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INDONESIA
Jurnal Mandiri IT
Published by Institute of Computer Science
ISSN : 23018984     EISSN : 28091884     DOI : https://doi.org/10.35335/mandiri
Core Subject : Science, Education,
Computer Science & IT Library & Information Science Mathematics
The Jurnal Mandiri IT is intended as a publication media to publish articles reporting the results of Computer Science and related research.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 202 Documents
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Application of apriori algorithm to find relationships between courses based on student grades STMIK YMI Tegal Hassan, Muhamad Nur; Gunawan, Gunawan; Arif, Zaenul
Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i4.281

Abstract

This research explores the application of the Apriori algorithm to investigate the relationship between courses based on student grades at STMIK YMI Tegal. This research focuses on analyzing the relationship between courses to support curriculum development that is responsive and relevant to industry needs and improves the quality of learning. The main objective of this research is to identify and understand relationship patterns between various courses based on student analysis scores using the Apriori algorithm, an effective data mining methodology for uncovering association rules between items in large datasets. By using a quantitative approach and quasi-experimental design, this research succeeded in analyzing grade data from various semesters, identifying combinations of courses that often appear together with high grades, indicating a positive correlation between related courses. The results of the analysis reveal that several basic courses play a significant role in forming a strong foundation for advanced courses, highlighting the importance of a capable curriculum structure. Although the lift scores show a neutral relationship, these findings provide important initial insights for further understanding of interactions between courses. The implication for curriculum development is the need to emphasize the integration of courses that have positive relationships to support a coherent learning process and increase student success.
Web-Based home marketing information system at CV Abah Ipin using Php and Mysql Rahman, Moh. Didi; Surono, Surono
Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v12i4.282

Abstract

CV Abah Ipin is a company engaged in the construction and marketing of houses in tegal district. In marketing activities, CV Abah Ipin still uses conventional methods, namely printing and distributing brochures. Conventional marketing techniques are inefficient because they are unable to reach a large enough number of potential customers. In addition, conventional marketing techniques are inefficient because they require a lot of money to produce promotional materials such as brochures. In order to build a web-based home marketing information system for CV Abah Ipin, the author examines these problems as a solution to the problems of traditional marketing that are deemed inefficient and unsuccessful. The technique employed in this design is Waterfall with UML tools, also using MYSQL functions as a database and the PHP programming language helps develop websites. The design of this house marketing information system will produce a web that is expected to be a way to increase the effectiveness and efficiency of CV Abah Ipin's property marketing.
Application of dijkstra algorithm to optimize waste transportation distribution routes in Tegal Regency Santoso, Bayu Aji; Surur, Misbahu; Syefudin, Syefudin; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.290

Abstract

Efficient waste management is essential for sustainable urban development, especially in densely populated areas such as Tegal Regency. The study addresses inefficiencies in current waste hauling routes that contribute to increased operational costs and environmental impacts due to long transit times and increased emissions. By applying the Dijkstra Algorithm, this study aims to optimize waste transportation routes to reduce these inefficiencies. This approach involves collecting primary and secondary data on the waste management system in Tegal, which is then analyzed using the dijkstra algorithm to determine the most efficient transport route. The findings show that route optimization can significantly reduce operational costs and carbon emissions, contributing to more sustainable waste management practices in the Tegal District. This study not only improves theoretical understanding of route optimization but also provides practical solutions to real problems in waste management systems.
The Control of skincare and bodycare inventory decisions using the Multi Attribute UtilityTheory (MAUT) Method Sinaga, Ismi Novitasari
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.292

Abstract

Skincare is a series of facial skin care activities to maintain the health and appearance of the skin, as well as overcome various problems with skin. This activity consists of using several types of products, each of which has a different function according to its contents. In companies operating in the supply and trading sector, it is one of the core variables of business operations. Inventory management is also very important in company operations. Inventory that is not managed properly will cause many operational problems, such as running out of products or raw materials when needed, losing customers, losing goods, and other aspects of loss that can have a significant impact on the company.  For this reason, this research aims to make decisions on controlling skincare supplies so that they can be guaranteed in sufficient quantities with decision support using the MAUT method. The data used in this research is the number of supplies that run out per day, per week, per month. It is hoped that the results of this research with the Multi Attribute Utility Theory (MAUT) method can help companies in making decisions on controlling skincare supplies very well.
Application of the haversine formula method to determine the closest distance to a minimarket Muttaqin, Anik; Murtopo, Aang Alim; Syefudin, Syefudin; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.293

Abstract

In a digital era that demands speed and efficiency, determining the closest distance to minimarkets is crucial for consumers and the logistics industry. This study proposes the use of the haversine method to improve the accuracy of distance calculations. Through quantitative and quasiexperimental approaches, this study describes the steps of data collection, pre-processing, and application of haversine formulas. The results demonstrate the reliability of the haversine method in estimating distances accurately, allowing users to make more informed decisions in planning trips or logistics strategies. These findings contribute to the academic literature and field practice by providing a more robust and applicable methodology for determining the closest distance. Keywords: haversine, closest distance, minimarket.
Prediction of Bank Central Asia stock prices after dividend distribution using ARIMA method Surorejo, Sarif; Sulthon, Muhammad; Anandianskha, Sawaviyya; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.294

