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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal of Economic, Bussines and Accounting (COSTING) INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science bit-Tech Zonasi: Jurnal Sistem Informasi INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal UNITEK Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Knowbase : International Journal of Knowledge in Database Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Elvia Budianita
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Arts Humanities 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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Application of Data Mining for Ceramic Sales Data Association Using Apriori Algorithm Habibi, M. Ilham; Nazir, Alwis; Haerani, Elin; Budianita, Elvia
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v5i2.8757

Abstract

This research is conducted to provide an understanding of consumer purchasing patterns at CV. Sukses Bersama by applying data mining using the association rules method and the Apriori algorithm to identify the relationships between one item that influences other items within a ceramic sales dataset at CV. Sukses Bersama. This information is expected to serve as a foundation for improving sales strategies, optimizing customer satisfaction, and expanding the company's market share. The Apriori algorithm is a popular algorithm implemented to identify association rules in data mining. The Apriori algorithm was chosen due to its ability to efficiently identify association rules and its good scalability in handling large datasets. This research begins with the collection of ceramic sales data, followed by data preprocessing to clean and prepare the data. The Apriori algorithm is then applied to discover the association rules, which generate two matrices: support and confidence, and the results are subsequently evaluated. This research was conducted using Google Colaboratory, a web application that is a cloud-based platform provided by Google to run Python code. The results of the study show that the Apriori algorithm can depict significant association structures between different ceramic brand types in the sales data of CV. Sukses Bersama. The calculation results show that the rule has the maximum support and confidence value, namely 67% support value and 84% confidence value in the rule "if you buy the DIAMD brand, you will buy the TOTAL brand"
Pengelompokan Tingkat Stres Akademik Pada Mahasiswa Menggunakan Algoritma K-Medoids Nurfadilah, Nova Siska; Budianita, Elvia; Nazir, Alwis; Insani, Fitri; Susanti, Reni
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7409

Abstract

Academic stress is one of the common problems issues by university students due to heavy with heavy workloads, grade pressure, and various academic This condition can have a negatively impact on mental health, productivity and overall academic performance. In the long term, unmaged stress may lead serious psychological disorders. Therefore, it is important to accurately identify and classify the levels of academic stress. This study aims to cluster students’ academic stress levels by utilizing the K-Medoids algorithm. The data analyzed in the research were collected through questionnaires that were filled out by 507 students from the 2021-2023 cohorts, based on a modified version of the Perception of Academic Stress Scale (PASS). The results show that the K-medoids algorithm successfully clustered the data in 2 groups: cluster 0, which represents a moderate stress level with 212 students, and cluster 1, which indicates a high stress level with 295 students. This high-stress cluster exhibited higher average cores on questions 12 and 13 (score 3-5), which fall under the favorable category and are suspected to be the main triggers of academic stress among students in this group. Based on two evalutation metrics-Silhouette Coeficient and Davies-Bouldin Index (DBI)-it can be concluded that the optimal number of clusters for this data set is K=2. However, the clustering separation was not optimal due to he variation in study programs and the uneven distribution of respondets across academic years. This research is expected to provide direction the development intervation policies and strategies to support student welfare.
Penerapan Algoritma K-Means Untuk Mengelompokkan Tingkat Stres Akademik Pada Mahasiswa Wiranti, Lusi Diah; Budianita, Elvia; Nazir, Alwis; Insani, Fitri; Susanti, Reni
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7410

Abstract

Academic stress is a prevalent concern among university students, often arising from various challenges within the academic environment. These challenges may include tight assignment deadlines, elevated expectations from both lecturers and parents, ineffective time management, and negative self-assessment. If left unaddressed, such stress can negatively impact students’ academic performance and mental well-being. This study focuses on categorizing student academic stress levels using the K-Means clustering algorithm. Data were collected from 507 participants through a customized version of the Perception of Academic Stress Scale (PASS) questionnaire, adapted to suit the study context. Prior to analysis, the data were preprocessed and converted into a numerical format. Clustering was performed using Python on the Google Colab platform. To assess the clustering performance, two evaluation metrics were used: the Davies-Bouldin Index (DBI) and the Silhouette Coefficient. Lower DBI values suggest that the clusters formed are more compact and distinct from each other, while higher Silhouette values indicate better clustering performance. From the evaluation, the best clustering result was found when the number of clusters was 2, with a DBI score of 1.43 and a Silhouette score of 0.27. Nonetheless, these values still fall short of the ideal range, likely due to the heterogeneous nature of the data, as participants came from five different departments within the Faculty of Science and Technology. Moreover, the number of responses varied across academic years (2021–2023). Cluster 1 comprised 229 students identified as having low levels of academic stress, as shown by their lower questionnaire scores. In contrast, Cluster 2 consisted of 278 students with higher levels of stress, as reflected in their higher scores (ranging from 3 to 5) on positively worded items.
Feature Selection using Information Gain on the K-Nearest Neighbor (KNN) and Modified K-Nearest Neighbor (MKNN) Methods for Chronic Kidney Disease Classification Ramadhan, Aweldri; Budianita, Elvia; Syafria, Fadhilah; Ramadhani, Siti
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.26834

