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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Ilmiah Kursor Journal of Innovation and Applied Technology International Journal of Local Economic Governance Journal of Environmental Engineering and Sustainable Technology Jurnal Pembangunan dan Alam Lestari Jurnal Teknologi Informasi dan Ilmu Komputer The International Journal of Accounting and Business Society Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Knowledge Engineering and Data Science Jambura Law Review Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Engineering, Science and Information Technology Indexia Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Bulletin of Culinary Art and Hospitality Bulletin of Social Informatics Theory and Application Jurnal ilmiah teknologi informasi Asia Signal and Image Processing Letters Jurnal Pengabdian Masyarakat Teknologi Informasi dan Informatika

Wayan Firdaus Mahmudy

Brawijaya University

Author-ID : 312490

Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Comparison of Bagging Ensemble Combination Rules for Imbalanced Text Sentiment Analysis Cahya, Reiza Adi; Bachtiar, Fitra A.; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 6 No. 1: April 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

The wealth of opinions expressed by users on micro-blogging sites can be beneficial for product manufacturers of service providers, as they can gain insights about certain aspects of their products or services. The most common approach for analyzing text opinion is using machine learning. However. opinion data are often imbalanced, e.g. the number of positive sentiments heavily outnumbered the negative sentiments. Ensemble technique, which combines multiple classification algorithms to make decisions, can be used to tackle imbalanced data to learn from multiple balanced datasets. The decision of ensemble is obtained by combining the decisions of individual classifiers using a certain rule. Therefore, rule selection is an important factor in ensemble design. This research aims to investigate the best decision combination rule for imbalanced text data. Multinomial NaÃ¯ve Bayes, Complement NaÃ¯ve Bayes, Support Vector Machine, and Softmax Regression are used for base classifiers, and max, min, product, sum, vote, and meta-classifier rules are considered for decision combination. The experiment is done on several Twitter datasets. From the experimental results, it is found that the Softmax Regression ensemble with meta-classifier combination rule performs the best in all except in one dataset. However, it is also found that the training of the Softmax Regression ensemble requires intensive computational resources.

Application of Density Based Spatial Clustering Application With Noise (DBSCAN) in Determining the Quality of Keprok Orange and Siam Orange Hybrid in the Research Center of Orange and Subtropic Plants Batu City Alqorni, Faiz; Mahmudy, Wayan Firdaus; Widodo, Agus Wahyu
Journal of Information Technology and Computer Science Vol. 6 No. 1: April 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. One of the tasks of the Indonesian Citrus and Subtropical Research Institute is research on crossing between citrus varieties to produce saplings with the best quality products through observation of the fruit produced. Because the amount of fruit production studied is very large, it requires a fast and accurate observation process, one of which is the clustering method of data mining. Observations were made using a clustering process or grouping Density Based Spatial Clustering Application with Noise (DBSCAN) on fruit characteristics that indicate quality. DBSCAN works by grouping data based on density, so that it is expected to find several data groups that are close to each other which shows the tendency of the quality of the observed fruit data as well as labeling outlays for data that are too far from the crowd. The results of the grouping will be analyzed to find out the number and characteristics of the groups formed where the results of the grouping are assessed using the Silhouette Coefficient method to determine the best parameter values. The results obtained in this study are obtained three group results which will be divided into medium quality, good, and not so good. The quality of grouping using the Silhouette Coefficient value of 0.69.

Website Visitors Forecasting using Recurrent Neural Network Method Arya, Putu Bagus; Firdaus Mahmudy, Wayan; Basuki, Achmad
Journal of Information Technology and Computer Science Vol. 6 No. 2: August 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Abstract. The number of visitors and content accessed by users on a site shows the performance of the site. Therefore, forecasting needs to be done to find out how many users a website will come. This study applies the Long Short Term Memory method which is a development of the Recurrent Neural Network method. Long Short Term Memory has the advantage that there is an architecture of remembering and forgetting the output to be processed back into the input. In addition, the ability of another Long Short Term Memory is to be able to maintain errors that occur when doing backpropagation so that it does not allow errors to increase. The comparison method used in this study is Backpropagation. Neural Network method that is often used in various fields. The testing using new visitor data and first time visitors from 2018 to 2019 with vulnerable time per month. The computational experiment prove that the Long Short Term Memory produces better result in term of the mean square error (MSE) comparable to those achieved by Backpropagation Neural Network method.

