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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Teknologi dan Sistem Komputer SISFOTENIKA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Informasi dan Komputer JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informatika dan Komputer ABSYARA: Jurnal Pengabdian Pada Masyarakat Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal Teknologi Sistem Informasi MDP Student Conference GEMBIRA (Pengabdian Kepada Masyarakat) AT-TAKLIM: Jurnal Pendidikan Multidisiplin INOVTEK Polbeng - Seri Informatika
Tinaliah, Tinaliah
Universitas Multi Data Palembang
Religion Agriculture, Biological Sciences & Forestry Humanities 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP INVESTASI KEUANGAN DI INDONESIA MENGGUNAKAN METODE NAIVE BAYES Andreas Danny Agus Wahyudi; Tinaliah
AT-TAKLIM: Jurnal Pendidikan Multidisiplin Vol. 2 No. 9 (2025): At-Taklim: Jurnal Pendidikan Multidisiplin (Edisi September)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/at-taklim.v2i9.725

Abstract

Financial investment is important for Indonesian people to prepare for their future financially. In this digital era, social media such as Twitter have become popular platforms for sharing opinions and views on various topics including financial investments. By leveraging the data available on Twitter, sentiment analysis can be used to understand user views and opinions regarding financial investments in Indonesia. The Naive Bayes method can be used to perform sentiment analysis on Twitter data by utilizing probability theory to classify tweets with positive, negative or neutral views about financial investment in Indonesia. The amount of tweet data is unbalanced, so it is necessary to do SMOTE over-sampling so that the dataset is balanced and do the testing using k-fold validation so that you can see the confision matrix and get the values for accuracy, precision, recall, and f1-score. Based on the sentiments obtained from Twitter social media, it shows that Twitter social media users have positive sentiments towards financial investment in Indonesia with a total number of positive sentiments of 426 data from a total of 1000 tweet data. Unbalanced data affects the classification results, namely an accuracy of 45% with the SMOTE up-sampling method and an accuracy of 89% without using the SMOTE up-sampling method.
Klasifikasi Lesi Benign Dan Malignant Pada Rongga Mulut Menggunakan Arsitektur ResNet50 Tinaliah, Tinaliah; Elizabeth, Triana
JATISI Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.6947

Abstract

It is very important to protect the human oral cavity to avoid various oral problems, one of which is tumors and oral cancer. Cell growth in the oral cavity is divided into benign oral cavity tumors (benign), precancerous lesions, and oral cavity cancer (malignant). Image classification of benign and malignant lesions can help to determine whether cells in the oral cavity are benign or malignant. CNN is a type of neural network that can be used to extract features from an image. In this research, image classification of benign and malignant lesions will be carried out by applying the ResNet50 architecture to the CNN method. The dataset used is the Oral Image Dataset, which has two classes, namely the benign class and the malignant class. Testing is carried out using testing data from each class using the Adam and SGD optimizers. Based on the test results, it can be concluded that ResNet50 can classify images of benign and malignant lesions well using the Adam optimizer with an accuracy value of 94%.
Classification of Diabetic Retinopathy Using ShuffleNet V2 and Real-ESRGAN with CLAHE Image Enhancement Edison, Nicholas; Tinaliah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/g1xj7p28

Abstract

Diabetic retinopathy (DR) is a microvascular complication of diabetes that can lead to blindness if not detected and treated early. Manual DR grading from fundus images is time-consuming and highly dependent on expert availability, motivating the need for automated and efficient decision-support systems. This study proposes a lightweight DR severity classification model using ShuffleNet V2 combined with a preprocessing pipeline consisting of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Real-ESRGAN-based super-resolution. Unlike prior works that mainly employ these enhancement techniques with deeper or computationally expensive networks, this study explicitly investigates their synergistic integration with ShuffleNet V2 to improve lesion visibility while preserving computational efficiency for resource-constrained environments. Experiments conducted on the APTOS 2019 dataset demonstrate that the proposed combination significantly improves classification performance, achieving a best accuracy of 90.70%, with balanced precision, recall, and F1-score when optimized using Adam. Comparative analysis with the SGD optimizer further reveals a trade-off between accuracy and inference speed. The results confirm that combining CLAHE and Real-ESRGAN with ShuffleNet V2 offers an effective and efficient solution for automated diabetic retinopathy grading, highlighting its suitability for large-scale screening and low-resource clinical deployment
Assessing the Impact of Image Preprocessing on Convnext Performance for Waste Classification Destian Luis, Ivander; Tinaliah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/fsa3at15

