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All Journal Journal of Information Technology and Computer Science JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Progresif: Jurnal Ilmiah Komputer Jurnal Ilmu Komputer Dan Informatika Jurnal Pengabdian Masyarakat Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Soft Computing Exploration
M Fadli Ridhani
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology Social Sciences

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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
Benchmarking deep transfer learning for imbalanced skin cancer classification: Integrating focal loss, explainable AI, and web deployment Yazid Aufar; Muhammad Daffa Abiyyu Rahman; M. Fadli Ridhani
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.20

Abstract

Non-melanoma skin cancer (NMSC) classification faces challenges like severe data imbalance and the "black-box" nature of AI, limiting clinical trust. This study benchmarks four pre-trained convolutional models (ConvNeXt-Tiny, EfficientNetV2-S, DenseNet121, MobileNetV3-Large) for the imbalanced multi-class classification of Squamous Cell Carcinoma, Actinic Keratosis, and benign Nevus. Images were preprocessed using morphological hair removal and inpainting. The methodology integrated a 5-fold Stratified Group-KFold cross-validation, Focal Loss to address class imbalance, and Grad-CAM for Explainable AI (XAI) transparency. Results showed ConvNeXt-Tiny achieved the highest and most stable performance with a Balanced Accuracy of 76.98% (± 0.31 standard deviation) and a Macro F1-Score of 0.7513, significantly outperforming the other architectures. Grad-CAM confirmed the model's precise focus on pathological lesion borders. Ultimately, the optimal model was deployed as a real-time Streamlit web application, establishing a robust and practical clinical decision-support system.
Pelatihan Visual Branding dalam Pembuatan Konten Kreatif Media Sosial bagi Siswa SMKN 2 Banjarmasin Jiki Romadoni; Muhammad Hidayat; M. Fadli Ridhani; Haji Ahmad Makie; M. Bahril Ilmi
Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2026): Juli
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat Politeknik Negeri Media Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46961/jpk.v6i1.2114

Abstract

Perkembangan media sosial menuntut kemampuan dalam menciptakan konten visual yang menarik dan konsisten, khususnya bagi siswa jurusan Desain Komunikasi Visual (DKV). Namun, sebagian siswa masih belum memahami konsep visual branding, struktur konten digital, serta penggunaan elemen visual seperti warna, tipografi, dan tata letak yang efektif. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan siswa dalam membuat konten kreatif media sosial berbasis visual branding. Kegiatan dilaksanakan dalam bentuk workshop dan praktik langsung kepada 29 siswa kelas X DKV SMKN 2 Banjarmasin. Metode yang digunakan meliputi penyampaian materi, diskusi, bedah desain, praktik redesign konten, serta evaluasi melalui observasi dan presentasi hasil karya peserta. Hasil kegiatan menunjukkan bahwa siswa mulai memahami konsep visual branding, penerapan struktur hook-core-CTA, serta pentingnya konsistensi warna, tipografi, dan elemen visual dalam pembuatan konten media sosial. Selain itu, peserta mampu menerapkan konsep dasar visual branding pada hasil redesign konten yang dibuat selama kegiatan berlangsung.
PENGEMBANGAN MEDIA MOBILE LEARNING BERBASIS AUGMENTED REALITY MENGGUNAKAN ZACHMAN FRAMEWORK PADA MATERI KESEIMBANGAN BENDA TEGAR Muhammad Hidayat; Jiki Romadoni; Haji Ahmad Makie; M. Fadli Ridhani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.10025

Abstract

Penelitian ini bertujuan mengembangkan media pembelajaran berbasis mobile learning dengan integrasi teknologi augmented reality (AR) pada materi keseimbangan benda tegar untuk siswa SMA. Pengembangan dilakukan dengan menggunakan Zachman Framework yang diterapkan pada tiga tahap awal, yaitu Scope Planner, Enterprise Model, dan System Model. Metode penelitian yang digunakan adalah Research and Development (RD) di kombinasi dengan Zachman Framework pada tahapan analisis kebutuhan, perancangan sistem, pengembangan aplikasi, serta validasi oleh ahli media, ahli materi, dan praktisi pembelajaran. Produk yang dihasilkan berupa aplikasi Android berbasis Unity 3D dengan dukungan Vuforia SDK, yang menyajikan visualisasi 3D benda tegar (stabil, labil, netral) melalui pemindaian marker QR Code, dilengkapi fitur interaksi dan kuis evaluasi. Hasil validasi menunjukkan tingkat kelayakan masing-masing sebesar 85,33% (ahli media), 84% (ahli materi), dan 88% (praktisi pembelajaran), dengan rata-rata kelayakan 85,78% dalam kategori “Sangat Baik”. Dengan demikian, media pembelajaran ini dinyatakan layak dan siap diimplementasikan guna mendukung pembelajaran fisika yang interaktif, kontekstual, dan sesuai dengan tuntutan pembelajaran di era digital.
Multi-Modal Ensemble Framework for Mental Health Disorder Prediction: A Novel Machine Learning Approach M. Fadli Ridhani; Tesdiq Prigel Kaloka; Yazid Aufar; Annisa Rizqiana
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 Annisa Rizqiana Arif, M Aidil Haji Ahmad Makie Haji Ahmad Makie Hidayat, Muhammad Jiki Romadoni M. BAHRIL ILMI Muhammad Daffa Abiyyu Rahman Tesdiq Prigel Kaloka Wayan Firdaus Mahmudy Yazid Aufar Yazid Aufar