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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Data and Science
Tasrif Hasanuddin
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Modifikasi Least Significant Bits pada Gambar sebagai Data Hiding Steganography A. Muh. Ramadhani; Tasrif Hasanuddin
Indonesian Journal of Data and Science Vol. 2 No. 2 (2021): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v2i3.48

Abstract

Penelitian ini menghasilkan kombinasi teknik steganografi dan kriptografi dengan metode LSB. merupakan teknik kriptografi yang populer dapat diterapkan pada citra digital. Nilai piksel citra digital hanya berkisar 0 sampai 255, Dalam penelitian ini diusulkan untuk mengkonversi nilai piksel citra menjadi 16bit sehingga kunci yang digunakan dapat lebih bervariasi. Hasil eksperimen membuktikan adanya peningkatan keamanan serta nilai imperceptibility yang tetap terjaga. Hal ini dibuktikan dengan hasil PSNR 77,79dB, MSE 0.0005dB
Smart Waste Bin Prototype for University Waste Management Fathrurahman, Fauzy; Dolly Indra; Tasrif Hasanuddin; Herdianti Darwis; Tanaka Kazuaki
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.324

Abstract

Background: Waste mismanagement remains a critical issue in Indonesian campuses, where ineffective segregation and collection practices contribute to environmental pollution. Smart technologies offer opportunities to improve waste handling efficiency and monitoring in university environments. Methods: This study developed a smart waste bin prototype that integrates Internet of Things (IoT) sensors, machine learning–based image classification (MobileNetV2 with TensorFlow Lite), GPS tracking, and LoRa communication. The system was designed to classify three types of waste—plastic bottles, snack packaging, and cans—while enabling fill-level monitoring, automated sorting, and real-time location reporting. Results: Experimental results showed strong classification accuracy for plastic bottles (100%), but lower performance for snack packaging (53–80%) and cans (40–67%), especially in low-light conditions or with darker materials. The overall real-time testing accuracy reached 45.1%. LoRa communication provided long-range connectivity but was affected by electromagnetic interference, while GPS tracking was reliable in open areas but inconsistent indoors. Conclusions: The prototype demonstrates the feasibility of integrating AI and IoT for scalable campus waste management. Despite environmental and hardware limitations, it offers a modular framework that can be refined with improved lighting, EMI shielding, and enhanced datasets. This research contributes a practical model for smart campus initiatives and supports the adoption of sustainable waste management practices in higher education environments.
Co-Authors A. Muh. Ramadhani Dolly Indra Fathrurahman, Fauzy Herdianti Darwis Tanaka Kazuaki