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All Journal Teknika ITEj (Information Technology Engineering Journals) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENDIDIKAN TAMBUSAI JURIKOM (Jurnal Riset Komputer) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) International Journal of Engineering, Science and Information Technology Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Algoritma MIMBAR INTEGRITAS Jurnal Malikussaleh Mengabdi INOVTEK Polbeng - Seri Informatika
Zahratul Fitri, Zahratul
Sekolah Tinggi Keguruan dan Ilmu Pendidikan (STKIP) Bumi Persada
Religion 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 Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Implementation of Content-Based Filtering Method for Data Recommendation Of Computational Thinking Students-Based Informatics Subject Fitri, Zahratul; Safriana, Safriana; Nurdin, Nurdin
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 1 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i1.22394

Abstract

Data recommendations are built by displaying the results of student subject recommendations based on students' computational thinking value. The process carried out is tokenization, stopword removal, stemming, and weighting. The extraction results were then compared using the cosine similarity approach. The greater the value of cosine similarity produced, the more similar the two data are, so that the material recommendations will be based on the smallest cosine similarity value between the extraction of student recommendation data. From the 535 data, several student data are included in 3 levels of material, namely recommendation 0 (low), recommendation 1 (medium), and recommendation 2 (high). Recommendation data was obtained from the results of students' computational thinking calculations by looking at decomposition value, pattern value, abstraction value, and algorithm value.
Classification of Coronary Heart Disease Based on Community Health Centre Medical Record Data Using SVM Algorithm Kausar, M Reza; Fuadi, Wahyu; Fitri, Zahratul
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/ng11kk81

Abstract

Coronary heart disease (CHD) is one of the leading causes of death worldwide and demands a fast and accurate diagnostic system, especially in community health centres (Puskesmas) where medical resources are limited. This study aims to develop a classification system for CHD using the Support Vector Machine (SVM) algorithm based on numerical medical record data. It also addresses the gap in previous studies that rarely applied SVM to tabular data from primary healthcare facilities. The methodology includes variable weighting, min-max normalization, model training with a linear kernel, and performance evaluation using a confusion matrix. The dataset consists of 100 patient records with variables such as age, blood pressure, heart rate, respiratory rate, and chest pain. The results show that the SVM model achieved an accuracy of 95%, a precision of 100%, recall of 88.9%, and an F1-score of 94.1%. The model was further integrated into a web-based application using Flask to support automated early diagnosis. This study demonstrates that SVM is effective in classifying heart disease based on medical records and offers a practical solution to improve healthcare service quality in Puskesmas.
Development of an Expert System for Identifying Students' Learning Styles Using the Euclidean Probability Method Rahma, Putri; Fitri, Zahratul; Fuadi, Wahyu
ITEJ (Information Technology Engineering Journals) Vol 10 No 1 (2025): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i1.214

Abstract

Learning styles play an important role in determining the most effective teaching strategies by aligning instructional methods with students’ individual preferences in receiving, processing, and understanding information. However, classroom teaching is often applied uniformly, disregarding the differences in learning styles among students. This can hinder the effectiveness of the learning process. This research aims to develop a web-based expert system using the Euclidean Probability method to identify the dominant learning styles of students at SMK Negeri 3 Lhokseumawe. The system processes input data representing student characteristics and calculates the proximity to each learning style category using the Euclidean distance formula. A total of 110 student data entries were analyzed, revealing that 32 students (29.09%) had a Visual learning style, 26 students (23.64%) were Auditory, 16 students (14.55%) were Read/Write, and 36 students (32.73%) were Kinesthetic learners. The results showed that the Kinesthetic learning style was the most dominant among students. Therefore, this expert system can efficiently assist in determining students' learning styles, allowing for quick and accurate identification of their learning preferences. This supports the development of more personalized and adaptive learning strategies, which are expected to enhance student engagement and learning outcomes.
Pemanfaatan Artificial Intelligence Tools untuk Melatih Skill Menulis Siswa Fitri, Zahratul; Emi Maulani; Liza Afra; Khairul Amna; Annisa Karima; Leni Maulinda
Jurnal Malikussaleh Mengabdi Vol. 4 No. 1 (2025): Jurnal Malikussaleh Mengabdi, April 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i1.22317

