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All Journal Sainteks International Journal of Artificial Intelligence Research JURNAL ILMIAH INFORMATIKA Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Journal of Innovation Information Technology and Application (JINITA) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) JPM: JURNAL PENGABDIAN MASYARAKAT Buletin Sistem Informasi dan Teknologi Islam Journal of Artificial Intelligence and Digital Business Journal of Software Engineering and Information System (SEIS) Journal of International Multidisciplinary Research IJMST Scientific Journal of Engineering Research Scientific: Journal of Computer Science and Informatics Scientific Journal of Computer Science Journix: Journal of Informatics and Computing
Wijaya, Setiawan Ardi
Universitas Muhammadiyah Riau
Aerospace Engineering Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : Scientific Journal of Computer Science

 1 
Machine Learning 5.0 In-depth Analysis Trends in Classification Dianda Rifaldi; Tri Stiyo Famuji; Setiawan Ardi Wijaya; Ahmed Jaber Abougarair; Phichitphon Chotikunnan; Alfian Ma'arif; Furizal
Scientific Journal of Computer Science Vol. 1 No. 1 (2025): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i1.2025.18

Abstract

In the era of Technology 5.0 Machine Learning continues to show significant advancements across various sectors. This study aims to examine the latest trends in Machine Learning classification, focusing on four key approaches Explainable Artificial Intelligence, Federated Learning, Transfer Learning, and Generative Adversarial Networks. The methodology involves a comprehensive literature review of research in Asia and experimentation with related datasets. The findings indicate that Explainable Artificial Intelligence enhances transparency and accuracy in data classification, Federated Learning enables decentralized learning while safeguarding data privacy, Transfer Learning improves accuracy with small datasets, and Generative Adversarial Networks aids in data augmentation for better model training. In conclusion, these techniques not only enhance the efficiency and accuracy of classification but also open up new opportunities for innovation in various fields, including healthcare, transportation, and cybersecurity.
A Statistical Approach to Crime Rate Prediction Using Multiple Linear Regression Wijaya, Setiawan Ardi; Arribe, Edo; Muhitualdi
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.47

Abstract

The high crime rate in Riau Province poses a serious threat to social stability and public safety, requiring accurate prediction strategies to support crime prevention efforts. Based on data from the Central Statistics Agency (BPS), Riau ranked seventh among the provinces with the highest crime rates in Indonesia in 2022, indicating that conventional prevention efforts remain insufficient. However, studies applying statistical data-based prediction models to crime in Riau are still limited, creating a gap in data-driven decision making. This study aims to develop a crime rate prediction model in Riau Province using the Multiple Linear Regression (MLR) method with BPS crime data from 2019–2023. The independent variables include six types of crime: corruption, drug dealers, drug users, terrorism, illegal logging, and human trafficking, while the dependent variable is the total number of crimes per district or city. The research process involved data collection, understanding, preprocessing, application of linear regression algorithms, model training and testing, and evaluation using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that Pekanbaru City recorded the highest number of cases, mostly related to drug crimes. The model predicts an increase in Pekanbaru’s cases from 3,331 in 2024 to 5,852 in 2027, while Dumai City is projected to decline from 543 to 397 cases. The model demonstrates high accuracy in most areas, particularly in Kampar (MAPE 0.28%), Siak (0.52%), and Rokan Hilir (0.94%), though less accurate in the Meranti Islands (565.99%) due to data instability. These findings prove that the Multiple Linear Regression method effectively predicts crime trends and can serve as a quantitative decision-making tool for law enforcement and local governments. Further research should include socioeconomic factors such as poverty and unemployment, and compare results with alternative forecasting methods like ARIMA and Exponential Smoothing to enhance prediction accuracy.
Co-Authors Abdennasser, Dahmani Abdul Fadlil Abid Yanuar Badharudin Abraar, M. Said Ada, Yosia Agus Prasetyo Marsaid Ahmad Bukhari, Ahmad Ahmed Jaber Abougarair Al-Sabur, Raheem Alfian Ma’arif Anton Yudhana Anton Yudhana Ariska Fitriyana Ningrum Aryanto Asno Azzawagama Firdaus Br Bangun, Elsi Titasari Brilianti Nafilah, Rizkiya Dahmani, Abdennasser Dianda Rifaldi Doni Winarso, Doni Driss, Zied Dwi Purnomo, Raka Edo Arribe, Edo Fanani, Galih Pramuja Inngam Fauzan, Al fana Fauzan, Wahyu Fazilla, Rahma Muti Firmansyah Firmansyah Fitri Anggraini Fladea, Saskia Azki Furizal Furizal, Furizal Gabriel Diemesor Galih Pramuja Inngam Fanani Gerry Julian Gunawan, Rahmad Hafsari, Rizka Hasanah, Rakyatul Hasanah, Zakiyah Kariyamin, Kariyamin Laia, Ertin Mardiyanto, Silvia Marisa, Vania Mihardi, Hansen Muhammad Amirul Mu'min Muhitualdi Murni Murni Muzdhalifatul Ijfi, Inessthasia Nakib, Arman Mohammad Novi Tristanti Novi Tristanti Nurhikmayani, Nurhikmayani Phichitphon Chotikunnan Pratama, Dzaky Medlin Pratama, Nanda Dean Putera, Ardi Maulia Putri, Jossie Mutiarani Putri, Tia Refviani Ramadhan, Aziz Rizki Rangga Alif Faresta Risnal Diansyah, Risnal Safuan, M. Chairil Sarohim, Nabil saskia, ananda Sharkawy, Abdel Nasser Sharkawy, Abdel-Nasser Silpandi, Dimas Sulistiani Sulistiani, Sulistiani Sunardi Sunardi sunardi sunardi Sunardi, Sunardi Suwarno, Iswanto Syahril Syaputri, Qori Monica Tri Stiyo Famuji Tristanti, Novi TSABITAH, NAYLA Wahyu Ningsih, Dwi Putri Wide Mulyana Wiyrabawa Kainna Putra Rachman Yana Safitri Yana Safitri Yana Safitri, Yana Yusuf Hendra Pratama Zahran Ramdani, Ibnu Ziad Iqbal, Muhammad