Abstract

Waste has become an increasingly urgent environmental issue in everyday life. The waste is constantly increasing due to population growth, urbanization, and consumption. The increasing amount of waste needs more intelligent systems to help with the management of waste, especially with the sorting of waste. Unfortunately, the absence of the public's awareness of the importance of waste management has led to the ineffective collection of waste. Thus, there is a need for classifying the waste into technological systems based on various waste types. This research has computing waste types using ConvNetX. The research methodology is based on the collection and preprocessing of data that includes different image enhancement techniques such as CLAHE and bilateral filtering. This study employed the ‘Garbage Classification Dataset’ found on Kaggle. The dataset is split into 80% of it as training data, 10% of it as testing data, and the last 10% of it as validation data. The ConvNeXt model was trained using one of the training sets after the data was split and was subsequently measured using the validation and test sets for the training of the model. This research analyzed the effects of image preprocessing by using a baseline, which was no preprocessing (Scenario 1), and then using preprocessing (Scenario 2). The results from the experiments showed Scenario 2 had a higher accuracy of 94% compared to the baseline of 90%. The use of CLAHE and bilateral filtering positively impacted the F1 score by increasing it to Glass (96%) and Plastic (92%) and having a full recall (100%) for Metal. Scenario 2 resulted in a total training time of 20.86 minutes, and Scenario 1 was 11.83 minutes, which means that Scenario 2 had a lower computational efficiency. Nevertheless, the additional time was well spent for the considerable consistency improvement in the classification of all categories. This makes it evident that substantial image preprocessing is necessary for the model to be able to generalize and classify images with complex visual details.
Application of EfficientNet Deep Learning with Wiener Filter for Freshwater Fish Disease Image Classification Setiawan, Christofer Evan; Tinaliah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/k1xeb958

Abstract

Challenges pertaining to the timely and accurate diagnosis of diseases in freshwater fish have adversely impacted the productivity of the aquaculture industry. Image classification using deep learning techniques has the potential to overcome such challenges. However, this potential has not been realized due to such problems as image noise, motion blur, and small dataset sizes. Most prior studies in this area employ the same Convolutional Neural Network (CNN) architectures and, while using the same or similar techniques generic to the studies, preprocess the images. The focus of this study is to compare and benchmark the image classification performance of the EfficientNet architectures (B0 to B7) using the Wiener filter as a preprocessing technique for the classification of diseases in freshwater fish. The experiments used a publicly available dataset of 1,750 images of seven diseases in fish, while maintaining identical training parameters to yield sixteen different experimental configurations. Metrics such as accuracy, precision, recall, and F1 score were exercised while evaluating model performance. The data show that medium-scale architectures surpass both smaller- and larger-sized variants. The optimal performance was achieved by EfficientNet-B4 and Wiener Filter with an accuracy of 94.89%, a precision of 95.15%, a recall of 94.92%, and an F1-score of 94.89%. The results confirm that preprocessing with a Wiener filter improves performance on classification tasks using medium-sized models and further elucidate the applicable value of the model developed in this study in aquaculture and its related interventions.
Classification of Indonesian Batik Patterns Using Convolutional Neural Network with MobileNetV3 Architecture Aldi, Aldi Ardiansyah; Tinaliah, Tinaliah
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3205

Abstract

Indonesia has a diversity of ethnic groups that give rise to cultural richness, one of which is batik as a national identity. According to the Great Dictionary of the Indonesian Language, batik is a patterned cloth made by applying wax onto the fabric and processed in a certain way. Batik is an Indonesian cultural heritage with a variety of motifs that reflect regional identity and philosophical values. However, public understanding of the range of motifs is still limited, so technological support is needed for automatic identification. Although batik has become a symbol of national culture, the public's knowledge about the types and meanings of its motifs remains limited. This study develops a system for classifying Indonesian batik motifs using a Convolutional Neural Network with MobileNetV3 Small, MobileNetV3 Large, and MobileNetV2 architectures based on transfer learning. The dataset consists of 3,000 batik images with 20 motif classes from public sources, processed through image resizing to 224×224 pixels, augmentation (rotation, flip, zoom, and random brightness), and splitting into training, validation, and test sets with proportions of 80%, 10%, and 10%. Evaluation was conducted using accuracy, precision, recall, and F1 score. The results showed that all architectures achieved accuracy above 88%, with the best values exceeding 99%. MobileNetV3 Large consistently maintained accuracy up to 99% in several configurations and proved to be the most stable architecture, whereas MobileNetV2 reached a maximum accuracy of 99.33% at a learning rate of 0.0001. Therefore, MobileNetV3 Large is recommended as the primary architecture for batik motif recognition applications.
Software Development for Swimmer Performance Prediction System Based on Physical Characteristics using XGBoost Tanzil, Surya Pratama; Tinaliah, Tinaliah; Widi, Anugerah
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12176

