Article Per Year (5 Year)
p-Index From 2021 - 2026
5.31
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Teknika SITEKIN: Jurnal Sains, Teknologi dan Industri CESS (Journal of Computer Engineering, System and Science) Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang Malikussaleh Journal of Mathematics Learning (MJML) Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Sains dan Informatika : Research of Science and Informatic JPLED
Jasril Jasril
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

Published : 53 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : bulletin of computer science research

 1 
Klasifikasi Sentimen Pada Dataset yang Terbatas Menggunakan Algoritma Convolutional Neural Network M Ridho Saputra; Surya Agustian; Jasril Jasril; Novriyanto Novriyanto
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.613

Abstract

This study aims to analyze public responses to the appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) using a sentiment classification approach based on the Convolutional Neural Network (CNN) algorithm. The primary dataset consists of 300 Indonesian-language tweets categorized into three sentiment classes: positive, negative, and neutral. The limited size of the training data presents a major challenge, as it can hinder the model's ability to generalize. To address this issue, data augmentation was carried out by incorporating external datasets with Covid-19 and Open Topic themes. The preprocessing stages include text cleaning, normalization, and tokenization. The developed CNN model utilizes a layered architecture and applies regularization techniques such as L2 and dropout to reduce the risk of overfitting. Accuracy, F1-score, precision, and recall were used as performance evaluation metrics. Experimental results show that the best performance was achieved when the Kaesang and Covid-19 datasets were combined, yielding an F1-score of 0.62 on the validation set and 0.51 on the test set. These findings indicate that adding external data can improve classification accuracy, even under limited data conditions. This study contributes to the development of deep learning-based sentiment classification methods for Indonesian-language texts.
Penggunaan Convolutional Neural Network NASNetLarge Dalam Klasifikasi Citra Daging Babi dan Sapi M Alfandri Aqilah; Jasril Jasril; Suwanto Sanjaya; Fitri Insani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.666

Abstract

The adulteration of beef with pork is a serious issue in Indonesia, particularly for Muslim consumers who are required to consume halal products. According to a Kompas (2020) report, a case of meat adulteration involving 100 kilograms of mixed meat sold as beef was discovered in Tangerang City. This practice not only violates religious laws but also poses threats to public health and consumer trust. To address this challenge, this study adopts a deep learning approach using NASNetLarge for the classification of pork, beef, and mixed meat images. Unlike previous research that utilized EfficientNet-B2 and achieved an accuracy of 98.23%, this study’s NASNetLarge approach produced a comparably competitive accuracy of 98.03%. The dataset used consists of 1,932 images sourced from the Kaggle platform, which were processed through preprocessing and augmentation stages. The data were then split into two distribution scenarios: the entire dataset and a balanced class dataset with 90:10 and 80:20 ratios. Evaluation results show that the best parameter combination was achieved in the first scenario with a 90:10 ratio using augmented images, a learning rate of 0.001, 128 dense units, and the Adam optimizer. The model achieved the highest accuracy of 98.03%, with a precision of 98.63%, recall of 98.40%, and an F1-score of 98.50%. These results indicate that NASNetLarge is effective in accurately and consistently classifying meat images. Image augmentation significantly improved model performance, and the 90:10 data ratio yielded more optimal results compared to 80:20. These findings have the potential to support food surveillance efforts by enabling rapid and accurate detection of meat adulteration.
Prediksi Saham Berdasarkan Data Teknikal Serta Fundamental Menggunakan Algoritma XGBoost Yoga Nur Pradana; Fitri Insani; Jasril Jasril; Siti Ramadhani
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1126

Abstract

The capital market has an important role in the economy as a means of investment and fundraising, with banking sector stocks being one of the main contributors to market capitalization in Indonesia. However, the investment decision-making process often faces obstacles in the form of limited investors' ability to comprehensively analyze fundamental and technical data, as well as irrational behavior that causes decisions to be less than optimal. This conditions encourage the need for a more objective and data-driven approach to help predict stock price movements. The results of the model evaluation on the test data showed excellent performance: BCA obtained a MAPE of 2.8% and an R² of 0.9488; BNI with MAPE 3.06% and R² 0.8863; Bank Mandiri with a MAPE of 4.70% and R² 0.9114; and BRI with MAPE of 2.48% and R² 0.8872. Based on this model, the results of the share price prediction for 2026 show that BCA is predicted to experience a significant increase from IDR 7,756 (January) to IDR 7,846 (June), while Bank Mandiri is predicted to grow from IDR 5,211 (January) to IDR 5,930 (June). BNI and BRI are predicted to experience an increase in share prices, respectively from IDR 3,327 (January) to IDR 3,683 (June) and from IDR 4,124 (January) to IDR 4,541 (June). This research contributes by presenting a stock prediction model that combines technical and fundamental data at once, applied to four major Indonesian banks Bank Central Asia, Bank Rakyat Indonesia, Bank Mandiri, dan Bank Negara Indonesia in a single modeling framework. This approach has proven to produce good accuracy with an average MAPE of 3.13% and R² 0.919, as well as being a more objective alternative for investors in analyzing stock price movements. However, the prediction results obtained in this study are analytical tools and are not intended as direct investment recommendations.
Co-Authors Afiana Nabilla Zulfa Ahmad Paisal Ahyani Junia Karlina Alifiyoni Rahmat Umara Alwis Nazir Amalia Hanifah Artya Dardiri Dardiri Deny Ardianto Dodi Efendi Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Elvia Budianita Fadhilah Syafria Febi Yanto Fikri Kurnia Rahman Fikri Utri Amri Firman Zahri Fitri Insani Fitri Insani Fitri Insani Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Hafez Almirza Iis Afrianty Ikhwan Habibi Ikhwanul Akhmad DLY Imam Hanafi Ismu Kusumanto Isnan Mellian Ramadhan Iwan Iskandar Lestari Handayani Lestari Handayani Lola Oktavia M Alfandri Aqilah M Ridho Saputra M Wandi Dwi Wirawan M. Fadil Martias M. Muda MAS`UD ZEIN Maulana Junihardi Meiky Surya Cahyana Mori Hovipah Mori Hovipah Muhammad Affandes Muhammad Affandes Muhammad Fauzan Muhammad Hasbi Assidiqqi Muhammad Irsyad Muhammad Mulky Mar'arif Muhammad Vio Hardiansyah Musa Thahir Mustakim Mustakim Mustakim Mustakim Nadila Handayani Putri Nazruddin Safaat H Novia Kumala Sari Novriyanto Novriyanto Nurul Gayatri Indah Reza Pizaini Pizaini Rahayu Anggraini Rahma Shinta Reski Mai Candra Rizuan Rizuan Said Nanda Saputra Saputra, Elvino Dwi Sarah Lasniari Sarah Lasniari Siti Ramadhani Sola Huddin Sonya Aulia Febriana Sonya Meitarice Surya Agustian Susiandri Suwanto Sanjaya Suwanto Suwanto Tiara Ramayanti Yoga Nur Pradana Yusra, Yusra Zulkifli M. Nuh