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Thermal Analysis of Greenhouse Environment Using Computational Fluid Dynamics (CFD), Case Study in ITERA Drantantiyas, Nike Dwi Grevika; Ramli, Asyarf Nur; Suaif, ahmad; Yehezkiel, Listra Ginting
Jurnal IPTEK Vol 28, No 2 (2024): December
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2024.v28i2.6808

Greenhouse is a modern agricultural technology that allows for increased agricultural yields. The purpose of this study was to analyze the thermal distribution in the greenhouse of the Sumatra Institute of Technology (ITERA) in 3 time conditions, namely in the morning, afternoon and evening. The method used is CFD modeling using Solidwork. The dimensions of the greenhouse are 12.5 x 25 x 4.26 m³. The greenhouse is divided into a grid into 20 thermal measurement points separated by 2.5 m. The greenhouse has 2 cooling pads and 2 exhaust fans separated by 12.5 m. Model validation using MAPE and R2. The results of the analysis show that 3 models have valid results with MAPE 10% and R2 0.75 and can continue in the review of thermal distribution. of the 3 time condition models that provide a thermal distribution of 28 - 37 ℃. Morning conditions are hotter than afternoon and evening. Cold air from the cooling pad sucked by the exhaust fan is only able to control an area 50%. So the thermal distribution of the greenhouse needs improvement.

IoT-Based Continuity Analysis of Oil Pipeline Leakages Malau, Nadia Sri Melati; Drantantiyas, Nike Dwi Grevika; Gani, Ferizandi Qauzar
International Journal of Innovation in Mechanical Engineering and Advanced Materials Vol 7, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijimeam.v7i2.33360

Oil pipeline leaks pose a serious challenge due to their potential to cause significant economic losses and severe environmental damage. These incidents can disrupt industrial operations and endanger nearby ecosystems and communities. Early detection and real-time monitoring are therefore essential for minimizing adverse impacts and enabling rapid response. This research develops an Internet of Things (IoT)-based oil pipeline leak monitoring system using integrated multi-sensor data collected from field-simulated scenarios, providing a realistic evaluation of system performance under near-operational conditions. The system incorporates an ultrasonic sensor (HC-SR04) to measure fluid levels, a temperature sensor (DS18B20) to detect thermal anomalies, and a pressure sensor to identify internal pressure fluctuations. Sensor data are wirelessly transmitted via a NodeMCU ESP32 microcontroller to a web-based dashboard for remote monitoring, while local readings are simultaneously displayed on an LCD screen for on-site observation. The system was evaluated through controlled experiments simulating variations in pressure, temperature, and induced leak conditions. Results showed that the system achieved over 95% accuracy in leak detection, with a response time of less than 60 seconds upon leak initiation. The flow rate deviations under leak conditions exceeded the ±3% detection threshold, triggering real-time alerts. In non-leak scenarios, flow rates remained steady between 1.5–2.1 L/min, with tank level variations within 1 cm, confirming strong mass balance and stability. Overall, the developed IoT-based monitoring platform demonstrated high reliability and effectiveness in real-time leak detection, enabling faster response and significantly reducing potential environmental and operational impacts.

Prediksi Penyakit Daun Pisang Menggunakan Metode LSTM (Long Short-Term Memory) Ba’its, Alfian Kafilah; Bagaskara, Radhinka; Setiawan, Andika; Yulita, Winda; Harmiansyah, Harmiansyah; Listiani, Amalia; Untoro, Meida Cahyo; Drantantiyas, Nike Dwi Grevika; Faisal, Amir; Anggraini, Leslie; Febrianto, Andre; Aprilianda, Mohamad Meazza; Fitrawan, Mhd. Kadar
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Dalam sektor pertanian, tanaman yang memiliki peran signifikan dalam skala global adalah pisang, yaitu buah yang mudah didapatkan, dapat tumbuh dimana saja, memiliki gizi yang tinggi, serta memiliki nilai ekonomi & budaya yang tinggi. Pisang mempunyai kontribusi yang signifikan terhadap pendapatan nasional Indonesia, terutama di Provinsi Lampung sebagai penghasil pisang nasional terbesar. Tetapi, proses produksi pisang seringkali mengalami kendala, salah satunya karena faktor serangan penyakit Black Sigatoka. Penyakit tersebut memberikan kerugian pada tanaman pisang, seperti daun yang meranggas, panen tertunda, bakal buah rontok, dan kualitas buah yang rendah, dan dapat menyebar melalui aliran udara atau percikan air hujan. Tingkat keparahan penyakit Black Sigatoka perlu diprediksi agar penyakit tersebut dapat dikontrol dan dapat dicegah sedini mungkin. Model yang digunakan untuk memprediksi permasalahan ini dalam jangka panjang adalah model Long Short-Term Memory (LSTM), salah satu jenis dari arsitektur Recurrent Neural Network (RNN), yang mempunyai kinerja yang baik dan mempunyai model yang prediktif. Aplikasi LSTM diterapkan terhadap dataset pohon pisang yang terdampak penyakit Black Sigatoka. Hasil dari model LSTM dalam melakukan prediksi penyakit Black Sigatoka menghasilkan model dengan nilai error yang kecil, dengan nilai MAE dan MAPE masing-masing sebesar 0.084 dan 5.7%

