Article Per Year (5 Year)
p-Index From 2020 - 2025
7.235
P-Index
This Author published in this journals
All Journal Teknotan: Jurnal Industri Teknologi Pertanian Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat IKRA-ITH Humaniora : Jurnal Sosial dan Humaniora JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal RESISTOR (Rekayasa Sistem Komputer) IKRA-ITH ABDIMAS Pontianak Nutrition Journal (PNJ) Journal of Industrial and Information Technology in Agriculture Jurnal Abdi Insani JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer DECODE: Jurnal Pendidikan Teknologi Informasi Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Composite: Jurnal Ilmu Pertanian Jurnal Sains dan Teknologi Pangan
Yana Cahyana
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 58 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Bulletin of Computer Science Research

 1 
Model Machine Learning Untuk Analisis Sentimen Masyarakat Terhadap Kenaikan PPN di Media Sosial X Ridho Pratama, Ilham; Cahyana, Yana; Rahmat; Wahiddin, Deden
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.v5i4.523

Abstract

This study examines people's reactions to the Indonesian government's plan to adjust the VAT rate from 11% to 12%, which is scheduled to take effect in 2025. This policy triggered a variety of opinions among netizens, especially on the social networking service X. To explore public opinion, data was collected through web crawling techniques from October to December 2024, resulting in 1,871 records. Then the dataset was preprocessed by text cleaning, case folding, tokenization, stopword removal, and stemming, and the dataset was reduced to 1806. In addition, up to 1000 data will be manually labeled, negative, neutral, positive, by language experts to ensure that each sentence has the appropriate label. These data are used for testing and training, then up to 806 unlabeled data are used as final testing. At the word weighting stage, the Term Frequency-Inverse Document Frequency (TF-IDF) method is used to perform the process. In this study, three machine learning algorithms were used to compare the classification performance, namely Support Vector Machine (SVM), Random Forest, and Decision Tree. Based on the evaluation results, the SVM algorithm recorded the highest accuracy rate of 94%, followed by Random Forest with 93% and Decision Tree with 91%. The results showed a predominance of negative sentiments, indicating public dissatisfaction with the policy. This study proves that machine learning techniques can be effectively used to capture public perceptions through social media, which in turn can be a benchmark for the government to make decisions that will be enforced.
Analisis Sentimen Masyarakat Terhadap Pembatasan BBM Pertalite Menggunakan Random Forest dan K-Nearest Neighbor Muhammad Fadillah, Farhan; Cahyana, Yana; Rahmat; Fauzi, Ahmad
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.v5i4.547

Abstract

This study aims to analyze public opinion regarding the policy of limiting the use of Pertalite fuel by examining user comments on the Instagram platform. To classify these opinions, classification approaches using K-Nearest Neighbor (KNN) and Random Forest algorithms were employed. Comments were categorized into three sentiment expressions: positive, negative, and neutral. The research stages included data collection (crawling), text cleaning and normalization, sentiment labeling, weighting using the TF-IDF technique, model development, and performance evaluation. A total of 2,081 comments were used, with 1,000 comments labeled by language experts as training data, and the remaining used for testing. Model evaluation was conducted using two data splitting ratios, 80:20 and 70:30, to assess classification stability and accuracy. The results indicate that the Random Forest algorithm consistently outperforms KNN, achieving the highest accuracy of 73% under the 80:20 scenario. The classification distribution suggests a dominance of negative sentiment in public opinion toward the policy. These findings reflect public dissatisfaction and serve as critical input for the government in reviewing the subsidized fuel distribution policy. This research also highlights the potential of social media as an alternative data source for real-time public perception analysis.
Prediksi Pola Pergerakan Saham Adro.Jk Melalui Model LSTM Berbasis Data Historis Iskandar, Muhammad Irsyad; Mudzakir, Tohirin Al; Cahyana, Yana; Pratama, Adi Rizky
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.v5i4.554

