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All Journal EKONOMIA Jurnal Informatika dan Teknik Elektro Terapan Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmiah Matrik JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknologi Sistem Informasi dan Aplikasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Psychology, Evaluation, and Technology in Educational Research Jurnal Abdimas Mahakam METIK JURNAL Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Didaktik : Jurnal Ilmiah PGSD STKIP Subang Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing BAKTI BANUA : JURNAL PENGABDIAN KEPADA MASYARAKAT Teknika Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Bangkit Indonesia Jurtik STMIK Bandung Jurnal Abdimas Lamin Journal of Innovative and Creativity Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Buffer Informatika INOVTEK Polbeng - Seri Informatika JSE Journal of Science and Engineering KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Taghfirul Azhima Yoga Siswa
Universitas Muhammadiyah Kalimantan Timur
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Penerapan Smote Pada Algoritma SVM Untuk Mengatasi Imbalance Data Kelayakan Donor Darah Enriko Chiesa Sipahutar; Taghfirul Azhima Yoga; Fendy Yulianto
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.3877

Abstract

Penelitian ini mengembangkan sistem klasifikasi kelayakan donor darah berbasis metode data mining dengan fitur penanganan ketidakseimbangan data untuk meningkatkan akurasi prediksi. Sistem ini menggunakan algoritma SVM (Support Vector Machine) sebagai model klasifikasi utama dan metode SMOTE (Synthetic Minority Over-sampling Technique) untuk menyeimbangkan distribusi data antar kelas. Dataset donor darah yang diperoleh dari PMI Samarinda tahun 2024–2025 diolah melalui tahap pembersihan, normalisasi, serta validasi silang (Cross-validation) untuk mencegah Overfitting. Prototipe model diuji untuk memastikan kinerja klasifikasi terhadap data minoritas dan mayoritas dapat berjalan optimal. Hasil pengujian menunjukkan bahwa penerapan SMOTE berhasil meningkatkan akurasi hingga 96,05%, serta mengurangi kesalahan klasifikasi pada data minoritas. Penelitian ini berkontribusi dalam menyediakan solusi analitik berbasis Machine learning yang dapat diterapkan untuk permasalahan serupa, khususnya dalam meningkatkan akurasi klasifikasi pada data yang tidak seimbang.
Komparasi Ekstraksi Fitur TF-IDF dan Word2Vec pada Naïve Bayes untuk analisis Sentimen Pembangunan IKN di YouTube Rahmad Fahrozi, Mu. Aldi; Siswa, Taghfirul Azhima Yoga; Verdikha, Naufal Azmi
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.9198

Abstract

The development of Indonesia’s New Capital City (IKN) has generated diverse public responses on social media, particularly YouTube, making sentiment analysis necessary to map public perceptions. Previous studies have reported relatively low classification accuracy, reaching only 60%, indicating the need for more effective approaches to improve performance. This study aims to compare the performance of the Naïve Bayes algorithm in classifying public sentiment toward the IKN development using two feature extraction methods, namely TF-IDF and Word2Vec. The data were collected from YouTube comments and processed through preprocessing stages, expert-based labeling, and evaluation using 10-Fold Cross Validation. The results show that the TF-IDF-based Multinomial Naïve Bayes model achieves the best performance with an accuracy of 83%, a positive recall of 82%, and a negative F1-score of 85%, outperforming the Word2Vec-based Gaussian Naïve Bayes model, which attains an accuracy of 82% with a lower positive recall of 76%. These findings confirm that TF-IDF is more effective and stable in handling short-text comment characteristics than Word2Vec, which requires a larger corpus for optimal semantic representation.
Revitalisasi Infrastruktur dan Digitalisasi UMKM Kerajinan Dayak di Desa Budaya Pampang Samarinda Dewi, Catur Kumala; Bahrudin, Faizal; Haryadi, Rina Masithoh; Siswa, Taghfirul Azima Yoga; Ekawati, Ekawati
Jurnal Pengabdian Masyarakat Lamin Vol 5, No 1 (2026)
Publisher : Fakultas Ekonomi dan Bisnis Universitas 17 Agustus 1945 Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31293/jpml.v5i1.9299

Abstract

Program pengabdian masyarakat ini bertujuan untuk mentransformasi kapasitas operasional dan jangkauan pasar pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) Lamin Pamung Tawai di Desa Budaya Pampang, Samarinda. Permasalahan utama yang diidentifikasi meliputi keterbatasan infrastruktur fisik tempat berjualan yang rentan terhadap faktor cuaca dan risiko keamanan, serta stagnasi strategi pemasaran yang masih bersifat konvensional. Melalui pendekatan Participatory Action Research (PAR), intervensi dilakukan dalam dua dimensi utama: revitalisasi fasilitas fisik melalui pemasangan folding gate dan partisi modular, serta akselerasi digital melalui pengembangan portal e-commerce pada domain budayapampang.web.id. Hasil implementasi menunjukkan peningkatan signifikan dalam kenyamanan kerja dan efisiensi manajemen inventaris. Selain itu, integrasi teknologi digital telah memperluas visibilitas produk kerajinan Dayak ke pasar nasional dan internasional. Keberlanjutan program dijamin melalui pembentukan tim pengelola fasilitas dan peningkatan literasi digital pemuda desa sebagai admin pemasaran. Sinergi antara penguatan fisik dan digital ini tidak hanya meningkatkan kesejahteraan ekonomi lokal, tetapi juga berfungsi sebagai mekanisme pelestarian warisan budaya Dayak Kenyah di era industri 4.0.
PENENTUAN JENIS KECELAKAAN LALU LINTA DI KOTA SAMARINDA MENGGUNAKAN METODE DECISION TREE Khanisa Octavia; Fendy Yulianto; Taghfirul Azhima Yoga Siswa
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 11 No. 01 (2025): Volume 11 No. 01 Maret 2025 In Proccess
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v11i01.5589

