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All Journal JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Dinamisia: Jurnal Pengabdian Kepada Masyarakat Polingua : Scientific journal of Linguistics, Literature and Language Education MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering Teknomekanik JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Vokasi Mekanika Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Proceeding Applied Business and Engineering Conference International Journal of Advanced Science Computing and Engineering Jurnal Akademik Pengabdian Masyarakat Engagement: Jurnal Pengabdian Kepada Masyarakat Jurnal Testing dan Implementasi Sistem Informasi
Fazrol Rozi, Fazrol
Department Of Information Technology, Politeknik Negeri Padang, Indonesia
Religion Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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An Expert System for Early Detection of Mental Health Conditions Using Certainty Factor and DASS-42 Indri Rahmayuni; Yance Sonatha; Tsalsabila Jilhan Haura; Fazrol Rozi
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2214

Abstract

Mental health problems such as depression, anxiety, and stress continue to increase in many countries, while access to professional services is still limited. Many digital screening systems use fixed scoring methods and do not consider uncertainty in user responses. This study developed a web-based expert system by combining the Depression Anxiety Stress Scales (DASS-42) and the Certainty Factor (CF) method to represent uncertainty in overlapping emotional symptoms and provide more flexible screening results. The knowledge base was prepared through consultation with a licensed clinical psychologist and converted into 42 production rules based on the DASS-42 items. Each rule was assigned a confidence value according to expert judgment. The system uses forward chaining to combine active rules and calculate confidence scores for depression, anxiety, and stress at the same time. System evaluation was conducted using 50 community cases aged 18–35 years and compared with independent expert assessment. The overall accuracy reached 86% (43 of 50 cases). The accuracy for each category was 88.2% for depression, 82.3% for anxiety, and 87.5% for stress. Most classification errors occurred between anxiety and stress, which may be related to overlapping symptoms in the DASS-42 instrument. The findings indicate that the proposed system can support early mental health screening through interpretable confidence-based results. However, this study used a limited dataset and only one expert in knowledge development. The system is intended as a screening support tool and not as a replacement for clinical diagnosis.
Penerapan Teknologi Spark AR dalam Pengenalan Suntiang Minangkabau Fanny Laila Safitri; Taufik Gusman; Fazrol Rozi
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 6 No 4 (2025)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.6.4.527

Abstract

Penelitian ini menjelaskan penerapan teknologi Spark AR dalam pengenalan Suntiang Minangkabau. Suntiang Minangkabau adalah simbol budaya dan identitas Minangkabau yang biasanya selalu dipakai oleh pengantin wanita atau anak daro di daerah tersebut. Namun pengetahuan dan pemahaman mengenai suntiang ini masih terbatas, terutama pada kalangan generasi muda termasuk masyarakat Minangkabau itu sendiri. Oleh karena itu, penelitian ini memanfaatkan teknologi Augmented Reality(AR) menggunakan Spark AR untuk menghadirkan pengalaman digital interaktif yang memungkinkan pengguna untuk secara virtual mengenakan dan mengenal Suntiang Minangkabau melalui suatu platform seperti instagram. Metode perancangan filter ini melibatkan tahap pengumpulan data, perancangan, implementasi dan pengujian. Rancangan filternya juga dibuat dengan metode prototype dengan menampilkan gambar suntiang dalam bentuk 2D serta informasi tentang Suntiang Minangkabau tersebut. Hasil penelitian ini tentunya bertujuan bahwa penerapan teknologi Spark AR dapat menjadi alat yang efektif dalam menambah pengetahuan dan pembelajaran tentang budaya Minangkabau salah satunya tentang Suntiang Minangkabau. Selain itu juga bisa berpotensi untuk mempromosikan warisan budaya dan edukasi melalui media digital
A Framework of image processing and machine learning utilization for flood disaster management Fazrol Rozi; Indri Rahmayuni; Ardi Syawaldipa; Fitri Nova; Primawati Primawati; Batara Batara
Teknomekanik Vol. 5 No. 2 (2022): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v5i2.17372

