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Rancang Bangun Sistem Monitoring Lingkungan (Suhu Air) Akuarium Berbasis Internet of Things Hendi Santoso; Rasional Sitepu; Andrew Joewono
Journal of Information Technology Vol 5 No 1 (2025): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v5i1.983

Abstract

Pemantauan suhu air dalam akuarium sangat penting untuk menjaga kesehatan ikan dan keseimbangan ekosistemnya. Perubahan suhu yang tidak sesuai dapat menyebabkan stres pada ikan, menurunkan daya tahan tubuh, dan bahkan menyebabkan kematian. Namun, pemantauan suhu secara manual kurang efisien dan memerlukan perhatian terus-menerus. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem pemantauan suhu air akuarium berbasis Internet of Things (IoT) yang dapat diakses secara real-time melalui website mandiri.Sistem ini menggunakan sensor DS18B20 untuk mengukur suhu air, yang terhubung dengan mikrokontroler NodeMCU ESP8266. Data suhu dikirimkan secara nirkabel ke server melalui koneksi WiFi dan disimpan dalam database. Informasi suhu kemudian ditampilkan pada website agar pengguna dapat memantau kondisi akuarium kapan saja dan di mana saja.Hasil pengujian menunjukkan bahwa sistem ini dapat memantau suhu air akuarium secara akurat dan memberikan kemudahan akses data secara real-time. Dengan adanya sistem ini, pemilik akuarium dapat lebih mudah mengontrol suhu air, sehingga kesehatan ikan tetap terjaga.
Transformasi Menuju Keberlanjutan: Analisis QoS dan Maintenance Jaringan Pada Diskominfo Bengkayang yang Mendukung SDGs Maya Sari; Andrew Joewono
Journal of Information Technology Vol 5 No 1 (2025): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v5i1.988

Abstract

Improving network quality and the effectiveness of digital infrastructure maintenance play an important role in supporting the achievement of the Sustainable Development Goals (SDGs). Through Quality of Service (QoS) analysis and network maintenance strategies at Diskominfo Bengkayang, this study shows that network quality generally meets ITU-T G.114 and Cisco standards, with a throughput of 6721 kb/s, packet loss of 0.200%, latency of 17.250699 ms, and jitter of 17.261801 ms. However, packet loss still exceeds the ideal limit (2%), which can affect the stability of digital services. The maintenance strategy implemented includes preventive and corrective maintenance, with an average recovery time of 1-3 hours. The main obstacles include limited technical human resources, lack of a real-time monitoring system, and the use of old network devices. This network quality improvement contributes to the achievement of SDG 9 (reliable infrastructure supporting the digitization of government services), SDG 11 (development of smart cities in Bengkayang), and SDG 17 (partnership with ISPs, academics, and the private sector). This research recommends the use of more comprehensive QoS parameters and infrastructure improvements to support better digital services in the future.
Analisis Teknologi Manajemen Energi Pada Kendaraan Listrik Hibrida Berbasis Tinjauan Pustaka Theophilus Ezra Nugroho Pandin; Bryan Hulio Santoso; Rasional Sitepu; Andrew Joewono
Widya Teknik Vol. 21 No. 2 (2022): November
Publisher : Fakultas Teknik, Universitas Katolik Widya Mandala Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33508/wt.v21i2.4531

Abstract

The application of hybrid electric vehicle technology has grown rapidly in recent years. This article aims to describe and discuss energy management strategies in hybrid electric vehicles. The research method is qualitative with a systematic literature review based on database searches on IEEE, Garuda SINTA, ArXiv, Preprints. The results obtained 13 articles from the IEEE database by describing the results of the energy management strategy of each article. The conclusion is that the technology used for energy management strategies includes algorithm settings, namely reinforcement learning and Q-learning combined with several control systems, namely predictive control models, Equivalent Consumption Minimization Strategy, and Dynamic Programming.