Abstract

Pemanfaatan Artificial Intelligence (AI) tools dalam melatih keterampilan menulis siswa sebagai respons terhadap tantangan literasi di era digital. Pemanfaatan alat kecerdasan buatan (AI) dalam pendidikan telah menunjukkan potensi yang signifikan untuk meningkatkan keterampilan menulis siswa. Dengan adanya alat seperti ChatGPT, pemrograman berbasis AI dapat membantu guru dalam merancang rencana pembelajaran yang efektif dan efisien, menghemat waktu dalam perencanaan dan pelaksanaan pengajaran. Dengan pendekatan deskriptif kualitatif, pengabdian ini berfocus pada jenis AI writing tools seperti Grammarly, ChatGPT, dan Quillbot yang terbukti mampu meningkatkan kemampuan menulis dari segi struktur, tata bahasa, dan koherensi ide. Berdasarkan permasalahan artificial intelligence tools terbukti memberikan umpan balik instan yang bersifat adaptif dan personal, memungkinkan siswa belajar secara mandiri dan berkelanjutan. Meskipun demikian, penelitian ini juga menyoroti risiko ketergantungan terhadap teknologi dan hilangnya proses berpikir kritis jika tidak disertai dengan pengawasan guru yang memadai. Oleh karena itu, integrasi Artificial Intelligence tools perlu dirancang secara sistematis agar menjadi media pembelajaran yang efektif, etis, dan relevan dengan kebutuhan kurikulum. Hasil pengabdian ini memberikan kontribusi terhadap strategi inovatif dalam pembelajaran literasi dan mendukung pengembangan keterampilan siswa secara lebih holistik.
Klasterisasi Data Stunting Pada Balita Di Puskesmas Xyz Dengan Menggunakan Metode Mixture Modelling Delianda, Anggun; Asrianda, Asrianda; Fitri, Zahratul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8580

Abstract

This research is motivated by the high prevalence of stunting in Indonesia, reflecting nutritional imbalances in early childhood. To address this issue, an information technology approach is employed to identify at-risk infant groups. The analyzed data consists of anthropometric information, including height, weight, and age of infants, collected from the Peusangan Health Center. The applied method is the Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to cluster the data into two groups: "Potential Stunting" and "Not Stunting." The research results indicate that several Posyandu and villages have notably high potential stunting rates, such as Posyandu Bungong Seulanga (141 infants) and Pante Gajah village (116 infants), with a higher prevalence among male infants (34.67%) and those aged 52–60 months (24.18%). Model evaluation using a confusion matrix on 1,465 data points showed a True Positive of 958 (65.36%), False Negative of 4 (0.27%), False Positive of 503 (34.33%), and True Negative of 0 (0%), with an accuracy of 65.36% and an error rate of 34.64%. However, a previous accuracy test on 1,665 data points only achieved 34.55%, indicating unsatisfactory individual prediction performance. In conclusion, Mixture Modelling is effective for clustering and identifying at-risk groups but lacks accuracy in individual predictions, with a bias toward the "Potential Stunting" class that requires improvement in future research.
Penerapan Algoritma K-Means Clustering untuk Segmentasi Kepadatan Penduduk Berbasis GIS Putri, Rizki Amelia; Safwandi, Safwandi; Fitri, Zahratul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8668

