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All Journal Jurnal TAM (Technology Acceptance Model) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JUTIM (Jurnal Teknik Informatika Musirawas) JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi Jurnal Informatika Global Jurnal Abdimas Mandiri TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Digital Teknologi Informasi Jurnal Pengabdian kepada Masyarakat JIKI (Jurnal Ilmu Komputer dan Informatika) IJAIRTec Aptekmas : Jurnal Pengabdian Kepada Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika Green Intelligent Systems and Applications Informatics and Enginering Dedication Jurnal Teknologi Sistem Informasi Klik - Jurnal Ilmu Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Jurnal Pengabdian Publikasi Hasil Pengabdian Kepada Masyarakat. Jurnal Ilmiah Teknik Informatika dan Komunikasi MDP Student Conference Jurnal Rekayasa Sistem Informasi dan Teknologi JISCOMP (Journal of Information System and Computer) INOVTEK Polbeng - Seri Informatika International Journal of Artificial Intelligence and Robotic Technology (IJAIRTec) Jurnal TAM (Technology Acceptance Model)

Hartati, Ery

Universitas Multi Data Palembang

Author-ID : 2742746

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Klasifikasi Sampah Daur Ulang Menggunakan Dukungan Vektor Machine Dengan Fitur Pola Biner Lokal Leonardo, Leonardo; Yohannes, Yohannes; Hartati, Ery
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Garbage is one of the problems that always arise in Indonesia and even in the world. Increasingly, the production of waste is increased along with the increase in population and consumption. Therefore, need a prevention to stop wasting or producing garbage through recycle. This research do garbage recycle classification of cardboard, glass, metal, paper and plastic by using Local Binary Pattern (LBP) texture feature extraction methode and Support Vector Machine (SVM) as classification methode. For examination technic and dataset distribution is using K-Fold Cross Validation methode type Leave One Out (LOO). From examination result had been done were using fold 5 until fold 10. Polynomial kernel get highest accuracy result from every fold used with mean point 87.82%. Based on SVM classification examination result whether linear kernel, polynomial nor gaussian by using fold 5 until fold 10. The best accuracy point for cardboard garbage is 96.01%. For glass garbage, the best accuracy point is 90.62%. Then, metal garbage get the best accuracy point 89.72%. While paper garbage with highest accuracy point 96.01%. And plastic garbage with highest accuracy point 87.64%.

Identifikasi Aksara Katakana Menggunakan Convolutional Neural Network Arsitektur LeNet Winardi, Eric Agustian; Hartati, Ery
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 2 (2022): April 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Penelitian ini mengangkat topik terkait dengan identifikasi menggunakan objek aksara katakana. Pada penelitian ini menggunakan beberapa Optimizer, namun belum diketahui penggunaan Optimizer dan Pooling Layer yang memiliki tingkat pengenalan yang terbaik dalam penelitian tersebut. Penelitian ini menggunakan Optimizer Adam, SGD dan RMSprop, kemudian Pooling Layer menggunakan Average dan Max Pooling. Data yang digunakan sebanyak 2070 citra yang terdiri dari 920 citra latih, 690 citra validasi dan 460 citra uji dengan total 46 kelas. Metode pengenalan menggunakan Convolutional Neural Network arsitektur LeNet, dengan input berupa citra yang telah melalui proses preprocessing menggunakan metode otsu dari citra aksara katakana. Skenario pengujian terdiri dari 6 skenario dengan Optimizer dan Pooling Layer yang berbeda-beda. Tingkat akurasi tertinggi didapatkan pada skenario pertama menggunakan Adam dan Average Pooling sebesar 90% dengan hasil pengenalan sebanyak 414 dari 460 data uji. Hasil dari penelitian ini dapat digunakan sebagai referensi pada penelitian lanjutan dengan metode ataupun objek yang sama.