Pengenalan Internet of Thing pada anak-anak Panti Asuhan Tiara Putri Korpri drantantiyas, nike dwi grevika; Suaif, Ahmad; Gani, Ferizandi Qausar; Khourinisa, Vera; Putra, Septia Eka Marsha
TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Vol 5 No 1 (2025): TEKNOKREATIF : Jurnal Pengabdian kepada Masyarakat Volume 5 No 1
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M), Institut Teknologi Sumatera, Lampung, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/teknokreatif.v5i1.2157

The purpose of this community service activity is to introduce the Internet of Things to children, particularly those in orphanages. The extraordinary development of digital communication technology offers significant positive impacts when properly understood. The method implemented is a knowledge transfer approach, which includes lectures and dialogues, along with examples of commonly used applications. The evaluation was conducted by dividing the participants into two groups: children and teenagers. For the children, the evaluation method involved a question-and-answer session with the success parameter being the number of participants raising their hands. For the teenagers, a pre-test and post-test consisting of multiple-choice questions were administered. The results were quite remarkable, with the success rate for the children reaching 62.5% and the teenagers nearly 88%, proving that the material was very well absorbed.

Prediksi Penyakit Daun Pisang Menggunakan Metode LSTM (Long Short-Term Memory) Ba’its, Alfian Kafilah; Bagaskara, Radhinka; Setiawan, Andika; Yulita, Winda; Harmiansyah, Harmiansyah; Listiani, Amalia; Untoro, Meida Cahyo; Drantantiyas, Nike Dwi Grevika; Faisal, Amir; Anggraini, Leslie; Febrianto, Andre; Aprilianda, Mohamad Meazza; Fitrawan, Mhd. Kadar
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Dalam sektor pertanian, tanaman yang memiliki peran signifikan dalam skala global adalah pisang, yaitu buah yang mudah didapatkan, dapat tumbuh dimana saja, memiliki gizi yang tinggi, serta memiliki nilai ekonomi & budaya yang tinggi. Pisang mempunyai kontribusi yang signifikan terhadap pendapatan nasional Indonesia, terutama di Provinsi Lampung sebagai penghasil pisang nasional terbesar. Tetapi, proses produksi pisang seringkali mengalami kendala, salah satunya karena faktor serangan penyakit Black Sigatoka. Penyakit tersebut memberikan kerugian pada tanaman pisang, seperti daun yang meranggas, panen tertunda, bakal buah rontok, dan kualitas buah yang rendah, dan dapat menyebar melalui aliran udara atau percikan air hujan. Tingkat keparahan penyakit Black Sigatoka perlu diprediksi agar penyakit tersebut dapat dikontrol dan dapat dicegah sedini mungkin. Model yang digunakan untuk memprediksi permasalahan ini dalam jangka panjang adalah model Long Short-Term Memory (LSTM), salah satu jenis dari arsitektur Recurrent Neural Network (RNN), yang mempunyai kinerja yang baik dan mempunyai model yang prediktif. Aplikasi LSTM diterapkan terhadap dataset pohon pisang yang terdampak penyakit Black Sigatoka. Hasil dari model LSTM dalam melakukan prediksi penyakit Black Sigatoka menghasilkan model dengan nilai error yang kecil, dengan nilai MAE dan MAPE masing-masing sebesar 0.084 dan 5.7%

