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All Journal Jurnal Ilmiah Admnistrasi Niaga Pendidikan Kewarganegaraan Indonesian Journal of Artificial Intelligence and Data Mining Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Ibnu Maja
Politeknik Negeri Sriwijaya
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PENGARUH MOTIVASI, METODE PEMBELAJARAN DAN DISIPLIN BELAJAR TERHADAP PRESTASI BELAJAR MATEMATIKA TEKNIK DI POLITEKNIK NEGERI SRIWIJAYA (Studi Penelitian pada Mahasiswa Jurusan Teknik Kimia) Ibnu Maja
Orasi Bisnis : Jurnal Ilmiah Administrasi Niaga Vol. 9 No. 3 (2013): Orasi Bisnis Edisi IX Mei 2013
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (777.465 KB)

Abstract

The low achievement of students and the lack of motivation, methods of learning and discipline in the students' learning of mathematics is a challenge for mathematics lecturers to continue to think creatively in order to teach the material to students in accordance with the standards of the curriculum and the learning process takes place by directly involving students in full in terms of the learning process that takes place can be run with fun. The low achievement of students was indirectly caused by the lack of students' motivation and discipline itself. Learning methods implemented by the faculty also affects the achievement of students majoring in chemical engineering. The learning method is a technique applied by teachers in the learning process, because the learning process is a process of knowledge transfer from faculty as a lecturer to the students as learners and for that there must be a special technique to be effective and well targeted. If the teaching methods employed by teachers precisely, the maximum learning outcomes clear and satisfactory.
Pkm Bagi Guru Smp Yang Mengalami Kesulitan Menggunakan Software Geogebra Dalam Pembelajaran Matematika Yulianto Wasiran; Ibnu Maja; Farida Husien
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 2, No.2 (2019) : APTEKMAS Volume 2 Nomor 2 2019
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (298.026 KB) | DOI: 10.36257/apts.v2i2.1601

Abstract

One reason is that the process of learning mathematics related to geometry is not yet optimal, it is the difficulty of the teacher to visualize abstract geometrical objects. Geogebra software can be used as a tool to construct, demonstrate or visualize abstract problems in learning geometry material. The problem faced by partners in implementing the geometry learning process by using Geogebra's assistance as a learning medium is the low knowledge of teachers in the use of Geogebra software and the low skills of teachers in using Geogebra software in learning geometry material. In an effort to overcome partner problems, the solution offered was to provide training and assistance to the Geogebra application to junior high school mathematics teachers in Banyuasin III to assist them in the process of learning mathematics. This activity includes theory, practice, and exercises given to participants so that skilled participants use Geogebra. This activity has succeeded in increasing teacher skills in knowledge and skills using Geogebra software for Mathematics learning. Participants understand the use of this software and are also skilled at using it for learning geometry material. This activity has also produced several instructional materials for Geogebra software assisted geometry materials.
PEMBELAJARAN DARING BERBASIS PENDIDIKAN KARAKTER Fransisca Ully Marshinta; Silvana Oktanisa; Ibnu Maja
Jurnal Pendidikan Kewarganegaraan Vol 12, No 01 (2022): Jurnal Pendidikan Kewarganegaraan
Publisher : Prodi PPKn ULM dan AP3KnI Kal-Sel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/kewarganegaraan.v12i01.12915

Abstract

Tujuannya penelitian ini untuk mengetahui bagaimana implementasi pembelajaran daring berbasis pendidikan karakter jujur, disiplin, mandiri dan tanggung jawab pada mata pelajaran PPKn. Metode yang digunakan adalah penelitian deskriptif dengan pendekatan kualitatif dengan metode snowball dan teknik proporsive sampling. Hasil yang didapat dalam penelitian ini menyatakan bahwa dalam pembelajaran daring sekolah atau guru tidak menyediakan lembar aktivitas belajar, tidak ada hukuman bagi peserta didik yang melanggar aturan pembelajaran daring, tidak melakukan kunjungan ke rumah peserta didik bermasalah dalam mengikuti pembelajaran daring selain itu tidak pernah memberikan hadiah bagi peserta didik yang berprestasi selama pembelajaran daring. Namun, upaya menerapkan pendidikan karakter sudah terlihat dari penilaian yang transparan, ketepatan waktu pengumpulan tugas serta bentuk-bentuk soal yang mendidik peserta didik tidak mencontek. Pembelajaran daring berbasis karakter berdaya manfaat apabila dalam pelaksanaannya mengikutsertakan lebih aktif orang tua atau wali dalam proses pembelajaran baik pada saat mulai sampai berakhirnya pembelajaran daring tersebut Kata Kunci: Pembelajaran Daring, Pendidikan Karakter, PPKn
MATLAB-Based Performance Evaluation of Lightweight YOLO Models for Waste Object Detection Nadhirah Meidiasty Maharani; Dewi Permata Sari; Ibnu Maja; Destra Andika Pratama; Ozkar F. Homzah
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i2.38984