PENGGUNAAN ALGORITMA RANDOM FOREST DALAM KLASIFIKASI BUAH SEGAR DAN BUSUK Santoso, Felix; Hartati, Ery
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v3i1.3404

Buah-buahan merupakan salah satu makanan yang sering dikonsumsi oleh berbagai kalangan umur karena sumber berbagai mineral, vitamin dan serat pangan. Untuk memperoleh manfaat yang terdapat pada buah, masyarakat harus mengonsumsi buah yang segar dan belum busuk. Secara fisik, kesegaran buah dapat dilihat karena tanda-tanda yang ada pada buah segar atau buah busuk mudah diamati.LBP (Local Binary Pattern) adalah metode ekstraksi fitur tekstur yang sederhana,namun efisien dalam mempresentasikan ciri tekstur, sedangkan HSV (Hue, Value dan Saturation) merupakan ruang warna yang cocok untuk mengidentifikasi warna-warna dasar yang akan digunakan dalam penelitian sebagai warna identifikasi cahaya dan bisa menoleransi perubahan intensitas cahaya. Penelitian ini menggunakan public dataset buah segar dan buah busuk. Proses di mulai dari resize menjadi ukuran 300 x 300 pixel dan selanjutnya dilakukan ekstraksi fitur LBP dan dilanjutkan dengan ekstraksi fitur HSV. Hasil ekstraksi fitur LBP dan HSV di gunakan sebagai input klasifikasi menggunakan algoritma random forest dengan nilai n_estimator 500,1000,1500,dan 2000. Hasil pengujian menggunakan algoritma random forest menghasilkan nilai Accuracy tertinggi sebesar 95,92% dengan nilai n_estimator 2000.

Pemanfaatan Wodershare Filmora Dalam Meningkatkan Kemampuan Sumber Daya Manusia Di Dinas Sosial Provinsi Sumatera Selatan Hartati, Ery; Ricoida, Desy Iba; Fransen, Lisa Amelia
FORDICATE Vol 1 No 1 (2021): November 2021
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Lack of skills in the ability to edit videos and has become anobstacle for the Human Resources department at the Social Service ofSouth Sumatra Province in presenting information that is attractiveand interesting to the public. Therefore, training in the use ofWodershare Filmora software is needed to improve the skills of theHuman Resources department in video editing. From the trainingprovided, it was seen that the participants were enthusiastic inparticipating and practicing directly the software used and increasingtheir understanding of using the Wodershare Filmora software. It ishoped that in the future the training can be carried out on a scheduledbasis so that the material presented can be more in-depth and theability of the trainees to increase.

Pelatihan Digital Marketing Dengan Market Place Toko Talk Pada Usaha Kuliner RM Pondok Kolam Sangabut Hartati, Ery; Gasim, Gasim; Inayatullah, Inayatullah; Michael, Michael
FORDICATE Vol 1 No 2 (2022): April 2022
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Tri Dharma perguruan tinggi adalah kewajiban bagi setiap dosen yang harus dijalankan setiap semester dalam tahun ajaran. Adapun Tri Dharma Perguruan Tinggi meliputi pengajaran, penelitian, dan pengabdian kepada masyarakat. Pengabdian kepada masyarakat memberikan esensi penting secara sosial agar memberikan manfaat secara langsung bagi masyarakat umum. Selain itu juga memberikan pengalaman bagi dosen yang bersangkutan agar dapat melakukan inovasi pengetahuan. Pengabdian kepada masyarakat ini dilaksanakan di RM Kuliner dan Pemancingan Sangabut Kayu Agung. Pengabdian ini dilaksanakan oleh dosen lingkungan Universitas MDP dengan persetujuan dari Rektor Universitas MDP. Usaha yang akan hadir yaitu para Usaha Kuliner untuk mengikuti Workshop Pemanfaatan Digital marketing dan Pembuatan Laporan Keuangan Bagi Usaha Usaha Kuliner. Dalam pelatihan ini nanti diharapkan para usaha dapat melakukan pembuatan Digital marketing dan Pembuatan Laporan Keuangan guna membantu dalam kegiatan operasional usaha mereka dan selain itu juga dapat lebih meningkatkan kompetensi para Usaha Kuliner RM Pondok Kolam Sangabut di Kota Kayu Agung.

