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All Journal Jurnal Ilmiah Informatika Komputer Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Technopreneur (JTech) Jurnal Teknologi INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Mantik Jurnal Sistem Informasi dan Informatika (SIMIKA) Buana Information Technology and Computer Sciences (BIT and CS) JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Intelligent Decision Support System (IDSS) Pena : Jurnal Ilmu Pengetahuan dan Teknologi Sustainable: Jurnal Kajian Mutu Pendidikan Jurnal Cahaya Mandalika Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Mandiri IT Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Journal of Practical Computer Science (JPCS) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Biner : Jurnal Ilmiah Informatika dan Komputer IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Jurnal Masyarakat Madani Indonesia Jurnal Ekonomi, Teknologi dan Bisnis Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia Journal of Elektronik Sistem InformasI Jurnal Pengabdian Masyarakat Terapan
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Journal : Jurnal Mantik

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Application of the Naïve Bayes and K-Nearest Neighbor Methods for Classifying Roses Gunawan Gunawan; Wresti Andriani; Aisyach Aminarti Santoso
Jurnal Mantik Vol. 7 No. 2 (2023): Agustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i2.3911

Abstract

They are faced with the rapid development of plant science, especially the science of rose flora. The rose has a sweet-smelling charm; beautiful color. Many people, like roses, are deliberately cultivated by the beauty industry as the main ingredient in making cosmetics. Roses have various varieties, and the types have similarities, so it is difficult to distinguish, know and determine the varieties of roses; in plain view, it requires a long time and precision. In this study, the Naïve Bayes and K-Nearest Neighbor applications were used. Algorithms will be carried out for the classification of roses in addition to proving the identification and classification of rose varieties based on morphological characteristics using K-NN and Naïve Bayes to understand the diversity of roses. The Naive Bayes method produced maximum accuracy with little training data. Meanwhile, K-Nearest Neighbor was chosen because it is robust against noise data. The performance of the two methods will be compared to determine which method is better for classifying roses. The results show that the Naive Bayes method performs better, with an accuracy rate of 75%, while the K-Nearest Neighbor method has an accuracy rate of 62.5%.
Implementation of sugeno fuzzy method on bandwidth management at STMIK YMI Tegal Gunawan Gunawan; Wresti Andriani; Dodi Setiawan
Jurnal Mantik Vol. 7 No. 3 (2023): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i3.4145

Abstract

In the lecture process, the Internet becomes very important, and access settings or bandwidth management is needed so that Internet use can run smoothly. Many users can cause the burden of computer access to be high, so internet use will be unbalanced if bandwidth management is not carried out. Bandwidth management aims to allocate bandwidth in an internet network so that the distribution is equal and the comfort between users and one another is not disturbed. The method used is fuzzy Sugeno to implement Sugeno's fuzzy logic into bandwidth management and optimize internet usage. Fuzzy inputs used include download, streaming, and browsing variables. Using three fuzzy sets, namely low, standard, and solid. The output is Download Limit, Streaming Limit, and Browsing Limit. System testing will be conducted using MATLAB. After testing, results were obtained through bandwidth limits from downloading, streaming, and browsing. One of the study's results included a given system input download = 700, streaming = 668, and browsing = 611. Produce output is Download Limit = 700, Streaming Limit = 481, and Browsing Limit = 319
Comparison of banking stock price movements using KNN and Linier Regresi methods Wresti Andriani; Gunawan; Sawavyya Anandianskha
Jurnal Mantik Vol. 7 No. 3 (2023): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i3.4304

