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All Journal SAMUDERA Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) INFORMAL: Informatics Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JISKa (Jurnal Informatika Sunan Kalijaga) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Informasi dan Teknologi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Engineering, Science and Information Technology Multica Science and Technology jeti TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi International Journal of Information System & Innovative Technology Multidisiplin Pengabdian Kepada Masyarakat (M-PKM) Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Scientific Journal of Informatics International Journal of Information System and Innovative Technology Smatika Jurnal : STIKI Informatika Jurnal Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Rozzi Kesuma Dinata
Program Studi Teknik Informatika, Unversitas Malikussaleh, Aceh Utara 24351, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Pemetaan Titik Penumpukan Sampah di Kota Lhokseumawe Menggunakan Metode Ant Colony Optimization Suhaeymi, Rozzi Kesuma Dinata, Zara Yunizar
Jurnal Elektronika dan Teknologi Informasi Vol 4 No 2 (2023): September 2023
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v4i2.413

Abstract

This research implements Ant Colony Optimization (ACO) to optimize waste collection routes in urban areas. The implementation utilizes the PHP, JS, and HTML programming languages, resulting in an interactive mapping application that facilitates community participation in identifying garbage accumulation locations. The research findings indicate that by employing ACO calculations with parameters α = 1.0 for pheromones and β = 2.0 for visibility, the best waste collection route was identified with a total distance of 9.565 km.The route begins at "Cunda Fish Market" (pheromone 0.1, visibility 3.321146121) heading towards "Beside the bus terminal" (distance 0.301 km), then continues to "Inpres Market" (pheromone 0.1, visibility 0.814261078, distance 1.228 km), "Pusong Lama Market" (pheromone 0.1, visibility 0.611779235, distance 1.635 km), "Lhokseumawe Reservoir behind the church" (pheromone 0.1, visibility 1.854365059, distance 0.539 km), and concludes at "Lhokseumawe State Polytechnic" (pheromone 0.1, visibility 0.170600122, distance 5.862 km).Each step reflects ant choices based on calculated probabilities, starting from the highest probability of 0.922322 in the first step to the lowest probability of 0.006391 in the last step. This research underscores the efficiency of the routes generated by ACO and demonstrates that bio-inspired algorithms such as ACO can be effectively applied to real logistics problems, providing responsive and adaptive solutions to the dynamics of urban environments.
Web-Based Asset Management Information System for Enhanced Asset Tracking at The Land Office of Bireuen District Rozzi Kesuma Dinata, Agus Maula Rizki
Jurnal Elektronika dan Teknologi Informasi Vol 5 No 1 (2024): Maret 2024
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v5i1.419

Abstract

The management and tracking of assets at the Land Office of Bireuen District have become increasingly complex, necessitating the development of a more efficient and systematic approach. This study proposes the design and implementation of a web-based Asset Management Information System (AMIS) to optimize asset tracking processes. The system aims to provide real-time asset data, improve accuracy, and streamline the management workflow. Key features include asset registration, location tracking, maintenance scheduling, and reporting capabilities. By utilizing web technologies, the AMIS ensures accessibility and ease of use for authorized personnel. The implementation of this system is expected to significantly enhance asset management efficiency, reduce human error, and provide comprehensive data analytics for better decision-making. Initial testing and feedback from users indicate a substantial improvement in the overall asset management process at the Land Office of Bireuen District.
Penerapan Algoritma Random Forest dalam Deteksi dan Klasifikasi Ransomware Alvanof, Mulia; Bustami; Rozzi Kesuma Dinata
Jurnal Elektronika dan Teknologi Informasi Vol 5 No 2 (2024): September 2024
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v5i2.488

Abstract

Ransomware is a type of malware that blocks access to computer systems or data until a ransom is paid by the victim. Ransomware attacks typically occur due to malicious files that are unknowingly downloaded and installed by the victim onto their computer system. Given the threats and potential losses posed, methods for detecting and classifying ransomware continue to be developed, one of which utilizes the Random Forest machine learning algorithm. Random Forest is chosen for its advantages in handling large datasets, short training time, high prediction accuracy, and its ability to reduce the risk of overfitting. Using 1380 ransomware samples from a dataset with 54 features, 10 best features were selected through Feature Selection where the built Random Forest model successfully predicted ransomware files with an accuracy of 98.79%.
Heart Disease Classification Based on Medical Record Data Using the Logistic Regression Method Iswari, Syahyana; Dinata, Rozzi Kesuma; Aidilof, Hafizh Al Kautsar
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.8867

