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Wirdayanti
Universitas Tadulako
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Implementation of ResNet-50-Based Convolutional Neural Network For Mobile Skin Cancer Classification Asriani, Asriani; Lapatta, Nouval Trezandy; Nugraha, Deny Wiria; Amriana, Amriana; Wirdayanti, Wirdayanti
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9696

Abstract

The skin is one of the most important parts of the human body, serving vital functions such as protecting internal organs from injury, shielding against direct bacterial exposure, regulating body temperature, and more. However, the skin is also susceptible to diseases, one of which is skin cancer. Skin cancer can be extremely dangerous if not treated promptly, as it can lead to death. Therefore, early detection is crucial. This study proposes a technology-based solution by classifying skin cancer using a convolutional neural network (CNN) with a ResNet50 architecture implemented into a mobile application via a REST API using Flask. The HAM10000 dataset, consisting of 10,015 skin lesion images across seven classes, was used for model training. Various testing scenarios were conducted to determine the optimal parameter combination. The best results were achieved with an accuracy of 83.84%, precision and recall of 83%, and an F1-score of 83%, using a training data configuration of 70%, dropout of 0.4, and a batch size of 64. The model implemented in this Android application can perform early detection of skin cancer quickly, practically, and easily accessible to the general public, though healthcare professionals must still supervise it. However, although this model can assist users in making early predictions, the prediction results from this model are only a tool for early detection and do not replace clinical diagnosis by professional medical personnel.2) Figure 8 shows the display for taking pictures through the gallery or camera. Users can choose the image they want to upload from the gallery or the camera to be analysed and predicted by the model.
Faktor-Faktor Yang Berhubungan Dengan Kejadian Diabetes Melitus di Puskesmas Kecamatan Tomoni Kabupaten Luwu Timur Nirwan, Nirwan; Warsid, Aisyah; Wirdayanti, Wirdayanti; Sari, Rafika; Semmagga, Nuraeni
Jurnal Promotif Preventif Vol 6 No 6 (2023): Desember 2023: JURNAL PROMOTIF PREVENTIF
Publisher : Fakultas Kesehatan Masyarakat Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47650/jpp.v6i6.1041

Abstract

Penyebaran diabetes melitus (DM) menjadi permasalahan yang meluas karena tingginya prevalensinya, dampak finansial yang signifikan dan Tingkat morbiditas yang besar. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang berhubungan dengan kejadian diabetes melitus di Puskesmas Kecamatan Tomoni Kabupaten Luwu Timur. Penelitian ini merupakan penelitian kuantitatif dengan rancangan penelitian yang digunakan adalah survei analitik dengan menggunakan pendekatan Cross Sectional. Populasi dalam penelitian ini adalah seluruh pasien yang berkunjung melakukan pengobatan di Puskesmas Kecamatan Tomoni sebanyak 2.444 orang dengan sampel berjumlah 96 orang. Cara pengambilan sampel menggunakan teknik Accidental Sampling dan dikumpulkan dengan menggunakan kuesioner. Analisis data penelitian menggunakan uji Chi-Square dengan tingkat kepercayaan 95%. Hasil uji statistik menunjukkan bahwa ada hubungan umur dengan kejadian diabetes melitus (p-value = 0,000), ada hubungan keturunan/riwayat keluarga dengan kejadian diabetes melitus (p-value = 0,000), ada hubungan pola makan dengan kejadian diabetes melitus (p-value = 0,000). Disarankan kepada tenaga kesehatan agar meningkatkan upaya promotif dan preventif untuk meningkatkan kewaspadaan pada masyarakat tentang penyakit diabetes melitus.
Usability and User Experience Evaluation on Extracurricular Website (SINEMA) Implementation using SUS and UEQ Methods Wirdayanti; AKBAR, MUHAMMAD; Sabarudin Saputra; Deni Luvi Jayanto; Sri Khaerawati Nur; Nouval Trezandy; Bakri
Information Technology International Journal Vol. 3 No. 2 (2025): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v3i2.57

