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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Masyarakat Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ekonomi Bisnis dan Kewirausahaan Journal of Applied Geospatial Information Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Innovation in Research of Informatics (INNOVATICS) International Journal of Artificial Intelligence and Robotics (IJAIR) Devotion: Journal of Research and Community Service
Sri Winarno
Faculty of Information Science and Technology, Multimedia University
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 Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Physics Social Sciences

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Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation Khoirunnisa, Emila; Alzami, Farrikh; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Naufal, Muhammad; Al-Azies, Harun; Winarno, Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14308

Abstract

Batik, an Indonesian cultural heritage recognized by UNESCO, faces challenges in pattern identification and documentation, particularly for the younger generation. Previous studies on batik classification have shown limitations in handling small datasets and maintaining accuracy with limited computational resources. This research proposes an enhanced classification approach for Semarang Batik motifs using MobileNetV2 architecture combined with strategic data augmentation techniques. The study utilizes a dataset of 3,020 images comprising 10 distinct Semarang Batik motifs, implementing horizontal flipping, rotation, and zoom transformations to address dataset limitations. Our methodology incorporates transfer learning through ImageNet pre-trained weights and custom layer modifications to optimize the MobileNetV2 architecture for batik-specific features. The model achieves 100% accuracy on validation data, with precision, recall, and F1-scores consistently above 0.98 across all classes. The confusion matrix analysis reveals minimal misclassification between similar motif patterns, particularly in the Batik Blekok Warak and Batik Kembang Sepatu classes. This research contributes to cultural heritage preservation by providing an efficient, resource-conscious solution for automated batik pattern recognition, potentially supporting educational and commercial applications in the batik industry.
Thyroid Disease Prediction Using Random Forest with KNNImputer for Missing Values Pratama, Raffy Nicandra Putra; Winarno, Sri; Wijaya, Tan Nicholas Octavian
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14334

Abstract

Thyroid disease is a health dysfunction that requires immediate and accurate diagnosis. This research seeks to design a classification model based on the Random Forest algorithm to detect the type of thyroid disease utilizing data from the UCI Repository. In the data processing stage, KNNImputer is used to handle missing data by calculating the average value of the nearest neighbors based on Euclidean distance, thus ensuring better data quality for model training. The developed model was evaluated utilizing the confusion matrix, which showed an accuracy of 98%, with precision, recall, and F1 score values ​​reached 98% based on weighted avg.These results corroborate that the proposed model is highly reliable in detecting various types of thyroid diseases, such as Negative, Hypothyroid, and Hyperthyroid. This research makes an important contribution to the application of data mining technology for medical diagnosis, while proving that optimal data processing through methods such as KNN Imputer can significantly improve model performance.
Analisis Pemain Terbaik Sepak Bola dengan menggunakan Algoritma K-Means Wardhana, Faviola Proba; Winarno, Sri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27105

Abstract

The selection of players in soccer is crucial for developing strategies in matches. It serves as a decision support system that can be used to choose the starting line-up. The data for this research was obtained from the official and reliable website of Liga 1 Indonesia for the 2023 season. This study aims to analyze the top soccer players using the K-means algorithm based on their statistical performance throughout the 2023 Liga 1 Indonesia season. Data collection for each player included their percentage of appearances in the starting line-up. We used the K-means algorithm, which helps identify patterns and cluster players based on statistical metrics from the matches, such as the number of goals, assists, and other physical statistics across various player positions. The data comprised 197 players competing in Liga 1 2023. Our findings reveal that 62 players belong to Cluster 1 out of the total 197 analyzed. These players exhibited the best statistics and could be potential options for Liga 1 coaching staff to recruit or sign in order to strengthen their teams for the next season. Our research indicates that the players in this cluster demonstrated outstanding performance, helping coaches identify categories such as "efficient strikers" or "strong defenders." Therefore, this study can assist coaches or managers in selecting the most suitable players to meet the team’s needs for the upcoming season.
Implementasi BERT dan Cosine Similarity untuk Rekomendasi Dosen Pembimbing berdasarkan Judul Tugas Akhir Sabilillah, Ferris Tita; Winarno, Sri; Abiyyi, Ryandhika Bintang
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27791