Redefining Competitiveness: The Role of Lean Production and Eco-Design in the Indonesian FMCG Industry Rachmawati, Christina; Mahmudy, Wayan Firdaus; Khusaini, Moh.; Kurniawan, Andi
Jurnal Pembangunan dan Alam Lestari Vol. 16 No. 1 (2025): Jurnal Pembangunan dan Alam Lestari
Publisher : Postgraduate School of Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jpal.2025.016.01.01

The Indonesian FMCG market is booming due to increased consumer spending power. However, this expansion brings challenges, including fierce competition and the potential for escalating production costs. FMCG companies must prioritize innovation to improve operational efficiency and environmental sustainability to stay competitive and profitable. This research explores the strategies employed by a leading Indonesian FMCG company to attain a competitive edge in sustainability by integrating eco-design and lean production principles. The concept of Eco-Design, which emphasizes minimizing environmental impact over the product's lifecycle, is integrated with Lean Production's emphasis on waste reduction and process efficiency. This company is redefining competitiveness by merging these two strategies. It's not just about economic success; it's about achieving that success in a way that prioritizes environmental stewardship and minimizes ecological impact. This holistic strategy seeks to harmonize financial and environmental objectives, creating a mutually reinforcing cycle that fosters a more sustainable and responsible approach to competitive advantage. This study used statistical analysis to confirm that Lean Production and Eco-Design strongly contribute to sustainable manufacturing in the FMCG sector. The findings highlight how combining these two approaches can boost efficiency and sustainability, offering a powerful roadmap for companies in Indonesia and beyond to achieve lasting success in a rapidly changing market. Keywords: eco-design, FMCG market, lean production, prioritize innovation

Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor Widodo, Agus Wahyu; Hernando, Deo; Mahmudy, Wayan Firdaus
Signal and Image Processing Letters Vol 1, No 3 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i3.6

Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

Legal Challenges and Reform Proposals for Algorithmic Contracts under Indonesia’s Information and Electronic Transactions Law Tejomurti, Kukuh; Sukarmi, Sukarmi; Mahmudy, Wayan Firdaus
Jambura Law Review VOLUME 7 NO. 2 JULY 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33756/jlr.v7i2.30617

The emergence of algorithmic contracts, made or carried out by independent systems, creates difficult legal problems, especially in Indonesia's regulatory environment. This study examines whether Indonesia’s Information and Electronic Transactions Law is adequate to deal with new problems with algorithmic pricing, EAs, and automated contract creation. The article also examines the different types of algorithmic contracts and how black-box algorithms are used in dynamic pricing in business competition. It shows how unclear the law is about agency, consent, and accountability. Using doctrinal legal research combined with conceptual, comparative, and interdisciplinary approaches, the study finds that Indonesia's Information and Electronic Transactions Law and Competition Law have become inadequate in responding to developments in AI-driven transactions. It suggests a legal framework that does not favour any one technology and recognises algorithms as helpful agents. It also calls for changes to the law to clarify how electronic agents can work together and negotiate. This method ensures that businesses are held accountable and that the law is clear in the age of self-driving digital contracts.

Gold Prices Prediction using Univariate Long Short Term Memory Method Aditama, Gustian; Yudistira, Novanto; Mahmudy, Wayan Firdaus
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102525

Gold is one of the precious metals that serves various purposes beyond being a jewelry material. When it comes to gold, it is often associated with the economy. Before the existence of currency, humans used gold as the base material for coins as a medium of exchange. Currently, one of the commonly utilized functions of gold is as an investment asset. Due to its utility and high demand, the price of gold can fluctuate over time. This research aims to predict the price of gold using the Long Short Term Memory (LSTM) method. LSTM is a deep learning technique that performs well when applied to time series data. The performance of LSTM can be assessed using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). Thus, this study proposes the prediction of gold prices using LSTM with an optimized architecture. In order to achieve it, testing is conducted based on sequence length and hidden size. The best results were achieved using Univariate LSTM with a sequence length of 25 and a hidden size of 150, that produce RMSE of 22.014 and MAPE of 1.133%.

Empowering Low-Resource Languages: Javanese Machine Translation Sulistyo, Danang Arbian; Aji Prasetya Wibawa; Wayan Firdaus Mahmudy; Fadhli Almu’iini Ahda; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6887

This study addresses the critical need to preserve and revitalize the Javanese language, which despite its widespread popularity, faces challenges as a low-resource language in Indonesia. The decline in Javanese proficiency among younger generations poses a significant threat to the language's cultural significance and heritage. To address this issue, this study introduces an innovative approach to machine translation, focusing on the development of a robust Indonesian-Javanese translation system. Utilizing advanced neural machine translation (NMT) techniques, including Long Short-Term Memory (LSTM) networks, the proposed system aims to bridge the linguistic gap between Indonesian and Javanese. Special attention was given to the unique linguistic characteristics and challenges of Javanese, with the goal of achieving exceptional translation accuracy and fluency. Through extensive experimentation and evaluation, this study aims to demonstrate the effectiveness of the translation system in facilitating cross-cultural communication and language preservation efforts within the Javanese-speaking community. By emphasizing the significance of Javanese as a widely spoken yet under-resourced language, this study underscores the importance of innovative technological solutions in safeguarding linguistic diversity and cultural heritage. Through its contributions, the research seeks to address the pressing need for language preservation and revitalization, particularly in the context of low-resource languages like Javanese.

