cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Fristi Riandari
Contact Email
hengkitamando26@gmail.com
Phone
+6281381251442
Journal Mail Official
hengkitamando26@gmail.com
Editorial Address
Romeby Lestari Housing Complex Blok C Number C14, North Sumatra, Indonesia
Location
Unknown,
Unknown
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 26 Documents
 1   2   3 
Search results for , issue "Vol. 13 No. 1 (2024): July: Computer Science and Field" : 26 Documents clear
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.
Application of centroid and geometric mean methods for face recognition Nugroho, Bangkit Indarmawan; Khasanah, Apriliani Maulidya; Arif, Zaenul; 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.v13i1.300

Abstract

Face recognition is one of the most important areas in artificial intelligence and image processing, with wide applications from attendance system security to human-computer interaction. This study aims to overcome the difficulties in classifying student faces in an academic environment by applying and comparing centroid and geometric mean methods. Student face data was collected and processed through conversion to grayscale, pixel intensity normalization, and statistical analysis using both methods. The results showed that both methods had the same performance with 70% accuracy, 75% precision, 60% recall, and 66.67% F1-score. The application of this method can improve the efficiency and accuracy of attendance management and security in the campus environment, especially for institutions with limited resources.
Implementation of the Fuzzy Tsukamoto method to determine the amount of beverage production Surorejo, Sarif; Firmansyah, Muchamad Aries; Arif, Zaenul; 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.v13i1.302

Abstract

Optimization of the amount of beverage production by applying the Fuzzy Tsukamoto Method at PT. Sariguna Primatirta Tbk. This study aims to develop a predictive model that can assist companies in determining the optimal amount of beverage production, minimizing production costs, and maximizing customer satisfaction. The research method uses a quantitative approach with a combination design of experimental methods, quantitative analysis, and model validation, including the collection of historical data on production, market demand, and raw material availability, data pre-processing, selection of input and output variables, implementation of the Fuzzy Tsukamoto algorithm, and model evaluation with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. The results showed that the Fuzzy Tsukamoto Method succeeded in determining the amount of beverage production with good accuracy, with an MAE of 0.25 and RMSE of 0.274 after the data was understated, proved effective in handling the uncertainty of market demand and providing optimal production recommendations based on fuzzy rules from expert knowledge. The implications of this research contribute to the scientific literature in the field of computer science and industrial management, as well as practical benefits for  PT. Sariguna Primatirta Tbk in improving its production effectiveness, with the potential to be adopted by similar industries to improve operational efficiency.

Page 1 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 14 No. 2 (2025): October: Computer Science and Field Vol. 14 No. 2 (2025): Computer Science and Field Vol. 14 No. 1 (2025): July: Computer Science and Field. Vol. 13 No. 4 (2025): April: Computer Science and Field. Vol. 13 No. 3 (2025): January: Computer Science and Field. Vol. 13 No. 2 (2024): CALL FOR PAPER !!! Scope Computer Science Vol. 13 No. 1 (2024): July: Computer Science and Field Vol. 12 No. 4 (2024): April: Computer Science and Field. Vol. 12 No. 3 (2024): January: Computer Science and Field. Vol. 12 No. 2 (2023): October: Computer Science and Field. Vol. 12 No. 1 (2023): July: Computer Science and Field. Vol. 11 No. 4 (2023): April : Computer Science and Field. Vol. 11 No. 3 (2023): January: Computer Science and Field. Vol. 11 No. 2 (2022): October: Computer Science and Field. Vol. 11 No. 1 (2022): July: Computer Science and Field. Vol. 10 No. 2 (2022): January: Computer Science and Field. Vol. 10 No. 1 (2021): July: Computer Science and Field. Vol. 9 No. 2 (2021): January: Computer Science and Field. Vol. 9 No. 1 (2020): July: Computer Science and related. Vol. 8 No. 2 (2020): January: Coputer Science and related. More Issue