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All Journal Journal of Information Technology and Computer Science Progresif: Jurnal Ilmiah Komputer Jurnal Ilmu Komputer Dan Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
M Fadli Ridhani
Politeknik Hasnur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Advancements in Fire Alarm Detection using Computer Vision and Machine Learning: A Literature Review M Fadli Ridhani; Wayan Firdaus Mahmudy
Journal of Information Technology and Computer Science Vol. 8 No. 2: August 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202382554

Abstract

Fire is one of the most common and increasing emergencies that threaten public safety and social development. This can cause significant loss of life and damage. Fire detection systems play an important role in the early detection of fires. The purpose of this study is to provide a brief survey of the latest literature in the field, which can provide a foundation for researchers to develop a Fire Alarm Detection System with a Computer Vision and Machine Learning approach. The Computer Vision and Machine Learning approaches are popular and have been extensively studied because the advantages. The main challenges in fire detection systems are high false alarm rates and slow response times. This research presents potentials and emerging trends through Computer Vision and Machine Learning approaches for Fire Alarm Detection Systems in the future, including the selection of input features to the use of appropriate methods and the process flow of Fire Alarm Detection Systems.
Evaluasi Tingkat Usability Sistem Informasi Perpustakaan Berbasis Website Menggunakan System Usability Scale (SUS) ROMADONI, JIKI; Ahmad Makie, Haji; Ridhani, M. Fadli; Hidayat, Muhammad
JUKOMIKA (Jurnal Ilmu Komputer dan Informatika) Vol. 8 No. 2 (2025): Desember
Publisher : LPPMPP Yayasan Sejahtera Bersama Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jukomika.v8i2.624

Abstract

Sistem informasi perpustakaan berbasis web telah banyak dikembangkan untuk mendukung pengelolaan layanan perpustakaan di lingkungan pendidikan. Namun, sebagian besar penelitian sebelumnya masih berfokus pada pengembangan sistem dan pengujian fungsional, tanpa mengevaluasi tingkat usability dari sudut pandang pengguna. Oleh karena itu, penelitian ini bertujuan untuk mengevaluasi tingkat usability sistem informasi perpustakaan berbasis web menggunakan metode System Usability Scale (SUS). Penelitian ini melibatkan 15 responden, terdiri dari 2 admin perpustakaan, 5 guru, dan 8 siswa, sebagai pengguna sistem. Instrumen penelitian menggunakan kuesioner SUS dengan 10 pernyataan standar yang dianalisis untuk memperoleh skor usability. Hasil pengujian menunjukkan bahwa sistem memperoleh skor SUS sebesar 78, yang termasuk dalam kategori Good dan Acceptable, sehingga sistem dinilai mudah digunakan dan dapat diterima oleh pengguna. Hasil penelitian ini diharapkan dapat memberikan kontribusi dalam evaluasi kualitas sistem informasi perpustakaan berbasis web serta menjadi bahan pertimbangan dalam pengembangan sistem serupa di lingkungan pendidikan.
Implementasi Backpropagation Neural Network pada Sistem Electronic Nose untuk Klasifikasi Aroma Teh Arif, M. Aidil; Hidayat, Muhammad; Ridhani, M. Fadli
Progresif: Jurnal Ilmiah Komputer Vol 22, No 1 (2026): Januari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i1.3482