Abstract

Purpose: Kidneys has an important role in the human excretory system. Unhealthy kidneys can affect kidney function. It is important to know the symptoms of chronic kidney disease. One data mining technique that can be applied is the classification technique to determine whether a person has chronic kidney disease or not based on the symptoms (attributes) obtained from medical records. The symptoms of chronic kidney disease obtained amount to 24 symptoms or attributes,Methods/Study design/approach: In this research, the classification of chronic kidney disease is performed using the information gain feature selection method and the KNN and MKNN classification methods. The number of data used is 400 data with 2 classes, namely chronic kidney disease (CKD) and non-chronic kidney disease (non-CKD).Result/Findings: Based on the test results, it was found that the hemo (Hemoglobin) attribute has the highest information gain value, which is 0.6255. The best accuracy for the KNN classification method is 96.61%, and for the MKNN method, it is 98%. Novelty/Originality/Value: The purpose of information gain feature selection is to choose features or attributes that significantly influence chronic kidney disease. Keywords: Chronic Kidney Disease, Information Gain, KNN, MKNN
Klasifikasi Sentimen Presepsi Masyarakat di Instagram Terhadap Paslon Pilpres 2024 Menggunakan Naïve Bayes Classifier (NBC) Akbar, Lionita Asa; Haerani, Elin; Syafria, Fadhilah; Nazir, Alwis; Budianita, Elvia
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11293

Abstract

The 2024 presidential election has attracted considerable attention as it has become a controversial issue among the public. Various positive and negative opinions generated can potentially turn into rumors. One of the means used by the public to express their opinions is the social media platform Instagram. Data on public opinions on Instagram can be processed into valuable information through sentiment classification. This research conducted sentiment classification on public perceptions towards the 2024 presidential candidates using a naïve Bayes classifier. The study utilized a dataset consisting of 1000 comments. These comments were collected from several posts on the social media platform Instagram discussing the presidential and vice-presidential candidates. The comments were manually labeled by an expert who is a lecturer in the Indonesian language. Classification was carried out after preprocessing and weighting TF-IDF stages. Based on the research findings, the naïve Bayes classifier method showed an accuracy of 82% and an F1-Score of 83.93% obtained from a 90%:10% split of training and testing data. These results indicate that the naïve Bayes classifier method is effective in classifying the sentiments of the public on Instagram towards the 2024 presidential candidates.
Penerapan Seleksi Fitur Information Gain dan Metode Backpropagation Neural Network Untuk Klasifikasi Atrisi Karyawan Dinyah Fithara; Elvia Budianita; Iis Afrianty; Siska Kurnia Gusti
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.922

Abstract

Employee attrition management is a critical challenge for organizations as it involves costs, time, and the risk of decision-making errors. This problem requires a data-driven business strategy to achieve more accurate predictions of employees who are potentially at risk of termination. This study applies the Information Gain feature selection method and the Backpropagation Neural Network (BPNN) algorithm in the employee attrition classification process with the aim of increasing the accuracy and efficiency of the prediction model. BPNN is chosen due to its simpler architecture, faster training time, and greater stability for small to medium sized datasets.  With the assistance of Information Gain feature selection, BPNN is able to achieve optimal performance without requiring a complex architecture. The dataset used consist of 35 attributes and 1.470 employee records covering various factor such as age, income level, and employment status. The research stages include feature selection based on information gain values with specific thresholds, data partitioning using k-fold cross validation, and model training using BPNN with variations of learning rates and hidden neuron counts. The results show that the combination of Information Gain and BPNN improves classification accuracy compared to models without feature selection, achieving the highest average accuracy of 87.28% when using 25 selected attributes, with a BPNN configuration of learning rate 0.001, 35 hidden neurons, and 50 epochs. The attributes with the highest Information Gain score include JobLevel, OverTime, MaritalStatus, and MonthlyIncome. This study demonstrates that the proposed approach successfully enhances the prediction performance of employee attrition and can serve as a foundation for developing data-driven models that support employee retention efforts.
Comparison of Various Deep Learning Techniques to Obtain the Best Technique for Detecting Brain Cancer Yanto, Febi; Budianita, Elvia; Wang, Shir Li
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.38599

