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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.
Application of WASPAS method in determining the best flour for nastar making Nugroho, Bangkit Indarmawan; Dewi, Errika Mutiara; Kurniawan, Rifki Dwi; 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.v13i1.303

Abstract

This study explores the use of the Weighted Aggregated Sum Product Assessment (WASPAS) Method in selecting the best wheat flour for pineapple cake production. The aim of this study is to develop a more systematic and quantitative approach in assessing flour quality, provide useful guidance for pineapple cake producers and enrich the academic literature in the field of food science and food technology. This study used quantitative methodology data analysis and model validation with WASPAS, aimed at overcoming the challenge of selecting the best wheat flour for pineapple cake making. Results showed that the WASPAS method was effective in identifying the best flour, with Bungasari Hana Emas flour obtaining the highest WASPAS score of 0.952863, followed by the Falcon Hijau with a score of 0.931373. This score indicates the optimal balance between cost and quality. The study emphasizes the importance of objective decision-making tools in the food industry, suggesting that such an approach can significantly improve product quality and production efficiency.
Expert system for diagnosing pests and diseases of shallot plants with naïve bayes method Surorejo, Sarif; Albana, Muhammad Syifa; Santoso, Nugroho Adhi; 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.v13i1.304

Abstract

The development of an expert system for diagnosing pests and diseases of onion plants is of great importance given the significant role of these crops in the agricultural industry. This research aims to design and develop an expert system that can diagnose various pests and diseases that attack onion plants using the Naive Bayes method. This method was chosen for its ability to classify data based on probability assuming independence between features. This system is designed to assist farmers in identifying pests and diseases more accurately and quickly so that appropriate control measures can be taken immediately.  The training data used in this study included symptoms that often occur in onion plants due to pest or disease attacks. Each symptom is associated with the probability of the appearance of a particular pest or disease. This expert system is designed with an easy-to-use interface for farmers, where they can enter the symptoms observed in plants. Based on these inputs, the system will analyze and provide a diagnosis along with recommendations for control actions that can be taken. The system testing results show that this expert system has good accuracy in diagnosing pests and diseases in onion plants. Thus, this system can be an effective tool for farmers in managing the health of their onion plants. Further research is recommended to improve disease and pest databases and expand the application of these systems to other plant types.
Application of fuzzy tsukamoto method in forecasting weather Murtopo, Aang Alim; Aslam, Muhammad Nur; 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.v13i1.305

Abstract

In today's information age, accurate weather prediction is essential given its far-reaching impact on various aspects of life and economic activity. This study aimed to test the effectiveness of Fuzzy Tsukamoto's method in predicting important weather variables such as temperature, humidity, and precipitation. This research method uses a combination design that includes experimental methods for model development, quantitative analysis of historical weather data, and model validation using separate data. The results showed that the Fuzzy Tsukamoto method was able to increase the accuracy of weather predictions compared to conventional methods, with a significant decrease in the value of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In conclusion, this study successfully demonstrates that Fuzzy Tsukamoto's method can be a more accurate alternative in weather prediction, making a significant contribution to the field of meteorology and its practical application in decision-making in various sectors that depend on weather prediction.
Application of the latent dirichlet allocation method to determine news text topics Surorejo, Sarif; Maulana, M Taufik Fajar; 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.v13i1.306

