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All Journal Sistemasi: Jurnal Sistem Informasi Progresif: Jurnal Ilmiah Komputer JURNAL PENELITIAN EKONOMI DAN AKUNTANSI (JPENSI) TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Media Jurnal Informatika Riset, Ekonomi, Akuntansi dan Perpajakan (REKAN) Rengganis Jurnal Pengabdian Masyarakat JCOMENT (Journal of Community Empowerment) Paradigma Bakti Sekawan : Jurnal Pengabdian Masyarakat
Muhammad Haris Nasri
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Local Wisdom-Based Treatment Recommendation System for Tropical Diseases Using Bayesian Network and Association Rule Mining Muhammad Haris Nasri; Rifqi Hammad; Pahrul Irfan; I Nyoman Switrayana; Rahayun Amrullah Husaini
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6290

Abstract

Tropical diseases remain a major public health problem in Indonesia, particularly in regions with limited access to healthcare facilities, leading communities to rely on traditional medicine based on local wisdom. However, the integration of traditional and modern treatment knowledge in intelligent recommendation systems remains limited. This study aimed to develop a tropical disease treatment recommendation system by integrating Bayesian Network (BN) and Association Rule Mining (ARM). Traditional and modern treatment knowledge were collected from scientific literature and expert interviews, validated by medical practitioners and traditional medicine experts, and incorporated into the system. A quantitative and experimental approach was conducted using a dataset of 150 tropical disease cases comprising dengue fever (42 cases), malaria (35), leptospirosis (28), tuberculosis (30), and leprosy (15). The dataset included 47 symptom attributes, 34 traditional treatment attributes, and 12 modern treatment attributes. Bayesian Network was used to model probabilistic relationships among symptoms, diagnoses, and treatments, while the Apriori algorithm in ARM was applied with minimum support and confidence thresholds of 0.3 and 0.7, respectively. Experimental evaluation on 30 testing cases showed that the integrated BN-ARM model achieved 86.7% accuracy and an F1-score of 86.0%, outperforming standalone BN (82.0% accuracy; F1-score 82.5%) and ARM (79.0% accuracy; F1-score 78.8%). The system generated accurate and contextually relevant treatment recommendations by combining local wisdom and modern medical knowledge.
Rice Leaf Disease Classification Based on ResNet50 and MobileNetV3 Feature Extraction Using Random Forest Gede Yogi Pratama; Rahayun Amrullah Husaini; Muhammad Haris Nasri; Rifqi Hammad
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5939

Abstract

Diseases in rice plants are one of the main factors contributing to decreased agricultural productivity. Early and accurate disease identification is crucial to support effective decision-making in plant disease management. This study aims to compare the performance of deep learning models based on Convolutional Neural Networks (CNN), namely ResNet50 and MobileNetV3, as well as their integration with the Random Forest (RF) algorithm for rice leaf disease classification. The dataset used consists of rice leaf images categorized into several disease classes. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics with a macro-average approach. The results show that the standalone ResNet50 and MobileNetV3 models achieved accuracies of 62.5% and 65.7%, respectively, with macro F1-scores below 0.65, indicating moderate classification performance. However, combining CNN models with Random Forest significantly improved classification performance. The ResNet50 + RF model achieved an accuracy of 99.6%, while the MobileNetV3 + RF model attained the highest accuracy of 99.8%, along with equally high macro-averaged precision, recall, and F1-score values. These findings demonstrate that integrating CNN-extracted features with the Random Forest algorithm enhances the model’s ability to distinguish disease classes more accurately and consistently. Therefore, the hybrid CNN–Random Forest approach shows strong potential as an effective solution for image-based rice plant disease detection systems.
Peningkatan Kompetensi Digital Siswa melalui Pelatihan Pengembangan Aplikasi Web dalam Mendukung Kualitas Sumber Daya Manusia Mohammad Najib Roodhi; Rahayun Amrullah Husaini; Gede Yogi Pratama; Rifqi Hammad; I Nyoman Switrayana; Muhammad Haris Nasri; Gilang Primajati
Rengganis Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): Mei 2026
Publisher : Pendidikan Matematika, FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/rengganis.v6i1.1102

Abstract

The rapid development of digital technology demands an increase in the quality of human resources (HR) that are adaptive to change, especially in the field of information technology. One of the competencies needed is the ability to develop web-based applications, which are increasingly relevant to industry needs. This community service activity aims to improve students' digital competencies and reduce the dynamic skills gap through web application development training. The partners in this activity were grade X students of SMKN 2 Mataram with a total of more than 20 participants. The training method used was a learning-by-doing approach, which included material delivery, demonstrations, direct practice, and evaluation. The results of the activity showed an increase in the average score of participants from 65 in the pre-test to 85 in the post-test, indicating a significant increase in participant understanding. In addition, participants were also able to develop simple web applications and demonstrated improved problem-solving skills and self-confidence. This activity contributes to improving digital competencies and strengthening the quality of human resources who are better prepared to face technological developments in the digital era.
Selection of Outstanding Students Using AHP and Profile Matching Muhammad Haris Nasri; Rifqi Hammad; Pahrul Irfan
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3189

Abstract

The determination of outstanding students is the giving of awards to those who excel in academic and non-academic fields, aimed at motivating increased achievement. However, this process is often hampered by various criteria that must be considered, such as English language skills, work results, awards, and so on. The solution offered to overcome this problem is the development of a decision support system for selecting outstanding students using the AHP and Profile Matching methods. So, the aim of this research is to develop a decision support system for selecting outstanding students using a combination of the AHP and Profile matching methods, where later the system developed can assist decision makers in determining outstanding students. The results obtained from this research are a decision support system that uses 8 criteria and 26 alternative sample data which shows that "Mahasiswa F" is an outstanding student with a score of 4.09. The results of manual calculations with the system show similarities, which shows that the system developed is in accordance with expectations.
Co-Authors Abdurahman Abdurrahman Baiq Dinda Puspita Ayu Christopher Michael Lauw Defel Septian Fadli, M. Najmul Gede Yogi Pratama Gede Yogi Pratama Gilang Primajati Hammad, Rifqi Husain Husain Husaini, Rahayun Amrullah I Nyoman Switrayana I Wayan Bratayasa Ika Ajiani Kartarina, Kartarina Lilik Widyawati Mardedi, Lalu Zazuli Azhar Melati Rosanensi Miftahul Madani Mohammad Najib Roodhi Muhammad Mujahid Dakwah Pahrul Irfan Pahrul Irfan Pratama, Gede Yogi Rahayun Amrullah Husaini Rifqi Hammad Rizky Raharjo Roodhi, Mohammad Najid Setiawan, Pandu Dwi Sirojul Hadi Tomi Tri Sujaka Triwijoyo, Bambang Krismono Wibisanaa, Bayu Widia Febriana Zamroni Alpian Muhtarom Zefanya Andryan Girsang