Perangkat Lunak untuk Memprediksi Harga Cryptocurrency Menggunakan Algoritma Support Vector Regression Cerwyn Asyraq; Ery Hartati
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1699

Cryptocurrency is a digital asset that continues to gain popularity due to its decentralized nature and potential for profit, but its high price volatility poses significant challenges for investors. This study aims to develop a price prediction software for Ethereum cryptocurrency using the Support Vector Regression (SVR) algorithm. Historical price data were collected, preprocessed, normalized using MinMaxScaler, and divided into training and testing datasets. The SVR model was optimized using the GridSearch method to obtain the best hyperparameters. Model performance was evaluated using MAE, RMSE, and MAPE, resulting in 199.61 (7.60%), 227.57 (8.66%), and 8.64%, respectively, indicating good predictive accuracy. The software was developed with the Flask framework and tested using Blackbox testing and stress testing via Locust, showing stable system performance with efficient response time. The developed software can serve as a decision-support tool for investors to predict Ethereum prices over various time ranges from 1 to 30 days or more

Klasifikasi Fraud Pada Transaksi Finansial Melalui Integrasi TabTransformer dan Oversampling Generatif CTGAN Prana Welas Sukma, Tangguh; Hartati, Ery
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9056

Extreme class imbalance in the BankSim dataset (1.2% fraud) is a major hurdle to building reliable detection systems. This study proposes the integration of the TabTransformer architecture with the Conditional Tabular GAN (CTGAN) oversampling technique to address majority class bias. Data quality evaluations indicate that CTGAN produces synthetic data with an overall quality score of 90.05% and a column pair correlation trend of 91.63%. Experimental findings prove the proposed model delivers superior performance, achieving an F1-Score of 85.34%, a Recall of 81.39%, and a Balanced Accuracy of 90.64%. These results significantly outperform the SMOTE technique, which recorded an F1-Score of 83.99% but suffered from probability calibration failure with an extreme optimal threshold of 0.98. In contrast, the CTGAN scenario demonstrates efficient decision threshold stability at 0.46. Validation through SHAP analysis confirms that engineered variables such as merchantRisk, custStepDiff, and amtZScoreByCat provide dominant contributions to model predictions. This research concludes that the synergy of the Data-Centric AI paradigm facilitates the creation of robust, precise, and highly accountable classification models for digital banking protection within financial transaction systems.

Klasifikasi Penyakit Alzheimer menggunakan CNN dengan pretrained VGG19 dan SMOTE berdasarkan Citra MRI Otak md, Ramanda; Hartati, Ery
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 2 (2025): December 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i2.15122

Early detection of Alzheimer's disease is crucial for effective treatment, and the use of brain MRI images has become a common method for diagnosis. However, many previous studies have faced challenges in addressing class imbalance in their datasets, leading to lower accuracy for minority classes. This study aims to address this issue by using a pretrained CNN architecture, VGG19, combined with the SMOTE method to address class integration and improve classification accuracy. This study contributes by introducing SMOTE to the Alzheimer's MRI image dataset to achieve a more balanced class distribution, which has not been fully explored in previous studies. The evaluation results show that the classification accuracy reaches 95%, higher than previous studies using VGG-19 with an accuracy of 77.66%. These results confirm that the use of VGG19 with SMOTE produces better performance, especially in addressing class representation, which is a key contribution of this study. This research has the potential to be applied in more efficient and accurate automated image-based detection systems, especially for the early diagnosis of Alzheimer's disease.

Enhancing Generalization of Tomato Leaf Disease Classification via TDR Model and Field-Conditioned Data Augmentation Fernando Feliansyah; Ery Hartati
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/tvgfx074

Tomato leaf diseases significantly affect agricultural productivity, particularly when detection systems are deployed under real-field conditions characterized by illumination variation, background clutter, and image noise. Although deep learning-based models have achieved high accuracy on laboratory datasets such as PlantVillage, their generalization performance often degrades when applied to real-world environments. This study proposes a lightweight CNN-based tomato leaf disease recognition model, referred to as the TDR-Model, combined with field-conditioned data augmentation strategies. The proposed model integrates MobileNetV3 with Convolutional Block Attention Module (CBAM) and Omni-Dimensional Dynamic Convolution (ODC) to enhance feature representation while maintaining computational efficiency. Field-conditioned augmentation using the Albumentations library to simulate real-world visual variations during training. The model is evaluated on the real-world tomato set consisting of 10 classes and 885 leaf images. Experimental results show that the proposed model achieves an overall test accuracy of 82.94%, with precision, recall, and F1-score of 85.06%, 83.04%, and 83.03%, respectively. Furthermore, the model requires only 3.47 million parameters, 0.23 GFLOPs, and an average inference time of 5.15 ms, making it suitable for real-time and resource-constrained agricultural applications. These results indicate that the proposed approach effectively balances accuracy and efficiency for practical tomato leaf disease detection.

