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All Journal Jurnal Mantik Journal of Intelligent Decision Support System (IDSS) Journal Automation Computer Information System (JACIS) Jurnal Mandiri IT Journal Of World Science Jurnal Teknologi Sistem Informasi PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Digital Business Jurnal Masyarakat Madani Indonesia
Arif, Zaenul
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences

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Journal : Jurnal Mandiri IT

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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.
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.
Co-Authors Aang Alim Murtopo Agustina, Catur Puji Andriani, Wresti Ariyani, Rizki Bangkit Indarmawan Nugroho Farkhan, Ali Firmansyah, Akhmad Lutfi Firmansyah, Muchamad Aries Gunawan Gunawan Gunawan Gunawan Hafid Subechi, Fadlan Hassan, Muhamad Nur Januarto, Sigit Khasanah, Apriliani Maulidya Miftakhudin, Muhammad Nurokhman, Akhmad Riyadi, Fajar Sugeng Santoso, Nugroho Adh Sarif Surorejo Syefudin, Syefudin