based on mechanical parameters, including tensile strength (Su), yield strength (Sy), elastic modulus (E), shear modulus (G), Poisson's ratio (μ), and density (ρ). The clustering results reveal that the data is divided into three main groups with the following distributions: Cluster 1 (321 data points), Cluster 2 (403 data points), and Cluster 3 (828 data points). Each cluster exhibits unique characteristics: Cluster 1 is dominated by materials with low Su and Sy values, moderate E and G values, and light ρ. Cluster 2 features materials with very high E values, while Su, Sy, and G values vary. Cluster 3 is characterized by moderate Su values, low Sy values, high E and G values, and light ρ. An evaluation using the Silhouette Score yielded a value of 0.492, indicating that the clustering quality is reasonably good, though there is evidence that some data points may lie near the boundaries between clusters.
Prediction of Shrimp Sales Using the ARIMA (AutoRegressive Integrated Moving Average) Method at UD Udang Makmur Peureulak Veri Ilhadi; Muliana Muliana; Zulfia , Anni; Ulya, Athiyatul; Sahputra , Ilham
Multica Science and Technology Vol 4 No 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.978

Abstract

UD. Udang Makmur is a shrimp farming business that often faces challenges in accurately predicting sales stock due to reliance on manual forecasting methods. This study aims to develop a web-based sales prediction application utilizing the AutoRegressive Integrated Moving Average (ARIMA) method. The application uses daily sales data from January to December 2023 for analysis. The results indicate that the ARIMA (2,1,1) model delivers accurate predictions, achieving a Mean Squared Error (MSE) of 0.264295. Forecasts for the next 24 periods demonstrate a stable projection, with predicted values converging around 2.5 and a narrow 95% confidence interval. These findings highlight the model's reliability and low uncertainty for the forecasted time frame. The application was successfully tested using the Black-Box method, confirming its functionality and effectiveness in supporting sales predictions.
Decision Support System for Land Suitability Assessment of Horticultural Crops of Legume Commodities Using AHP-VIKOR Ilham Sahputra; Rizky Putra Phonna; Natasya Natasya; Annisa Karima; T. Sukma Achriadi Sukiman
Multica Science and Technology Vol 4 No 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.979

Abstract

This Decision Support System (DSS) is designed to evaluate land suitability for horticultural crops, specifically legumes, using a combination of Analytical Hierarchy Process (AHP) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) methods. The system aids farmers in determining the appropriate crops based on the available land conditions. The research includes problem identification, literature review, data collection, and system design. The implementation of the AHP-VIKOR methods has proven effective and accurate in providing horticultural crop recommendations. This system adds value to modern and efficient agricultural land management. The research results show that the AHP-VIKOR methods successfully applied in determining the suitability of land for legumes in the areas of Bireun, Bukit Rata, Sawang, and Pesisir Pelabuhan Kreung Geukuh with satisfactory outcomes. Therefore, the AHP-VIKOR methods are considered optimal for weighting criteria and ranking alternatives in selecting land for legume crops
PROTECTION SYSTEM INI SOLAR PANEL AGAINST INDUCTION MOTOR LOAD Hadiyanto Hadiyanto; Ali Abrar; Rudiansyah Rudiansyah
Multica Science and Technology Vol 5 No 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

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

Abstract

Solar Power Plants (PLTS) have become one of the solutions to meet the need for clean and sustainable energy. However, like other electrical systems, PLTS is also susceptible to disturbances and problems that can interfere with its performance and reliability. This study aims to design and build a voltage and current protection system at the PLTS inverter output to protect the 1-phase induction motor from damage due to unstable voltage and current. The protection system used is Over/Under Voltage Protector, and MCB for overvoltage and undervoltage protection and load current limitation, in order to protect electrical equipment connected to the PLTS system. The test results show that the protection system implemented on the Inverter Output can protect against overvoltage above 250 V, undervoltage below 185 V, and overcurrent.where if this happens then the electricity supply to the load will be automatically cut off
DESIGN AND DEVELOPMENT OF A PV INVERTER SYSTEM WITH A REAL POWER CONCEPT FOR INDUCTION MOTOR LOADS Hadiyanto Hadiyanto; Ali Abrar; Syahruddin Syahruddin; Muhammad Rifaldy Zhaqi
Multica Science and Technology Vol 5 No 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v5i1.997