Abstract

Swimmer performance assessment in Indonesia still largely depends on coaches’ intuition, which may lead to subjective decisions and inconsistencies in training program planning, particularly in environments where frequent changes in coaches and sports administrators occur. The lack of structured and data-driven performance assessment tools further limits the continuity and objectivity of athlete development. This study aims to develop a web-based system capable of predicting swimmers’ performance potential by estimating race times based on physical characteristics using the XGBoost model. The proposed system is designed to support coaches in identifying athlete performance potential in a more objective and data-driven manner. Model evaluation results indicate that the XGBoost model achieved an R² value of 0.9190, demonstrating a very high level of prediction accuracy, with an average prediction time of 7.036 seconds. Software testing results confirm that the system operates as intended and is able to present prediction outputs in the form of estimated swimming time, performance percentage, and performance classification into four categories: Very High, High, Medium, and Low. Furthermore, usability evaluation using the USE method yielded excellent results, with an average score of 88.16%.
Klasifikasi Genre Musik Menggunakan CNN Dengan Arsitektur Resnet-50 Dan Gradient Boost LightGBM Alessandro, Roberto; Tinaliah, Tinaliah
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3458

Abstract

The rapid growth of digital music has driven the need for accurate and efficient automated music genre classification systems. This study evaluates a hybrid approach that integrates the ResNet-50 architecture as a feature extractor through transfer learning and LightGBM as a classifier. Using the GTZAN dataset represented as Mel-spectrograms, the research compares the effectiveness of hyperparameter optimization using Random Search and Grid Search methods. Based on performance evaluation, the hybrid scenario optimized with Grid Search yielded the best performance with an accuracy of 81.20%, outperforming the Random Search method. Nevertheless, the overall experimental results reveal that the end-to-end ResNet-50 model still provides superior performance compared to the hybrid approach. This indicates that the deep features from ResNet-50 are highly representative for separating genre classes, such that the addition of an external ensemble classifier does not yield significant improvements, although the hybrid approach still offers valuable empirical insights as a stable alternative model.Keywords: Convolutional Neural Network; ResNet-50; LightGBM; Mel-Spectogram; Klasifikasi Genre Musik;AbstrakPertumbuhan pesat musik digital mendorong kebutuhan akan sistem klasifikasi genre musik otomatis yang akurat dan efisien. Penelitian ini mengevaluasi pendekatan hibrida yang mengintegrasikan arsitektur ResNet-50 sebagai pengekstraksi fitur melalui teknik transfer learning dan LightGBM sebagai classifier. Menggunakan dataset GTZAN yang direpresentasikan dalam bentuk Mel-spectrogram, penelitian ini membandingkan efektivitas optimasi hyperparameter menggunakan metode Random Search dan Grid Search. Berdasarkan evaluasi kinerja, skenario hibrida dengan optimasi Grid Search terbukti menghasilkan kinerja terbaik dengan akurasi 81,20%, mengungguli metode Random Search. Kendati demikian, hasil eksperimen secara keseluruhan mengungkapkan bahwa model ResNet-50 end-to-end masih memberikan performa yang lebih unggul dibandingkan pendekatan hibrida. Hal ini mengindikasikan bahwa fitur mendalam dari ResNet-50 sudah sangat representatif untuk memisahkan kelas genre, sehingga penambahan classifier eksternal tidak memberikan peningkatan signifikan, meskipun pendekatan hibrida tetap menawarkan wawasan empiris penting sebagai model alternatif yang stabil. 
Co-Authors Abdul Rahman Aldi, Aldi Ardiansyah Alessandro, Roberto Amri Amri Andreas Danny Agus Wahyudi Bautista, Christian Chandra Saputra Cyndi Monica Deky Berniawan Derry Alamsyah Destian Luis, Ivander Dicky Pratama Dicky Pratama Edison, Nicholas gasim, Gasim Hafiz Irsyad Inayatullah Inayatullah Inayatullah, Inayatullah Johannes Petrus Lucretia, Jolyn Molavi Arman Niko Pirnando, Hendra Nata Novan Wijaya Pratama, Samuel Effendi Putri, Agnes Anastasia Putri, Anisah Dhiyaa Azzahra Setiawan, Christofer Evan Susanto, Rafael Ivan Tanzil, Surya Pratama Triana Elizabeth, Triana Wahyu Mustika Anggara Widi, Anugerah Wilcent Yoannita Yoannita, Yoannita