Abstract

The rapid integration of web-based platforms in higher education highlights the importance of usability and user experience in supporting students’ extracurricular activities. This study evaluates the usability and user experience of the Student Extracurricular Information System (SINEMA) developed at Tadulako University. A total of 99 respondents participated, selected from a population of 5,581 active users through Slovin’s formula. Two standardized instruments were applied: the System Usability Scale (SUS) to capture global usability perceptions and the User Experience Questionnaire (UEQ) to assess six dimensions of user experience. The SUS results indicate a mean score of 76.06, which falls within Grade B and the “Good” category, exceeding the global benchmark. This suggests that the system is generally usable, although certain respondents reported minor challenges requiring further improvement. The UEQ results show that Perspicuity (1.94), Dependability (1.94), and Stimulation (1.93) achieved the “Excellent” category, reflecting clarity, reliability, and engagement. Meanwhile, Attractiveness (1.65), Efficiency (1.58), and Novelty (1.72) were rated “Good,” highlighting positive perceptions but also opportunities for optimization. Overall, the findings demonstrate that SINEMA effectively supports extracurricular management with satisfactory usability and strong user experience. The study contributes novelty by integrating SUS and UEQ for comprehensive evaluation within a higher education extracurricular context. Recommendations include enhancing efficiency and novelty to elevate user satisfaction and system adoption.
Analisis Sentimen Terhadap Kinerja Awal Pemerintahan Menggunakan IndoBERT Dan SMOTE Pada Media Sosial X Ihalauw, Sahron Angelina; Trezandy Lapatta, Nouval; Wiria Nugraha, Deny; Wirdayanti; Ar Lamasitudju, Chairunnisa
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2957

Abstract

Social media platform X has become a key channel for expressing public opinion on political issues, including evaluating the early performance of the government. The first 100 days of an administration are a strategic period to assess policy direction and public perception. This study aims to apply and evaluate the IndoBERT model for sentiment analysis of Indonesian-language tweets discussing the 100-day performance of the Prabowo–Gibran administration, as well as to assess the impact of using the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance. A total of 15,027 tweets were collected through API crawling and processed through several stages: preprocessing, labeling using the InSet Lexicon, data splitting, and fine-tuning IndoBERT. Two scenarios were tested — without SMOTE and with SMOTE oversampling. The results show that both models achieved the same overall accuracy of 87%, but performance varied across sentiment classes. The model without SMOTE performed better in the positive class with 93% precision, whereas the SMOTE-applied model improved performance in the neutral class (F1-score increased from 70% to 71%; recall from 69% to 71%) and in the negative class (precision increased from 88% to 90%). Considering the balance across classes, the SMOTE-based model was selected as the final model and implemented into a Streamlit application for interactive sentiment analysis. This study expands the application of IndoBERT in the Indonesian political domain by combining the lexical InSet approach with SMOTE oversampling — a combination rarely applied in Indonesian political sentiment analysis. The findings highlight the importance of data balancing strategies in improving transformer-based model performance on imbalanced datasets. Future research is encouraged to explore alternative balancing methods, expand training data, and test other transformer variants to enhance accuracy and generalization.
Implementation of Long Short-Term Memory Algorithms on Cryptocurrency Price Prediction with High Accuracy on Volatile Assets Nursiana Zasqia, Andi Nirina; Laila, Rahmah; Trezandy Lapatta, Nouval; Yazdi Pusadan, Mohammad; Santi, Dessy; Wirdayanti
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2422

