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All Journal CommIT (Communication & Information Technology) Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-SAKTI (Jurnal Sains Komputer dan Informatika) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Indonesian Journal of Electrical Engineering and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Iskandar Fitri, Iskandar
Bina Nusantara University Master of Information Technology
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy

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Analisis Sentimen Terhadap Kebijakan Pemerintah Tentang Larangan Mudik Hari Raya Idulfitri di Indonesia Tahun 2021 Menggunkan Metode Naïve Bayes Aziz, Abdul; Fauziah, F; Fitri, Iskandar
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.381

Abstract

Social media as a place to access and disseminate information has grown very rapidly, one of which is Twitter. Twitter, as a place for information flow, is a rich source for seeking public opinion and sentiment analysis. Twitter in this study was used as a source to obtain data about the 2021 homecoming in Indonesia. The purpose of this study is to determine public satisfaction with government policies regarding the ban on going home in Indonesia in 2021. The data to be processed is Indonesian-language tweets, the keywords are #mudik and #diarangmudik, the length of data collection is 1 week, with lots of data generated as many as 1000. Sentiment analysis in this study using the Naïve Bayes Classification method. The steps in this study are first crawling Twitter data which is then stored in csv format, second preprocessing which consists of tokenizer, case folding, cleansing and stop removal, third Naive Bayes classification which will be carried out after going through the Pre-processing stage, where the results of the classification tweets tend to be positive or negative or neutral. The results of this study obtained an accuracy of 56.52% with each positive sentiment value of 62.28%, negative sentiment as much as 46.72% and neutral sentiment as much as 66.50%.
Development of ResNet-18 architecture to lesion identification in breast ultrasound images Andini, Silfia; Sumijan, Sumijan; Fitri, Iskandar
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1236-1248

Abstract

Breast ultrasound (USG) is widely used for early breast cancer detection, but challenges such as noise, low contrast, and resolution limitations hinder accurate lesion identification. This study proposes a modified residual network-18 (ResNet-18) architecture for breast lesion segmentation, aimed at improving detection accuracy. The methodology involves preprocessing steps including red green blue (RGB) to Grayscale conversion, contrast stretching, and median filtering to enhance image quality. The modified ResNet-18 model introduces additional convolutional layers to refine feature extraction. The proposed model was trained and validated on 30 breast ultrasound images, with evaluation metrics including accuracy, sensitivity, and specificity. Experimental results indicate that the modified architecture outperforms the baseline model, achieving an average accuracy of 0.97093, sensitivity of 0.90056, and specificity of 0.97705. Validation by a radiology specialist confirms the model’s clinical relevance. These findings suggest that the enhanced ResNet-18 model has the potential to assist radiologists in more accurately identifying breast lesions. Future research should focus on expanding the dataset, integrating multi-modal imaging, and optimizing model generalizability for real-time clinical applications. The study contributes to advancing artificial intelligence (AI)-driven breast cancer diagnostics, supporting early detection, and improving patient outcomes.
Co-Authors Abdul Aziz Abdullah, Mohammad Aldinugroho Abdullah, Syamsun Nahar Agus Iskandar Albaar Rubhasy Aldisa, Rima Tamara Alhadi, Alhadi Andrianingsih Ardiansyah, Aldi Aris Gunaryati, Aris Arundy, Vicky Agnes Azamta, Firnas Benrahman Burhani, Hanif Rahman Cahyadi, Iman Deny Hidayatullah Devita, Retno Endah Tri Esti Handayani Ernawati Ernawati Fauziah Fauziah Fauziah, F Finny Fitry Yani Furqaansyah, Yoga Gunaryarti, Aris Gunaryati, Agus Haryono, Muklis Hasbulloh, Hasbulloh Herlan, Agus Humansyah, Hanggita Ahmad Iriandi, Aditya Nuryudha Irsandi, Johar Saputra Jayanti, Riza Dwi Khuzaifi, Ahmad Lesmana, Delles Mardia, Ahmad Muslih Mardiani, Eri Muchlis, Muhammad Muttaqin Muhammad Khairul Muhammad Ridwan Muhammad, Fauzan Nehe, Kurniadin Novi Dian Nathasia Nurhayati Nurhayati Oka Saputra Pangestu, Danang Aji Prayogi, Bayu Setyo Putra, Muhammad Aldi Aditia Putra, Wisnu Andika Rahman, Ben Rini Nuraini, Rini Rohadi, Chairil Sari Ningsih Sari Ningsih Shofi, Niam Sholihatih, Ira Diana Silfia Andini, Silfia Situmorang, Daud Sopiyan, Muhamad Sumarhadi, Sumarhadi Sumijan Sumijan Supriyanto Supriyanto Suryadi Suryadi Syafii, Muhammad Faiz Syafrika Deni Rizki, Syafrika Deni Syiamtoni, Eky Pambudi Takyudin, Takyudin Taslim, Denis Utomo, Budi Tri Wilianto Wilianto Yauma, Alfath Yudhi Setiawan Yuhandri Yuhandri, Yuhandri Zayana, Muhammad Rafiq Zikrullah, Alhadi Putra