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All Journal CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Inotera Jurnal Sisfokom (Sistem Informasi dan Komputer) METIK JURNAL Jurnal Eduscience (JES) Jurnal Informasi dan Teknologi Jurnal AbdiMas Nusa Mandiri Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Minfo Polgan (JMP) Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Algoritma Jurnal Teknologi Terapan and Sains 4.0 Dikdimas : Jurnal Pengabdian Kepada Masyarakat Bulletin of Network Engineer and Informatics (BUFNETS) Jurnal Pengabdian Inovasi Masyarakat Indonesia Jurnal Rekayasa Sistem Informasi dan Teknologi Journal Of Artificial Intelligence And Software Engineering Pengabdian Pendidikan Indonesia (PPI) Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Polimesin Portal Riset dan Inovasi Sistem Perangkat Lunak Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) ITEJ (Information Technology Engineering Journals) Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Rizki Suwanda
Universitas Malikussaleh, Aceh, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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IMPLEMENTASI FRAMEWORK CODEIGNITER DALAM PENGEMBANGAN SISTEM MANAJEMEN DATA DAN INFORMASI ALUMNI BERBASIS WEB Rizki Suwanda; Said Fadlan Anshari; Rizal Rizal
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 6 No. 2 (2022): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2022
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v6i2.10470

Abstract

Data alumni salah unsur yang paling penting dalam peningkatan mutu perguruan tinggi. Alumni menjadi perpanjangan tangan sebagai pembentuk jaringan kerja yang diharapkan dapat membuka relasi instansi untuk memudahkan para mahasiswa dan alumni  saling berbagi informasi dalam dunia kerja dan ilmu pengetahuan. Sebagai upaya pengembangan dan peningkatan sistem manajemen informasi alumni diperlukan sebuah sistem pengelolaan data yang bisa diakses dengan waktu yang tidak terbatas sesuai dengan kebutuhan. Pengembangan sistem berbasis web dengan menerapkan Codeigniter berbasis php dengan menerapkan konsep  MVC. Codeigniter menjadi sebuah toolkit yang diminati ditujukan kepada pengembang aplikasi web dalam bahasa PHP dengan berbagai macam library yang disediakan yang dapat mempermudah dalam pengembangan aplikasi web. Hasil penelitian ini dapat diterapkan pada penelusuran data alumni dengan sistem manajemen data alumni berbasis web yang terkomputerisasi dan terstruktur dapat memudahkan program studi maupun universitas dalam mengelola data dan informasi para lulusan.
Implementation of Convolutional Neural Network for Leaf Disease Detection in Cayenne Pepper Plantsaper Ayu Suningsih; Zara Yunizar; Rizki Suwanda
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.27552

Abstract

Cayenne pepper (Capsicum frutescens L.) is an economically important horticultural crop in Indonesia; however, its productivity is frequently affected by leaf diseases, including leaf curl, leaf spot, and yellow leaf disease. Conventional disease identification mainly relies on visual inspection, making the diagnosis highly dependent on farmers’ experience and increasing the possibility of inaccurate identification and delayed treatment. This study proposes a Convolutional Neural Network (CNN)-based approach for automatic chili leaf disease classification and implements the trained model in an Android application for offline real-time detection. A total of 2,000 images were collected from chili plantations located in Lhokseumawe City and Aceh Tamiang Regency. The dataset was organized into five balanced categories, namely healthy leaf, leaf curl, leaf spot, yellow leaf, and non-leaf, with 400 images assigned to each category. The addition of a non-leaf category enables the application to distinguish chili leaves from irrelevant objects during mobile-based detection. Before training, all images were resized to 150 × 150 pixels, normalized, and partitioned into training, validation, and testing sets using an 80:10:10 ratio. The proposed CNN architecture comprised three convolutional layers followed by max-pooling layers, a flatten layer, a fully connected layer, a dropout layer, and a Softmax output layer. Experimental evaluation on the testing dataset produced an overall accuracy of 89.50%, while the macro-average precision, recall, and F1-score reached 90%, 89%, and 89%, respectively. The trained model was successfully converted into TensorFlow Lite (TFLite) format and integrated into an Android application capable of providing real-time disease prediction, confidence scores, disease descriptions, treatment recommendations, and detection history without requiring an Internet connection. These results indicate that the proposed system is suitable for practical field deployment to support early identification of chili leaf diseases.
Student Creativity Education in Plastic Waste Processing Innovation Tulus Setiawan; Rizki Suwanda; Said Fadlan Anshari; Nur Fazri Husna; Tiara Sartika
DIKDIMAS : Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 3 (2024): DIKDIMAS : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Asosiasi Profesi Multimedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/dikdimas.v3i3.350

