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All Journal Journal of Applied Data Sciences Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Applied Informatics Research
Faris Abdi El hakim
Universitas Negeri Surabaya
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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Designing an Internet of Things-Based Fire and Gas Leak Detection System M Adamu Islam Mashuri; Binti Kholifah; Faris Abdi El Hakim
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44451

Abstract

LPG currently plays a crucial role in daily human activities, both in households and industries. However, gas leaks from LPG cylinders have often led to fire incidents, primarily due to undetected gas emissions. To address this issue, a real-time gas and smoke detection system has been developed using the MQ-2 gas sensor and the ESP32 microcontroller. The system is designed to monitor gas concentration levels and provide early warnings through the Blynk application on smartphones, utilizing the Internet of Things (IoT) concept. The system can detect dangerous gas concentrations above 80 ppm and responds by activating a buzzer and LED indicators. In addition, warning notifications are sent to the user’s smartphone. Test results show the system can accurately detect gas within a 10 cm radius of the sensor, with wireless notification capabilities reaching up to 500 meters. The system proves to be effective in enhancing safety and minimizing fire risks caused by undetected LPG leaks.
Analisis Sentimen Pengguna Aplikasi Mobile JKN Menggunakan Metode Stochastic Gradient Descent (SGD) Faris Abdi El Hakim; M Adamu Islam Mashuri
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.409

Abstract

Kesehatan merupakan aspek penting yang harus dimiliki setiap individu untuk menjalankan aktivitas sehari-hari dan mendukung kesejahteraan masyarakat. Dalam upaya meningkatkan layanan kesehatan, BPJS Kesehatan meluncurkan aplikasi Mobile JKN sejak 2017 untuk mempermudah akses layanan kesehatan secara online seperti pendaftaran antrean, konsultasi dokter, dan informasi fasilitas kesehatan. Namun, aplikasi ini menghadapi berbagai kendala teknis yang menyebabkan ketidakpuasan pengguna yang tercermin dari banyaknya ulasan negatif di Google Play Store. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna Mobile JKN secara otomatis menggunakan metode Stochastic Gradient Descent (SGD). Sebanyak 100.000 data ulasan diunduh dan diproses melalui tahapan pelabelan sentimen, preprocessing teks, dan ekstraksi fitur TF-IDF. Evaluasi model menggunakan K-Fold Cross Validation menghasilkan akurasi tertinggi 91.00% dengan hyperparameter optimal. Hasil ini menunjukkan bahwa metode SGD efektif dan efisien dalam mengolah data ulasan berukuran besar sehingga dapat memberikan masukan berharga bagi pengembang aplikasi Mobile JKN untuk meningkatkan kualitas layanan dan kepuasan pengguna.
Pembuatan dan Pelatihan Sistem Informasi Pondok Pesantren Putri KH. Ahmad Basthomi Sebagai Media Promosi dan Informasi Faris Abdi El Hakim; Moch Deny Pratama; Asmunin Asmunin; Fitria Fitria; Rosita Rosita
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 6 (2025): November 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v5i6.940

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk mengembangkan Sistem Informasi Pondok Pesantren Putri KH. Ahmad Basthomi berbasis web sebagai media promosi dan informasi resmi lembaga. Pengembangan dilakukan karena sebelumnya pondok pesantren belum memiliki sarana digital untuk menyebarkan informasi secara luas dan efisien. Metode pelaksanaan meliputi analisis kebutuhan, pengembangan sistem menggunakan framework Laravel, implementasi, sosialisasi, serta pelatihan pengelolaan konten. Evaluasi dilakukan melalui survei kepuasan pengguna menggunakan skala Likert terhadap aspek tampilan, navigasi, kecepatan akses, akurasi informasi, dan kepuasan umum. Hasil menunjukkan nilai rata-rata kepuasan sebesar 4,51 yang menandakan bahwa sistem mudah digunakan, informatif, dan responsif. Penerapan sistem informasi ini terbukti meningkatkan efektivitas penyebaran informasi dan citra pesantren di masyarakat serta mendorong pesantren beradaptasi dengan perkembangan teknologi digital.
Hybrid Transformer-XGBOOST Model Optimized with Ant Colony Algorithm for Early Heart Disease Detection: A Risk Factor-Driven and Interpretable Method Moch Deny Pratama; Faris Abdi El Hakim; Dimas Novian Aditia Syahputra; Dodik Arwin Dermawan; Asmunin Asmunin; Salamun Rohman Nudin; Andi Iwan Nurhidayat
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.969

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with significant socioeconomic consequences due to premature death and chronic disability. Although clinical screening techniques have evolved, early and accurate prediction of heart disease is still partial due to the limited capacity of conventional machine learning algorithms to model the complex nonlinear interactions among various contributing risk factors e.g., hypertension, diabetes, hyperlipidemia, and genetic predisposition. To address these challenges, this research introduces a hybrid framework that combines the Transformer architecture known for its robust self-attention mechanism and high representational capabilities with Ant Colony Optimization (ACO), a nature-inspired metaheuristic algorithm modeled on the foraging behavior of ants, to enable adaptive and efficient hyperparameter optimization. The proposed model processes structured clinical data by encoding categorical variables into embeddings and normalizing numerical features, resulting in a unified tabular representation suitable for transformer-based analysis. ACO improves model efficiency by optimizing key parameters e.g., embedding configuration, learning rate, and depth, reducing manual intervention and computational overhead. The proposed Hybrid Transformer-ACO model focuses on interpretable clinical features to provide actionable risk stratification. Model evaluation was performed using classification metrics e.g., accuracy, precision, recall, F1 score, and time complexity to measure predictive performance and computational efficiency during the training and inference phases. These evaluation criteria provide evidence of the model's diagnostic reliability, generalizability, and practical feasibility for clinical application.. The model achieved 100% accuracy, sensitivity, specificity, and F1-score, outperforming several models. Time complexity analysis demonstrated efficient training and testing, while the model interpretability supports transparency and trust.
Co-Authors Andi Iwan Nurhidayat Asmunin Asmunin Binti Kholifah Dimas Novian Aditia Syahputra Dodik Arwin Dermawan Fitria Fitria M Adamu Islam Mashuri Moch Deny Pratama Rosita Rosita SALAMUN ROHMAN NUDIN