Abstract

Flood is one of the annual disasters in many places. It has not been well-managed yet both pre-disaster and post-disaster. Image processing and machine learning are commonly utilized for disaster management systems such as forecasting any potential flood by monitoring the water level in rivers and dams. However, it has a limited framework to be implemented as a strategic plan in flood management. Thus, this study aims to develop a framework for image processing and machine learning utilization for flood management. This study involves Padang, West Sumatera, Indonesia as a sample. It was conducted in three stages; 1) categorize the strategic plans and policies; 2) gather relevant literature; 3) analyze data. As findings, this study proposes a framework consisting of enhanced disaster preparedness, improved coping capacity, and completion of post-disaster reconstruction and rehabilitation. Involvement of the government, researchers and industry are mandatory. Government and researchers should collaborate to establish policies and regulations. Researchers should conduct studies with financial support from the industry. Meanwhile, the industry should be a public-private partnership with the government. In addition, the involvement of the private sector and the government are important factors that must exist to support research in this field.
APPLIED MATHEMATICAL MODELING FOR 3D KINEMATIC SPATIAL RECONSTRUCTION IN A LOW-COST MONOCULAR WEBCAM-BASED SQUAT ANALYSIS SYSTEM Fazrol Rozi; Primawati Primawati; Anton Komaini; Sri Gusti Handayani
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 1 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i1.2197

Abstract

Squat is a fundamental exercise for improving lower body strength; however, improper execution may increase the risk of musculoskeletal injury. Conventional motion analysis systems, such as marker-based technologies, provide high accuracy but require expensive equipment and controlled environments, while monocular camera-based approaches often suffer from limited three-dimensional representation. Therefore, this study proposes a low-cost squat analysis system, gui_mocap, which integrates monocular computer vision with vector-based mathematical modeling for real-time motion analysis. The system employs pose estimation to detect body landmarks and reconstructs joint kinematics in three-dimensional space using geometric vector operations. Knee joint angles are computed using the dot product formulation, and an Exponential Moving Average (EMA) filter is applied to improve measurement stability. Experimental evaluation was conducted using multiple squat repetitions to analyze motion patterns and consistency. The results demonstrate that the system can accurately identify key movement phases, including standing, deep flexion, and return to standing, while producing smooth and stable joint angle trajectories. Furthermore, the system is capable of analyzing repeated movements and generating descriptive statistics, such as average joint angles and range of motion, indicating consistent performance across repetitions. This study contributes a practical and affordable solution for real-time motion analysis using a monocular webcam, with potential applications in home-based exercise monitoring and basic rehabilitation.
An Expert System for Early Detection of Mental Health Conditions Using Certainty Factor and DASS-42 Indri Rahmayuni; Yance Sonatha; Tsalsabila Jilhan Haura; Fazrol Rozi
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2214

Abstract

Mental health problems such as depression, anxiety, and stress continue to increase in many countries, while access to professional services is still limited. Many digital screening systems use fixed scoring methods and do not consider uncertainty in user responses. This study developed a web-based expert system by combining the Depression Anxiety Stress Scales (DASS-42) and the Certainty Factor (CF) method to represent uncertainty in overlapping emotional symptoms and provide more flexible screening results. The knowledge base was prepared through consultation with a licensed clinical psychologist and converted into 42 production rules based on the DASS-42 items. Each rule was assigned a confidence value according to expert judgment. The system uses forward chaining to combine active rules and calculate confidence scores for depression, anxiety, and stress at the same time. System evaluation was conducted using 50 community cases aged 18–35 years and compared with independent expert assessment. The overall accuracy reached 86% (43 of 50 cases). The accuracy for each category was 88.2% for depression, 82.3% for anxiety, and 87.5% for stress. Most classification errors occurred between anxiety and stress, which may be related to overlapping symptoms in the DASS-42 instrument. The findings indicate that the proposed system can support early mental health screening through interpretable confidence-based results. However, this study used a limited dataset and only one expert in knowledge development. The system is intended as a screening support tool and not as a replacement for clinical diagnosis.
EFEKTIVITAS PENERAPAN FLIPPED CLASSROOM DENGAN IMAGE PROCESSING UNTUK MENINGKATKAN KETERAMPILAN PEMROGRAMAN KOMPUTER PADA MAHASISWA TEKNIK MESIN Primawati Primawati; Fazrol Rozi; Rizki Ema Wulansari; Febri Prasetya; Ambiyar Ambiyar; Fiki Efendi
Jurnal Vokasi Mekanika (VoMek) Vol 6 No 3 (2024): Jurnal Vokasi Mekanika
Publisher : Unversitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/vomek.v6i3.732