Abstract

This community service activity aims to address environmental problems, emphasizing project-based learning, where participants are invited to solve real-world problems in their environment. The implementation of the Kurikulum Merdeka (Independent Curriculum) offers a great opportunity to utilize the Project for Strengthening Pancasila Student Profiles (P5) as a platform to encourage student involvement in environmental issues. One tangible form of P5 implementation is through a project converting plastic waste into oil. The activity was conducted at SMA Negeri 5 Kota Lhokseumawe, employing the creative education method for plastic waste processing, including education and training for teachers and students, as well as project assignments for processing plastic waste through the pyrolysis process. The results of the community service activity showed that more than 80% of respondents rated the educational activities, such as material delivery, awareness-raising, and providing new insights on plastic waste processing, as excellent. Moreover, 95% expressed satisfaction with the implementation of the plastic waste processing project via the pyrolysis process, which successfully provided educational, technical, and motivational experiences to participants. Education and the plastic waste processing project have proven to offer practical solutions that not only enhance students' knowledge and skills but also drive behavioral and attitudinal changes toward the environment.
Decision Tree-Based Web Expert System for Preliminary Screening of Refractive Eye Disorders Dini Fariha; Rizal Tjut Adek; Rizki Suwanda
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13213

Abstract

Refractive eye disorders remain one of the leading causes of visual impairment worldwide, while limited access to ophthalmology services often delays early diagnosis. This study investigates the effectiveness of the Decision Tree algorithm for symptom-based classification of refractive eye disorders and its implementation within a web-based expert system for preliminary eye health screening. Data were collected from 505 respondents using a structured Google Forms questionnaire. After preprocessing and labeling, the dataset was divided into 80% training data and 20% testing data. Model performance was evaluated using a confusion matrix together with accuracy, precision, recall, and F1-score metrics to provide a comprehensive assessment under an imbalanced class distribution. Experimental results showed that the proposed Decision Tree model achieved an overall accuracy of 89.11% and demonstrated satisfactory performance in identifying dominant diagnostic classes while maintaining transparent and interpretable decision rules. The developed web-based system provides symptom-based diagnosis, examination history management, and PDF report generation to support preliminary eye health assessment. These findings indicate that the proposed approach is suitable as an accessible decision-support tool for preliminary refractive eye disorder screening, particularly in areas with limited access to ophthalmology services.
The Application of Naïve Bayes Algorithm in Detecting Hoaxes on National News Portals in Indonesia Cut Rifa Salsabil; Nurdin Nurdin; Rizki Suwanda
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13306

Abstract

The rapid advancement of information technology in Indonesia has led to a massive spread of digital disinformation, commonly known as an infodemic. The inability to filter inaccurate information manually necessitates a reliable, automated hoax detection system. This study aims to implement and evaluate the Multinomial Naïve Bayes algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction to classify news articles as either factual or hoax. The research utilizes a dataset of 2,910 Indonesian news articles published in 2025, collected from verified national news portals and fact-checking websites. The text data underwent comprehensive preprocessing—including case folding, cleansing, stopword removal, and stemming—before being evaluated using 5-Fold Cross-Validation and an 80:20 data split. Experimental results demonstrate that the Naïve Bayes model achieves highly stable and competitive performance, recording an accuracy of 93.81%, a precision of 93.84%, a recall of 93.81%, an F1-Score of 93.82%, and a 5-Fold Cross-Validation F1-Score of 93.39%. Notably, the algorithm exhibited a significantly low False Negative rate, missing only 15 hoax documents out of 582 test samples. Furthermore, the trained model was successfully integrated into a real-time, web-based user interface using Streamlit. This practical implementation provides an accessible and efficient initial screening tool for the general public and journalists to assist in verifying news authenticity, thereby supporting efforts to mitigate the impact of digital hoaxes.
Accuracy Analysis of the Capsule Network Method in Cataract Fundus Image Classification Salshabila Putri; Arnawan Hasibuan; Rizki Suwanda
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13557

