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All Journal Jurnal Informatika dan Teknik Elektro Terapan Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Integrasi Kesehatan dan Sains Journal of Computing Theories and Applications
Rahman, Imam Fathur
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health

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TINJAUAN PUSTAKA: “ANALISIS PROSES ISOLASI LIMONEN DARI MINYAK ATSIRI MENGGUNAKAN BERBAGAI TEKNIK DISTILASI” Alfiyah, Mutiara; Rahman, Imam Fathur
Jurnal Integrasi Kesehatan dan Sains Vol 6, No 2 (2024): Jurnal Integrasi Kesehatan dan Sains
Publisher : Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jiks.v6i2.13746

Abstract

AbstrakMinyak atsiri merupakan minyak yang mudah menguap yang dapat ditemukan di berbagai tanaman. Dalam literatur review ini, kami mengeksplorasi efisiensi isolasi minyak atsiri dari berbagai tanaman dengan menggunakan berbagai metode. Pada literatur review ini bertujuan mengidentifikasi karakteristik dan kualitas beberapa metode yang digunakan pada sampel dalam proses isolasi senyawa limonen. Artikel ini juga berfungsi sebagai referensi bagi pembaca dan peneliti selanjutnya. Selain itu, artikel ini dapat membantu memudahkan pengembangan isolasi senyawa limonen sebab sumber dan referensi mengenai senyawa ini masih terbatas. Metodologi artikel tinjauan melibatkan pencarian di Google Scholar dan pubmed untuk artikel yang relevan, meninjau 20 makalah terkait, dan memilih lima yang paling sesuai dengan topik yang dibahas. Hasil literatur review ini, yaitu metode yang sering digunakan untuk mengisolasi senyawa minyak atsiri adalah metode distilasi uap-air. Literature Review: Analysis of Limonene Isolation Process from Essential Oils Using Various Distillation TechniquesAbstractEssential oils are volatile oils that can be found in various plants. This literature review uses multiple methods to explore the efficiency of critical oil isolation from different plants. This literature review aims to identify the characteristics and quality of several techniques used in the isolation process of the compound limonene. The article also serves as a reference for readers and future researchers. Additionally, this article can help facilitate the development of limonene isolation since sources and references about this compound still need to be improved. The review methodology involves searching Google Scholar and PubMed for relevant articles, reviewing 20 related papers, and selecting the five most appropriate to the discussed topic. The results of this literature review indicate that the steam-water distillation method is frequently used to isolate essential oil compounds.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI SAMSAT DIGIITAL NASIONAL (SIGNAL) DENGAN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Rahman, Imam Fathur; Hasanah, Anisa Nur; Heryana, Nono
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 2 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i2.4073

Abstract

Aplikasi Samsat Digital Nasional (SIGNAL) adalah sebuah aplikasi yang memungkinkan pembayaran pajak kendaraan bermotor secara online. Aplikasi ini memiliki banyak fitur dan insentif yang ditujukan untuk meningkatkan kenyamanan dan kepatuhan perpajakan pengguna. Namun, tidak semua pengguna merasa puas dengan aplikasi ini. Beberapa pengguna mengeluhkan masalah teknis, kesalahan data, atau layanan pelanggan yang kurang responsif. Tujuan dari penelitian ini yaitu melakukan analisis sentimen dari ulasan pengguna aplikasi SIGNAL yang tersedia di Google Play Store dengan menggunakan metode Naïve Bayes Classifier. Penelitian ini juga bermaksud untuk mengetahui faktor-faktor yang mempengaruhi kepuasan dan ketidakpuasan pengguna dan rekomendasi terhadap pengembang aplikasi untuk meningkatkan kualitas layanan dan fitur yang ditawarkan. Setelah dilakukannya penelitian ini penulis berharap dapat berkontribusi terhadap pengembangan ilmu pengetahuan dan teknologi, khususnya dalam bidang analisis sentimen dan Aplikasi Samsat Digital Nasional (SIGNAL).
Dual-Domain Temporal–Spatial Denoising Approach for Autism Spectrum Disorder EEG Signals Based on Stationary Wavelet Transform and SPHARA Syiva, Cut Siti Azola; Melinda, Melinda; Syahrial, Syahrial; Rahman, Imam Fathur; Das, Souvik; Heryanto, M. Ary
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15875