Abstract

This study aims to address the difficulty of comparing deep learning–based brain cancer detection methods due to differences in datasets and parameter settings, which limits the generalizability of previous findings. The purpose of this research is to evaluate the performance of several convolutional neural network (CNN) architectures using identical datasets and experimental configurations to determine the most effective technique for early brain cancer detection. The study builds a comparative framework using the Keras API on TensorFlow, supported by libraries such as NumPy, Pandas, Matplotlib, and Seaborn. All datasets were split into stratified training, validation, and test sets, and preprocessing included resizing images to 224×224 pixels, converting them to 3-channel RGB, normalizing the inputs, and applying data augmentation. CNN architectures, including VGG16, ResNet50, GoogleNet, and AlexNet, were trained with consistent parameter settings, including epoch count, batch size, learning rate optimization, and training protocols. Performance evaluation using accuracy, precision, recall, and F1-score shows that GoogleNet and ResNet50 achieve the highest results across datasets (average >94%), with GoogleNet slightly outperforming ResNet50. AlexNet performs poorly on the Kaggle dataset but shows potential on the private dataset, while VGG16 demonstrates moderate but less consistent performance. The originality of this study lies in providing a unified evaluation framework that enables fair comparison across CNN models, offering valuable insights for selecting optimal architectures for brain cancer detection.
Sistem Prediksi Produksi Kelapa Sawit Berbasis Gradio Menggunakan Algoritma Regresi Linear Berganda Matondang, Irfan Jamal; Budianita, Elvia; Syafria, Fadhilah; Afrianty, Iis
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.994

Abstract

The instability of oil palm production often leads to discrepancies between production targets and actual outputs, thereby necessitating an accurate prediction model to support operational planning. This study aims to develop an oil palm production prediction model and to identify the most influential variables affecting production outcomes as a basis for data-driven decision-making. The model was developed using the Multiple Linear Regression method based on historical data from 2020–2024, consisting of 60 monthly observations with variables including number of trees, land area, rainfall, number of fruit bunches, and plant age. The research stages included data preprocessing, variable selection through testing several feature combinations, model development, and performance evaluation using the coefficient of determination (R²), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). The results indicate that the combination of number of trees, land area, number of fruit bunches, and plant age produced the best performance, with an R² value of 0.85 on the training data and 0.81 on the testing data. The MAE values were 125,307 kg and 176,984 kg, the MSE values were 28,870,838,455 kg² and 52,809,954,662 kg², and the RMSE values were 169,914 kg and 229,804 kg, respectively. Based on the regression coefficients, the number of fruit bunches was identified as the most dominant variable, with a coefficient value of 637,720 kg. The model was subsequently implemented using the Python Gradio library in the form of an interactive interface to support production planning effectiveness and minimize the risk of inaccurate decision-making in oil palm plantation management.
KLASIFIKASI MINAT BACA MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Hafiz, Muhammad; Budianita, Elvia; Nazir, Alwis; Gusti, Siska Kurnia
Journal of Economic, Bussines and Accounting (COSTING) Vol. 9 No. 1 (2026): COSTING : Journal of Economic, Bussines and Accounting
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/gcqhr236

Abstract

Minat baca merupakan faktor penting dalam mendukung keberhasilan akademik siswa, namun pengukurannya masih sering dilakukan secara subjektif. Penelitian ini bertujuan untuk mengklasifikasikan tingkat minat baca siswa MTsN 1 Payakumbuh menggunakan metode Naïve Bayes Classifier. Atribut yang digunakan sebagai input berupa 20 butir pernyataan kuesioner skala Likert yang merepresentasikan kebiasaan, frekuensi, motivasi, serta preferensi membaca siswa. Data penelitian diperoleh dari 911 responden yang dikelompokkan ke dalam tiga kelas tingkat minat baca, yaitu tinggi (329 data), sedang (501 data), dan rendah (81 data). Pengujian model dilakukan menggunakan tiga skema pembagian data latih dan data uji, yaitu 90:10, 80:20, dan 70:30. Evaluasi performa model menggunakan confusion matrix menunjukkan bahwa skema 90:10 menghasilkan akurasi sebesar 96,74%, skema 80:20 sebesar 97,81%, dan skema 70:30 sebesar 98,18%. Hasil tersebut menunjukkan bahwa metode Naïve Bayes Classifier memiliki performa yang sangat baik dan konsisten dalam mengklasifikasikan tingkat minat baca siswa berdasarkan data kuesioner.
Thyroid Disease Classification Using Support Vector Machine and Recursive Feature Elimination Method Citra Wulandari; lis Afrianty; Elvia Budianita; Siska Kurnia Gusti
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3454