Abstract

This study aims to develop a clustering system using the K-means algorithm to analyze demographic data of sub-districts from 2020 to 2023. The system is designed to cluster sub-districts based on variables such as population size, population percentage, population density, and gender ratio. The clustering results reveal different grouping patterns each year, reflecting the dynamics of demographic data over time. Evaluation using the Davies-Bouldin Index (DBI) indicates that the clustering results are of reasonably good quality, with DBI values of 1.1492 in 2020, 0.6859 in 2021, 1.2470 in 2022, and 0.6805 in 2023. The best DBI value was recorded in 2023 at 0.6805, demonstrating that the clustering results in that year were the most optimal compared to other years. The system also facilitates Users with interactive map visualizations, supporting better data analysis and decision-making processes. This research is expected to contribute to the management of demographic data and support more accurate data-driven policy-making.
Application of Linear Discriminant Analysis Method With Gray Level Cooccurrence Matrix Method for Classification of Lung Disease Diagnosis Based on X-Ray Results Nuriana, Nuriana; Fitri, Zahratul; Razi, Ar
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.v9i4.9908

Abstract

This study aims to classify lung diseases from X-ray images using a combination of Gray Level Cooccurrence Matrix (GLCM) and Linear Discriminant Analysis (LDA) methods. GLCM was used to extract texture features such as contrast, correlation, energy, and homogeneity from 300 lung X-ray images representing four categories: Normal, Pneumonia, Tuberculosis, and Bronchitis. The LDA method was then applied for classification based on these features. The results showed that Tuberculosis had the highest classification accuracy at 80%, while the overall model accuracy was 61.67%. Evaluation using precision, recall, F1-score, and confusion matrix confirmed that the GLCM and LDA combination performed best in identifying tuberculosis cases. However, overlapping features between Normal, Bronchitis, and Pneumonia classes reduced the classification performance. These findings suggest that the proposed method provides promising results and could be improved further with advanced feature extraction or classification techniques.
Implementasi Algoritma XGBoost dengan Walk Forward Validation untuk Prediksi Harga Emas Antam Hisyam, Mochammad; Fitri, Zahratul; Aidilof, Hafizh Al Kautsar
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8693

Abstract

Accurate gold price prediction is crucial in supporting financial and investment decision-making. This study aims to develop and optimize a daily gold price prediction model using the Extreme Gradient Boosting (XGBoost) algorithm based on historical price data and technical indicators. The model was constructed to predict two types of prices, namely "Close" and "Buyback" prices in IDR/gram. Optimization was carried out using Bayesian Optimization to obtain the best hyperparameter combinations. The model was evaluated using a Walk Forward Validation (WFV) approach with a 14-day sliding window and two main evaluation metrics: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that the model provides excellent predictive performance, with an average RMSE of 15,431.92 and MAPE of 1.03% for Close price, and RMSE of 15,382.64 and MAPE of 1.15% for Buyback price. The prediction visualizations indicate that the model consistently follows the actual price trend. Feature importance analysis reveals that technical indicators such as RSI, EMA, and MACD significantly contribute to the model. The success of this study demonstrates that an optimized XGBoost model can serve as a reliable approach for gold price forecasting and opens opportunities for developing more advanced predictive models in future research.
Kontribusi Penguasaan Kosakata terhadap Keterampilan Menulis Teks Eksposisi Siswa Kelas VIII SMP Negeri 2 Lubuk Basung Fitri, Zahratul; Anggraini, Dewi
Jurnal Pendidikan Tambusai Vol. 8 No. 2 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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