Abstract

Heart disease remains one of the primary causes of mortality globally and poses a significant public health concern, including in Indonesia. Early identification of individuals at risk is essential for lowering death rates and enhancing the success of medical interventions. This research focuses on developing a classification model for heart disease using the Logistic Regression technique, utilizing data extracted from patient medical records. The dataset comprises 100 entries, each containing six key features: age, gender, blood pressure, heart rate, respiratory rate, and chest pain. The model was trained on 80% of the data and evaluated using the remaining 20%. Model performance was assessed using several metrics, including accuracy, precision, recall (sensitivity), F1-score, confusion matrix, and the ROC (Receiver Operating Characteristic) curve. The evaluation results revealed an accuracy of 95%, precision of 100%, recall of 88.89%, F1-score of 94.12%, and an AUC score of 0.99. These outcomes suggest that Logistic Regression is highly effective for classifying heart disease risk and can serve as a valuable tool in early detection systems supported by medical record data.
SOSIALISASI PEMANFAATAN CHATGPT SEBAGAI ALAT BANTU CERDAS DALAM MENINGKATKAN LITERASI DIGITAL SANTRI PESANTREN Hasdyna, Novia; Kesuma Dinata, Rozzi; Fadhilah, Cut; Irfan Fajri, T.; Mutasar, Mutasar
Multidisiplin Pengabdian Kepada Masyarakat Vol. 4 No. 03 (2025): Multidisiplin Pengabdian Kepada Masyarakat, 2025
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pesantren sebagai lembaga pendidikan berbasis keagamaan menghadapi tantangan yang cukup besar dalam meningkatkan literasi digital di era modern. Keterbatasan pemahaman santri terhadap pemanfaatan teknologi informasi menyebabkan akses terhadap sumber belajar yang relevan dan mutakhir menjadi terbatas. Program Pengabdian kepada Masyarakat (PKM) ini dilaksanakan untuk mengatasi permasalahan tersebut melalui sosialisasi pemanfaatan ChatGPT sebagai alat bantu cerdas dalam mendukung proses pembelajaran dan peningkatan literasi digital. Tujuan utama kegiatan ini adalah membekali santri dengan keterampilan dasar dalam menggunakan ChatGPT secara efektif, etis, dan produktif sebagai media pencarian informasi, penyusunan tulisan, serta pendukung pembelajaran mandiri. Metode pelaksanaan menggunakan pendekatan pelatihan interaktif yang meliputi sosialisasi konsep literasi digital, demonstrasi penggunaan ChatGPT, sesi praktik langsung, serta pendampingan dan evaluasi. Kegiatan ini dilaksanakan di Dayah Darul Ulum Al Munawarah, Cunda, Kota Lhokseumawe, pada Kamis, 18 September 2025. Hasil evaluasi menunjukkan adanya peningkatan pemahaman santri terhadap pemanfaatan ChatGPT sebesar 78%, berdasarkan perbandingan hasil pre-test dan post-test, disertai perubahan sikap positif terhadap penggunaan teknologi digital secara bertanggung jawab. Program ini memberikan dampak nyata dalam meningkatkan literasi digital dan kemandirian belajar santri, sekaligus menjadi langkah awal dalam membangun ekosistem pendidikan pesantren yang adaptif terhadap perkembangan teknologi berbasis Artificial Intelligence.
A Web-Based Decision Support System Implementation for Evaluating Premier Smartphone Brands Using Weighted Product Method Novia Hasdyna; Rozzi Kesuma Dinata; Sujacka Retno
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 13 No 02 (2023): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v13i02.939