Imputation of missing microclimate data of coffee-pine agroforestry with machine learning Nurwarsito, Heru; Suprayogo, Didik; Sakti, Setyawan Purnomo; Prayogo, Cahyo; Yudistira, Novanto; Fauzi, Muhammad Rifqi; Oakley, Simon; Mahmudy, Wayan Firdaus
International Journal of Advances in Intelligent Informatics Vol 10, No 1 (2024): February 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i1.1439

This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffee-pine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, K-Nearest Neighbors (KNN), and Linear Regression methods across multiple time frames - 6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.

Penentuan Upah Minimum Kota Berdasarkan Tingkat Inflasi Menggunakan Backpropagation Neural Network (BPNN) Yohannes, Ervin; Mahmudy, Wayan Firdaus; Rahmi, Asyrofa
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2 No 1: April 2015
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Upah Minimum Kota (UMK) adalah sebuah standardisasi upah atau gaji karyawan atau pegawai untuk diterapkan diperusahaan baik itu BUMN, BUMS, maupun perusahaan lain yang berskala besar. Faktor yang mempengaruhi UMK sangat banyak dan beragam salah satunya adalah rata-rata inflasi pengeluaran dimana terdapat 8 kategori yang dipakai. Tulisan ini memaparkan penggunaan Backpropagation Neural Network (BPNN) untuk memprediksi besarnya UMK. Pada tahap uji coba data dibagi menjadi dua bagian yaitu data latih dan data uji, dimana data latih digunakan untuk mencari jumlah iterasi, jumlah hidden layer, dan nilai learning rate yang optimal. Pengujian data latih memberikan hasil yakni jumlah iterasi optimal diperoleh pada saat iterasi 80, sedangkan untuk jumlah hidden layer yang optimal adalah sebanyak satu hidden layer dan untuk nilai learning rate optimal yakni pada saat bernilai 0.8. Semua variabel yang diperoleh dikatakan optimal karena memiliki rata-rata MSE paling kecil dibandingkan dengan data lainnya. Hasil yang diperoleh saat data uji dengan menggunakan iterasi, jumlah hidden layer, dan nilai learning rate yang optimal didapatkan hasil MSE sebesar 0.07280534710552478.