Abstract

The fluctuating nature of stock price movements presents a significant challenge in investment decision-making. To address this issue, a predictive model capable of capturing historical patterns and accurately forecasting stock prices is required. This study aims to develop a stock price prediction model for PT Alamtri Resources Indonesia Tbk (ADRO.JK) using the Long Short-Term Memory (LSTM) algorithm. The dataset comprises daily closing prices from January 1, 2020, to December 30, 2024, obtained from Yahoo Finance. The data was processed in a time series format using a sliding window approach, employing 30 historical data points to predict the next price point. The model was constructed using two LSTM layers, one Dense layer, and techniques such as Dropout and EarlyStopping to prevent overfitting.The training and testing results indicate that the model performs exceptionally well, achieving a Mean Absolute Percentage Error (MAPE) of 0.0341 or 3.41%, corresponding to a prediction accuracy of 96.59%. In a short-term prediction scenario over seven days, the model achieved an accuracy of 99.07% (MAPE = 0.0093), while in a medium-term scenario up to May 19, 2025, it achieved an accuracy of 98.76% (MAPE = 0.0124). The predicted stock price on May 19, 2025, is estimated at IDR 1,913.76. With its high accuracy and low error rate, the LSTM model has proven to be a reliable tool for forecasting stock prices based on historical data.
Co-Authors Abdullah Darussalam Adi Rizky Pratama Adi Susilo Aenul Fuadah Agustin, Rachmayanti Tri Ahmad Fauzi Ajijah, Melia Siti Alifa, Naila Ratu Ambarwati, Evi Karlina Amid Rakhman amril siregar Anisa Itiawanti Annisa Nurhalizah Aqib Zhaky Ardiyani, Mery Awal, Elsa Elvira Ayu Juwita Azzahra, Reva Baihaqi, Kiki Ahmad Banafshah Shafa Bramandito Affandi Deden Wahiddin Dewi, Indah Purnama Didik Remaldhi Direja, Azhar Ferbista DWI KUSUMANINGRUM Een Nurhasanah Een Sukarminah Efri Mardawati Enjelia, Lola Faisal, Sutan Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Fitria, Denisa Gumilar, Rizki Bintang Hanan, Sofiah Marwah Hanny Hikmayanti Handayani Hartono Wijaya, Sony Heri Hermawan Herlina Marta Hilda Novita Imas Siti Setiasih In-In Hanidah Iskandar, Muhammad Irsyad Jovan Pangestu Juwita, Ayu Ratna Khoerunnisa, Nurani Kiki Baihaqi Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita M. Budi Kusarpoko Miptahul Ulum Mochamad Djali Mohammad Djali Mudzakir, Tohirin Al Muhamad Amirrullah Muhammad Fadillah, Farhan Muhammad Ramadhan Narwan Nahrudin Nina Puspitaloka Nisa, Azizatun Nofie Prasetiyo Nova Wulandari Nurani Khoerunnisa Nurjanah, Kartika Dewi Praditya Putri Utami Pratama, Adi Rizky Pratiwi, Sinta Amanda Puspitaloka, Nina Putri, Septiani Nuruldharma Rachmawati, Dhea Rahmat Rahmat Rahmat Rahmat Rahmat Restiana, Resti Ricky Steven Chandra Ridho Pratama, Ilham Rizka Ayu Permana Rizki Nur Annisa Rizky Nugraha Rizky Riyanto Robi Andoyo Rohana, Tatang Sabirin Sandra Intan Sari Santi Lestari Siregar, Amril Siregar, Amril Mutoi Siregar, Amril Mutoi Sukmawati, Cici Emilia Sulistya, Dwi Suningwar Mujiana Surya Martha Pratiwi Sutan Faisal Syahril, Ade Tatang Rohana Tita Rialita Tjong Wan Sen Tohirin Al Mudzakir Tukino, Tukino Utama, Duhita D Utama, Duhita Diantiparamudita Wahiddin, Deden Wenda Adi Kusnaya Widiharto, Banani