Classification of Tomato Fruit Ripeness Level Using Convolutional Neural Network–Support Vector Machine Based on Digital Image Saputra Edika, Nelson; Hartati, Ery
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: 10.35314/1r0wh197

Tomato ripeness classification is an important task in post-harvest quality management, as the ripeness level directly influences taste, shelf life, and market value. Conventional ripeness assessment methods based on manual visual inspection are inherently subjective and often yield inconsistent results. To address this limitation, this study proposes an image-based tomato ripeness classification model using a hybrid Convolutional Neural Network–Support Vector Machine (CNN–SVM) approach. In the proposed model, a pretrained ResNet-50 architecture is employed as a fixed feature extractor to derive deep visual representations, while a Support Vector Machine with a Radial Basis Function kernel is utilized for final classification. The model is evaluated using a publicly available tomato image dataset, with the analysis limited to unripe and ripe categories. Image preprocessing procedures include resizing, normalization, and data augmentation, followed by an 80:20 train–test split strategy. Experimental results demonstrate that the proposed CNN–SVM model achieves strong and balanced performance, with an accuracy of 96.56%, a weighted precision of 96.80%, a recall of 96.56%, and an F1-score of 96.57%. These findings indicate that integrating deep feature extraction with an SVM classifier provides an effective and robust solution for tomato ripeness classification, particularly under limited data conditions.

Co-Authors ., Dewa Adrian Chandra Akhfir, Muhammad Fadly Ukhrowi Amarullah, Rendy Ambrosius Dwi Cahyadi Andreas, Kevin Aneke Windari Ardiansyah, Aldi Ariansyah, Nova Awalludin, Nur Bertnas Valentino, Calvin Budi, Raden George Samuel Candra candra Cerwyn Asyraq Chaesa, Linus Ardel Chandra, Kelvin William Christy, Christy Deka Putra Pamungkas Derry Alamsyah Desy Iba Ricoida Djunaidi, Sherdian Eka Puji Widiyanto Fathimah Azzahra Fatimah Nadia FELICIA Felix Santoso Ferdian Indrahadi Fernando Fernando Feliansyah Fernando Fernando, Fernando Firnando, Januar Franko, Billy Fransiska, Julita gasim, Gasim Graciela, Michelle Hafiz Irsyad Hakiki, Muhammad Anugrah Hebert, Hocwin Inayatullah, Inayatullah Intan Sanu Ivander Destian Luis Jennifer Jocelyn Jennifer Velensia Santoti Jeovanni Wong Jumhari Jumhari Kelly, Angel Keristin, Usnia Wati Kesuma, Dorie P. Kevin Antonio Khairani, Siti Kotan, Jendraja Husin Kusuma, Dorie P. Leonardo Leonardo Lisa Amelia M Rifqi Virgiansyah M. Kurniawan, M. Maria Elana Maulana, Muhammad Ishaq Md, Ramanda Michael Michael Muhammad Maisep Muhammad Nirraca Mutia, Silvi Nataliatara Nataliatara Nicholas Edison Nicholas, Nicholas Nirraca, Muhammad Nurrahman, Wahyu Aji Oktaviani, Ayu Sri Peter Reynard Susanto Prana Welas Sukma, Tangguh Prasetyo, Zavier Billy Putra Darmansius, Albertus Dwi Andhika Putra Ganda Dewata Qois Al Qorni Renaldo, Florence Reza Ardana Richie Jonathan Chaniago Ricko Andreas Kartono Ricky Ricky Rikky, Rikky Sahpira, Mulia Saputra Edika, Nelson Saputra, Ade Rocky Saputra, Riganda Sasongko, Randie Se, Abd Rosyiid Selvie Selvie Sihombing, Mecha Bella Permata Steven Hartanto Sudiadi Sudiadi Sudiadi Sudiadi Suluh Arif Wibowo Tan, Handy Christianto Tandoballa, Lucky Tanzil, Surya Pratama Teo Yulio Sihotang Umar Muhdhor Umi Karolina Vasco Dee Gamma Bororing Verdy Verrino Adityya Virginia, Callista Widyakusuma, Rafael Lois Wijaya, Frisky Wilyanto, Nicholas Winardi, Eric Agustian Yogie Prakoso Yohannes Yohannes Yulistia Yulistia

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