Abstract

Conventional tea aroma quality assessment is subjective and slow. This study aims to design and implement an Arduino Uno-based automatic Electronic Nose (e-nose) system with a TGS sensor array (880, 826, 822, 825) combined with a Backpropagation Neural Network (BPNN) for tea aroma classification. The method includes signal acquisition, normalization, feature extraction, and sensor correlation analysis to form a chemical fingerprint before modeling. Testing with a confusion matrix on three types of tea (black, green, and jasmine) showed performance with an accuracy of 0.71, precision of 0.71, recall of 0.72, and f-measure of 0.71. The results of this study provide an objective, fast, economical, and non-destructive aroma evaluation method and contribute to the development of smart sensor technology to support the competitiveness of Indonesian tea products. The main novelty of this study is the integration of sensor correlation analysis into the modeling pipeline with an end-to-end classification system that combines sensor correlation analysis to optimize the performance of the BPNN model on the tea aroma dataset.Keywords: Arduino; Tea Aroma; Backpropagation; Electronic Nose; TGS Sensor AbstrakPenilaian mutu aroma teh secara konvensional bersifat subjektif dan lambat. Penelitian ini bertujuan merancang dan mengimplementasikan sistem Electronic Nose (e-nose) otomatis berbasis Arduino Uno dengan array sensor TGS (880, 826, 822, 825) yang dikombinasikan Backpropagation Neural Network (BPNN) untuk klasifikasi aroma teh. Metode mencakup akuisisi sinyal, normalisasi, ekstraksi fitur, dan analisis korelasi sensor untuk membentuk chemical fingerprint sebelum pemodelan. Pengujian dengan confusion matrix pada tiga jenis teh (hitam, hijau, wangi melati) menunjukkan performa dengan akurasi 0,71, presisi 0,71, recall 0,72, dan f-measure 0,71. Hasil penelitian memberikan metode evaluasi aroma yang objektif, cepat, ekonomis, dan non destruktif, serta berkontribusi pada pengembangan teknologi sensor cerdas untuk mendukung daya saing produk teh Indonesia. Kebaruan utama penelitian ini adalah pada integrasi analisis korelasi sensor ke dalam pipeline pemodelan dengan sistem klasifikasi end-to-end yang menggabungkan analisis korelasi sensor untuk mengoptimalkan performa model BPNN pada dataset aroma teh.Kata kunci: Arduino; Aroma Teh; Backpropagation; Electronic Nose; Sensor TGS
Multi-Modal Ensemble Framework for Mental Health Disorder Prediction: A Novel Machine Learning Approach M. Fadli Ridhani; Tesdiq Prigel Kaloka; Yazid Aufar; Rizqiana, Annisa
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.300

Abstract

Mental health disorders constitute a major global public health concern, affecting millions of individuals across diverse socioeconomic and cultural contexts. Accurate prediction of mental health outcomes at the population level remains challenging due to the complex and non-linear relationships among co-occurring disorders. Previous studies relying on traditional statistical approaches, particularly linear regression, have reported limited predictive performance, with an R² of approximately  0.7175. This limitation highlights the need for more advanced analytical frameworks capable of capturing comorbidity patterns and non-linear interactions among mental health conditions. This study proposes and evaluates a novel multi-modal ensemble machine learning framework to improve the prediction accuracy of eating disorder prevalence using global mental health data. The analysis utilizes country-level prevalence data for schizophrenia, depression, anxiety, bipolar disorder, and eating disorders across multiple countries and years. Eating disorder prevalence is modeled as the primary target variable, while other mental health disorders are incorporated as predictive features to represent clinically established comorbidity relationships. To enhance the representational capacity of the data, an extensive feature engineering strategy was applied, generating 19 additional features through polynomial transformations, interaction terms, ratio-based indicators, and aggregate burden measures. Unsupervised clustering techniques, including K-Means, DBSCAN, and hierarchical clustering, were employed to identify natural groupings of countries based on their mental health profiles. Furthermore, ten machine learning algorithms were systematically evaluated, including linear models, tree-based methods, neural networks, and support vector regression. The best-performing models were subsequently integrated into a stacking ensemble architecture. Experimental results demonstrate that the proposed stacking ensemble achieved a test R² score of 0.9955, corresponding to a 42.2% improvement over the baseline linear regression model. These results indicate that multi-modal ensemble approaches substantially enhance predictive accuracy and provide valuable insights to support evidence-based global mental health policy and targeted intervention planning.
Co-Authors Arif, M. Aidil Haji Ahmad Makie Hidayat, Muhammad Jiki Romadoni Rizqiana, Annisa Tesdiq Prigel Kaloka Wayan Firdaus Mahmudy Yazid Aufar