Perancangan Sistem Deteksi Peta Panas (Heatmap) Keramaian Pengunjung di Area Publik Selama Pandemi COVID-19 Berbasis YOLOV4-Tiny Harahap, Al Barra; Drantantiyas, Nike Dwi Grevika; Sasmita, Ismoyo Aji
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 19 No. 3 (2025)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v19n3.2796

Pandemi COVID-19 mengakibatkan masyarakat melakukan aktivitas dengan cara baru yaitu dengan penerapan protokol kesehatan. Pengawasan yang ketat sangat penting dilakukan pada kawasan wisata di area publik untuk menjaga jarak aman dan menghindari kerumuman untuk menekan angka penyebaran virus. Salah satu metode pemantauan aktivitas di ruang terbuka yang dapat digunakan adalah dengan penerapan deep learning. Pada penelitian ini dilakukan perancangan sistem deteksi objek untuk pemetaan kerumunan, kemudian dilakukan penilaian akurasi metode algoritma deteksi objek, dan dilakukan pengujian kemampuan metode algoritma deteksi objek dalam melakukan pemetaan kerumunan. Peta Panas kerumunan dibentuk dengan pendekatan prediktif menggunakan algoritma deteksi objek satu tahap You Only Look Once (YOLO) v4-Tiny berdasarkan pengurangan latar belakang (background subtraction). Pada penelitian ini berhasil dirancang algoritma deteksi objek di area keramaian dengan akurasi 95% dan model akurasi rata-rata 59,45%. Hasil visual pemetaan heatmap kerumunan pengunjung dibentuk dengan warna hitam dan kuning berdasarkan kepadatan arus pengunjung yang melalui suatu lokasi. Warna kuning pada peta di suatu lokasi menandakan pada jalur tersebut terdapat tingkat lalu-lintas manusia yang padat.

Decision Making for The Most Outstanding Students Award using TOPSIS: a Case Study at Institut Teknologi Sumatera Pratama, Borneo Satria; Drantantiyas, Nike Dwi Grevika; Marvie, Ilham; Sembiring, Noveliska Br; Nadi, Muhammad Abi Berkah
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.2114

The internal selection of Pemilihan Mahasiswa Berprestasi, or known as Pilmapres, is an annual competition held by Institut Teknologi Sumatera (ITERA) to award the most outstanding student of the year which will be further sent to compete in regional and national event of Pilmapres held by Balai Pengembangan Talenta Indonesia. This study aimed to implement TOPSIS as a decision-making tool to determine the winner of Pilmapres ITERA in 2023. The criteria used in this study were general achievements, English competencies, and creative ideas, with weight of 50, 20, and 30, respectively. The scores for the criteria for each of the students are obtained from nine members of the board of jury in the final stage of Pilmapres ITERA in 2023. The calculation result using TOPSIS concluded that the 1st, 2nd, and 3rd winners of the internal selection of Pilmapres ITERA in 2023 were Alpha, Beta, and Omega, with the final preference scores of 0.995, 0.799, and 0.795, respectively.

Computational Fluid Dynamics-Based Performance Evaluation of an Air Cooler for University Classroom Conditioning: A Case Study of Classroom E304, ITERA Ningsih, Titis Pajar; Drantantiyas, Nike Dwi Grevika; Khasanah, Rizky Anisatul; Bagaskara, Muhammad Fadli; Situmorang, Zefanya Frandita
Jurnal IPTEK Vol 29, No 2 (2025): December
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2025.v29i2.8439

The advancement of cooling technologies aims to enhance indoor comfort, but conventional air conditioners (AC) raise sustainability concerns due to high energy consumption. This study evaluated the performance of an air cooler in classroom E304, characterized by high occupancy and initial temperatures of 28–30°C with 55–57% relative humidity, exceeding comfort limits per SNI 03-6572-2001. After installing a single air cooler, CFD simulations indicated a temperature reduction to 22.50–23.08°C and relative humidity of 54.89–62.34%, within the comfort range. Model validation demonstrated high accuracy, with RMSE below 1°C and MAPE below 3%, confirming the simulation’s reliability for classroom cooling design. The results demonstrate that air coolers provide an effective and energy-efficient solution for tropical classrooms.

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