Abstract

This study explores the prediction of Bank Central Asia (BBCA) stock prices following the annual dividend distribution using the Autoregressive Integrated Moving Average (ARIMA) method. The primary goal is to provide a robust forecasting tool to aid investors and financial analysts in making informed decisions. The research employs a quantitative approach with a quasi-experimental design, analyzing weekly BBCA stock price data from January 2019 to February 2024. The findings demonstrate that the ARIMA (2, 1, 2) model provides stable and reliable predictions of BBCA stock prices, showing slight weekly variations but overall stability. Practically, these predictive models can be integrated into a web-based platform, allowing real-time stock price forecasting and broader accessibility for users. This study contributes to the financial literature by validating the ARIMA model's applicability in the Indonesian stock market and suggesting the exploration of hybrid models and external economic factors for future research.
Application of weighted aggregated sum product assessment method in determining the best flour to produce vermicelli Surorejo, Sarif; Rivaldiansyah, Rafik; Dwi Kurniawan, Rifki; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.296

Abstract

This study explores the application of the Weighted Aggregated Sum Product Assessment (WASPAS) method's selection of the best wheat flour for vermicelli production, which aims to improve product quality and production efficiency. The study aimed to integrate experimental data with sophisticated decision-making models to identify the most suitable type of flour based on a comprehensive set of criteria. Using a quantitative approach, this study combines experimental methods, quantitative analysis, and model validation, using the WASPAS method to evaluate and rank various flours. The results showed significant differences among flour types, with selected flours showing superior performance across multiple parameters, including chemical composition and functional properties. The study's findings underscore the potential of advanced decision-making tools such as WASPAS in improving food production processes, demonstrating broader applicability across the food industry to optimise raw material selection.
Comparison of naïve bayes and KNN for herbal leaf classification Nugroho, Bangkit Indarmawan; Khusni, Muhammad Wazid; Ananda, Pingky Septiana; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.297

Abstract

This study aims to compare the effectiveness of two classification algorithms, namely Naïve Bayes Classifier and K-Nearest Neighbor (KNN), in classifying herbal leaves. This research design uses a quantitative approach with experimental analysis and model validation. The dataset consisted of images of papaya leaves, pandanus, cat's whiskers, and betel nut taken in different lighting conditions. The methodology includes pre-processing of data by converting images into grayscale, feature extraction using Gray Level Co-occurrence Matrix (GLCM), and application of Naïve Bayes and KNN algorithms. The main results showed that KNN achieved 90.00% accuracy with precision, recall, and F1-score of 88.33% respectively, higher than Naïve Bayes which had 82.50% accuracy, 81.46% precision, 85.83% recall, and 82.27% F1-score. In conclusion, KNN is superior in the classification of herbal leaves to Naïve Bayes, although it requires a longer computational time. Further research is recommended to optimize algorithm parameters and explore the integration of deep learning techniques to improve classification accuracy and efficiency.
Comparison of dijkstra and genetic algorithms for shortest path guci Surorejo, Sarif; Al Fattah, Muhammad Raikhan; Andriani, Wresti; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.298

Abstract

This study aims to compare the performance of the Dijkstra algorithm and the Genetics algorithm in determining the shortest path to the Guci tourist destination. The research design combines experimental methods, quantitative analysis, and model validation. The data used is the distance between points on two alternative routes to Guci. Data pre-processing is done to ensure quality and consistency. The relevant variables are selected, and model optimization is performed to obtain the best parameter configuration for both algorithms. Dijkstra and Genetics algorithms are implemented using Python, taking into account computational efficiency and ease of integration. Model evaluation is done through a series of tests with time execution and convergence metrics. The results showed that Dijkstra's algorithm was superior in finding the shortest path with a distance of 43.0 km and an execution time of 0.0017 seconds, compared to the Genetics algorithm which found a path with a distance of 44.7 km and an execution time of 0.0048 seconds. It can be concluded that Dijkstra's algorithm is more effective and efficient in this case, but Genetics algorithms have the potential for more complex optimization problems.
Application of fuzzy genetic system to predict the number of outpatient visits Surorejo, Sarif; Cahyo, Septian Dwi; Fadilah, Nurul; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.299

Abstract

Improving the management and use of resources in outpatient care is a challenge faced by health facilities in today's digital era. The inability to accurately predict patient flow can result in inadequacies in staff scheduling and effective space management. Therefore, this study aims to develop a predictive model of outpatient visits using the fuzzy system genetic method. The research methods used include the design of a combination of experimental methods, quantitative analysis, and model validation. Outpatient visit data is taken from a hospital and processed using the Fuzzy Genetics System which optimizes fuzzy rules with genetic algorithms. The results of the model implementation show accurate and adaptive predictions to variations and uncertainties in patient visiting patterns. Based on the results of the study, it can be concluded that the use of fuzzy system genetic methods in predicting outpatient visits can improve the operational efficiency of health facilities. The developed prediction model is able to provide predictions that are more accurate, adaptive, and responsive to the real needs of health facilities. With the adoption of this method, health facilities can optimize management and resources in outpatient health services. This research contributes significantly to the development of predictive models that are more efficient and applicable in the dynamic context of healthcare.

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