Abstract

In the current modern era, smartphones have become an indispensable part of daily life, extensively utilized across a multitude of activities, particularly through online platforms. This underscores the imperative of aiding individuals in making precise decisions regarding the smartphone that aligns most with their needs. To address this exigency, the development of a Decision Support System (DSS) employing the Weighted Product method assumes paramount significance in this research. This DSS empowers users to select the most fitting smartphone by assigning weight values to various performance metrics. The criteria used in this research are price, RAM, ROM, battery capacity, and Android version. The successful implementation of this system streamlines the smartphone selection process, enabling users to make judicious choices that perfectly cater to their requirements while optimizing performance metrics.. In this research, Poco X3 Pro has the highest Vector V value of 0.255441, making it the best-recommended smartphone.
Implementation Of Support Vector Regression In Prediction Air Quakity Index In Banda Aceh City Rizky Fasya Ramdhani; Rozzi Kesuma Dinata; Ar Razi
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Air quality is one of the important aspects in maintaining environmental balance and public health. Increasing air quality in the environment is a matter of concern. Therefore, a method that can predict the Air Quality Index (AQI) effectively is needed to be able to monitor and support decision making on environmental impacts. This study aims to predict the Air Quality Index in Banda Aceh City using the Support Vector Regression algorithm, with five main parameters used in the study, namely particulate matter , Sulfur dioxide, Nitrogen dioxide, Carbon monoxide , and Ozone . In this research, the Support Vector Regression algorithm was chosen because of its ability to handle non-linear data and also because it can provide accurate predictions on data. The prediction system designed will be web-based using the flask framework and MySQL database, while the Support Vector Regression modeling will be done on google colab for the media used. In the process of modeling the data will be divided into 80% training data and 20% test data to ensure the model can capture long and short-term patterns. The results of the prediction will be compared using the Root Mean Squarred Error (RMSE) and Mean Squarred Error (MSE) evaluation metrics. The results of the evaluation using both metrics yielded RMSE values of 1.9001 and MSE of 3.6015. These values indicate good performance of the model in predicting the data. This research is expected to provide insight for future similar research in terms of prediction using the Support Vector Regression algorithm.
Application of the K-Medoids Clustering Method for Grouping High-Risk Areas of Violence Against Women and Children Annisa Afrilia Zahra Annisa; Rozzi Kesuma Dinata; Maryana
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Violence against women and children has been increasing in both quantity and variety, necessitating special attention. This study aims to cluster areas prone to violence against women and children in North Aceh using the K-Medoids Clustering method. The data used includes physical, sexual, exploitation, and neglect violence, obtained from 542 villages sourced from Unit II PPA Polres North Aceh for the period of 2021-2023. The clustering is categorized into three clusters: very prone, prone, and not prone. The results show that in 2021, there were 16 very prone villages, 22 prone villages, and 506 not prone villages, with the smallest DBI value of 0.12263 from 8 trials. In 2022, there were 22 very prone villages, 18 prone villages, and 502 not prone villages, with a DBI value of 0.10517 from 10 trials. In 2023, there were 15 very prone villages, 11 prone villages, and 516 not prone villages, with a DBI value of 0.21408 from 6 trials. The developed web-based system, using PHP and UML, is expected to assist authorities in preventing and addressing violence in prone areas, thereby reducing the incidence of violence in North Aceh.
Implementasi Metode Fisher-Yates Shuffle Dan Metode Finite State Machine Pada Game Edukasi Untuk Meningkatkan Minat Belajar Siswa Anak Sekolah Dasar Rizal, Muhammad; Rozzi Kesuma Dinata; Zahratul Fitri
Jurnal Elektronika dan Teknologi Informasi Vol 7 No 1 (2026): Maret 2026
Publisher : LPPM-UNIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5201/jet.v7i1.582

