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All Journal Jurnal Pengabdian UntukMu NegeRI JURTEKSI PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan Jasmani Khatulistiwa Jurnal Sistem Informasi dan Teknik Informasi Filantropi: Jurnal Manajemen Zakat dan Wakaf Journal Sport Academy (JSA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Software Engineering and Information System (SEIS) Journal Sport Academy
Rahmad Firdaus
Fakultas Kedokteran Universitas Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Physics Public Health Social Sciences Other

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Efektivitas Sosialisasi Narkotika, Psikotropika, dan Zat Adiktif (NAPZA) dalam Meningkatkan Pengetahuan dan Kesadaran Siswa SMP Negeri 05 Sungai Apit Anugerah Putra, Bayu; Soni, Soni; Gunawan, Rahmad; Fatma, Yulia; Firdaus, Rahmad; Taufiq, Reny Medikawati; Handayani, Fitri; Mukhtar, Harun; Mualfah, Desti; Azim, Fauzan; Aprilya, Regiesta Lintang; Ramadhan, Rafi Fakhri; Abadi, Abdi Nauli
Jurnal Pengabdian UntukMu NegeRI Vol. 10 No. 2 (2026): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v10i2.11302

Abstract

The abuse of narcotics, psychotropic drugs, and addictive substances (NAPZA) is a major problem that threatens the future of adolescents, especially junior high school students. Many students do not fully understand the dangers of NAPZA, making them vulnerable to the influence of their surroundings. Therefore, this study was conducted to determine the extent to which NAPZA awareness activities have succeeded in increasing the knowledge and awareness of students at SMP Negeri 05 Sungai Apit regarding the dangers of drug abuse. This study used a qualitative descriptive method with a field study approach. Data were obtained through material presentations and questions given to students after the socialization was conducted. The results showed an increase in students' knowledge about the types of NAPZA, their negative effects, and ways to prevent their abuse. Students were also better able to recognize the factors that could trigger abuse and showed a refusal to try NAPZA. The conclusion of this study states that NAPZA socialization is effective in increasing knowledge and forming a preventive attitude among students at SMP Negeri 05 Sungai Apit. Therefore, activities such as this need to be carried out regularly and continuously to create a healthy, safe, and NAPZA-free school environment.
Prediksi Keberhasilan Akademik Siswa Berbasis Fitur Kategorikal Na-tive dengan Explainable AI (SHAP) menggunakan CatBoost vs LightGBM Anugerah Putra, Bayu; Soni, Soni; Firdaus, Rahmad; Putri, Ayunda; Dwi Sanggar Wati, Anisa
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.12360

Abstract

Prediction of students academic success is important to support decision-making in education. Educational datasets are generally dominated by categorical variables that require encoding before modeling, which may cause information loss and reduce accuracy. This study applies the CatBoost algorithm, which processes categorical variables natively without additional encoding, to predict students' Exam Score on the Student Performance Factors dataset from Kaggle, with LightGBM used as a comparison model. Evaluation was carried out under three data-split schemes (70:30, 80:20, 90:10) using k-fold cross-validation and three regression metrics (R², MAE, RMSE), followed by model interpretation using Shapley Additive Explanations (SHAP). The results show that CatBoost consistently outperforms LightGBM across all schemes, with the best performance obtained under the 90:10 scheme (CatBoost: R² = 0.851, MAE = 0.475, RMSE = 1.414; LightGBM: R² = 0.809, MAE = 0.758, RMSE = 1.599). SHAP analysis identifies Attendance, Hours_Studied, and Previous_Scores as the most influential features in the prediction. These findings confirm that combining CatBoost with SHAP produces an academic prediction model that is both accurate and transparen.
IMPLEMENTASI SISTEM SMART VENTILATION BERBASIS IOT MENGGUNAKAN THRESHOLD KUALITAS UDARA Hamid, Muhammad Almas Albirra; Pratama, Dicky Aldian; Alghifari, Gian; Aprialna, Ravi Rabbani; Perkasa, Anan Satria; Firdaus, Rahmad; Wenando, Febby Apri
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 2 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i2.11039

