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All Journal JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Fisika Indonesia Journal of Innovation Information Technology and Application (JINITA) International Journal of Innovation in Mechanical Engineering and Advanced Materials Electrician : Jurnal Rekayasa dan Teknologi Elektro AMMA : Jurnal Pengabdian Masyarakat
Nike Dwi Grevika Drantantiyas
Institut Teknologi Sumatera
Humanities Astronomy Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics

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Potensi Cahaya Laser sebagai Sensor Kadar Asam Lemak Jenuh pada Minyak Jelantah Nike Dwi Grevika Drantantiyas; Okky Fajar Tri Maryana; Idra Herlina; Prio Santoso
Jurnal Fisika Indonesia Vol 24, No 3 (2020)
Publisher : Department of Physics Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jfi.v24i3.57418

Abstract

Penelitian ini bertujuan untuk menentukan panjang gelombang cahaya yang selektif sebagai sensor kadar asam lemak pada minyak jelantah. Minyak jelantah diperoleh dari limbah konsumsi rumah tangga dan pedagang kaki lima. Sampel minyak jelantah terbagi menjadi jumlah konsumsi yaitu 3 kali pengunaan, 6 kali penggunaan dan 9 kali penggunaan. Detektor cahaya yang digunakan adalah light dependent resistance yang dikendalikan oleh Arduino. Sumebr cahaya adalah laser dengan tiga panjang gelombang yaitu merah, hijau dan biru. Hasil yang diperoleh adalah perbedaan besar resisitansi antara cahaya yang melewati material dan cahaya tanpa melewai material. Berdasarkan hasil, akan dijelaskan interaksi cahaya yang melewati minyak jelantah dan panjang gelombang cahaya. Kadar asam lemak bebas pada minyak jelantah diukur dengan uji asam lemak bebas laboratorium. Berdasarkan kedua hasil pengukuran akan dikonversikan sehingga mendapat model sistem pengukuran. Berdasarkan model tersebut, panjang gelombang biru lebih sensitif sebagai sensor daripada panjang gelombang merah dan hijau.
Decision Making for The Most Outstanding Students Award using TOPSIS: a Case Study at Institut Teknologi Sumatera Borneo Satria Pratama; Nike Dwi Grevika Drantantiyas; Ilham Marvie; Noveliska Br Sembiring; Muhammad Abi Berkah Nadi
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

Abstract

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.
Performasi Deteksi Jumlah Manusia Menggunakan YOLOv8 Nike Dwi Grevika Drantantiyas; Winda Yulita; Naufal Taufiq Ridwan; Uri Arta Ramadhani; Rahman Indra Kesuma; Arkham Zahri Rakhman; Radhinka Bagaskara; Afit Miranto; Zunanik Mufidah
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 5 No. 2 (2023): Desember
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v5i2.11605

Abstract

Pengembangan deteksi kepala sudah meningkat dengan adanya peningkatan algoritma kecerdasan buatan. Peningkatan ini dapat pula dengan penambahan tugas yaitu menghitung jumlah orang dengan mendeteksi jumlah kepala. Tujuan penelitian ini adalah menentukan performansi model sistem penghitung jumlah kepala dengan menggunakan algoritma Yolov8. Penelitian ini hanya berfokus membuat model deteksi jumlah orang. Jumlah dataset yang dirancang berjumlah 2390 gambar yang diperoleh dari dataset Roboflow, dengan pemisahan data sebesar 70:20:10 untuk masing-masing, data latih; data uji ; data validasi. Besar Epoch pada pelatihan model yang digunakan adalah 50. Algoritma deteksi jumlah kepala meggunakan YOLOv8. Nilai yang diukur adalah performasi dari model data training, nilai confusion matrix dan nilai evaluasi dari confusion matrix. Nilai evaluasi yang akan dihitung adalah nilai presisi, nilai akurasi, recall dan f1-score.  Diperoleh hasil pengujian nilai akurasi sebesar 87,56 %, nilai presisi 83,74%, nilai recall  100% dan nilai F1-score 91,15%. Kurva presisi memberikan nilai tertinggi 1 pada tingkat kepercayaan 0,857, recall bernilai 0,8 pada tingkat kepercayaan 0, f1 0,716 pada kepercayaan 0,36 dan presisi-recall 0,771 pada 0,5 mAP. Berdasarkan nilai ini, model sudah cukup mendeteksi jumlah kepala.
Pelatihan Perakitan Robot Line Follower Pada Siswa SMAN 6 Metro Lampung Al Barra Harahap; Listra Yehezkiel Ginting; Nike Dwi Grevika Drantantiyas; Amrina Mustaqim; Hesti Wahyu Handani
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 11 : Desember (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

 Robotic system is a combination of various elements working together to control the behaviour, movement, and decisions made by the robot. The components are made of hardwares, softwares, user interface,  communication, mechanical structure, energy source, and safety. Using this technology, students of SMAN 6 Metro can stay up to date with recent technologies, and can implement it to solve problems as necessary. The aim for this community service is to introduce knowledge about the utility of robotic systems related to the components of the system and its implementation. The target of this event is students of SMAN 6 Metro and also the university students attending this event. Introduction starts with definition of robotic systems, components of a robot, methodology, and implementation. As a result of this event, we find that the knowledge of the students of SMAN 6 Metro seem to be improving significantly. The experience proved to be a fascinating experience for the students involved.
IoT-Based Continuity Analysis of Oil Pipeline Leakages Nadia Sri Melati Malau; Nike Dwi Grevika Drantantiyas; Ferizandi Qauzar Gani
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

Abstract

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.
Perancangan Sistem Deteksi Peta Panas (Heatmap) Keramaian Pengunjung di Area Publik Selama Pandemi COVID-19 Berbasis YOLOV4-Tiny Al Barra Harahap; Nike Dwi Grevika Drantantiyas; Ismoyo Aji Sasmita
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

Abstract

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.
Co-Authors Afit Miranto Al Barra Harahap Amrina Mustaqim Arkham Zahri Rakhman Borneo Satria Pratama Ferizandi Qauzar Gani Harahap, Al Barra Hesti Wahyu Handani Idra Herlina Ilham Marvie Ismoyo Aji Sasmita Listra Yehezkiel Ginting Mufidah, Zunanik Nadi, Muhammad Abi Berkah Nadia Sri Melati Malau Naufal Taufiq Ridwan Noveliska Br Sembiring Okky Fajar Tri Maryana Prio Santoso Radhinka Bagaskara Rahman Indra Kesuma URI ARTA RAMADHANI Winda Yulita