Abstract

Cryptocurrencies have emerged as one of the most popular digital assets, characterized by high volatility, which presents a significant challenge in forecasting their price movements accurately. This study aims to implement the Long Short-Term Memory (LSTM) algorithm to predict the prices of selected cryptocurrencies, including Bitcoin (BTC), Binance Coin (BNB), Ethereum (ETH), Dogecoin (DOGE), Solana (SOL), and Shiba Inu (SHIB). The LSTM model is trained using the Adam optimizer and employs early stopping to mitigate overfitting. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results indicate that the LSTM model achieves strong predictive accuracy for relatively low-volatility assets such as Dogecoin and Solana, with R² scores of 0.9795 and 0.9523, respectively. In contrast, its performance declines when applied to highly volatile assets like Bitcoin and Binance Coin. The findings also suggest that LSTM performs best in short-to-medium-term forecasts (7 to 30 days), but shows limitations in long-term predictions. This study contributes to the field by demonstrating the applicability of LSTM in financial forecasting and highlighting its strengths and constraints across different volatility profiles. Practically, the findings can assist traders and financial analysts in making data-driven decisions by applying LSTM models for more reliable short-term predictions, while emphasizing the need to integrate external market factors to enhance long-term forecast accuracy.
Strategi Pemasaran dan Tantangan Penjualan di Era Digitalisasi : Studi Kasus UMKM Cemilan Keripik Tempe Kenzi di Jl. Danau Maninjau LK. IV Kelurahan Padang Merbau Kota Tebing Tinggi Aziti, Tria Meisya; Saragih, Linda Hertaty; Wirdayanti, Wirdayanti
Community Service Progress Vol. 4 No. 2 (2025): Community Service Progress Edisi Desember 2025
Publisher : STIE Bina Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70021/csp.v4i2.277

Abstract

This study aims to analyze and propose effective digital marketing strategies for tempeh producers, who often still rely on traditional marketing methods, and to help MSMEs adapt to changing consumer behavior in the digital era. Tempeh product marketing strategies in the digital era generally discuss the importance of adopting digital technology by tempeh Micro, Small, and Medium Enterprises (MSMEs) to expand market reach and increase competitiveness. It also identifies various sales strategies and challenges they face.
Co-Authors ., Rezki A. Masse, Burhannudin Abul Azis, Muhammad Dzaky Aisyah Warsid Aldy, Moh.Fajrin Sigit Amriana Amriana Amriana Amriana Andi Hendra Anita Ahmad Kasim ARDIANSYAH ARDIANSYAH Ardiansyah, R Ar Lamasitudju, Chairunnisa Asriani Asriani, Asriani Bakri Budiman, Wahyu Chairunnisa Ar. Lamasitudju Deni Irawan, Deni Deni Luvi Jayanto Deny Wiria Nugraha Dessy Santi Dwi Shinta Angreni Hajra Rasmita Ngemba Ihalauw, Sahron Angelina Indah Safitri Kade Dwi Arsana, I Gusti Ngurah Agung Laala, Jonathan Zebina Laila, Rahma Masykur, M Miftah Miftah Miftah, Miftah Mohammad Yazdi Pusadan Mufthi, Mufthi Muhammad Akbar MUHAMMAD BAKRI Nadhar, Muhammad Ngurah, Gusti Nirwan Nirwan Nouval Trezandy Lapatta Noviantika, Noviantika Nursalim, Moh. Agung Nursiana Zasqia, Andi Nirina Qofifa, Sitti Nurlaili Rafika Sari Rahmah Laila Reinaldy Mansa, Jeremy Rinianty, Rinianty Rizka Ardiansyah Rizqa Raaiqa Bintana Ryfial Azhar, Ryfial Sabarudin Saputra Sahril Sahril Saragih, Linda Hertaty Semmagga, Nuraeni Septiano Anggun Pratama Sri Khaerawati Nur Suryani Putri, Fadiah Syahrullah Syahrullah Syahrullah Syaiful Hendra Tria Meisya Aziti Wahyu Setiawan Wongkar, Noel Marcell Jonathan Yanti, Alda Yudhaswana, Yuri Yuri Yudhaswana Joefrie Yusuf Anshori