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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL SISTEM INFORMASI BISNIS Bulletin of Electrical Engineering and Informatics AJIE (Asian Journal of Innovation and Entrepreneurship) CESS (Journal of Computer Engineering, System and Science) Jurnal Chemurgy ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) ELECTROPS : Jurnal Ilmiah Teknik Elektro
Didit Suprihanto, Didit
Fakultas Teknik, Universitas Mulawarman, Samarinda
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Bulletin of Electrical Engineering and Informatics

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A comparative study of machine learning methods for drug type classification Tejawati, Andi; Suprihanto, Didit; Ery Burhandenny, Aji; Saipul, Saipul; Puspitasari, Novianti; Septiarini, Anindita
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9477

Abstract

Drugs, commonly called narcotics, are dangerous substances that, if consumed excessively, can result in addiction and even death. Drug abuse in Indonesia has reached a concerning stage. In 2017, the National Narcotics Agency detected 46,537 drug-related incidents, including methamphetamine, marijuana, and ecstasy. There are 4 types of substances that can affect drug users, such as hallucinogens, depressants, opioids, and stimulants. A machine learning approach can detect these substances using user symptom data as input. This study uses six different methods in classifying, including decision tree, C.45, K-nearest neighbor (KNN), random forest, and support vector machine (SVM). The dataset comprises 144 data and 21 attributes based on the user's symptoms. The evaluation method in this study uses cross-validation with K-fold values of 5 and 10 and uses three parameters: precision, recall, and accuracy. KNN yields the most optimal results by using K=1 and K-fold 10 in the Euclidean and Minkowski types. The model achieves precision, recall, and accuracy of 91.9%, 91.7%, and 91.67%, respectively.
Comparative performance analysis of LSTM, GRU, and bidirectional neural networks for political ideology classification Afuan, Lasmedi; Hidayat, Nurul; Permadi, Ipung; Iqbal, Iqbal; Suprihanto, Didit; Bintang Pradana Yosua, Panky; Alfarez Marchelian, Reyno
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9980

Abstract

Political ideology classification is crucial for understanding social polarization, monitoring democratic processes, and identifying bias on online platforms. This study compares the performance of long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional GRU (Bi-GRU) neural network models in classifying liberal and conservative political ideologies from social media text data. The Bi-GRU achieved the best results with 88.75% accuracy and 89.16% F1-score, highlighting its strength in contextual analysis. These findings suggest their applicability in areas such as election monitoring and the analysis of political discourse. This study contributes to the field of political text classification by offering a comparative analysis of deep learning architectures. The dataset utilized covers a wide range of issues, including social, political, economic, religious, and racial topics, demonstrating its comprehensive nature. Visualizations using WordCloud and uniform manifold approximation and projection (UMAP) reveal distinct ideological patterns, validating the dataset’s quality for training models. The findings underscore the importance of utilizing advanced bidirectional architectures for nuanced tasks, such as ideology classification, where contextual understanding is crucial. These insights open avenues for future research, such as the application of Bi-GRU in analyzing multilingual political ideologies or real-time sentiment tracking during election campaigns.
Co-Authors 'Aisyah 'Aisyah Afrilla, Lidwina Nadya Agus Qomaruddin Munir Aji Ery Burhandenny Alfarez Marchelian, Reyno Amirul Hadi, Amirul Andri Syafrianto Anindita Septiarini, Anindita Ari Susandi Sanjaya Ari Susandy Sanjaya, Ari Susandy Arif Harjanto Arif Harjanto Aris Sugiharto Aryani., Retno Bambang Purnama Bangkit Bekti Nurdianto Bintang Pradana Yosua, Panky Budi Nining Widarti Budi Rahmani Budi Yannur Burhandenny, Aji Ery Delwizar, Muhammad Arya Dermawan, Muhammad Ali Dhea Yolanda Edhi Sarwono Ery Burhandenny, Aji Fatkhul Hani Rumawan Fatmawati Patang Hairul Huda Hamdani Hamdani Hamdani Hamdani Happy Nugroho Haviluddin Haviluddin Heru Ismanto Hetty Manurung Hetty Manurung, Hetty Hidayat, Aldi Muhammad Ipung Permadi Iqbal Iqbal Lasmedi Afuan Muhammad Akbar Muslimin Muslimin Nugroho, Happy Nugroho3, Rudy Agung Nurmadewi, Dita Nurul Hidayat Olivia Angelica Murtioso Palus, Petrus Prahastika, Widha Pranoto, Sarwo Purnama, Bambang Puspitasari, Novianti PUTU SUGIARTAWAN Rahmadhani, Rizal Restu Mukti Utomo Retno Aryani. Rudianto Rudianto Rudianto Rudianto Rudy Agung Nugroho Rumawan, Fatkhul Hani Saipul, Saipul Sali, Apriyanto Salsabila, Nur Maya Sanjaya, Ari Susandi Sari, Wulan Iyhig Ratna Sri Murdhani, I Dewa Ayu Tejawati, Andi Toni Prahasto Viny Christanti M Wati, Masna Widha Prahastika Wirawan, Adi Pandu Wulan Iyhig Ratna Sari Yudi Sukmono, Yudi Yudiana, I Made Yulianto, Fransiskus Federico