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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 RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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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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
A Comparative Performance Evaluation of Unsupervised Learning Algorithms for Clustering Stunting Prevalence in Aceh Province Novia Hasdyna; Rozzi Kesuma Dinata; Baringin Sianipar
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27009

Abstract

Stunting remains a significant public health issue in Indonesia, particularly in Aceh Province, where considerable disparities continue to exist across districts and municipalities. Identifying regional prevalence patterns is crucial for developing evidence-based intervention strategies. This study assesses the performance of four unsupervised learning algorithms, namely K-Means, Hierarchical Clustering, Gaussian Mixture Model (GMM), and Fuzzy C-Means (FCM), for clustering district-level stunting data in Aceh Province across five observation periods. Algorithm performance was evaluated using the Calinski-Harabasz Index, convergence efficiency, and cluster interpretability. The findings demonstrate that Fuzzy C-Means outperformed the other methods, achieving the highest Calinski-Harabasz score of 49.75, followed by GMM with 42.61, Hierarchical Clustering with 36.48, and K-Means with 25.30. In addition, FCM showed the fastest convergence, requiring only three iterations. Three stable regional clusters were identified, representing high, moderate, and low prevalence levels. High-prevalence areas included Aceh Barat, Aceh Utara, Aceh Tenggara, Pidie Jaya, Aceh Barat Daya, Simeulue, and Bener Meriah, whereas Subulussalam constituted the low-prevalence cluster. These findings indicate that Fuzzy C-Means provides a reliable approach for regional stunting classification and may contribute to more targeted policy interventions in Aceh Province.
Implementasi Metode Double Exponential Smoothing untuk Prediksi Jumlah Kebutuhan Air di PDAM Tirta Mon Pase Rahmatin Nisak; Arnawan Hasibuan; Said Fadlan Anshari; Rozzi Kesuma Dinata; Fadlisyah Fadlisyah
JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Vol 11, No 1 (2026): Januari 2026
Publisher : Universitas Al Azhar Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36722/sst.v11i1.5210

Abstract

Clean water is an essential human need, yet its provision is frequently disrupted by demand uncertainty, as experienced by PDAM Tirta Mon Pase with recurring public complaints regarding water supply interruptions. This study aims to design and implement a water demand forecasting system using the Double Exponential Smoothing (Holt’s Linear Trend) method and to evaluate its accuracy. The research utilized monthly historical water production data from January 2022 to December 2024 (36 observations) obtained from PDAM Tirta Mon Pase. The model was applied with smoothing parameters α = 0.8 and β = 0.2, and accuracy was measured using Mean Absolute Percentage Error (MAPE). The results show a very high level of accuracy with an overall MAPE of 3.56% (2022: 4.18%; 2023: 3.91%; 2024: 2.65%), and the forecast predicts water demand in December 2027 will reach 1,131,071.39 m³. It can be concluded that the Double Exponential Smoothing method is highly accurate and effective for forecasting water demand at PDAM Tirta Mon Pase. The developed system is therefore strongly recommended for operational adoption as a strategic decision-support tool in water resource planning, production, and infrastructure development.
Implementation Clustering Diabetes Suffering Areas Using Web-Based Dbscan Algorithm North Aceh District Ahmad Fauzi Abdillah; Rozzi Kesuma Dinata; 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.
PENERAPAN DATA MINING PENJUALAN SEPATU MENGGUNAKAN METODE ALGORITMA APRIORI DAN REGRESI LINIER BERGANDA BERBASIS WEB: THE IMPLEMENTATION OF DATA MINING FOR SHOE SALES USING THE APRIORI ALGORITHM METHOD AND MULTIPLE LINEAR REGRESSION BASED ON THE WEB Deffiyani; Rozzi Kesuma Dinata; Yessy Afrillia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6521