Abstract

Traffic accidents are one of the main causes of death in Indonesia, so comprehensive research is needed to determine the causal factors. The aim of this research is to classify the level of traffic accidents in Samarinda City using the Decision Tree method. The data used is the result of data collection from the Samarinda City Sector Police taken from 2021 to 2024. Data collection, preprocessing, method training, and evaluation are the steps carried out in this research process. The Decision Tree method was chosen because it has the advantage of a simple and easy to understand structure. The evaluation was carried out using K-Fold Cross Validation to ensure the method's performance remains stable and avoids overfitting. The classification results in the research show that the Decision Tree method produces an accuracy of 93% with evaluation metrics such as precision, Recall, F1-Score which also show good performance. It is hoped that the results of this research will provide a useful contribution in creating data-based safety regulations and can help reduce accident rates in Samarinda City.
Hybrid Support Vector Regression-Genetic Algorithm Model for Forecasting Stock Prices Albab, Muhammad Ulil; Yoga Siswa, Taghfirul Azhima; Hasudungan, Rofilde
Indonesian Journal of Artificial Intelligence and Data Mining Vol 9, No 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i1.39057

Abstract

The stock market exhibits a high level of volatility, which often leads to significant price fluctuations and increases the risk of financial losses for investors. Therefore, stock price prediction is an important tool to support investment decision-making, particularly for PT Aneka Tambang Tbk (ANTM.JK). This study aims to predict ANTM stock prices by applying the Support Vector Regression (SVR) method optimized using a Genetic Algorithm (GA). The data used in this study consist of 1202 historical stock price data of ANTM from September 11, 2020 to September 11, 2025, obtained from Investing.com, and the data are normalized using the Min-Max normalization method. The dataset is divided into training data and testing data using an 80:20 ratio, where 80% of the data are used for training and 20% for testing. The SVR model is constructed using the Radial Basis Function (RBF) kernel, while the GA is employed to optimize the SVR parameters in order to obtain the optimal parameter combination, with main GA parameters including population size of 50, 30 generations, crossover rate of 0.8, and mutation rate of 0.1. Model performance is evaluated by comparing the prediction results of SVR without optimization and GA-optimized SVR using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results indicate that the application of the GA improves the predictive performance of the model. The SVR model without optimization produces RMSE, MAE, and MAPE values of 85.48, 59.02, and 2.62%, respectively. After parameter optimization using GA, the model performance improves as indicated by reduced error values, with RMSE of 75.97, MAE of 52.42, and MAPE of 2.42%
Co-Authors Abdul Rahim Abdul Rahim Abror, Irfan Fiqry Agustya Nanda Pratiwi Akbar, Zakaria Ihza Albab, Muhammad Ulil Anis Siti Nurrohkayati Anitasari, Dini Anton Prafanto Anton Saputra Arbansyah Arbansyah Ari Ahmad Dhani Ariyadi, Dedy Asnur Karima Aspianur Bahrudin, Faizal Betris Dea Maretta, Nanda Damari, Azwar Darmawan Setiya Budi Dewi, Catur Kumala Dzul Rachman, Dzul Ekawati Ekawati Enriko Chiesa Sipahutar FAUZI Fendy Yulianto Fendy Yulianto Gubtha Mahendra Putra Haryadi, Rina Mashitoh Haryadi, Rina Masithoh Hasudungan, Rofilde Heri Abijono Hery Kurniawan Hidayati Ramadhani, Novia Istimaroh Istimaroh Joko Pranoto, Wawan Jubaidi Khanisa Octavia Khatimah, Khusnul lia, Alvina Lidya Sari Mardiana Mardiana Muhammad Fadly Ramadhani Muhammad Najeri Al Syahrin Muhammad Norhalimi Muhammad Rhosyid Akhmad Muhammad Wildan Hadinata Naufal Azmi Verdikha Pambudi, Faldy Alfareza Paula Mariana Kustiawan Pitoyo Pitoyo Pitoyo, Pitoyo Poernamawan, Ahmad Nugraha Prihandoko . Putri, Azzahra Namira Raenald Syaputra Rahmad Fahrozi, Mu. Aldi Rahman, Febrian Nor Ramadhani, Daib Jidan Renaldi Panji Wibowo Restu, Anggiq Karisma Aji Rivaldo, Vito Junivan Rizky Aspiah Rochman, Bagus Fathur Rudiman, R Rudiman, Rudiman Salsabila, Cindy Azra Santi Yatnikasari Satria, Bima Sidiq, Reza June Siti Muawwanah Taufiq, Ilham Taufiqurrahman Taufiqurrahman Wahyu Hidayat Wawan Joko Pranoto Wawan Joko Pranoto Wawan Joko Pranoto Widyastuti, Dessy Yoga Priantama