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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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Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions Hasdyna, Novia; Dinata, Rozzi Kesuma; Yafis, Balqis
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.1-15

Abstract

The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to enhance the performance of the K-Means algorithm by integrating the Purity method, with a specific focus on clustering regions renowned for oil palm production in North Aceh. Oil palm cultivation is a vital agricultural sector in North Aceh, contributing significantly to the local economy and employment. This study examines two clustering techniques: the conventional K-Means algorithm and an optimized version, Purity K-Means. Integrating the Purity method enhances the efficiency of K-Means by reducing the number of required convergence iterations. The data used for clustering analysis is sourced from the Department of Agriculture and Food in North Aceh Regency and pertains to oil palm production in 2023. The findings indicate that the Purity K-Means approach notably reduces the iteration count and improves cluster quality. The average Davies-Bouldin Index (DBI) for standard K-Means is 0.45, whereas the Purity K-Means method lowers it to 0.30. Furthermore, applying the Purity method reduced the number of K-Means iterations from 15 to just 3. These results highlight an enhancement in clustering performance and overall efficiency.
K Fold Cross Validation Analysis for Electricity Meter Classification at PLN Lhoksukon Using K-NN and SVM Methods Zuboili, Zuboili; Dinata, Rozzi Kesuma; Syahputra, Irwanda
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.21340

Abstract

Electricity consumption continues to increase every year in line with the increase in national economic growth. Predicting current electricity demand is important to understand the overall electricity supplied to each region. The problem is that currently, the electricity supply in various areas of Lhoksukon has not matched the needs of the community. In addition, problems can arise if the power generated is less than the load power requirements, causing energy shortages in an area. To find out whether the electricity provided is appropriate or not, a classification using Supervised Learning method is used. After classification, we will use K-fold Cross Validation to measure how good the accuracy is between the methods. This study will use 200 electricity meter data consisting of 150 test data and 50 training data with a composition of 75%: 25%. The testing process where the data process that has been divided is then carried out in the testing process where the data process is obtained from manual calculations. So that in this study get results in the form of the K-NN method with 99.3% accuracy, 100% precision, 99.29% recall and the SVM method with 94.00% accuracy, 94.00% precision, 100% recall. And to find out how well the performance of the method is based on Supervised Learning method, it will be checked using K-Fold Cross Validation with the results of K-NN 99.53% and SVM 96.00%, with the conclusion that the K-Nearest Neighbor method has a better accuracy rate.
Sosialisasi dan Pengenalan Dasar Kecerdasan Buatan bagi Santri Dayah Almubarakah Aceh Utara Hasdyna, Novia; Kesuma Dinata, Rozzi; Fajri, T. Irfan; Mutasar, Mutasar; Fadhilah, Cut
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 2 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi April - Juni
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i2.5954

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi digital di kalangan santri melalui pengenalan kecerdasan buatan (Artificial Intelligence) di Dayah Almubarakah, Aceh Utara. Permasalahan yang dihadapi adalah masih rendahnya pemahaman santri terhadap teknologi modern, khususnya kecerdasan buatan, yang berpotensi dimanfaatkan dalam pendidikan dan dakwah. Metode pelaksanaan kegiatan meliputi ceramah interaktif, studi kasus, dan demonstrasi aplikasi berbasis kecerdasan buatan yang relevan dengan kehidupan sehari-hari dan nilai-nilai Islam. Materi yang disampaikan mencakup pengenalan dasar Artificial Intelligence, aplikasinya dalam berbagai bidang, serta tantangan dan peluangnya dari perspektif Islam. Hasil kegiatan menunjukkan antusiasme yang tinggi dari peserta, dengan lebih dari 80% santri mampu menjelaskan kembali konsep dasar kecerdasan buatan setelah mengikuti kegiatan. Selain itu, diskusi yang berlangsung menunjukkan adanya minat lanjutan untuk mendalami topik ini dalam konteks dakwah dan pendidikan. Simpulan dari kegiatan ini adalah bahwa pengenalan kecerdasan buatan secara kontekstual dan aplikatif dapat menjadi langkah awal yang efektif dalam membangun literasi digital Islami di kalangan santri, sekaligus membuka wawasan mereka terhadap perkembangan teknologi yang relevan dengan nilai-nilai keislaman.
Public Facility Recommendation System in Subulussalam City Using Fuzzy C-Means Algorithm Berutu, Indah Fachlira; Dinata, Rozzi Kesuma; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.873