Abstract

Penelitian ini mengkaji penerapan teknik data mining untuk menganalisis pola penjualan sepatu di Toko Tunelbrand, Aceh Utara. Tujuan utama adalah mengidentifikasi keterkaitan antarproduk melalui algoritma Apriori serta memprediksi volume penjualan berdasarkan variabel harga dan promosi menggunakan metode regresi linier berganda. Proses analisis mencakup evaluasi data transaksi penjualan guna menghasilkan frequent itemsets dan aturan asosiasi berdasarkan nilai ambang support dan confidence tertentu, serta pengembangan model regresi linier untuk estimasi penjualan. Hasil dari algoritma Apriori mengungkap adanya pola pembelian yang signifikan, seperti kombinasi produk Adidas-Nike dengan support sebesar 50% confidence 67%, NikeAdidas dengan support sebesar 50% dan confidence 100%, serta Puma-Adidas dengan support 33% dan confidence 100%. Model regresi linier menunjukkan nilai koefisien determinasi (R²) sebesar 0,3813, RMSE sebesar 3,41 dan Mean Squared Error (MSE) dalam rentang 2,7 yang menandakan bahwa performa prediksinya masih terbatas. Sistem berbasis web yang dikembangkan dalam penelitian ini mampu menyajikan visualisasi hasil analisis secara informatif, sehingga dapat digunakan untuk mendukung pengambilan keputusan dalam strategi penempatan produk, promosi bundling, serta peramalan permintaan secara lebih efisien.
PERBANDINGAN METODE LOGISTIC REGRESSION DAN RANDOM FOREST DALAM KLASIFIKASI PENYAKIT KULIT MULTIKELAS: COMPARISON OF LOGISTIC REGRESSION AND RANDOM FOREST METHODS IN MULTICLASS SKIN DISEASE CLASSIFICATION Syatriani Jauhari; Rozzi Kesuma Dinata; Ar Razi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6551

Abstract

This study compares two classification algorithms, namely Logistic Regression and Random Forest, in classifying eight types of skin diseases based on ten clinical symptoms. The data used consists of 271 patient medical records from Cut Meutia General Hospital, which are divided into 80% training data and 20% test data. The pre-processing stage included imputing missing data, encoding categorical variables, and normalizing numerical features for the Logistic Regression model. Both algorithms were implemented using the Scikit-learn library in the Python programming language. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that Logistic Regression achieved an accuracy of 94.55%, slightly higher than Random Forest, which reached 92.73%. Validation using 5-fold cross-validation and a paired t-test yielded a p-value of 0.0371, indicating that the performance difference between the two models is statistically significant. However, limitations such as the relatively small amount of data and class imbalance impacted the low performance of the model in minority categories such as Psoriasis. This study is expected to serve as a foundation for the development of data-based medical diagnosis assistance systems to improve efficiency and accuracy in healthcare services.
IMPLEMENTASI AUGMENTED REALITY UNTUK PENGENALAN TANAMAN TOGA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Melita Saldila; Rozzi Kesuma Dinata; Said Fadlan Anshari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6602

Abstract

This research aims to develop an Augmented Reality (AR) based application integrated with Convolutional Neural Network (CNN) method to help communities recognize Family Medicinal Plants (TOGA) interactively and increase awareness of their potential benefits. The developed application uses AR technology to provide direct information about TOGA plants detected through mobile phone cameras, with a dataset covering 10 types of TOGA plants, each containing 200 images per label. The research results show that the system successfully performs plant recognition in real-time with an accuracy rate of 58.53%, precision of 58.76%, and recall of 99.40%. The CNN model is capable of recognizing various visual variations of plants under different lighting conditions and viewing angles. Model training was conducted up to 125,000 steps with the best performance achieved at the 72,000th checkpoint. Although the application can provide an engaging and effective learning experience, the main challenge faced is the diversity of physical forms of plants within each category that affects system accuracy. This research proves that the combination of AR and CNN technologies can be used as an innovative solution for medicinal plant education, although further development is still needed to improve recognition accuracy.
IMPLEMENTASI METODE CONTENT-BASED FILTERING DALAM REKOMENDASI KEDAI KOPI DI KOTA LHOKSEUMAWE Muhammad Arrayyan; Rozzi Kesuma Dinata; Said Fadlan Anshari; Fadlisyah; Maryana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6641