Abstract

Thyroid disease is a common endocrine disorder that can cause serious metabolic and cardiovascular complications, so accurate early detection is clinically essential. This study proposes a Support Vector Machine (SVM) classifier enhanced with Recursive Feature Elimination (RFE) to select the most informative attributes and Adaptive Synthetic Sampling (ADASYN) to handle class imbalance in a Kaggle thyroid dataset of 3,771 clinical records. The data contain 25 diagnostic attributes with a strongly skewed distribution between healthy and thyroid cases. The model’s robustness was examined using three train–test split ratios. The best configuration, SVM with a Linear kernel and 20 RFE-selected features under an 80:20 split, achieved 98.39% accuracy, with precision, recall, and F1-score all reaching 0.98, indicating consistently strong performance across classes. RFE contributes by removing redundant or weakly relevant variables, helping the classifier construct a more stable and interpretable decision boundary. ADASYN further improves the representation of the minority class, yielding higher recall and F1-score for thyroid cases and reducing the risk of missed diagnoses. Overall, the combined use of feature selection and adaptive oversampling produces a balanced and computationally efficient model for thyroid disease classification. These findings suggest that the proposed approach can support clinical decision-making, reduce diagnostic errors in imbalanced data settings, and strengthen early detection efforts in endocrine health assessment. By offering high sensitivity for thyroid cases while maintaining robust specificity for healthy patients, the model is well suited for integration into clinical decision-support and routine screening workflows.
Co-Authors Abdul Halim Adzhima, Fauzan Afriyanti, Iis Agnesti, Syafira Agung Syaiful Rahman Agustina, Auliyah Aji Pangestu Adek Akbar, Lionita Asa Akhyar, Amany Al Rasyid, Nabila Alfaiza, Raihan Zia Alfarabi.B, Alif Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Ammar Muhammad Anggi Pranata Aprilia, Tasya Aprima, Muhammad Dzaky Arif Pratama Budiman Ayu Sri Wahyuni Azhima, Mohd Baehaqi Boni Iqbal buhfi arides hanyodi Chely Aulia Misrun Citra Wulandari Damayanti, Elok Desra Rizki Riyandi Dicky Abimanyu Dinyah Fithara Dodi Efendi doli fancius silalahi Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia Eka Suryani Indra Septiawati Elin Haerani Elin Haerani Elin Haerani Elin Haerani Ellin Haerani Fadhilah Syafria Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Yanto Fikri Utri Amri Fikry Utri Amri Fitri Astuti Fitri Insani Fitri Insani Fitri Insani Fitri Insani Fitri, Anisa Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Habibi, M. Ilham Hara Novina Putri Hariansyah, Jul Ibnu Afdhal Ichsan Permana Putra Ihda Syurfi Ihlal Hanafi Harahap Iis Afrianty Iis Afrianty Ikhsanul Hamdi Ilham Habibi Hasibuan Indah Wulandari Isra Almahsa, Muhammad Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril jasril jasril jasril Jeki Dwi Arisandi Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati lis Afrianty Liza Afrianti Lola Oktavia M Fikry M Ikhsan Maulana M ridwan Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Matondang, Irfan Jamal Mawadda Warohma Mazdavilaya, T Kaisyarendika Megawati Megawati Meiky Surya Cahyana Mhd. Kadarman Mohd. Ridho Zarkasih Rahim Muhammad Affandes Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Hafiz Muhammad Irsyad Muhammad Rizky Ramadhan Mulyati, Sabar Mulyono, Makmur Musa Irfan Mustasaruddin Mustasaruddin Nabyl Alfahrez Ramadhan Amril Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Neni Sari Putri Juana Novi Yanti Novi Yanti Novriyanto Novriyanto Nur Iza Nuradha Liza Utami Nurafni Syahfitri Nurfadilah, Nova Siska Okfalisa Okfalisa Pasiolo, Lugas Permata, Rizkiya Indah Pizaini Pizaini Putri, Widya Maulida Rahmad Abdillah Rahmad Kurniawan Ramadani, Repi Ramadhan, Aweldri Ramadhani, Astrid Ramadhani, Siti Reni Susanti Reski Mai Candra Reski Mai Candra Rinaldi Syarfianto Robby Azhar Roni Salambue Rusnedy, Hidayati Said Nurfan Hidayad Tillah Saktioto Saktioto Sephia Pratista Silfia Silfia Siti Sri Rahayu Surya Agustian Suwanto Sanjaya Syahputra, Armadani Trisia Intan Berliana Ulti Desi Arni, Ulti Desi Ummy Agustina Putri Wang, Shir Li Widodo Prijodiprodjo Wiranti, Lusi Diah Yeni Fariati Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra Zabihullah, Fayat Zulastri, Zulastri Zulkarnain Zulkarnain