Article Per Year (5 Year)
p-Index From 2021 - 2026
2.078
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electrical Engineering and Computer Science Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) ABDI SABHA (Jurnal Pengabdian kepada Masyarakat) Hello World Journal of Artificial Intelligence and Engineering Applications (JAIEA) Nusantara Journal of Multidisciplinary Science Khidmat Jurnal Sains, Teknologi dan Komputer Portal Riset dan Inovasi Sistem Perangkat Lunak Tsabit Jurnal Informatika Dan Tekonologi Komputer
Firahmi Rizky
Universitas Muhammadiyah Sumatera Utara
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Design and development of an IoT-based smart waste bin with automatic incineration and remote monitoring Fahri Syahnanda; Firahmi Rizky
Jurnal Sains, Teknologi & Komputer Vol. 3 No. 1 (2026): Jurnal Sains, Teknologi & Komputer (SAINTEK)
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/saintek.v3i1.1315

Abstract

Household waste management challenges encourage the development of technologies that support more integrated waste handling. This study aims to design and develop an Internet of Things (IoT)-based smart waste bin prototype with an automatic incineration mechanism and remote monitoring. The system was developed using the Prototype Model by integrating a NodeMCU ESP8266, an HC-SR04 ultrasonic sensor for waste-level detection, a limit switch as a fail-safe mechanism, and a servo motor and flame gun for automatic incineration. System status notifications were delivered to users through a Telegram Bot. The results showed that the prototype successfully performed waste-level detection, timer-based automatic incineration, fail-safe operation, and status notification according to the defined test scenarios. In the combustion test using 500 g of mixed paper and cardboard, 22 g of residual mass remained, corresponding to a mass reduction of 95.6%. The ignition mechanism succeeded in 10 out of 10 trials, while Telegram notifications were received with a delay of approximately 2–4 seconds. These results demonstrate that waste detection, automatic incineration, operational safety, and remote monitoring functions can be integrated into a single IoT-based prototype.
Klasifikasi Jenis Tanah Berbasis Deep Learning Menggunakan Algoritma CNN Eka Nurul Sabrina; Firahmi Rizky
Hello World Jurnal Ilmu Komputer Vol. 5 No. 2 (2026): Edisi Juli 2026
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hello_world.v5i2.1320

Abstract

Klasifikasi jenis tanah seperti aluvial, inceptisol, dan entisol memiliki peranan penting dalam perencanaan penggunaan lahan, pertanian, serta pengembangan infrastruktur. Proses identifikasi jenis tanah secara manual melalui observasi lapangan dan analisis laboratorium seringkali memakan waktu, biaya besar, serta memerlukan keahlian khusus. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis terhadap jenis tanah aluvial, inceptisol, dan entisol menggunakan metode deep learning dengan algoritma Convolutional Neural Network (CNN). Dataset berupa citra tanah diperoleh dari sumber primer (pengambilan langsung) dan sekunder (pangkalan data online). Proses penelitian mencakup preprocessing data seperti normalisasi, augmentasi, dan perubahan ukuran citra, dilanjutkan dengan pelabelan dan pelatihan model CNN. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan ketiga jenis tanah tersebut dengan tingkat akurasi yang tinggi, sehingga pendekatan ini efektif untuk mempercepat proses klasifikasi dan mengurangi ketergantungan pada metode manual. Temuan ini diharapkan dapat menjadi solusi inovatif dalam mendukung pengambilan keputusan berbasis teknologi di bidang geoteknik dan lingkungan.
Sistem Pakar Diagnosa Penyakit Tanaman Tomat (Solanum Lycopersicum) Menggunakan Metode Certainy Factor Di Desa Merek Mei Widianti; Firahmi Rizky
Nusantara Journal of Multidisciplinary Science Vol. 3 No. 10 (2026): NJMS - Mei 2026
Publisher : PT. Inovasi Teknologi Komputer

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

Abstract

Tanaman tomat merupakan komoditas hortikultura yang rentan terhadap berbagai penyakit. Keterbatasan akses petani terhadap pakar serta kesulitan dalam melakukan diagnosis menjadi permasalahan utama. Penelitian ini bertujuan untuk membangun sistem pakar berbasis website untuk mendiagnosis penyakit tanaman tomat menggunakan metode Certainty Factor (CF).Metode Certainty Factor diguna kan untuk mengatasi ketidakpastian dengan menggabungkan nilai keyakinan dari pakar dan pengguna. Data diperoleh dari wawancara dengan pakar dan pengujian menggunakan data enam responden. Hasil penelitian menunjukkan bahwa sistem mampu memberikan diagnosis penyakit seperti Busuk Daun (Late Blight), Virus Leaf Curl, Bercak Daun, Leaf Mold, dan Layu Fusarium dengan nilai tingkat keyakinan tertentu.Sistem yang dibangun diharapkan dapat membantu petani dalam melakukan diagnosis awal secara cepat dan praktis
Analisis Sentimen Nasabah Terhadap Pengguna M-Banking Raya Indonesia Menggunakan Metode Naïve Bayes Berbasis NLP Yusi Meilanda; Firahmi Rizky
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 2 (2026): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i2.11282

Abstract

This study aims to analyze customer sentiment toward the use of the Raya Indonesia M-Banking app based on reviews on the Google Play Store using a Naïve Bayes method grounded in Natural Language Processing (NLP). Data was collected via web scraping, yielding 501 reviews, of which 474 were deemed valid after the selection process. The analysis stages included text preprocessing (case folding, cleansing, tokenizing, stopword removal, and stemming), feature extraction using TF-IDF, and classification using Multinomial Naïve Bayes with a training and testing data split of 80:20. The results of the study show that positive sentiment dominates at 62.66%, followed by negative sentiment at 31.43%, and neutral sentiment at 5.91%. Model evaluation yielded an accuracy of 75.79%, precision of 73.17%, recall of 75.79%, and an F1-score of 70.97%, indicating that the model performed quite well. Further analysis revealed that the main user complaints relate to login failures, transaction issues, and slow service responses; Thus, these findings provide a basis for evaluating and improving digital banking service quality.
Co-Authors Abdi Sudrazad Ahmad Fitry Boy Al Khowarizmi Amrullah Dedi Setiawan Egi Afandi Eka Nurul Sabrina Fahri Syahnanda Fathahul Ammar Hasibuan Jenisya Wulandari Lussy Ria Arista Luthfi Pratama Nst Marnoko Marnoko Mei Widianti Muhammad Adra Al Ichsan Nazwa Putri Ananda Shofiah Adelia Surya Lesmana Syukri Annisa Pasaribu Trinanda Syahputra Usti Fatimah Sari Sitorus Pane Yusi Meilanda