Abstract

Pengajaran pemrograman komputer dalam konteks teknik mesin sering kali terbatas pada konsep-konsep dasar, tanpa penerapan langsung dalam kasus-kasus teknik mesin yang konkret. Penelitian ini bertujuan untuk mengevaluasi efektivitas penerapan image processing dalam pembelajaran pemrograman komputer melalui pendekatan flipped classroom, dengan fokus pada aplikasi dalam memprediksi laju korosi. Penelitian ini dilakukan dalam dua siklus di kelas pemrograman komputer bagi mahasiswa teknik mesin. Pada siklus pertama, metode pembelajaran case method digunakan. Mahasiswa diberikan modul ajar tentang image processing dengan MATLAB dan kasus studi prediksi laju korosi yang harus dipelajari sebelum pertemuan kelas. Pertemuan kelas digunakan untuk diskusi, bimbingan, dan tes untuk mengukur pemahaman dan keterampilan mahasiswa, dengan hasil ketuntasan sebesar 60%. Siklus kedua menggunakan pendekatan pembelajaran berbasis proyek tim (team-based project). Mahasiswa dibagi menjadi kelompok kecil, diberi waktu untuk berdiskusi, dan mengerjakan proyek bersama. Tes yang dilakukan setelah pembelajaran menunjukkan peningkatan ketuntasan hingga 82,31%. Hasil penelitian menunjukkan bahwa pendekatan flipped classroom yang dikombinasikan dengan pembelajaran berbasis proyek tim dapat meningkatkan pemahaman dan keterampilan praktis mahasiswa dalam image processing. Implikasi praktis dari penelitian ini termasuk relevansinya dalam meningkatkan efektivitas pengajaran pemrograman komputer dalam pendidikan teknik mesin.
Impact of Speckle Reduction Filters on Machine Learning-Based Detection of Polycystic Ovary Syndrome from Ovarian Ultrasound Images Fazrol Rozi; Syafrizal Sy; Adiwijaya; Admi Nazra; Primawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7658

Abstract

Polycystic Ovary Syndrome (PCOS) is commonly assessed with ovarian ultrasonography, but speckle can conceal follicular margins and reduce the robustness of automated interpretation. Although automated PCOS studies increasingly employ machine learning, the contribution of conventional despeckling to subsequent segmentation and classification has not been examined consistently. This study compares five classical filters - Mean, Median, Lee, Frost, and Kuan - within an interpretable machine-learning pipeline for ovarian ultrasound analysis. From a public collection of 12,680 images, a balanced sample of 300 scans (150 PCOS and 150 non-PCOS) was selected. Two radiologists produced follicle annotations, and disagreements were resolved with a third expert to obtain consensus masks. Each filtered image was segmented by adaptive thresholding with morphological refinement, after which geometric and intensity descriptors were extracted. Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Logistic Regression (LR) were trained using a stratified 70/30 train-test split with cross-validated hyperparameter tuning. The Kuan-LR configuration yielded the strongest result, reaching 94.44% accuracy and an AUC of 0.98, together with the best edge-preservation score and segmentation agreement. The results indicate that preprocessing materially affects the reliability of an interpretable PCOS detection pipeline and provide quantitative guidance for selecting a speckle-reduction strategy before segmentation and classification.
Co-Authors - Primawati - Yulherniwati Abdul Marfuad Adiwijaya Admi Nazra Aldo Erianda Ambiyar, Ambiyar Ardi Syawaldipa Batara Batara Batara, Batara Bobby Kurniawan Cipto Prabowo Dedi Tri Laksono, Dedi Tri Dony Marzuki Efendi, Novi Fadhilah Oriyasmi Fadhilah Oriyasmi Fadilillah, Fadli Fadli Fadilillah Faisal Amir Fanny Laila Safitri Fanny Laura Fauziah Wulandari Febri Prasetya Fiki Efendi Fithratul Miladiyenti Fitri Nova Fitri Nova Fitri Nova Fitri Nova, Fitri Fitriani Fitriani Furqon, Muhammad Ariful Gun Faisal Gusman, Taufik Handayani, Sri Gusti Hanif Aulia Sabri Haslina, Welsi Hidra Amnur Humaira, Humaira Ikhsan Yusda Prima Putra Indri Rahmayuni Indri Rahmayuni Indri Rahmayuni Indri Rahmayuni IndriRahamyuni Irfan Kurniawan Komaini, Anton Mardalisa, - Maulana, Raihan Efel Meri Azmi Miladiyenti, Fithratul Muhammad Irsad Alfajri Muhammad Ridwan Kurniawan Najwa, Nina Fadilah Novi Yanti Primawati Primawati Primawati Primawati Primawati Primawati, Primawati Primawati, - Putri Huriati Rasyidah Rasyidah Rasyidah, - Rika Idmayanti Risa Safira Riyanto Rizki Ema Wulansari Sariani, - Sariani, Sariani SONY SUHANDONO Syafira Indah Maryam Syafrizal Sy Syawaldipa, Ardi Tiara Ramayani Tsalsabila Jilhan Haura Yance Sonatha Yufrizal Yufrizal ZukronAlviandyRahmadhan