Abstract

Challenges in completing final projects, which often contribute to delays in student graduation, are frequently due to a mismatch between students' research topics and the expertise of their supervisors. Therefore, a method is needed to address this misalignment in the final project process. This study aims to implement a Bidirectional Encoder Representations from Transformers (BERT) model and cosine similarity to recommend supervisors based on students' final project titles. The research dataset includes 3,723 research titles collected through web scraping from Google Scholar and ResearchGate, representing the expertise of 63 lecturers in the Informatics Engineering Program at Universitas Dian Nuswantoro. Data processing includes preprocessing to generate embedding vectors from lecturers' research titles, which are then compared with students' final project titles. Our findings indicate that the developed recommendation model achieves an accuracy of 90% in identifying relevant supervisors based on topic alignment between students' final project titles and lecturers' areas of expertise, as reflected in their publications. This result can make a significant contribution to supporting students in completing their final projects more efficiently and improving the quality of academic supervision by facilitating more appropriate supervisor selection.
Classification of Key and Time Signature in Western Musical Notation by using CRNN Algorithm with Bounding Box Soeroso, Dennis Adiwinata Irwan; Winarno, Sri; Luthfiarta, Ardytha; Aryanti, Firda Ayu Dwi
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6510

Abstract

This research seeks to employ the Convolutional Recurrent Neural Network (CRNN) algorithm to develop a method for classifying key and time signatures from sheet music images. The research design involved compiling a dataset of 285 sheet music images, which includes 15 types of key signatures and 19 types of time signatures. The methodology encompasses annotation using the bounding box technique, image preprocessing, and applying the CRNN model for classification using K-Fold Cross Validation because of the limited dataset. Then, the model is evaluated using the Multi Class Confusion Matrix and performance metrics. The primary findings of this study reveal that the developed model achieves 96% accuracy in key signature classification and 95% in time signature classification when utilizing bounding boxes. Conversely, the absence of bounding boxes substantially negatively impacted the accuracy of key signature classification, resulting in only a 58% accuracy rate. Time signature classification performed even worse, with an accuracy of just 19%. This research highlights the substantial accuracy enhancements achievable by incorporating bounding boxes. Therefore, we anticipate that this research will help singers, especially those in choirs, to understand and express music better using existing technologies while enhancing the accuracy of optical music recognition using the CRNN model.
Prediction of Catfish Yield To Fulfill Community Needs Using Multiple Linear Regression Algorithm Method Nurazizah, Syifa; Winarno, Sri
Devotion : Journal of Research and Community Service Vol. 3 No. 13 (2022): Special Issue
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/dev.v3i13.270

Abstract

Fishery is one sector that is important for human life, because most of human needs come from fisheries, one example is the need for catfish. Currently, there is a gap between crop yields and community needs, which is indicated by very high demand while yields are low. Therefore, this study was conducted to predict catfish yields using a linear regression algorithm so that community needs are met. The method used for the training process in this study uses Multiple Linear Regression. Multiple Linear Regression is an analysis conducted on the dependent variable / dependent variable and two or more independent or independent variables. In contrast to simple regression which only has one independent variable and one dependent variable. Predicting the size of the dependent variable using the independent variable data which is already known. The results of this study analyzed the value of Root Mean Squared Error, and Mean Absolute Error. The result of the line equation from the training data for catfish harvesting is yˆ = -310.6119 + 0.0759x1 + 0.0245x2 + 0.6104x3
Myopia Identification by Fundus Photo Image Classification Using Convolutional Neural Network Laksono, Giffari Ilham; Winarno, Sri
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10624

Abstract

The myopia is an increasing refraction of the eye in various populations and requires accurate early detection methods to minimize its effects. The study developed a convolutional neural network (CNN) prediction model (deep learning), an architectural superior in image processing. Eye image data is used as input to train models in order to identify visual characteristics that are linked to the myopia. Experiments have shown that the proposed model of CNN achieves high predictive accuracy in classifying the condition of the eyes of the canopy and non-myopia. This approach is potentially used as an effective diagnostic aid in the medical world, providing a quick and accurate solution to clinical decision-making.
Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model Cahya, Leno Dwi; Luthfiarta, Ardytha; Krisna, Julius Immanuel Theo; Winarno, Sri; Nugraha, Adhitya
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 3 (2023): Desember 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.290-298