Abstract

Stock price movements are a reflection of various situations and factors such as economics, politics and markets. Influencing factors such as economic news both foreign and domestic such as monetary policy, changes in interest rates and political events such as general elections. Indonesian politics is currently holding a general election process, the Presidential election can influence stock movements. Causing investors to be more careful about investing in this case in banks in Indonesia that have state-owned status, such as BTN, BRI, BNI and Bank Mandiri. This research predicts stock price movements using the Linear Regression method compared to the k-NN method, to find the best evaluation results from the two methods in selecting banks that are more profitable and do not influence political process factors. The results obtained were that Bank Mandiri was safer and promised profits using the Linear Regression method which was better than k-NN with RMSE 281,012, MAE 97,909 and MSE 80348,873. Bank Mandiri is safer and promises profits
Impact of Palestine-Israel conflict on multinational stock prices use neural network and support vector machine comparison Andriani, Wresti; Gunawan, Gunawan; Wahyuning Naja, Naella Nabila Putri
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5196

Abstract

One form of prolonged geopolitical event is the conflict between Palestine and Israel, which has complex historical, political, and religious roots in the Middle East. This research aims to determine whether this conflict influences the share prices of the companies Unilever, McDonald's, and KFC. These three large companies are known as allies of one of the disputing countries. The method used by the Neural Network is compared with Support Vector Machine to find the best accuracy using RMSE and MAE. The greater the error value, the more affected the company is by this geopolitical factor. As a result, the accuracy of the SVM method is better than NN; the company most affected is KFC, with the RMSE value of 0.111, MAE of 0.020, followed by Unilever with RMSE 0.034, MAE 0.025 then McDonald's with RMSE 0.026 and MAE 0.116, is expected to help investors choose to invest in the company McDonald’s then Unilever.
Performance evaluation of single moving average and exponential smoothing in shallot production prediction Santoso, Aisyach Aminarti; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5205

Abstract

Shallots are a strategic commodity that has significant health benefits, including its ability to prevent cancer. The commodity also plays an important role in the agricultural economy, especially in Indonesia, where high demand in domestic and international markets contributes greatly to farmer’s income. However, fluctuations in shallot production often lead to price instability, which has a negative impact not only on consumers but also on the sustainability of farmers' income. This research aims to develop a forecasting model that can assist in more effective planning of shallot production. To achieve this goal, the study tested and compared two forecasting methods: Single Moving Average (SMA) and Single Exponential Smoothing (SES), which are known for their ease of implementation and accuracy in predicting time series data. Using a dataset of shallot production from Brebes Regency over the period 2020-2023, the study found that Single Exponential Smoothing consistently provided more accurate results than Single Moving Average. SES performance is more responsive to recent changes in production data, which is particularly important given the rapid fluctuations that often occur in the agricultural sector. The findings suggest that the application of the SES method in shallot production forecasting can facilitate more informed decision-making in production management and distribution planning, potentially stabilizing market prices and improving farmers' economic conditions
Application of machine learning for short-term climate prediction in Indonesia Gunawan, Gunawan; Andriani, Wresti; Aimar Akbar, Aminnur
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5215

Abstract

This study explores the Application of Machine Learning for Short-Term Climate Prediction in Indonesia, focusing on enhancing forecast accuracy through advanced computational models. The primary objective was to develop and validate Random Forest and Support Vector Machine (SVM) models to predict short-term climate conditions accurately across ten major Indonesian cities. Employing a quantitative approach, the study utilized experimental design, rigorous data analysis, and model validation using historical weather data from April 2024 provided by the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The results indicate that both Random Forest and SVM significantly outperform traditional climate prediction models, with Random Forest achieving an average accuracy of 87.5% and SVM 85.2%. These findings underscore the potential of machine learning to revolutionize short-term climate predictions in regions with complex meteorological dynamics like Indonesia, offering substantial implications for disaster preparedness, agricultural planning, and urban management. Future research can expand upon these models by incorporating real-time data and exploring deep learning techniques to enhance predictive reliability further
Optimizing the viola-jones algorithm for robust face recognition in variable lighting and orientation conditions Gunawan, Gunawan; Aisyah, Nur; Santoso, Nugroho Adhi
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5220