Abstract

Electroencephalography (EEG) signals are highly susceptible to noise and artifacts, which can degrade analysis accuracy, particularly in Autism Spectrum Disorder (ASD) studies. Therefore, effective preprocessing is required to improve signal quality prior to further analysis. This study proposes an integrated EEG preprocessing pipeline that combines a Finite Impulse Response (FIR) band-pass filter (0.5–70 Hz) with notch filtering and detrending, followed by temporal denoising using the Stationary Wavelet Transform (SWT) with the Daubechies 4 mother wavelet and spatial filtering based on SPHARA. This dual-domain approach is designed to address both temporal and spatial noise in multichannel EEG signals. Experimental results demonstrate that the proposed FIR combined with SWT and SPHARA pipeline consistently outperforms single-domain preprocessing methods, achieving a maximum Signal-to-Noise Ratio (SNR) of 31.93 dB. The proposed method also produces the lowest Mean Absolute Error (MAE) (16.81 µV) and Standard Deviation (SD) (0.75 µV), indicating high signal stability with minimal amplitude distortion. Root Mean Square Error (RMSE) values remain stable within the range of 29.5–592.3 µV, with a minimum RMSE of 29.5 µV, demonstrating effective noise suppression while preserving signal energy. These results confirm that integrating temporal and spatial preprocessing significantly improves EEG signal quality and supports more reliable EEG analysis for ASD-related studies.
CNN-Based Facial Image Analysis for Pediatric Down Syndrome Classification Yunidar, Yunidar; Harahap, Inda Mariana; Melinda, Melinda; Rosmawinda, Rosmawinda; Basir, Nurlida; Rafiki, Aufa; Rahman, Imam Fathur
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1523

Abstract

Down syndrome (trisomy 21) is a genetic disorder caused by an extra copy of chromosome 21, resulting in distinctive developmental facial characteristics and intellectual delays. Early detection is crucial to enable timely medical intervention. However, conventional diagnostic procedures still rely on clinical observation and genetic testing, which can be invasive and expensive. This study proposes a facial image–based classification system for detecting Down syndrome using a Convolutional Neural Network (CNN) approach. Seven CNN architectures were evaluated, namely EfficientNetB0, MobileNetV2, ResNet34, ShuffleNetV2, AlexNet, VGG19, and InceptionV3, under two training scenarios: with and without early stopping. The dataset consisted of 1,000 facial images of children with and without Down syndrome, split into training, validation, and test sets at 60:20:20. Face detection was performed using the Haar Cascade Classifier, followed by data augmentation techniques including rotation, zoom, translation, horizontal flipping, and Gaussian noise to improve model generalization and reduce overfitting. Experimental results show that the VGG19 architecture achieved the best performance, with an accuracy of 94.5%, precision of 91.59%, recall of 98%, and an F1-score of 94.69%. A one-way ANOVA test yielded an F-value of 0.003 and a p-value of 0.955 (> 0.05), indicating no statistically significant difference between models trained with and without early stopping. Grad-CAM visualization highlighted key facial regions, namely the eyes, nose, and mouth, as the primary contributors to classification, while analysis using 68 facial landmark points revealed distinctive morphological patterns associated with Down syndrome. The integration of CNN models, Grad-CAM visualization, and facial landmark analysis demonstrates a promising, interpretable, and non-invasive approach to supporting early Down syndrome screening using facial images
Co-Authors Alfiyah, Mutiara Basir, Nurlida Das, Souvik Hasanah, Anisa Nur Heryana, Nono Inda Mariana Harahap M Ary Heryanto Melinda Melinda Rafiki, Aufa Rosmawinda, Rosmawinda SYAHRIAL SYAHRIAL Syiva, Cut Siti Azola Yunidar