Abstract

Subulussalam City, as one of the autonomous regions in Aceh Province, Indonesia, has excellent potential to develop public facilities to improve the quality of life for its residents. Recommendation systems have become an effective solution in helping users find relevant information based on the preferences and needs of the community. This research focuses on developing a recommendation system using the Fuzzy C-Means algorithm. This algorithm is one of the clustering methods capable of handling uncertainty and ambiguity in data. This study aims to develop and analyze a public facility recommendation system in Subulussalam City using the Fuzzy C-Means algorithm. The dataset in this study was obtained from the Youth, Sports, and Tourism Office of Subulussalam City and the results of a research questionnaire. Regarding the names of each public facility, it provides information about the location and various forms of visitor assessments, including evaluations related to accessibility, facilities, costs, environment, and visitor experiences, using a rating scale of 1-5. Based on the testing results, the Fuzzy C-Means clustering algorithm can group facilities based on characteristics and user preferences, resulting in more personalized and relevant recommendations. The data to be clustered is divided into two categories: recommended and not recommended. The study's results using the Fuzzy C-Means algorithm show the final grouping based on the degree of membership from the last iteration of each public facility, with cluster 1 containing 31 locations and cluster 2 containing 31 locations.
Comparison of the Results of the Weighted Moving Average Method and the Least Absolute Shrinkage and Selection Operator Method for Predicting Total Palm Oil Production at PT. Mora Niaga Jaya Ardiansyah, Sakha; Dinata, Rozzi Kesuma; Ar Razi, Ar Razi
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.v5i2.862

Abstract

This study compares two prediction methods, Weighted Moving Average (WMA) and Least Absolute Shrinkage and Selection Operator (LASSO), in forecasting the total palm oil production at PT. Mora Niaga Jaya. Accurate forecasting is essential in the palm oil industry to support decision-making, optimize production planning, and manage supply chains efficiently. The WMA method produced more realistic prediction results, with a Mean Absolute Error (MAE) of 114,854 tons and a Mean Absolute Percentage Error (MAPE) of 220.45%, despite still having a considerable margin of error. These values suggest that while WMA is not perfectly accurate, it performs moderately well, given the complexity and variability inherent in agricultural production data. On the other hand, the LASSO method yielded significantly worse results, with an extremely high and unrealistic MAE and a MAPE of 291,456.000%, indicating that this approach is unsuitable for palm oil production forecasting in this specific case. The underperformance of the LASSO method may be due to the nature of the data used, which may not meet the assumptions required for LASSO to function optimally, such as linear relationships and minimal noise. This highlights the importance of aligning forecasting methods with the dataset's characteristics. Based on the comparison, it can be concluded that the WMA method is more appropriate for predicting palm oil production than LASSO. However, further steps such as parameter optimization, data normalization, and outlier removal should be undertaken to achieve better predictive accuracy. This research provides valuable insights into the importance of selecting the correct predictive method and ensuring data quality in forecasting. Ultimately, careful model selection and data preprocessing support effective operational and strategic decisions in the palm oil industry.
Classification Of Outpatient Visit Status Walking at Dr. Zubir Mahmud Hospital Using Algoritma C4.5 Fikria, Putri; Dinata, Rozzi Kesuma; afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.865

Abstract

This study aims to classify the status of outpatient visits at RSUD Dr. Zubir Mahmud into three main categories, namely "Very Urgent", "Urgent", and "Not Urgent”, using the C4.5 algorithm. The web-based system uses the PHP programming language and MySQL database to ensure ease of implementation and efficient data management. The classification process is done by setting threshold parameters, calculating entropy, and the gain ratio to form an accurate and reliable decision tree. The results show that the C4.5 algorithm can classify patient visit data with a reasonably high accuracy rate, which is 93.75% for 2022 data and reaches 100% for 2023 data. In 2022 the “Very Urgent" category had 9 True Positives (TP); in 2023, the number remained consistent. However, in both years, there were also False Negatives in the same category, with 4 cases in 2022 and 5 cases in 2023. The "Urgent" and "Not Urgent" categories show suboptimal classification performance due to uneven data distribution, which causes the precision and recall values in these categories low. Model evaluation was conducted using evaluation metrics such as precision, recall, and F1 score. The evaluation results show that the model works very well in identifying high-priority categories, but further development is needed to improve classification in other categories. This system is expected to be a reliable tool in decision-making in health services, especially in determining the priority of patient services appropriately and efficiently. With further development, this system has the potential to be widely applied in various other hospitals.
Implementation Clustering Diabetes Suffering Areas Using Web-Based Dbscan Algorithm North Aceh District Abdillah, Ahmad Fauzi; Dinata, Rozzi Kesuma; Maryana
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.622