Abstract

Poor air quality in enclosed workspaces, particularly in Micro, Small, and Medium Enterprises (MSMEs) that operate CNC laser cutting machines for acrylic processing, poses potential health risks and reduces worker comfort. This study aims to design and implement an Internet of Things (IoT)–based Smart Ventilation system capable of automatically monitoring and controlling indoor air quality based on predefined threshold values. The system integrates air quality sensors to detect smoke or hazardous gases, an ESP32 microcontroller as the central processing unit, and an exhaust fan as the actuator. Sensor data are processed in real time and used as the basis for activating or deactivating the ventilation system, while monitoring information is displayed through an IoT platform for remote supervision. Experimental results indicate that the proposed system can accurately identify changes in air quality and respond automatically to hazardous conditions in accordance with the defined thresholds. The implementation of this system is expected to improve indoor air quality, support occupational health and safety, and enhance energy efficiency through condition-based ventilation control.
MACHINE LEARNING UNTUK PREDIKSI SUHU: SEBUAH TINJAUAN Adityo, Tri Novian; Mukhtar, Harun; Firdaus, Rahmad; Taufiq, Reny Medikawati; Gunawan, Rahmad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 6 No. 2 (2026)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v6i2.12177

Abstract

Global climate change has increased the demand for accurate temperature prediction to support decision-making in sectors such as agriculture, disaster mitigation, and energy management. Machine Learning (ML) and Deep Learning (DL) approaches have been widely applied to model the non-linear and dynamic characteristics of temperature data. This study presents a Systematic Literature Review (SLR) following the PRISMA protocol. From 125 identified articles, 45 studies published between 2021 and 2025 were selected for detailed analysis. The results indicate that Long Short-Term Memory (LSTM) is the most frequently used algorithm, both as a standalone model and within hybrid architectures. Most studies employ multivariate datasets sourced from BMKG, ERA5 Reanalysis, satellite imagery, and the Internet of Things (IoT). Data preprocessing techniques, particularly norssmalization and time-series construction, play a crucial role in improving model stability. However, challenges remain, including hyperparameter sensitivity, complex weather data characteristics, and geographical variability. Future research opportunities include adaptive model development, multi-source data integration, and comprehensive comparative studies among algorithms.
Co-Authors Abadi, Abdi Nauli Aditya Ahmad Fauzi Adityo, Tri Novian Alghifari, Gian Alris Gusnanda Amran, Hasanatul Fu'adah Andinata Anton, Anton Kurnia Hadisaputro Apri Wenando, Febby Aprialna, Ravi Rabbani Aprilya, Regiesta Lintang Ayunda Putri Bayu Anugerah Putra Br Bangun, Elsi Titasari Dian Utami Dicky Pratama Dwi Sanggar Wati, Anisa Fadila, Wahyu Fakhira Frisya Ramadhani Fatma, Yulia Fauzan Azim Febby Apri Wenando Fithriawan Nugroho Fitri Handayani Fitri, Nurkhairi Fu'adah Amran, Hasanatul Gunawan, Rahmad Hamid, Muhammad Almas Albirra Hanum Salsabila Harun Mukhtar Hayami, Regiolina Hermanto Hermanto Jihan Aulia Karan Khasanah, Diva Nayla Marna, Marna Maryani, Cik Mualfah, Desti Muarif, Putri Fadhilla Muhammad Suhairi, Perkasa, Anan Satria Pratama, Dicky Aldian Prihatin, Ananda Putra, Gope Mandala Rafdi, Faris Abi Rajaguguk, Rifaldi Febriansyah Ramadhan, Rafi Fakhri Rico Apriandika Rizqy, Riza Rindriani Sartika, Cici Septiana Srinandini Soni Soni Soni, Soni Suhardi Suhardi Sunanto Sunanto Sunanto Suwanto, Fredy Syahril Syahril Taufiq, Reny Medikawati Wahyudi, Ashari Yeremia, Deryl Andeya Zidan, Abiyyu Al Thoriq Zusyah Porja Daryanto