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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Bekti Maryuni Susanto
Politeknik Negeri Jember, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Sistem Monitoring Suhu dan Pengairan Otomatis Pada Tanaman Stroberi Berbasis Website Akhmad Farizi; Bekti Maryuni Susanto; Ery Setiyawan Jullev Atmadji; Agus Hariyanto; Elly Antika
Jurnal Teknologi Informasi dan Terapan Vol 8 No 2 (2021)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v8i2.255

Abstract

Abstract— Strawberry plants need a certain amount of soil moisture to thrive. In the process of watering strawberry plants, farmers have to go to the garden every day to do watering and monitor the condition of the garden manually, the process of watering manually is like using a bucket and a pitcher, this process is a waste of energy and time, if the farmer's house is far from the garden , farmers or cultivators must make observations when the time is right for watering according to the soil conditions of the strawberry plant. This research implements a website-based automatic temperature monitoring and irrigation system where the website can be accessed via the public Internet network, not only on the local network. The results of observations on the growth of strawberry plants showed that plants that were given automatic irrigation based on the Internet of Things (IoT) grew better when compared to plants that were given manual irrigation. Keywords—strawberry; monitoring; temperature; waterring; microcontroller; Internet of Things. Abstrak— Tanaman stroberi membutuhkan kelembapan tanah tertentu agar dapat berkembang dengan baik. Pada proses penyiraman tanaman stroberi, petani harus pergi ke kebun setiap hari untuk melakukan penyiraman dan memonitoring kondisi kebun secara manual, proses penyiraman secara manual seperti menggunakan ember dan teko kocor, proses ini sangatlah membuang tenaga dan juga waktu, apabila rumah petani jauh dari kebun tersebut, petani atau pembudidaya harus melakukan pengamatan kapan waktu yang tempat untuk melakukan penyiraman sesuai dengan kondisi tanah dari tanaman stroberi. Penelitian ini mengimplementasikan sistem monitoring suhu dan pengairan otomatis berbasis website dimana website dapat diakses melalui jaringan publik Internet bukan hanya di jaringan lokal. Hasil pengamatan pertumbuhan tanaman stroberi menunjukkan bahwa tanaman yang diberikan pengairan otomatis berbasis Internet of Things (IoT) tumbuh lebih baik jika dibandingkan dengan tanaman yang diberikan pengairan secara manual. Keywords—stroberi; monitoring; suhu; pengairan; mikrokontroller; Internet of Things.
Implementation of the Template Matching Algorithm for Smart Light Control through Speech Recognition for People with Disabilities Sholihah Ayu Wulandari; Adisty Pramudita Putri Rudi; Adi Sucipto; Bekti Maryuni Susanto; Dhony Manggala Putra
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.449

Abstract

Voice control systems in smart homes provide significant convenience for people with disabilities, especially in operating household devices such as lights without physical interaction. This study develops a voice-based light control system that runs locally on IoT devices using the template matching method. This system utilizes Mel-Frequency Cepstral Coefficients (MFCC) for voice feature extraction and Dynamic Time Warping (DTW) to match test voices with pre-recorded templates. Out of 66 voice samples tested, the system successfully recognized 13 out of 22 voices belonging to the primary user and rejected 43 out of 44 voices from other users, with an accuracy rate of 84.85%. Thus, this system shows potential as an inclusive, efficient, and disability-friendly voice control solution for smart home environments
Multivariate LSTM with SLO-Aware Loss for Virtual Machine Workload Prediction on Cloud Data Center Agus Hariyanto; Ahmad Fahriyannur Rosyady; Adi Sucipto; Bekti Maryuni Susanto; Sapta Nugraha; Nicolas Chenu
Jurnal Teknologi Informasi dan Terapan Vol 13 No 1 (2026): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v13i1.490

Abstract

Accurate virtual machine (VM) workload prediction is a key component of cloud resource management, particularly to support auto-scaling and to maintain Service Level Objectives (SLOs). In conventional prediction models that rely on symmetric loss functions such as Mean Squared Error (MSE), under-prediction errors are treated equivalently to over-prediction errors, even though under-prediction carries significantly more severe operational consequences — it directly triggers capacity shortages and SLO violations. This study proposes a CPU workload prediction approach based on a multivariate Long Short-Term Memory (LSTM) network enhanced with an SLO-aware loss, an asymmetric loss function that penalizes under-prediction ten times more heavily than over-prediction. Experiments are conducted on a subset of 25,000 rows from the Bitbrain GWA-T-12 fastStorage dataset with four input features (CPU, memory, network received, network transmitted), using a fixed random seed for reproducibility. Two models are trained and compared: one with SLO-aware loss and one with standard MSE as baseline, both sharing identical architecture and hyperparameters. The primary evaluation metric is the under-prediction rate, which directly quantifies SLO violation risk. Results show that the SLO-aware model achieves an under-prediction rate of 0.04%, compared to 0.16% for the MSE baseline — a fourfold reduction. These findings empirically confirm that SLO-aware loss effectively directs the model toward conservative predictions that protect SLO compliance, establishing loss function design as a critical and actionable dimension in cloud VM workload prediction.
Co-Authors Adi Sucipto Adisty Pramudita Putri Rudi Agus Hariyanto Agus Hariyanto Ahmad Fahriyannur Rosyady Akhmad Farizi Dhony Manggala Putra Elly Antika Ery Setiyawan Jullev Atmadji Nicolas Chenu Sapta Nugraha Sholihah Ayu Wulandari