Abstract

Lhokseumawe a city known for its numerous coffee shops, serves as the focus of this study, which aims to develop a coffee shop recommendation system using a content-based filtering approach based on Google Maps review analysis. A total of 54 coffee shops were collected through web scraping and filtered to 32, as only these shops provided sufficient and relevant reviews according to the selected keywords. User reviews were processed through preprocessing, TF-IDF weighting, and cosine similarity to measure the alignment between user preferences and shop characteristics. A scenario-based evaluation was conducted by using keywords such as “noodles,” “parking,” “spacious,” “toilet,” and “watching together” to represent user preferences. The results show that the system generates recommendations consistent with the presence and relevance of these keywords, with shops such as AN Coffee and Arabica Kopi frequently appearing as top suggestions. Although the evaluation is limited to scenario-based testing, the system demonstrates potential in assisting users in selecting suitable coffee shops. Future work may include hybrid filtering, machine learning methods, automated keyword extraction through topic modeling, and user-based evaluation to improve recommendation quality.
Peningkatan Kompetensi Guru melalui Pelatihan Koding dan Artificial Intelligence di Kabupaten Aceh Utara Mutasar Mutasar; Novia Hasdyna; Rozzi Kesuma Dinata; Chaeroen Niesa; Cut Fadhilah
Multidisiplin Pengabdian Kepada Masyarakat Vol. 5 No. 02 (2026): Multidisiplin Pengabdian Kepada Masyarakat, April-July 2026
Publisher : Sean Institute

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Abstract

Guru sebagai tenaga pendidik memiliki peran strategis dalam menghadapi tantangan peningkatan literasi digital di era modern. Namun, keterbatasan pemahaman terhadap pemanfaatan teknologi, khususnya koding dan Artificial Intelligence (AI), menyebabkan proses pembelajaran berbasis digital belum optimal. Program Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan kompetensi guru melalui pelatihan koding dan pemanfaatan AI sebagai alat bantu pembelajaran. Metode pelaksanaan menggunakan pendekatan pelatihan interaktif yang meliputi sosialisasi literasi digital, demonstrasi penggunaan koding dan AI, praktik langsung, serta evaluasi melalui pre-test dan post-test. Kegiatan ini dilaksanakan selama dua hari, yaitu pada tanggal 2–3 April 2026 di Kabupaten Aceh Utara, dengan melibatkan guru dari berbagai jenjang pendidikan. Hasil evaluasi menunjukkan adanya peningkatan pemahaman peserta terhadap pemanfaatan koding dan AI sebesar 78%, disertai perubahan sikap positif dalam penggunaan teknologi secara etis dan produktif. Peserta juga mampu mengimplementasikan dasar-dasar koding serta memanfaatkan AI dalam penyusunan materi pembelajaran. Program ini memberikan dampak nyata dalam meningkatkan literasi digital dan kompetensi guru, serta mendukung transformasi pembelajaran berbasis teknologi.
Co-Authors ., Yustizar Ahmad Fauzi Abdillah Aidilof, Hafizh Al Kautsar Akbar, Hafizal Akram, Rizalul Alvanof, Mulia Andik Bintoro Annisa Afrilia Zahra Annisa Anya Regina Putri Ar Razi 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 Chaeroen Niesa Cut Fadhilah Deffiyani Eva Darnila Fadlisyah 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 Irwanda Syahputra Iswari, Syahyana Jasmin, Nadya Khairul Muttaqin Khairunnisa Khairunnisa Khairunnisa Khairunnisa Lubis, Aulia Azzahra Ma'aruf Maryana Maryana Maryana Melita Saldila Muhammad Al Hafiz Hasmar Muhammad Alif Muhammad Arasyi Muhammad Arrayyan Muhammad Fikry Muhammad Iqbal Muhammad Nurfahmi Muhammad Rivai Muhammad Rizal MUHAMMAD RIZAL Munirul Ula Mursyidah Mursyidah Mutammimul Ula Mutasar Muttaqin Muttaqin Narita Taskia Novia Hasdyna Novianda Novianda Nur Azizah Nurwijayanti Rahmat Hidayat Rahmat Hidayat Rahmatin Nisak Risawandi, Risawandi Rizki Suwanda Rizky Fasya Ramdhani Safwandi Safwandi Safwandi Safwandi Sahputra, Ilham Said Fadlan Anshari Selly Alfika Suci Ramadani Sujacka Retno Sujacka Retno Syatriani Jauhari T Irfan Fajri Tahir, Hasan Ulfa, Septia Mulya Yafis, Balqis Yessy Afrillia Yesy Afrillia Zahratul Fitri Zahratul Fitri Zara Yunizar Zuboili, Zuboili Zulfa Zulfa