Abstract

Cataract is one of the leading causes of blindness worldwide and requires early detection to prevent vision deterioration. Advances in artificial intelligence, particularly deep learning, have enabled the development of automated systems for medical image classification. However, conventional Convolutional Neural Networks (CNNs) tend to lose important spatial information due to pooling operations, which may affect classification performance. This study aims to analyze the accuracy of the Capsule Network (CapsNet) method for cataract classification using fundus images. The proposed method employs image preprocessing techniques, including resizing, data augmentation, and normalization, before training the CapsNet model. The model was developed using Python and the PyTorch framework. Performance evaluation was conducted using a Confusion Matrix and several metrics, namely Accuracy, Precision, Recall, Specificity, F1-score, and Area Under the Curve (AUC). Experimental results showed that the model achieved a training accuracy of 86.29% and a best validation accuracy of 81.34%. Furthermore, testing on 1,813 fundus images resulted in an accuracy of 89.85%, precision of 94.56%, recall of 84.53%, specificity of 95.15%, F1-score of 89.26%, AUC-ROC of 95.27%, and Average Precision (AP) of 94.71%. These findings indicate that CapsNet is capable of effectively classifying cataract fundus images. Although the obtained performance is consistent with the theoretical capability of CapsNet to preserve spatial relationships among image features, this capability was not directly evaluated in the present study.
Multifunctional performance validation of coconut frond fibre-reinforced composite prototype for automotive interior application Muhammad Habibi; Rizki Suwanda; Wardhiah Wardhiah; Maisarah Maisarah; Muhammad Iqbal; Usman Usman
Jurnal Polimesin Vol 24, No 4 (2026): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i4.9196

Abstract

The automotive industry increasingly demands sustainable, multifunctional materials for interior components to simultaneously reduce environmental impact and improve occupant comfort. This study presents a comprehensive experimental validation of a coconut frond fibre-reinforced composite prototype intended for automotive door trim panel application. The prototype, developed at Technology Readiness Level (TRL) 2, was evaluated across three critical performance domains: mechanical properties (tensile, flexural, impact, and hardness), acoustic performance (sound absorption coefficient), and thermal characteristics (thermal conductivity and heat deflection temperature). Specimens were prepared and tested in accordance with internationally recognised standards (ASTM D3039, D790, D256, D2240, E1050, E1530, D648). Mean and standard deviation from five replicate specimens per test revealed tensile strength of 28.0 ± 1.4 MPa, flexural strength of 48.1 ± 0.8 MPa, impact toughness of 7.59 ± 0.54 kJ/m², and Shore D hardness of 69.4 ± 1.1. Acoustic evaluation yielded a Noise Reduction Coefficient (NRC) of 0.37 ± 0.01, with a peak absorption coefficient of 0.53 at 1000 Hz, significantly outperforming conventional ABS and PP interior materials. Thermal conductivity was measured at 0.142 ± 0.003 W/mK, which is lower than commercial ABS, while the heat deflection temperature of 93.0 ± 0.9°C exceeds the maximum cabin operating temperature of 85°C. All 11 evaluated parameters met or exceeded their respective automotive-grade target specifications, confirming TRL 3 achievement and suitability for advancement to TRL 4 environmental validation. Scanning electron microscopy (SEM) analysis revealed moderate fibre–matrix interfacial bonding with fibre pull-out as the dominant failure mechanism. The findings establish a robust technical database for the future scale-up and industrial adoption of this bio-based composite.
Implementation of Data Mining for Nutrition Clustering of Toddlers at Posyandu Using K-Means Algorithm Muna Kamilah; Dahlan Abdullah; Rizki Suwanda
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.718

Abstract

This study aims to determine the nutritional status of toddlers in the Peulimbang sub-district using a clustering method that can group toddlers based on their dietary indicators, such as gender (jk), age (u), height (tb), weight (bb), and upper arm circumference (Lila). By using data analysis techniques such as K-Means clustering, this study successfully identified several groups of toddlers with different nutritional statuses, ranging from malnutrition to good nutrition to obesity nutritional status. The data for this study were taken directly from the Peulimbang Health Center, and as many as 765 toddler data were collected from 16 villages in the Peulimbang area. The programming language used in this study is PHP, which functions in web development and is often used to process data sent via web formula, interact with databases, and manage user sessions. Based on the results of clustering with the K-Means method using Euclidean Distance as a measurement between points, toddler data has been grouped into three Clusters, namely C1, C2, and C3. Cluster C1 covers 22.81% of 147 malnourished toddlers, C2 covers 48% of 323 well-nourished toddlers, and C3 covers 29.19% of 205 obese toddlers. Based on the clustering results, improving nutrition education programs for parents is essential, especially in areas with poor or lacking nutritional status. This program can include counseling on the importance of balanced nutrition, nutritious cooking methods, and choosing the right Food for toddlers. Thus, this study is expected to contribute to improving toddlers' nutritional status and overall public health in the Peulimbang District area.
Implementation of the First Come First Served Method in a Website-Based Sports Facility Reservation Service System Irsan Efendi Rangkuti; Taufiq Taufiq; Rizki Suwanda
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.859