Abstract

Tujuan penelitian ini mendeskripsikan tiga hal berikut. Pertama, mendeskripsikan penguasaan kosakata siswa kelas VIII SMP Negeri 2 Lubuk Basung. Kedua, mendeskripsikan keterampilan menulis teks eksposisi siswa kelas VIII SMP Negeri 2 Lubuk Basung. Ketiga, menjelaskan kontribusi penguasaan kosakata terhadap keterampilan menulis teks eksposisi siswa kelas VIII SMP Negeri 2 Lubuk Basung. Jenis Penelitian ini adalah kuantitatif dengan metode deskriptif. Desain penelitian yang digunakan dalam penelitian ini adalah desain korelasional. Populasi penelitian ini adalah siswa kelas VIII SMP Negeri 2 Lubuk Basung yang terdaftar pada tahun ajaran 2023/2024 sebanyak 212 siswa. Sampel penelitian ini sebanyak 42 siswa. Hasil penelitian ini ada tiga. Pertama, penguasaan kosakata siswa kelas VIII SMP Negeri 2 Lubuk Basung berada pada kualifikasi Baik (B) dengan nilai rata-rata 76,36. Kedua, keterampilan menulis teks eksposisi siswa kelas VIII SMP Negeri 2 Lubuk Basung berada pada kualifikasi Baik (B) dengan nilai rata-rata 84. Ketiga, penguasaan kosakata berkontribusi sebesar 50,4% terhadap keterampilan menulis teks eksposisi siswa kelas VIII SMP Negeri 2 Lubuk Basung. Berdasarkan hasil penelitian tersebut, dapat disimpulkan bahwa penguasaan kosakata diperlukan untuk menunjang keterampilan menulis teks eksposisi.
Klasterisasi Data Stunting Pada Balita Di Puskesmas Xyz Dengan Menggunakan Metode Mixture Modelling Delianda, Anggun; Asrianda, Asrianda; Fitri, Zahratul
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8580

Abstract

This research is motivated by the high prevalence of stunting in Indonesia, reflecting nutritional imbalances in early childhood. To address this issue, an information technology approach is employed to identify at-risk infant groups. The analyzed data consists of anthropometric information, including height, weight, and age of infants, collected from the Peusangan Health Center. The applied method is the Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to cluster the data into two groups: "Potential Stunting" and "Not Stunting." The research results indicate that several Posyandu and villages have notably high potential stunting rates, such as Posyandu Bungong Seulanga (141 infants) and Pante Gajah village (116 infants), with a higher prevalence among male infants (34.67%) and those aged 52–60 months (24.18%). Model evaluation using a confusion matrix on 1,465 data points showed a True Positive of 958 (65.36%), False Negative of 4 (0.27%), False Positive of 503 (34.33%), and True Negative of 0 (0%), with an accuracy of 65.36% and an error rate of 34.64%. However, a previous accuracy test on 1,665 data points only achieved 34.55%, indicating unsatisfactory individual prediction performance. In conclusion, Mixture Modelling is effective for clustering and identifying at-risk groups but lacks accuracy in individual predictions, with a bias toward the "Potential Stunting" class that requires improvement in future research.
Co-Authors - Fakhrurrazi Ade Irfan Aidilof, Hafizh Al Kautsar Akbar, Muhammad Zulfat Ananda Faridhatul Ulva Andik Bintoro Angga Pratama ANNISA KARIMA Asrianda Asrianda Bakhtiar Bakhtiar Dahlan Abdullah Delianda, Anggun Dewi Anggraini Dita Amelia, Dita Emi Maulani Eva Darnila Fadliani, Fadliani Fuadi, Wahyu Hisyam, Mochammad Irwansyah, Defi Kausar, M Reza Khairul Amna Laksana, Fatih Dwi Leni Maulinda Liza Afra Merdu, Bunga Mawarni MUHAMMAD TEGUH, MUHAMMAD Muhibuddin Muhibuddin, Muhibuddin Mutammimul Ula Nazimah Nazimah Nisa, Miftahul Nisfiani, Nisfiani Nunsina, Nunsina Nurdin Nurdin Nurdin Nurdin Nuriana, Nuriana Putri, Rizki Amelia Rahayu, Cut Fitriani Rahma, Putri Razi, Ar Rita, Fatia Rizal S.Si., M.IT, Rizal Rizki Suwanda Safriana Safriana Safwandi Safwandi, Safwandi Sahira, Dianda Samsuar, Samsuar Saptari, Mochamad Ari Shaleha, Cut Dini Siregar, Siti Chairani Sri Wahyuni Syathira, Najwa Ummi Kalsum Winandar, Aris Zainal Abidin Zul Akli