Abstract

Diabetes has shown a significant increase in Indonesia, including in the North Aceh District. This research implements the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise) web-based method to map diabetes distribution patterns in 27 North Aceh sub-districts. This system was built using the PHP programming language and database MySQL. Proses clustering utilizing data on population, number of sufferers, and number of deaths from 2021-2023 obtained from Prima Inti Medika Hospital and Cut Meutia RSU, with parameters epsilon = 0.5 and MinPts = 3. Results clustering shows an increase in high-risk areas from year to year. In 2021, 2 high-risk sub-districts were identified, Dewantara and Lhoksukon, increasing to 3 sub-districts in 2022 Dewantara, Lhoksukon, and Nisam, in 2023 to 4 sub-districts Dewantara, Lhoksukon, Nisam and Muara Batu. The resulting web-based system succeeded in visualizing diabetes distribution patterns and can be used to plan more effective and targeted health programs.
Hiace Transportation Departure Scheduling Information System in Lhokseumawe With Genetic Algorithm Taskia, Narita; Dinata, Rozzi Kesuma; Retno, Sujacka
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.878

Abstract

This study aims to address challenges in transportation scheduling by employing a suitable algorithm to ensure the scheduling process operates efficiently and effectively. One algorithm identified as appropriate for this task is the Genetic Algorithm, which is widely recognized for its robust capabilities in optimization tasks. Known for its adaptability and robustness, the Genetic Algorithm is well-suited for scheduling applications, including academic timetabling, as it can handle complex problems involving multiple criteria and objectives. Inspired by principles of biological evolution and natural selection, this algorithm iteratively explores solutions to approach optimal outcomes, refining the schedule in each iteration until an effective solution is achieved. Based on the analysis of experimental results using real-world data and evaluation of the system's design, the study concludes that the Hiace transportation departure scheduling system was successfully developed using a web-based approach. This web-based system offers significant advantages, as it facilitates more efficient management of departure schedules and eliminates the need for manual checks. As a result, it reduces the risk of human error and allows for better resource allocation. The integration of Genetic Algorithms into the development of the Hiace transportation scheduling system demonstrates the potential of evolutionary computation in solving practical, real-life scheduling problems. The resulting system is supported by internet-based technologies, providing easy access to passengers and system administrators. Despite the positive outcomes achieved, the current implementation is not without limitations. Further refinement and continued development are essential to enhance system performance, increase reliability, and ensure it can adapt to evolving needs and operational complexities, ensuring its long-term effectiveness.
Comparison of Linear Regression and Polynomial Regression for Predicting Rice Prices in Lhokseumawe City Muhammad Iqbal; Rozzi Kesuma Dinata; Rizki Suwanda
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2396

Abstract

Rice is a strategic food commodity in Indonesia, and its price fluctuations significantly impact inflation, economic stability, and poverty levels. Accurate price prediction is, therefore, essential for effective policymaking. The objective of this research is to develop a system for predicting the price of rice in Lhokseumawe City, employing a comparison of the accuracy of linear and polynomial regression models. To this end, daily price data from the Strategic Food Price Information Center (PIHPS) from 2020 to 2024 were utilized, with both models being implemented in Python. The findings indicate that 4th-order polynomial regression exhibited optimal performance, attaining a mean absolute percentage error (MAPE) of 1.85%, a mean absolute error (MAE) of 205.23, and a root mean squared error (RMSE) of 284.88. Conversely, the implementation of linear regression resulted in substantially elevated error metrics, with a mean absolute percentage error (MAPE) of 5.16%, a mean absolute error (MAE) of 553.91, and a root mean square error (RMSE) of 614.14. The findings indicate that 4th-order polynomial regression is a substantially more effective model for predicting rice prices in Lhokseumawe. The latter's superiority suggests that local rice price dynamics are characterized by significant non-linear patterns, rendering it a more robust tool for capturing data volatility and supporting data-driven policy.
Implementation of The Logistic Regression Algorithm to Analyze Poverty Factors in Aceh Province Mursyidah, Mursyidah; Kesuma Dinata, Rozzi; Yunizar, Zara
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9715

Abstract

Aceh Province continues to face a high poverty rate despite its abundant natural resources. This study aims to analyze the factors influencing poverty status in Aceh Province by applying a binary logistic regression algorithm. The research specifically focuses on an inferential analytical approach to reveal significant relationships among socioeconomic variables. Secondary data were obtained from the Aceh Provincial Statistics Agency (Badan Pusat Statistik/BPS) for the period 2019–2023. Inferential analysis was conducted using the entire dataset through the statsmodels library to identify variables that are statistically significant to poverty status. In addition, a classification approach was implemented using scikit-learn, with a data split between training data (2019–2022) and testing data (2023), yielding an accuracy of 0.70, precision of 0.81, recall of 0.70, F1-score of 0.66, and AUC of 0.69. These findings provide empirical evidence that improving access to education and equitable infrastructure development in densely populated areas can serve as effective policy focuses in efforts to alleviate poverty in Aceh Province.
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 Berutu, Indah Fachlira Bustami Bustami Bustami Bustami Bustami 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 Nisak, Rahmatin Novia Hasdyna Novianda Novianda Nur Azizah Nurwijayanti Putra Barumun Daulay, Azrai Putri, Anya Regina Rahmat Hidayat Rahmat Hidayat 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