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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JURNAL NASIONAL TEKNIK ELEKTRO Jurnal Pendidikan Informatika dan Sains Jurnal CoreIT JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Infomedia DoubleClick : Journal of Computer and Information Technology JuSiTik : Jurnal Sistem dan Teknologi Informasi Komunikasi CSRID (Computer Science Research and Its Development Journal) JURNAL SURYA ENERGY Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JISKa (Jurnal Informatika Sunan Kalijaga) INTI Nusa Mandiri Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JURMATIS : Jurnal Manajemen Teknologi dan Teknik Industri Jurnal Generic Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Arcitech: Journal of Computer Science and Artificial Intelligence Digital Transformation Technology (Digitech) MDP Student Conference Journal of Embedded Systems, Security and Intelligent Systems Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Riau Jurnal Teknik Informatika Scientific Journal of Informatics IPSSJ
Dedy Hermanto
Universitas Multi Data Palembang
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Physics Public Health Social Sciences Other

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Penentuan Epochs Hasil Model Terbaik: Studi Kasus Algoritma YOLOv8 Jonathan, Jasen; Dedy Hermanto
Digital Transformation Technology Vol. 4 No. 2 (2024): Periode September 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i2.4640

Abstract

Salah satu pengembangan machine learning yaitu deep learning merupakan salah satu metode inti dalam artificial intelligence yang sedang berkembang dengan pesat, dikarenakan kemampuannya dalam mempelajari informasi dalam jumlah besar. Salah satu cabang dari deep learning adalah computer vision, dan Convolutional Neural Network (CNN) yang merupakan metode yang paling banyak digunakan untuk melakukan pemrosesan citra. YOLOv8 merupakan salah satu algoritma yang menggunakan CNN yang telah dimodifikasi sebagai dasar, YOLOv8 merupakan algoritma open-source yang paling banyak digunakan dikarenakan menghasilkan hasil pengenalan objek yang akurat, cepat, dan mudah untuk di implementasikan. Proses pelatihan model dari YOLOv8 membutuhkan perangkat yang cukup memadai dengan jumlah epochs yang ditentukan secara manual. Penelitian ini bertujuan untuk mengetahui jumlah epoch yang dibutuhkan dalam membuat model YOLOv8 sesuai dengan kriteria yang di tentukan pada penelitian ini. Pelatihan akan dilakukan dengan menggunakan 50 epochs, 100 epochs, 150 epochs, 200 epochs, 250 epochs, dan 300 epochs. Pelatihan akan di jalankan dengan menggunakan dataset citra bibit ikan lele yang terdiri dari 753 gambar bibit ikan lele yang telah di anotasikan. Pelatihan dijalankan dengan menggunakan CPU Ryzen 5 4600H. Berdasarkan dari hasil pelatihan didapatkan bahwa 50 epochs memiliki waktu pelatihan tercepat dengan hasil yang kurang baik. Hasil terbaik terdapat pada 200-300 epochs dengan rata-rata precision sebesar 96% dengan waktu pelatihan yang cukup lama.
Deteksi Penyakit Daun Teh Berdasarkan Citra Menggunakan Deep Learning Saputra, Andreas; Hermanto, Dedy
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 2 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i2.5657

Abstract

Tea plant (Camellia sinensis) originates from China and is one of the most widely consumed beverages in the world. Tea plants are vulnerable to leaf diseases such as Tea Leaf Blight, Tea Red Leaf Spot, and Tea Red Scab, which can reduce the quality and productivity of the harvest. Manual disease identification is still commonly used, but this method has many limitations, such as dependence on farmers’ experience and inaccuracy in early detection. This study aims to apply the YOLOv11 algorithm as an object detection method to automatically, quickly, and accurately detect four classes of tea leaf conditions (three diseases and one healthy). The dataset used consists of 3,960 high-resolution tea leaf images that have undergone segmentation, augmentation, and normalization processes. The research was carried out through image preprocessing, YOLOv11 model training, and model performance evaluation using precision, recall, F1-score, and mean Average Precision (mAP) metrics. The results of tea leaf disease detection using YOLOv11 achieved an average precision of 97.2%, recall of 98.2%, mAP@0.5 of 98.8%, and mAP@0.5:0.95 of 95.5%. This model can be used to help farmers identify tea leaf diseases more quickly and reduce the risk of crop yield losses.
Prediksi Kelayakan Kredit Nasabah Dengan Penerapan Cost-Sensitive Random Forest Lucretia, Jolyn; Hermanto, Dedy
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3401