Abstract

Facial recognition is a critical technology in digital security, driven by significant advances in computer vision. This research focuses on optimizing the Viola-Jones algorithm to improve the accuracy and speed of face detection by adjusting parameters and integrating more sophisticated image processing techniques. Facing challenges such as suboptimal lighting and variations in face orientation, the study adopted a rigorous experimental design, in-depth quantitative analysis, and robust model validation. Of the ten facial images collected, all were intensively processed using Haar-like features to identify significant patterns and adjust algorithm parameters in Python. This optimization process increased performance from 7 identified faces to 9 post-optimization identified faces and a substantial decrease in detection time from 0.0065 seconds to 0.0017 seconds per image. The comprehensive evaluation showed an increase in accuracy from 70% to 90%, recall from 70.0% to 90.0%, Precision remained constant at 100.0%, and F1-score from 82.35% to 94.74%. These results show that the optimization has increased the algorithm's sensitivity to changes in light intensity and face orientation and improved the effectiveness of facial recognition systems in complex and dynamic security scenarios while providing concrete evidence of the benefits of using Haar-like features in the Viola-Jones algorithm
Application of ant colony algorithm to optimize waste transport distribution routes in Tegal Gunawan, Gunawan; Handayani, Sri; Anandianskha, Sawaviyya
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5223

Abstract

Effective and efficient waste management is an essential challenge in developing cities like Tegal City. Optimizing waste transport routes can reduce operational costs and environmental impact. This study aims to implement the Ant Colony Algorithm (ACO) to optimize waste distribution routes in Tegal City. This method was chosen for its proven ability to solve route optimization problems. This study developed a model for the simulation and analysis of waste transportation routes using actual location data from the Integrated Waste Treatment Site (TPST) to the landfill (TPA). The results showed that the implementation of ACO reduced the total mileage from 27.50 km to 21.05 km, a significant reduction that shows the algorithm's efficiency in determining the optimal route. The conclusion of this study confirms that ACO can be effectively used to improve waste transportation operations
Application of artificial neural network with optimization of genetic algorithms for weather prediction Gunawan, Gunawan; Miftakhudin, Muhammad; Arif, Zaenul
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5225

Abstract

This research integrates Artificial Neural Network (ANN) with Genetic Algorithm Optimization (GA) to improve the accuracy of weather prediction. This method utilizes ANN-optimized GA, creating a model that can adapt to the dynamics of weather patterns. Using a dataset that includes meteorological variables such as temperature, humidity, and precipitation from January 1, 2023, to October 28, 2023, the model was tested for its ability to predict weather conditions accurately. The process begins with data preprocessing, ANN training, and GA optimisation. The evaluation showed that the optimized model was able to reduce the Mean Absolute Error (MAE) from 1.6865 to 0.8701, the Mean Absolute Percentage Error (MAPE) from 5.9864 to 3.1408, and the Root Mean Squared Error (RMSE) from 2.253 to 1.039, signalling a significant improvement in prediction accuracy and efficiency. This research confirms the potential of ANN and GA integration in improving weather prediction, providing new insights for developing more accurate and reliable prediction models for various applications, from agriculture to disaster management.
Application of the analytic network process method in the selection of raw material suppliers for yarn Gunawan, Gunawan; Hafid Subechi, Fadlan; Arif, Zaenul
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5227

Abstract

This study applies the Analytic Network Process (ANP) Method for selecting raw material suppliers for yarn, a crucial factor in boosting production efficiency and quality within the textile industry. The research aims to develop and validate a decision-making model that enhances supplier selection by integrating ANP with rigorous quantitative analyses. The methodology incorporates a series of experiments, thorough examination of historical data, and robust model validation processes to confirm the accuracy and dependability of the findings. The results demonstrate significant improvements in the precision of supplier selection, underscored by a high Pearson correlation coefficient of 0.89. This validates the model's effectiveness and reliability, suggesting that the developed framework not only supports data-driven and objective decision-making in the textile industry but also has potential applications in other sectors to enhance operational efficiency and sustainability
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