Abstract

Sentiment and emotion analysis is a common classification task aimed at enhancing the benefit and comfort of consumers of a product. However, the data obtained often lacks balance between each class or aspect to be analyzed, commonly known as an imbalanced dataset. Imbalanced datasets are frequently challenging in machine learning tasks, particularly text datasets. Our research tackles imbalanced datasets using two techniques, namely SMOTE and Augmentation. In the SMOTE technique, text datasets need to undergo numerical representation using TF-IDF. The classification model employed is the IndoBERT model. Both oversampling techniques can address data imbalance by generating synthetic and new data. The newly created dataset enhances the classification model's performance. With the Augmentation technique, the classification model's performance improves by up to 20%, with accuracy reaching 78%, precision at 85%, recall at 82%, and an F1-score of 83%. On the other hand, using the SMOTE technique, the evaluation results achieve the best values between the two techniques, enhancing the model's accuracy to a high 82% with precision at 87%, recall at 85%, and an F1-score of 86%.
Deep Learning Factor Investing in the Indonesian Stock Market Atha Rohmatullah, Fawwaz; Alzami, Farrikh; Rakhmat Sani, Ramadhan; Novita Dewi, Ika; Winarno, Sri; Sulistyono, Teguh
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112549

Abstract

Traditional linear factor models often fail to capture the complex, non-linear dynamics of emerging stock markets. This research designs and validates a novel Recurrence Plot (RP) matrices with β-VAE deep learning methodology to discover non-linear investment factors within the Indonesian context. We demonstrate that this framework is a systematically superior "factor factory" compared to a linear RP with PCA baseline, discovering twice as many high-quality factors (Sharpe > 0.3) and generating 7-fold more alpha on average. A key finding is the model's ability to disentangle high-frequency predictive signals (identified by SHAP) from more valuable, low-frequency profitable trends (validated by backtesting). The champion factor from this process yields a robust annualized alpha of 6.65% with a minimal max drawdown of -7.73% from 2018 to 2025. This study concludes that the RP -> β -VAE approach is a robust and resilient framework for discovering safer, non-linear sources of return unexplained by conventional models.
Co-Authors Abiyyi, Ryandhika Bintang Adhitya Nugraha Al-Azies, Harun Alzami, Farrikh Andrean, Muhammad Niko Ardytha Luthfiarta Arga Retha, Helynda Mulya Ariansyah, M. Hafidz Aryanti, Firda Ayu Dwi Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Cahya, Leno Dwi Cahyani, Ailsa Nurina Erika Devi Udayanti Fikri Budiman Firmansyah, Gustian Angga Ganiswari, Syuhra Putri Guruh Fajar Shidik Harun Al Azies Ika Novita Dewi Indra Gamayanto Junta Zeniarja Kamarudin, Fatkhurridlo Pranoto Khoirunnisa, Emila Krisna, Julius Immanuel Theo Kurniawan, Defri Laksono, Giffari Ilham Maulani, Ahmad Alaik Megantara, Rama Aria Muhammad Naufal, Muhammad Nur Fitri, Esmi Pratama, Raffy Nicandra Putra Pratama, Rifky Ariya Pratiwi, Yunita Ayu Pulung Nurtantio Andono Putra, Permana Langgeng Wicaksono Ellwid Putri, Rusyda Tsaniya Eka Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rony, Zahara Tussoleha Sabilillah, Ferris Tita Sasono Wibowo Senata, Denny Soeroso, Dennis Adiwinata Irwan Sukamto, Titien Suhartini Sulistyono, Teguh Syifa Nurazizah, Syifa Wardhana, Faviola Proba Wijaya, Tan Nicholas Octavian Yudantiar, Mayang Arinda Zami, Farrikh Al