Abstract

The high risk of credit default and class imbalance in customer data pose major challenges in developing accurate credit scoring systems. This condition causes predictive models to be biased toward the majority class, thereby reducing the ability to detect high-risk borrowers. This study develops a credit scoring model for imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE) and cost-sensitive Random Forest with hyperparameter optimization via GridSearchCV. The dataset consists of 32,581 customer records. Experimental results show that the best configuration with n_estimators = 200 achieves a cross-validation F1-score of 0.813750. On the test data, the model attains an accuracy of 0.927267, precision of 0.911458, recall of 0.738397, and an F1-score of 0.815851, indicating improved and more balanced detection of high-risk borrowers.Keywords: Random Forest; SMOTE; Cost-sensitive learning; GridSearchCV AbstrakTingginya risiko gagal bayar kredit dan ketidakseimbangan kelas pada data nasabah menjadi tantangan utama dalam pengembangan sistem credit scoring yang akurat. Kondisi ini menyebabkan model prediksi cenderung bias terhadap kelas mayoritas sehingga kemampuan deteksi debitur berisiko menjadi kurang optimal. Penelitian ini mengembangkan model credit scoring pada data tidak seimbang menggunakan Synthetic Minority Oversampling Technique (SMOTE) dan Cost-Sensitive Random Forest dengan optimasi hyperparameter GridSearchCV. Dataset yang digunakan berjumlah 32.581 data nasabah. Hasil pengujian menunjukkan konfigurasi terbaik dengan n_estimators = 200 menghasilkan F1-score validasi silang sebesar 0,813750. Pada data uji, model mencapai akurasi 0,927267, precision 0,911458, recall 0,738397, dan F1-score 0,815851, yang menunjukkan peningkatan kemampuan deteksi debitur berisiko secara lebih seimbang.Kata kunci: Random Forest; SMOTE; Cost-sensitive learning; GridSearchCV.
Klasifikasi Indikasi Penyakit Jantung Pada Manusia Menggunakan Algoritma Fuzzy KNN Kgs. M. Ammar Yazid; Dedy Hermanto
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/dv8p2p83

Abstract

The high mortality rate from heart disease in Indonesia is largely caused by delayed diagnosis, which stems from low public awareness regarding early screenings. Limited access to accurate health information exacerbates this situation, creating a critical gap between disease onset and medical intervention. This research proposes the development of a classification model for the early detection of heart disease using the Fuzzy K-Nearest Neighbor (Fuzzy KNN) algorithm. This method was chosen for its ability to indicate whether an individual has heart disease and to manage the uncertainty within symptom data, aiming to provide an initial recommendation that can increase public awareness. The model's performance was rigorously evaluated using k-fold cross-validation to ensure valid results. The findings show a significant trade-off. At a k-value of 9, the model achieved a recall of 0.64. However, this was accompanied by a precision of 0.23 and an average accuracy of approximately 0.75. Nevertheless, Fuzzy KNN shows significant potential as an early detection tool due to its strong capability in minimizing the risk of missed patients (false negatives).
Klasifikasi Risiko Diabetes Menggunakan Support Vector Machine Berbasis Feature Selection dan Hyperparameter Tuning Nova Ariansyah; Dedy Hermanto
Riau Jurnal Teknik Informatika Vol. 5 No. 1 (2026): Maret 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i1.4301

Abstract

Diabetes Mellitus merupakan penyakit metabolik kronis dengan prevalensi yang terus meningkat secara global. Deteksi dini risiko diabetes menjadi langkah strategis untuk mencegah komplikasi serius seperti penyakit kardiovaskular dan gagal ginjal. Penelitian ini bertujuan mengimplementasikan algoritma Support Vector Machine (SVM) dalam klasifikasi risiko diabetes serta menganalisis pengaruh Feature Selection dan Hyperparameter Tuning  terhadap kinerja model. Dataset yang digunakan berjumlah 100.000 data pasien yang diperoleh dari Kaggle  dengan distribusi kelas yang tidak seimbang antara pasien diabetes dan non-diabetes. Preprocessing dilakukan menggunakan Min-Max Normalization. Seleksi fitur diterapkan menggunakan metode ReliefF dan Mutual Information. Evaluasi model menggunakan 10-Fold Cross Validation untuk meminimalkan bias estimasi performa. Hyperparameter Tuning  dilakukan menggunakan Optuna dengan optimasi parameter C, gamma, dan degree pada kernel Polynomial dan Radial Basis Function. Hasil eksperimen menunjukkan bahwa Hyperparameter Tuning  meningkatkan recall kelas Diabetes dari 0,67 menjadi 0,94 serta meningkatkan akurasi keseluruhan dari 94,76% menjadi 98,90%. Jumlah false negative menurun dari 4.851 menjadi 915 kasus. Temuan ini menunjukkan bahwa optimasi parameter dan seleksi fitur berperan penting dalam meningkatkan sensitivitas model terhadap kelas minoritas pada dataset medis yang tidak seimbang.
Implementasi Arsitektur YOLOv11 untuk Deteksi Penyakit Daun Tebu Daniel Daniel; Dedy Hermanto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 3 (2026): JULY 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i3.6138

Abstract

Sugarcane (Saccharum officinarum L.) plays an important role in the national sugar industry, but its productivity has declined due to leaf diseases such as mosaic, red rot, rust, and yellow leaf. Manual identification is often inefficient, especially for farmers in remote areas. This study proposes a YOLOv11 architecture for the detection and classification of sugarcane leaf diseases based on digital images, with performance analysis compared to previous deep learning models and the effect of image augmentation on accuracy. The dataset from the Sugarcane Leaf Disease Dataset on Kaggle includes 2,521 images with five classes (healthy, mosaic, red rot, rust, yellow). The data was processed through preprocessing, division (80% training, 10% validation, 10% testing), and augmentation (rotation, translation, flip). The results show an average precision of 97.2%, recall of 98.2%, mAP@0.5 of 98.8%, and mAP@0.5:0.95 of 95.5%, proving the effectiveness of YOLOv11 in accurate and fast detection.
Classification of molly ornamental fish using VGG16 architecture Adikara Alif Nurrahman; Dedy Hermanto
Jurnal Pendidikan Informatika dan Sains Vol. 14 No. 2 (2025): Jurnal Pendidikan Informatika dan Sains
Publisher : Universitas PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/saintek.v14i2.9889

Abstract

Molly fish (Poecilia sphenops) is one of the ornamental fish species that is widely cultured. This study aims to develop a classification system for ornamental molly fish using the VGG16 model, trained with on-the-fly data augmentation techniques (flip, zoom, rotation, and translation). The dataset used consists of 1,750 images of molly fish, divided into seven different species: Black, Blue Electric, Calico, Dalmatian, Golden Black, Platinum, and Sunkist. Data augmentation is performed dynamically during the training process without saving the transformation results, aiming to increase data diversity and help the model recognize patterns more accurately. The experimental results show that the optimal combination of parameters, namely a learning rate of 1e-5, a batch size of 32, and 50 epochs, achieved a training accuracy of 97.80%, validation accuracy of 99.61%, and test accuracy of 99.62%. Additionally, very high precision (99.63%), recall (99.62%), and F1-Score (99.62%) values were achieved. Although there were minor classification errors in the "Black" class predicted as "Sunkist," these errors were minimal and did not affect the overall results. This study shows that with the right parameter settings and the use of augmentation techniques, the VGG16 model can provide classification results with fairly high accuracy for molly ornamental fish. This model also has the potential to be applied in the ornamental fish aquaculture industry, particularly in image-based automatic detection systems.
PELATIHAN PENGGUNAAN SISTEM MONITORING KUALITAS AIR KOLAM DI DINAS PERIKANAN OGAN KOMERING ILIR SUMATERA SELATAN Abdul Rahman; Dedy Hermanto; Mulyati Mulyati; Inayatulah Inayatulah
FORDICATE Vol 5 No 2 (2026): April 2026
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v5i2.15744

Abstract

Inefficient water monitoring remains a challenge for aquaculture in Ogan Komering Ilir. This community service initiative aims to equip local Fishery Service staff with the skills to operate digital water quality monitoring systems. Using socialization, tool demonstrations, and hands-on practice, participants were trained to accurately monitor crucial parameters such as pH, temperature, and oxygen levels. The results indicate a strengthening of technical capacity in adopting sensory systems for pond oversight. By transitioning from manual to digital methods, staff can now detect water quality degradation early to prevent farmer losses. This program is expected to drive the modernization of the fishery sector in South Sumatra by optimizing the supervisory functions of the relevant agency.
Klasifikasi Motif Kain Jumputan Palembang Menggunakan Metode CNN dengan Arsitektur Resnet-50 Muhammad Mauladi; Dedy Hermanto
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.v5i2.15310

Abstract

This study develops an automated classification system for Palembang jumputan textile motifs based on computer vision to address inter-motif pattern similarities that often challenge non-expert users and hinder the digital documentation of textile cultural heritage. Unlike traditional textile studies that typically employ generic Convolutional Neural Networks (CNNs), this research applies transfer learning using the ResNet-50 architecture on a primary dataset consisting of five motif classes: lilin, titik 7, titik 9, bunga tabur, and akoprin daun. The dataset is divided into training, validation, and testing sets, followed by preprocessing and image augmentation to enhance data variability. The model is trained with learning rate tuning, and the best configuration achieves a training accuracy of 95.57%, a validation accuracy of 87.33%, and a testing accuracy of 88%. Evaluation using a classification report and confusion matrix indicates excellent performance for the titik 9 and bunga tabur motifs, with precision and recall values approaching 1.00, while misclassifications still occur in the lilin motif due to visual similarity. These results confirm the effectiveness of ResNet-50 for jumputan motif classification and support cultural preservation through faster and more consistent motif identification.
Klasifikasi Tingkat Kematangan Buah Kelapa Sawit Menggunakan EfficientNet-B7 Valen Julyo Armando Davincylin; Dedy Hermanto
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3399

Abstract

Determining the ripeness level of oil palm fresh fruit bunches (FFB) is a crucial factor affecting oil yield and quality; however, field assessment is still largely performed manually and is prone to subjectivity and errors. This study aims to develop an image-based classification system for oil palm fruit ripeness using a Convolutional Neural Network (CNN) with the EfficientNet-B7 architecture. The proposed method applies transfer learning and fine-tuning on the public dataset “An Ordinal Dataset for Ripeness Level Classification in Oil Palm Fruit Quality Grading,” which contains 4,728 images across five ripeness classes. The methodology includes image preprocessing, normalization, and data augmentation techniques such as rotation, flipping, and zooming. The model is trained using the Adam optimizer and evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results show that the proposed model achieves an accuracy of 93.23% with stable performance across all classes. These findings indicate that EfficientNet-B7 is effective for oil palm fruit ripeness classification and has strong potential to be implemented as a decision-support system for more objective and consistent harvest timing.Keywords: EfficientNet-B7; Convolutional Neural Network; Ripeness classification AbstrakPenentuan tingkat kematangan tandan buah segar (TBS) kelapa sawit merupakan faktor penting yang memengaruhi rendemen dan kualitas minyak sawit, namun proses penilaiannya di lapangan masih dilakukan secara manual sehingga rentan terhadap subjektivitas dan kesalahan. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi tingkat kematangan buah kelapa sawit berbasis citra digital menggunakan algoritma Convolutional Neural Network (CNN) dengan arsitektur EfficientNet-B7. Metode yang digunakan meliputi transfer learning dan fine-tuning pada dataset publik “An Ordinal Dataset for Ripeness Level Classification in Oil Palm Fruit Quality Grading” yang terdiri dari 4.728 citra dalam lima kelas kematangan. Tahapan penelitian mencakup preprocessing citra, normalisasi, serta augmentasi data berupa rotasi, flip, dan zoom. Model dilatih menggunakan optimizer Adam dan dievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model mencapai accuracy sebesar 93,23% dengan performa klasifikasi yang stabil pada seluruh kelas. Berdasarkan hasil tersebut, EfficientNet-B7 terbukti efektif untuk klasifikasi tingkat kematangan buah sawit dan berpotensi diterapkan sebagai sistem pendukung keputusan dalam penentuan waktu panen yang lebih objektif.  
Co-Authors Abdul Rahman Adikara Alif Nurrahman Ahmad Rizky Ahmad, Prima Anam, M. Syaiful Andreas Saputra, Andreas Daniel Daniel Deka Putra Pamungkas Deris Stiawan Dermawan, Sepri Desy Iba Ricoida Dina Lestari Putri Eka Puji Widiyanto Eldo Suryadi Emi Yuliati Evan Susanto Feby Ardianto Hafiz Irsyad Iis Iis Pradesan Inayatulah Inayatulah Indri Ramayanti Ivan Ricardo Jiwanata Jonathan, Jasen Joseph Alberto Jovian Aristo Kgs. M. Ammar Yazid Kornelia* Kornelia* Kynta, Diva Putri Laksono, Ivan Luthfi Latius Hermawan Lucretia, Jolyn Meilinda, Meilinda Muhammad Fahmi Muhammad Mauladi Muhammad Rizky Pribadi Mulyati Mulyati Negara, Edi Surya Nova Ariansyah Pibriana, Desi Qois Al Qorni Richardo Anggara Rizqie, Muhammad Qurhanul Rusbandi rusbandi rusbandi, rusbandi Saputra, Darwin Sepri Dermawan Shinta Puspasari Suluh Arif Wibowo Syakurah, Rizma Adlia Theng, Arifin Therino Elevan, Rivaldo Thingkilia Finnatia Husin Tri Hartati Utami Valen Julyo Armando Davincylin Valentino, Calvin Bertnas Vannes Wijaya Wijaya, Frisky Yuniarti, Erliza