Abstract

Accurate waste object detection is important for enabling efficient automated recycling and environmental management. While lightweight YOLO models are often managed in Python, integration and evaluation of the models in MATLAB remains a technical challenge due to limited support and documentation. This study intends to fill that gap by evaluating the performance of YOLOv5s, YOLOv7-Tiny, and YOLOv8n within the MATLAB environment for waste object detection using the TrashNet dataset. A semi-automatic labeling approach was employed, combining manual annotation with pseudo-labeling using a pretrained YOLOv8n model. The models were trained and exported to the ONNX format for MATLAB-based inference and analysis. Experimental results show that YOLOv8n achieved the highest mAP@0.5 of 0.954, while YOLOv5s demonstrated the most stable inference performance in MATLAB, consistently producing confidence scores above 90% and real-time speeds of up to 15.9 fps. In contrast, YOLOv7-Tiny achieved the fastest inference speed (up to 24.4 fps) but exhibited reduced classification consistency. Notably, YOLOv8n experienced confidence score degradation during MATLAB inference, suggesting post-processing discrepancies between native Python and ONNX-imported workflows. This research highlights MATLAB’s capability to serve as a functional evaluation platform for modern lightweight detectors and emphasizes its potential for expanding accessible AI applications in waste management systems.
Authorized Occupant Detection System in Smart Rooms under Daylight and Nighttime Lighting Conditions Reza Fahlevi; Dewi Permata Sari; Ibnu Maja
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 2 (2026): July 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v9i2.39259

Abstract

This study discusses the development of a legitimate occupant detection system in smart rooms using the YOLOv8 algorithm, tested under daytime and nighttime lighting conditions. The system is designed using a Raspberry Pi connected to a webcam for real-time monitoring. The aim of this study is to evaluate the system's performance under different light intensities. Data were obtained by capturing images during the day and night, which were then used as a training dataset for the YOLOv8 model. With a mAP@0.5 of 0.91 and precision, recall, and F1-score values of 0.90, 0.88, and 0.89, respectively, the evaluation findings demonstrate that the system operates effectively under ideal lighting conditions. This shows that the model can recognize things accurately and consistently in real time. However, performance drastically declines in low light, with mAP@0.5 falling to 0.68 and precision, recall, and F1-score falling to 0.70, 0.65, and 0.67, respectively. This indicates a rise in false and missed detections (FP and FN). Reduced image quality, including inadequate illumination, noise, and poor feature visibility, is the primary cause of this degradation. However, it has been demonstrated that using more light sources increases detection accuracy
Prediksi Temporal Medan Listrik Atmosfer Menggunakan Lstm Berbasis Data Electric Field Mill Muhammad Alfin Rafanda; Masayu Anisah; Ibnu Maja
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 5 No. 2 (2026): Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v5i2.6236

Abstract

This study aimed to develop a temporal prediction model for atmospheric electric field variations using Long Short-Term Memory (LSTM) based on Electric Field Mill (EFM) data. Atmospheric electric field variation is an important indicator of charge accumulation in clouds and can support early warning systems for lightning potential. This study employed a descriptive quantitative approach using time-series data acquired from an ESP32-S3-based EFM system. The collected data included time, ADC value, raw voltage, filtered voltage, electric field value in kV/m, atmospheric status, and lightning potential. Before model training, the data were processed through low-pass filtering, Min-Max normalization, and sliding-window sequence formation with a window size of 60 observations. A Bidirectional LSTM architecture was applied for regression and classification tasks. The regression model produced an MSE of 0.558, RMSE of 0.747, and MAE of 0.580. Meanwhile, the classification model achieved an accuracy of 83.1% for atmospheric status classification and 99.20% for weather condition classification. These findings indicate that the proposed LSTM model can effectively learn temporal patterns in atmospheric electric field data and has potential to support the development of an early lightning warning system.
Co-Authors Destra Andika Pratama Dewi Permata Sari Farida Husien Fransisca Ully Marshinta Homzah, Ozkar F Masayu Anisah Muhammad Alfin Rafanda Nadhirah Meidiasty Maharani REZA FAHLEVI SILVANA OKTANISA, SILVANA Wasiran, Yulianto