Abstract

This research discusses the application of the Latent Dirichlet Allocation (LDA) method to determine news text topics, providing new insights into media content analysis. This research aims to develop a model that can increase the accuracy and efficiency of topic identification in Indonesian news texts. The research uses a quantitative approach with experimental methods, quantitative analysis, and model validation, where news text data is processed and analyzed using LDA. The results show that the developed model can accurately identify news topics, showing significant improvements compared to existing methods. The implications are substantial for practitioners and researchers in journalism and media analysis, offering more efficient and effective strategies for managing and understanding large flows of information and opening new directions for advanced research in news text analysis.
Co-Authors Aang Alim Murtopo Aditdya, Maulana Ahmad Zulfikri Aimar Akbar, Aminnur Aisyach Aminarti Santoso Al Fattah, Muhammad Raikhan Alan Eka Prayoga Albana, Muhammad Syifa Ali Murtopo, Aang Amalani, Mukhamad Zulfa Bakhtiar Ananda, Pingky Septiana Anandaianskha, Sawaviyya Anandianshka, Sawaviyya Anandianska, Sawaviyya Anandianskha, Sawaviyya Andriani, Wresti Andriani, Wresty Anshori, Abu Hasan Al Arianti, Tezya Sekar Arif, Zaenul Arifiyah, Nur Latifatul Arrohman, Zidni Dlia Aslam, Muhammad Nur Aziz, Taufiq Azmi, Isni Azmi, Muchamad Nauval Bangkit Indarmawan Nugroho Budiono, Wahyu Cahyo, Septian Dwi Catur Supriyanto Dari, Mayang Melan Dewi, Errika Mutiara Didiek Trisatya Dodi Setiawan Dodi Setiawan Dwi Fina Fahirah Dwi Kurniawan, Rifki Fadila, Nurul Fahirah, Dwi Fina Fanti, Azizah Permata Farkhan, Muhammad Fatkhurrohman Fatkhurrohman, Fatkhurrohman Firmansyah, Akhmad Lutfi Firmansyah, Hasbi Firmansyah, Muchamad Aries Gunawan Gunawan Hafid Subechi, Fadlan Handayani, Sri Harefa, Reyvan Sinatria Haris Fadillah Hassan, Muhamad Nur Hidayatullah, Bryan Adam Intan Mayla Faiza Intan Mayla Faiza Januarto, Sigit Khadziqul Humam Munfi Khasanah, Apriliani Maulidya Khusni, Muhammad Wazid Kurniawan, Rifki Dwi Lestari, Nindy Putri Limaknun, Lulu Lutfayza, Rezi Marzuqi, Maezun Nafis Maulana, M Taufik Fajar Miftakhuddin, Ahmad Miftakhudin, Muhammad Milkhatunisya, Milkhatunisya Moonap, Dinar Auranisa Muchamad Nauval Azmi Muh Ridwan Muhammad Sulthon Mutaqin, Ahadan Fauzan Muttaqin, Anik Naja, Naella Nabila Putri Wahyuning Ningrum, Isna Lidia Nughroho, Bangkit Indarmawan Nugroho Adhi Santoso Nugroho, Bangkit Indramawan Nur Aisyah Nur Tulus Ujianto Nurokhman, Akhmad Nursahid, Wahyu Nursidik, Maulia Nurul Fadhilah Nurul Fadilah, Nurul Prayoga, Alan Eka Priyo Haryoko Purwanto Purwanto Putra, Alif Sya’Bani Qurrotu Aini, Atikah Rafhina, Ana Ramadhan, Ilham Gema Rifki Dwi Kurniawan Rivaldiansyah, Rafik Riyadi, Fajar Sugeng Santoso, Aisyach Aminarti Santoso, Bayu Aji Santoso, Nughroho Adhi Santoso, Nugroho Adh Santoso, Nugroho Adhi Santoso, Nugroho Adi Saputra, Aryan Dandi Sarif Surorejo Sawaviyya Anandianskha Sawaviyya Anandianskha Sawaviyya Anandianskha Sawavyya Anandianskha Septian Ari Wibowo Septiana Ananda, Pingky Septiana, Pingky Setiawati, Windi Surur, Misbahu Sya’bani, Adhita Zulfa Syefudin, Syefudin Triwinanto, Mohammad Amin Triwinanto Ubaidillah, Muhamad Rizal Ujianto, Nur Tulus W.N, Naella Nabila Putri Wahyu Pratama, Raka Wahyuning Naja, Naella Nabila Putri Wilda Shabrina Wresti Andriani Wresti Andriani Wresti Andriani Yan Kurniawan Yan Kurniawan, Yan Yulison Herry Chrisnanto Zaenul Arif Zain Hidayatullah, Fikri Zain, Ahmad Muzakky