Co-Authors A.N. Afandi Abdul Latief Abadi Abdul Latief Abadi Achmad Arwan Achmad Basuki Achmad Ridok Adimoelja, Ariawan Aditama, Gustian Adyan Nur Alfiyatin Agi Putra Kharisma Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Agung Setia Budi Agung Setia Budi, Agung Setia Agus Naba Agus Wahyu Widodo Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Afif Supianto Aji Prasetya Wibawa Al Khuluqi, Mabafasa Alauddin, Mukhammad Wildan Alfiani Fitri Alfita Rakhmandasari Alfiyatin, Adyan Nur Alqorni, Faiz Amalia Kartika Ariyani Amalia Kartika Ariyani Amalia Kartika Ariyani Anantha Yullian Sukmadewa Andi Kurniawan Andi Maulidinnawati A K Parewe Andi Maulidinnawati A. K. Parewe Andreas Nugroho Sihananto Andreas Pardede Andreas Patuan G. Pardede Andrew Nafalski Angga Vidianto Anissa, Retno Dewi Aprilia Nur Fauziyah Aprilia Nur Fauziyah Arief Andy Soebroto Arinda Hapsari Achnas Armanda, Rifki Setya Arviananda Bahtiar Arya, Putu Bagus Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi, Asyrofa Aulia, Yudha Alif Bagus Priambodo Bagus Priambodo Bayu Rahayudi Binti Robiyatul Musanah Budi Darma Setiawan Burhan, M.Shochibul Cahya, Reiza Adi Cahyo Prayogo, Cahyo Candra Dewi Candra Fajri Ananda Cleoputri Yusainy Darmawan, Abizard Hashfi Dea Widya Hutami Dhaifullah, Afif Naufal Diah Anggraeni Pitaloka Didik Suprayogo Dinda Novitasari Dinda Novitasari, Dinda Diny Melsye Nurul Fajri Dita Sundarningsih Durrotul Fakhiroh Dyan Putri Mahardika Dzikron, Ahmad Ahdani Edi Satriyanto Edy Santoso Effendi, Mas’ud Eko Widaryanto Elta Sonalitha Ervin Yohannes Evi Nur Azizah Fadhli Almu’iini Ahda Fais Al Huda Fajri, Diny Melsye Nurul Fatchurrochman Fatchurrochman Fatwa Ramdani, Fatwa Fauzi, Muhammad Rifqi Fauziatul Munawaroh Fazlur Ihzanurahman Febriyana, Ria Fendy Yulianto Fitra Abdurrachman Bachtiar Fitri Anggarsari Fitria Dwi Nurhayati Gayatri Dwi Santika Ghozali Maski Grady Davinsyah Gusti Ahmad Fanshuri Alfarisy Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri Gusti Eka Yuliastuti Hafidz Ubaidillah Hamdianah, Andi Hanggara , Buce Trias Herman Tolle Hernando, Deo Heru Nurwarsito Hidayat, Luthfi Hilman Nuril Hadi Ida Wahyuni Ihsan Akram, Faiz Imada Nur Afifah Imam Cholisoddin Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Santoso Indriati Indriati Irvi Oktanisa Ishardita Pambudi Tama Ismiarta Aknuranda Jauhari, Farid Khozaimi, Ach. Kukuh Tejomurti, Kukuh Kuncahyo Setyo Nugroho Kuncahyo Setyo Nugroho Kurnianingtyas, Diva Lailil Muflikhah Lily Montarcih Limantara M Chandra Cahyo Utomo M Fadli Ridhani M Shochibul Burhan, M Shochibul M. Shochibul Burhan M. Zainal Arifin Mabafasa Al Khuluqi Mar'i, Farhanna Marji Marji Mayang Anglingsari Putri, Mayang Anglingsari Mochammad Anshori Moh. Khusaini Moh. Sholichin Moh. Zoqi Sarwani Mohammad Zoqi Sarwani Mohammad Zoqi Sarwani, Mohammad Zoqi Muâ€™asyaroh, Fita Lathifatul Muh Arif Rahman Muh. Arif Rahman Muhammad Ardhian Megatama Muhammad Faris Mas'ud Muhammad Halim Natsir Muhammad Isradi Azhar Muhammad Khaerul Ardi Muhammad Noor Taufiq Muhammad Rivai Muhammad Rofiq Nadia Roosmalita Sari Nadya Oktavia Rahardiani Nashi Widodo Ni Wayan Surya Wardhani Nindynar Rikatsih Novanto Yudistira Novi Nur Putriwijaya Nurizal Dwi Priandani Nurul Hidayat Oakley, Simon Oktanisa, Irvi Pardede, Andreas Philip Faster Eka Adipraja Pratama, Muhammad Fajarivan Prayudi Lestantyo Prima Zulvarina, Prima Purnomo Budi Santoso Putra, Firnanda Al Islama Achyunda Putri Hasan, Vitara Nindya Putu Indah Ciptayani Qoirul Kotimah Rachmansyah, Ghenniy Rachmawati, Christina Rani Kurnia Rayandra Yala Pratama, Rayandra Yala Retno Astuti Retno Dewi Anissa Riani, Garsinia Ely Rifa’i, Muhaimin Rinda Wahyuni Rizal Setya Perdana Rizal Setya Perdana Rizdania, Rizdania Rizki Ramadhan Rody, Rafiuddin Ruth Ema Febrita Ryan Iriany S, M Zaki Samaher . Saputro, Arkaditya Lanang Saragih, Triando Hamonangan Selly Kurnia Sari Setyawan Purnomo Sakti Sudarto Sudarto Suhana, Rizka Sukarmi Sukarmi, Sukarmi Sulistyo, Danang Arbian Sutrisno . Sutrisno Sutrisno Syafrial Syafrial Syafrial Syafrial Syaiful Anam Syandri, Hafrijal Tanjung, Lutfi Mitra Tirana Noor Fatyanosa, Tirana Noor Titiek Yulianti Titiek Yulianti Titiek YULIANTI Tomi Yahya Christyawan Tri Halomoan Simanjuntak Ubaidillah, Hafidz Ullump Pratiwi Utaminingrum, Fitri Utomo, M. Chandra Cahyo Vivi Nur Wijayaningrum Wahyuni, Ida Widdia Lesmawati Windi Artha Setyowati Yeni Herawati Yogi Pinanda Yogie Susdyastama Putra Yudha Alif Aulia Yudha Alif Auliya Yudha Alif Auliya, Yudha Alif Yulia Trianandi Yusuf Priyo Anggodo Yusuf Priyo Anggodo Yusuf Priyo Anggodo Yusuf Priyo Anggodo, Yusuf Priyo

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