Abstract

The use of educational games as interactive learning media is one solution to increase learning motivation and understanding among elementary school students. This study aims to implement the Fisher–Yates Shuffle (FYS) and Finite State Machine (FSM) methods in the development of a Unity-based educational game for Natural Sciences (IPA) and Social Sciences (IPS), and to evaluate its effectiveness in improving students’ learning outcomes. The system was developed using the Multimedia Development Life Cycle (MDLC), which consists of concept, design, assembly, testing, and distribution stages. FYS was applied to randomize quiz questions and answer options, while FSM was used to manage game flow and scene transitions in a structured manner. System testing was conducted using black-box testing, and learning effectiveness was evaluated through pre-test and post-test involving grade III and IV elementary school students. The results indicate an increase in students’ average scores after using the educational game, with improvement percentages ranging from 22% to 25%. In addition, teacher questionnaire results show that the game is feasible, easy to use, and beneficial as a supporting learning medium. Therefore, the developed Unity-based educational game is effective in enhancing students’ understanding of IPA and IPS subjects
Comparison of the K-Nearest Neighbor and Random Forest Methods in Classifying the Best Selling Medicines at Khan Pharmacy Matang Glumpang Dua Putri, Anya Regina; Rozzi Kesuma Dinata; Maryana
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25183

Abstract

Khan Matang Glumpang Dua Pharmacy faces difficulties in analyzing drug sales patterns that affect inventory efficiency and customer satisfaction. The need to anticipate demand and reduce the risk of stockouts or excess stock requires an effective classification system for best-selling drugs. This study aims to test the K-Nearest Neighbor (KNN) and Random Forest methods to perform and find the best classification model. The data used in this study consisted of 382 data points. This study compared two classification models on pharmacy sales data. The K-Nearest Neighbor (KNN) model was tested using the parameter k=3, while the Random Forest model was tested with 100 trees and a max depth of 5. The results showed that the KNN and Random Forest (RF) algorithms. The Random Forest (RF) model outperformed KNN on all metrics: RF achieved an Accuracy and F1-Score of 94.81%, while KNN recorded an Accuracy of 93.51% and an F1-Score of 93.44%.
Co-Authors ., Yustizar Abdillah, Ahmad Fauzi Aidilof, Hafizh Al Kautsar Akbar, Hafizal Akram, Rizalul Alfika, Selly Alvanof, Mulia Andik Bintoro Annisa Afrilia Zahra Annisa Ar Razi Ar Razi, Ar Razi Ardiansyah, Sakha Arif, M. Arif Saputra Arnawan Hasibuan Asrianda Asrianda Azrai Putra Barumun Daulay Badriana, Badriana Baringin Sianipar Berutu, Indah Fachlira Bustami Bustami Bustami Bustami Bustami Cut Fadhilah Ditha, Ananda Eva Darnila Fadhilah, Cut Fadlisyah Fadlisyah Fajri, T Irfan Fajriana, Fajriana Fiasari, Fiasari Fikria, Putri Fuadi, Wahyu Gadis Ayu Sofiana Hafizal Akbar Haried Novriando Hasan Tahir Hasmar, Muhammad Al Hafiz Irfan Fajri, T. Irwanda Syahputra Iswari, Syahyana Jasmin, Nadya Khairul Muttaqin Khairunnisa Khairunnisa Khairunnisa Khairunnisa Lubis, Aulia Azzahra Ma'aruf Maryana Maryana Maryana, Maryana Muhammad Al Hafiz Hasmar Muhammad Alif Muhammad Arasyi Muhammad Fikry Muhammad Iqbal Muhammad Nurfahmi Muhammad Rivai MUHAMMAD RIZAL Munirul Ula Mursyidah Mursyidah Mutammimul Ula Mutasar Muttaqin Muttaqin Niesa, Chaeroen Novia Hasdyna Novianda Novianda Nur Azizah Nurwijayanti Putra Barumun Daulay, Azrai Putri, Anya Regina Rahmat Hidayat Rahmat Hidayat Rahmatin Nisak Risawandi, Risawandi Rizki Suwanda Rizky Fasya Ramdhani Safwandi Safwandi Safwandi Safwandi Sahputra, Ilham Said Fadlan Anshari Suci Ramadani Sujacka Retno T Irfan Fajri Tahir, Hasan Taskia, Narita Tjut Adek, Rizal Ulfa, Septia Mulya Yafis, Balqis Yesy Afrillia Zahratul Fitri Zara Yunizar Zuboili, Zuboili Zulfa Zulfa