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All Journal Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Informatika dan Teknik Elektro Terapan International conference on Information Technology and Business (ICITB) JURNAL MEDIA INFORMATIKA BUDIDARMA JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Antivirus : Jurnal Ilmiah Teknik Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Journal of Information Systems and Informatics bit-Tech Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jifosi Nusantara Science and Technology Proceedings Jurnal Ilmiah Teknologi Informasi dan Robotika Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Sewagati
Yisti Vita Via
Universitas Pembangunan Nasional Veteran Jawa Timur
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Comparison of Recurrent Neural Network and Naive Bayes Algorithms in Identifying Stunting in Toddlers Sujayanti, Forentina Kerti Pratiwi; Via, Yisti Vita; Haromainy, Muhammad Muharrom Al
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.33946

Abstract

Stunting in toddlers is a health issue that affects their quality of life. This study aims to predict stunting status using three classification methods: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gaussian Naive Bayes. The dataset from Kaggle was split into 70% for training and 30% for testing to ensure optimal model evaluation. The RNN model was built with three hidden layers of 64 units each, while the LSTM model had four hidden layers with the same number of units. Both models utilized hidden states to capture temporal patterns and employed the tanh activation function to detect complex data patterns. The ADAM optimizer with a learning rate of 0.001 was applied to accelerate convergence. In contrast, the Gaussian Naive Bayes model used a simple probabilistic approach without temporal patterns, making it suitable for simpler datasets. Evaluation using accuracy and RMSE showed that LSTM achieved the highest accuracy (91%), followed by RNN (90%), though both exhibited signs of overfitting. Gaussian Naive Bayes attained 72% accuracy with stable performance. While LSTM and RNN effectively capture complex temporal patterns, they are prone to overfitting, whereas Gaussian Naive Bayes is suitable for initial implementation or simpler datasets, supporting early intervention for stunted toddlers.
Balinese Script Handwriting Recognition Using CNN and ELM Hybrid Algorithms Mas Diyasa, I Gede Susrama; Wijaya, Pandu Ali; via, Yisti Vita
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87524

Abstract

One of the foundational scripts used in Balinese culture is the Balinese script, known as “Aksara Bali”. In its writing, Aksara Bali follows specific rules regarding distinctive stroke shapes that must be carefully maintained to preserve authenticity and readability. This study proposes the use of a hybrid algorithm combining Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) to recognize handwritten Balinese script characters. The preprocessing stage includes dataset splitting, rescaling, data augmentation, batch size adjustment, and visualization of class distribution. The training stage utilizes the Adam Optimizer to enhance model accuracy. Using 1,691 images of various Balinese script characters, the dataset is divided into an 80:10:10 ratio for training, validation, and testing. Experimental results show that the best accuracy achieved is 91%, indicating that the CNN-ELM hybrid model effectively recognizes Balinese script characters.
Analisis Perbandingan Metode Algoritma C4.5 dan KNN dalam Prediksi Nilai Kebutuhan Gizi Ibu Hamil di Kecamatan Pandaan Miftahul Nuril Silviyah; Budi Nugroho; Yisti Vita Via
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i2.1171

Abstract

This study aims to compare the performance of the C4.5 algorithm and the K-Nearest Neighbor (KNN) method in predicting the nutritional needs of pregnant women. The research method involves six main stages: field data collection, dataset reading, basic data exploration, data preprocessing, predictive model development, and model evaluation using test data. The dataset was collected through a Google Form distributed to pregnant women in the Pandaan sub-district and then underwent a preprocessing phase to clean and prepare the data for further analysis. The C4.5 and KNN algorithms were built using the preprocessed data, and the complexity of each model was evaluated to determine their prediction accuracy. These methods were used to predict the nutritional requirements of pregnant women. The findings of the study indicate that the C4.5 algorithm achieved a higher accuracy rate of 95%, compared to 87.50% achieved by the KNN algorithm. Based on these results, it can be concluded that the C4.5 algorithm is more accurate and reliable for predicting the nutritional needs of pregnant women.
Random Forest – Deep Convolutional Neural Network Ensemble Model for Skin Disease Classification Kurniawan, Ananda Rheza; Via, Yisti Vita; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2528

Abstract

Skin diseases such as psoriasis, atopic dermatitis, and tinea are chronic conditions that significantly affect quality of life and require rapid and accurate classification to support early treatment. However, limited medical personnel and inadequate classification tools in various regions remain major challenges in handling these cases. This study proposes an automatic skin disease classification system based on digital images using an ensemble method that combines Deep Convolutional Neural Network (DCNN) and Random Forest (RF). The dataset used comprises 4,246 images categorized into four classes (psoriasis, atopic dermatitis, tinea, and normal skin), sourced from Kaggle and DermNet. Preprocessing steps include image resizing, normalization, and data augmentation, while hyperparameter tuning is conducted using Bayesian Optimization. The ensemble model applies a soft voting mechanism to integrate predictions from both DCNN and RF. Experimental results show that the RF-DCNN model achieves an accuracy of up to 84.35% in the 80:10:10 data split scenario, surpassing the performance of the conventional CNN model. These results suggest that the hybrid DCNN-RF approach enhances accuracy, stability, and generalization in skin disease classification. The proposed model holds strong potential for implementation in artificial intelligence-based clinical decision support systems, especially in regions with limited access to dermatology specialists. Future work is encouraged to explore more advanced architectures such as EfficientNet and Swin Transformer for further performance improvements.
Implementasi Vision Transformer untuk Klasifikasi Penyakit Pneumonia melalui Citra Chest X-Ray Maulana, Raihan Thobie Nabil; Via, Yisti Vita; Mandyartha, Eka Prakarsa
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 2 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i2.3599

Abstract

Pneumonia is a type of respiratory infection in the respiratory tract that is often caused by viruses or bacteria. Poor air quality in Jakarta's urban areas increases people's risk of developing pneumonia, acute respiratory infections (ARI), and asthma. 2019 showed that more than 740,180 children under the age of 5 died from pneumonia cases, about 14% of all early childhood deaths. In overcoming pneumonia, medical researchers have conducted many studies related to the problem of early diagnosis of pneumonia. One of the techniques to detect pneumonia is through chest x-rays that have been developed for classification. Vision Transformer (ViT) is one of the Deep Learning architectures developed specifically for image processing. The purpose of this study is to implement the classification task of pneumonia with ViT which is expected to help detect pneumonia early so that it can be treated faster and better. The results of the study show that the ViT model has good performance after applying several variations of augmentation, and is stable both in training and testing. at a small Learning Rate of 0.00001, it produces 80% accuracy for the case of pneumonia disease classification through Chest X-Ray Images.
Penerapan Metode RAD dalam Rancang Website Pemasaran Rumah Pada PT Bumi Lingga Pertiwi Affandi, Masfi Ulil; Via, Yisti Vita; Mumpuni, Retno
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 2 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i2.3735

Abstract

Industri properti terus berkembang seiring meningkatnya kebutuhan akan hunian yang layak. PT Bumi Lingga Pertiwi (BLP Property) berupaya meningkatkan kepercayaan konsumen melalui pengembangan situs web pemasaran yang dilengkapi Sistem Pendukung Keputusan (SPK) berbasis metode AHP-SMARTER. Penelitian ini menggunakan metode Rapid Application Development (RAD) untuk mempercepat proses pengembangan situs web dengan melibatkan pengguna secara aktif. Framework Laravel, React, dan MySQL digunakan untuk membangun sistem yang efisien dan terstruktur. Hasil implementasi menunjukkan bahwa situs web ini dapat menyajikan informasi properti secara interaktif serta memberikan rekomendasi hunian yang sesuai dengan preferensi pengguna. Pengujian menggunakan blackbox mendapatkan hasil yang sesuai dan berjalan sesuai dengan fungsionalitasnya. Dengan adanya situs web ini, BLP Property diharapkan dapat memperkuat citra positif perusahaan, meningkatkan pengalaman pengguna, dan mendukung pemasaran properti secara efektif.
Penerapan Algoritma K-Nearest Neighbor Dalam Klasifikasi Penyakit Daun Padi Menggunakan Ekstraksi HOG Yana, Baktiar Yudha; Via, Yisti Vita; Nurlaili, Afina Lina
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || 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.v6i1.13306

Abstract

Rice (Oryza sativa) is a strategic Indonesian food commodity that is susceptible to leaf disease attacks, causing decreased productivity and even crop failure. Conventional detection methods based on visual observation have limited accuracy and consistency, so an automated approach based on computer vision technology is needed for more effective early detection. This study applies the K-Nearest Neighbors (KNN) algorithm in rice leaf disease classification using Histogram of Oriented Gradients (HOG) feature extraction. A secondary dataset from Kaggle of 1,400 images covers four categories: Bacterial Leaf Blight, Brown Spot, Leaf Blast, and Healthy. The methodology includes image preprocessing (resize, augmentation, grayscale conversion, normalization), HOG feature extraction, and KNN classification with evaluation on a training-test data ratio of 85:15. The results show that KNN with k=2 achieves optimal performance at a ratio of 85:15 with an accuracy of 90.24%, a precision of 90.27%, a recall of 90.24%, an F1-score of 90.23%, and an efficient computational time of 3.34 seconds. The combination of HOG and KNN is proven to be effective for the automatic classification of rice leaf diseases with high accuracy and good computational efficiency.
Analisis Ekstraksi Fitur LBP, GLCM Dan HSV Untuk Klasifikasi Kualitas Cabai Rawit Menggunakan Xgboost ZAMAZANI, ZAIN MUZADID; Puspaningrum, Eva Yulia; Via, Yisti Vita
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || 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.v6i1.13307

Abstract

Cayenne pepper (Capsicum frutescens L.) is a horticultural commodity of high economic value, so determining its quality is an important factor in determining the selling price and suitability for consumption. So far, quality assessment is still mostly done manually, but this method tends to be subjective and less efficient. To overcome this, this research evaluates the quality classification of cayenne pepper based on digital image processing using the XGBoost algorithm with three types of features, namely Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), and Hue, Saturation, Value (HSV). The primary dataset used consists of 1,200 images of six quality classes (raw, undercooked, cooked, dry, rotten, and anthracnose). The methodology stages include pre-processing in the form of background removal, resizing, and data augmentation. Next, LBP, GLCM, and HSV feature extraction is carried out, then classification by dividing the test training data by 80:20. The test results show that the best configuration is obtained with the HSV feature, using learning rate parameters 0.1, n_estimators 100, and max depth 12, which produces accuracy (98.92%), higher than using GLCM (88.08%) or LBP (79.17%). These findings confirm that color information is more dominant than texture in supporting automatic quality classification of cayenne peppers.
IMPLEMENTASI ALGORITMA WEIGHTED TREE SIMILARITY DAN CONTENT BASED FILTERING DALAM PENCARIAN SKRIPSI Matondang, Natalia; Via, Yisti Vita; Akbar, Fawwaz Ali
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4807

Abstract

UPN “Veteran” Jawa Timur telah memiliki sebuah sistem repository untuk menyimpan semua data terkait skripsi yang telah diselesaikan oleh mahasiswa. Sistem tersebut dapat diakses secara online oleh seluruh mahasiswa UPN “Veteran” Jawa Timur. Namun terdapat kekurangan pada sistem tersebut, yaitu fitur pencarian yang belum dapat memberikan hasil yang relevan dengan input yang diberikan oleh pengguna. Oleh karena itu, penulis membuat sebuah sistem pencarian hasil penelitian skripsi agar mahasiswa dapat menemukan daftar judul yang relevan dengan topik yang ingin dicari oleh mahasiswa. Sistem menggunakan algoritma Weighted tree similarity dan Content based filtering agar hasil pencarian berorientasi pada atribut skripsi. Dilakukan pengujian pada sistem menggunakan recall dan precision dan mendapatkan hasil precision 74% dan recall 83%. Dengan demikian, sistem ini diharapkan mampu membantu mahasiswa untuk menemukan skripsi sesuai dengan topik yang diinginkan dan mengurangi peluang terjadinya plagiarisme atau kesamaan judul skripsi.
PENERAPAN HOLT-WINTERS UNTUK PERAMALAN HARGA BERAS DI PROVINSI JAWA TIMUR DENGAN PENDEKATAN TIME SERIES Isnaini, Frisda Dita; Via, Yisti Vita; Mandyartha, Eka Prakarsa
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4890

Abstract

Beras adalah makanan pokok mayoritas masyarakat Indonesia, dengan padi sawah sebagai komoditas utama. Pada 2024, harga beras mencapai Rp19.550 di seluruh Indonesia, termasuk Jawa Timur, karena masa paceklik. Pola pergerakan harga beras penting untuk diketahui guna membantu pengambil kebijakan dan petani dalam menjaga stabilitas harga. Penelitian ini bertujuan memodelkan peramalan harga beras di 20 wilayah Provinsi Jawa Timur dari 2017 – 2023 menggunakan Holt-Winters Exponential Smoothing, yang mempertimbangkan komponen level, tren, dan musiman. Hasil penelitian menunjukkan harga beras cenderung naik saat pergantian tahun dan menurun di pertengahan tahun. Pengujian dilakukan dengan K-Fold (k = 3 dan k = 5) dan menguji rentang parameter alpha, beta, dan gamma dari 0,1 – 0,9 dan 0,01 – 0,9. Parameter optimal ditemukan pada nilai alpha 0,9, beta 0,01, dan gamma 0,9 dengan k = 5. Model ini menghasilkan nilai error MAPE terbaik di Banyuwangi sebesar 0,03%.
Co-Authors Affandi, Masfi Ulil Akbar, Fawwaz Ali Alfath Daryl Alhajir Andreas Nugroho Sihananto Anggraini Puspita Sari Budi Nugroho Dhian Satria Yudha Kartika Dita Atasa Diyasa, I Gede Susrama Mas Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fetty Tri Anggraeny Intan Yuniar Purbasari, Intan Yuniar Isnaini, Frisda Dita Kurniawan, Ananda Rheza Mas Diyasa, I Gede Susrama Matondang, Natalia Maulana, Hendra Maulana, Raihan Thobie Nabil Miftahul Nuril Silviyah Muhammad Muharrom Al Haromainy Natalia Matondang Nurlaili, Afina Lina Pande Putu Sunaryang Yogam Prasanta Prayoga, Aji Putra Dwi Wira Gardha Yuniahans Retno Mumpuni Sadewa, Rahma Danu SALAMUN ROHMAN NUDIN Saputra, Dewa Raka Krisna Sari, Nabella Permata Shodiq, Mochammad Lukman Sufi Miftakhoneki SUGIARTO - Sujayanti, Forentina Kerti Pratiwi Syahrul Hidayat Tataq Distasianto Teguh Yulianto, Teguh Utami, Hapsari Wiji Wahyu Saifullah Jauharis Saputra Wahyu Syaifullah Jauharis Saputra Wijaya, Pandu Ali Yana, Baktiar Yudha Yudha K., Dhian Satria ZAMAZANI, ZAIN MUZADID