Abstract

The type of inverter used greatly affects the efficiency of the PLTS in distributing electrical power to the load to be used. The use of an inappropriate inverter type will cause electrical equipment to quickly become damaged [4]. This study was conducted to help determine the type of inverter that is more appropriate for use in a PLTS system with inductive and resistive loads, so that the quality of the electrical power produced is maximized. This study compared the 1000 Watt Pure Sine Wave Inverter and the 1000 Watt Modified Sine Wave Inverter by measuring the waveform, voltage output value, current output value, power output value, voltage Total Harmonic Distortion (THD) value and current Total Harmonic Distortion value. The final results of the study showed that the Pure Sine Wave Inverter was superior in producing output power, had lower voltage and current distortion values and could produce output waves with a pure sine shape
IMPLEMENTATION OF NEURAL NETWORK IN PREDICTING STOCK PRICE OF PT BANK RAKYAT INDONESIA (PERSERO) TBK Nurmayanti, Wiwit Pura; Ni Luh Desvita Pratiwi; Nariza Wanti Wulan Sari; Desi Yuniarti; Erlyne Nadhilah Widyaningrum; Thesya Atarezcha Pangruruk
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/dwkza342

Abstract

Forecasting involves estimating future outcomes by examining patterns in both historical and present data. A commonly used data type in forecasting is time series data, characterized by observations collected at consistent time intervals. One forecasting technique that has gained significant attention is the Neural Network, particularly through the Backpropagation method utilized in this study. In the context of the stock market, price fluctuations are influenced by a variety of factors, including shareholder rights, company performance, and the balance between supply and demand. Typically, a rise in stock prices leads to decreased demand, while a decline tends to stimulate it. Predicting stock prices, such as those of Bank Rakyat Indonesia (BRI), can support investors in making well-informed decisions. This research seeks to identify the optimal number of neurons in the hidden layer for forecasting BRI stock prices by minimizing error metrics such as MAPE, MSE, and MAE. The analysis revealed that forecasting the stock price of PT Bank Rakyat Indonesia (Persero) Tbk. using a neural network with one hidden neuron resulted in a MAPE of 1.22248 and an MAE of 61.30548.
TRAFFIC ACCIDENT VICTIM CLASSIFICATION IN BONTANG USING NW-KNN AND BACKWARD ELIMINATION Mangalik, Gerald; Nariza Wanti Wulan Sari; Surya Prangga; Wiwit Pura Nurmayanti; Ika Purnamasari
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/yfbspb33

Abstract

Traffic accidents have been a serious problem caused by various factors such as road conditions, driver behavior, and weather. To understand the pattern of victim severity, a classification approach capable of handling imbalanced data and irrelevant features was needed. This study aimed to classify the status of accident victims using the Neighbor Weighted K-Nearest Neighbor (NW-KNN) method, equipped with backward elimination for feature selection. Backward elimination was employed to reduce insignificant features and improve accuracy.The case study for this research involved the status of accident victims in Bontang City, with a sample size of 93 cases. There were nine features in this study: accident victim status, accident time, road density, road function, road surface condition, speed limit at the location, road slope, and road status.The research results showed that the best parameter combination for classification using the NW-KNN method with backward elimination was K = 7 and E = 3. The "type of accident" feature was eliminated, leaving 8 features. Classification results using the NW-KNN method with backward elimination yielded an accuracy of 88.89%, demonstrating an improvement in classification performance for identifying the status of traffic accident victims. Thus, this method proved to be an effective approach for traffic accident analysis in Bontang City.
USER EXPERIENCE (UX) AND USER INTERFACE (UI) DESIGN FOR E-GOVERNMENT SERVICES IN EAST KALIMANTAN: ENHANCING PUBLIC SERVICE ADOPTION THROUGH USER-CENTERED DESIGN Mundzir Mundzir
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/wr3h6h10

Abstract

This study investigates how user-centered User Experience (UX) and User Interface (UI) design influence the adoption of e-government services in East Kalimantan, Indonesia. Despite efforts to digitize public services, citizen engagement with these platforms remains low, often due to usability issues. Employing a qualitative descriptive approach grounded in the User-Centered Design (UCD) framework, data were collected through semi-structured interviews, direct user observations, and policy document analysis. Thematic analysis using NVivo revealed key pain points such as inconsistent interface design, cognitive overload, poor error recovery, and low user trust. Quantitative insights from observational data highlighted that task abandonment often occurred at CAPTCHA verification and form submission stages. The findings emphasize the importance of integrating UX/UI design principles, including heuristic evaluation and participatory design, into the development of digital public services. This study contributes to the discourse on e-government by demonstrating that citizen-centered design is crucial not only for improving service usability but also for fostering public trust and long-term adoption. Future work should focus on the development of standardized UX frameworks for regional governments to ensure inclusive, accessible, and responsive digital services.

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