Abstract

The people of Kotanopan have a high interest in sports such as futsal and badminton, an interesting phenomenon that shows the popularity of these sports and opens up many business opportunities. Most sports facilities for rent still use a conventional system where prospective renters must come directly to the field or call the manager to make a reservation, making it prone to errors in the reservation process. From the management's side, they often encounter obstacles when processing tenant reservations, especially when sorting the schedules that the tenants have created. This research aims to build a sports facility reservation service system using the First Come, First Served (FCFS) method. This method is the most suitable scheduling system for the sports facility reservation system in Kotanopan. The results of the First Come First Served (FCFS) method experiment with a random trial of 5 users, each with execution times of 3, 3, 5, 5, and 9, yielded an average waiting time of 1 minute and an average reservation completion time of 6. With the test results showing that the system can meet the needs and has functions accessible to every system user, it is hoped that this system can facilitate users in making reservations online.
Application of the C5.0 Algorithm to Determine the Eligibility of BPJS Contribution Assistance Recipients in the National Health Insurance Program Muhammad Furqan; Nurdin Nurdin; Rizki Suwanda
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.856

Abstract

The National Health Insurance (JKN) is a government program to provide health insurance to all Indonesian citizens. In its implementation, determining the eligibility of premium assistance recipients still faces challenges regarding accuracy and efficiency. This study aims to implement the C5.0 algorithm in classifying the eligibility of JKN premium assistance recipients and measure the accuracy of the resulting classification model. The method used is the C5.0 algorithm with the boosting technique with 10 trials and global pruning, with a 70:30 training and testing data split ratio, minimum cases of 2, and confidence level of 0.2. The research results show that the classification model performs excellently with an accuracy rate of 94.27%, precision of 83.78%, Recall of 90.29%, F1-Score of 86.92%, and AUC of 92.81%. The Specificity, reaching 95.34%, demonstrates the model's reliability in identifying ineligible participants for assistance. With a total algorithm execution time of 0.2224 seconds, these results indicate that the C5.0 algorithm can be implemented as an effective decision support system in determining the eligibility of JKN premium assistance recipients.
Co-Authors ., Bustami Absa, Munzir Adek, Rizal Tjut Amanda, Sherly Dwi Ananda, Ria Anggara, Aji Armelia Dafrina Arnawan Hasibuan Ayu Rosmala, Ayu Rosmala Ayu Suningsih Bayu Winata Bustami Bustami . Bustami Bustami Cut Rifa Salsabil Dahlan Abdullah Defry Hamdhana Dendy K. Pramudito Desi Dini Fariha Efendi, Syahril Erna Budhiarti Eva Darnila Fadillah, Rizky Fadlisyah Fadlisyah Fadlisyah Fanita Fanita, Fanita Fatia Naura Fatih Dwi Laksana Haj, Shafly Ulya Halimatus Sakdiah, Halimatus Hamdhana, Defry Irsan Efendi Rangkuti Judijanto, Loso Laksana, Fatih Dwi Maisarah Mardhatillah, Mardhatillah Maryana Maryana Muchlis Abdul Muthalib Muh Fahrudin Alawi Muhammad Daud Muhammad Fathurrahman Fathurrahman Muhammad Fikry Muhammad Furqan Muhammad Habibi Muhammad Iqbal Muhammad Iqbal Muhammad Khardawi Muhammad Khoiruddin Harahap Muhammad Naufal Muhammad Zakaria Muhammad Zikri Mukti Qamal Muna Kamilah Muna, Ninal Munirul Ula Munzir Nazaruddin Ahmad Nur Fazri Husna Nur Mauliza Nurdin Nurdin Nurul Nafisa Pangestu, Aridho Raudhatul Fazira Rini Meiyanti Rivalzi, Tri Ananda Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal Tjut Adek Rizky Putra Fhonna Roslaini Roslaini Rozi Kesuma Dinata Rozzi Kesuma Dinata Safwandi Safwandi Safwandi Safwandi, Safwandi Sagala, Wulandari Shakinahwati Said Fadlan Anshari Said Fadlan Anshari Salshabila Putri Siti Nadilla Sutri Wandani Syahriani Putri Ayu Syibral Malasyi, Syibral Taufiq Taufiq Teguh Brahmana Tiara Sartika Tri Ananda Rivalzi Tri Ramdhany Tulus Setiawan Usman Usman Wardhiah Wardhiah Wardina Ningsih Yudhi Franata Zahra, Annisa Afrilia Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar