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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika INTECOMS: Journal of Information Technology and Computer Science
Sofhia, Maya
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Computer Science & IT

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Raw Material Weighing Application Through Visual-Based RS-232 Cable Port Sofhia, Maya; Manawan, Junio Fegri Wira Manawan
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12158

Abstract

Officers who record incoming weighing data using a manual weighing machine experience difficulties when interacting with the weighing device. It is difficult to press the buttons, the storage memory cannot be more than three digits, and the display is difficult for officials to understand which can hinder the performance of recording the scales. Lack of capacity to store scale data on Officers who record incoming weighing data using a manual weighing machine experience difficulties when interacting with the weighing device. It is difficult to press the buttons, the storage memory cannot be more than three digits, and the display is difficult for officials to understand which can hinder the performance of recording the scales. Lack of capacity to store scale data on machine, so it can only store a maximum of 3 data scales. Inflexible on-machine data storage system. That is, the data scales that have been stored cannot be moved apart from within the machine itself. The large size of the machine is enough to take up space. So it is necessary to design a signal connection path from the scales to the computer via cable. With a computerized weighing application through the RS-232 communication port, where data input can be done using a visual-based weighing application. This data is then processed and produces an accurate report according to the data recorded by the scales. The testing process is carried out by entering data on the scales 19 times along with the check-in and check-out process for each incoming truck of raw materials for transportation. The testing process is carried out so that the application can run properly. machine, so it can only store a maximum of 3 data scales. Inflexible on-machine data storage system. That is, the data scales that have been stored cannot be moved apart from within the machine itself. The large size of the machine is enough to take up space. So it is necessary to design a signal connection path from the scales to the computer via cable. With a computerized weighing application through the RS-232 communication port, where data input can be done using a visual-based weighing application. This data is then processed and produces an accurate report according to the data recorded by the scales. The testing process is carried out by entering data on the scales 19 times along with the check-in and check-out process for each incoming truck of raw materials for transportation. The testing process is carried out so that the application can run properly.
Penerapan Data Science Untuk Memprediksi Transaksi Tidak Valid Pada Sistem Voting Berbasis Blockchain Simamora, Daniel; Purba, Yusuf Deardo; Zamili, Kristianos; Notavianus, Finky; Sofhia, Maya
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 3 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v8i3.15723

Abstract

Keamanan sistem pemungutan suara elektronik (e-voting) berbasis blockchain menjadi perhatian utama di era digital. Penelitian ini bertujuan membangun model evaluasi keamanan e-voting dengan pendekatan data mining, khususnya menggunakan algoritma Support Vector Machine (SVM) dan Random Forest. Dataset yang digunakan terdiri dari lebih dari 500.000 entri transaksi blockchain. Untuk mengatasi ketidakseimbangan kelas antara transaksi sah dan mencurigakan, dilakukan teknik undersampling sehingga diperoleh 100.000 data seimbang sebagai basis pelatihan dan pengujian model. Hasil eksperimen menunjukkan bahwa model Random Forest memiliki akurasi sebesar 95%, namun performanya rendah dalam mendeteksi transaksi mencurigakan, dengan nilai recall hanya 0,00 sebelum tuning dan tetap rendah setelah optimasi. Sebaliknya, model SVM menunjukkan peningkatan signifikan setelah dilakukan hyperparameter tuning, dengan recall pada kelas minoritas meningkat menjadi 0,42 meskipun akurasi keseluruhan menurun menjadi 57%. Temuan ini menunjukkan bahwa model SVM lebih sensitif dalam mengenali pola anomali, meskipun dengan kompromi pada performa kelas mayoritas. Penelitian ini menyoroti tantangan penerapan sistem evaluasi keamanan berbasis machine learning terhadap data blockchain yang tidak seimbang. Diperlukan pengembangan lebih lanjut dengan teknik penanganan data imbalance yang lebih canggih, serta penerapan arsitektur model yang mampu menangani skala besar untuk implementasi secara real-time.
Comparative Evaluation of YOLOv8 and YOLOv11 for Student Behavior Detection in Classroom CCTV Environments Sofhia, Maya
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15648

Abstract

Monitoring student behavior during classroom learning is important for supporting learning quality and teacher performance. This study presents a pilot comparison between YOLOv8 and YOLOv11 for detecting student classroom behaviors from CCTV images. Six elementary behaviors are consistently defined and used throughout the work: lookup, raise-hand, read, stand, turn-head, and write. The available SCB dataset contains 4,934 labeled images, but this study deliberately uses a front-facing subset of 100 images that best represent clear posture and behavior. After augmentation, the dataset grows to 220 images, split into 180 training, 30 validation, and 10 testing images. Both models are trained for 25 epochs on a T4 GPU with comparable configurations. At the detector level, YOLOv11 achieves higher mean average precision (mAP) of 42.9% compared to 28.9% for YOLOv8. At the behavior level, overall classification accuracy on the test set is 43.3% for YOLOv8 and 37.5% for YOLOv11. These results indicate a trade-off: YOLOv11 provides stronger bounding-box detection performance, while YOLOv8 produces slightly more stable behavior-level predictions on this very small and imbalanced dataset. The study emphasizes that these findings are exploratory baselines rather than definitive benchmarks, because the dataset is small and no statistical significance testing is performed. Future work must use a larger portion of the SCB dataset, more balanced class distributions, repeated experiments, and statistical analysis to obtain more robust conclusion.
Co-Authors Manawan, Junio Fegri Wira Manawan Notavianus, Finky Purba, Yusuf Deardo SIMAMORA, DANIEL Zamili, Kristianos