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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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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.
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.
Application of Backpropagation Neural Network Using Random Oversampling and Robust Scaler for Classification Thyroid Ummy Agustina Putri; Iis Afrianty; Elvia Budianita; Fadhilah Syafria
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

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

Abstract

Thyroid disease is a fairly common endocrine disorder that requires rapid and accurate diagnosis so that patients can receive appropriate treatment. This study was conducted to improve the system's ability to classify thyroid disease by utilizing data preprocessing techniques with RobustScaler and Random Over Sampling (ROS), as well as the Backpropagation Neural Network (BPNN) algorithm. The research dataset consisted of 3,771 patient data with 25 clinical attributes describing the condition and function of the thyroid. The data preprocessing process involved data selection, data cleaning, and data transformation using RobustScaler so that each feature had a more stable scale and was not affected by extreme values. The class imbalance problem was overcome using ROS so that the amount of data increased to 6,834 samples and the class distribution became more balanced. The Backpropagation Neural Network algorithm was applied in model training by testing various variations in the number of neurons in the hidden layer (38 and 49) and learning rate (0.01 and 0.001). Training was conducted for 5,000 and 10,000 epochs. Evaluation was performed using the 10-Fold Cross Validation method to obtain more consistent results. The results of the study show that the model is capable of achieving very high accuracy, up to 99.85%, on several parameters. The results show that proper data processing and appropriate parameter selection greatly affect model performance. Overall, the use of RobustScaler and ROS has been proven to significantly improve the accuracy of thyroid disease classification.
Klasifikasi Sentiment Review Aplikasi MyPertamina Menggunakan Word Embedding FastText dan SVM (Support Vector Machine) Mustasaruddin Mustasaruddin; Elvia Budianita; M Fikry; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5695

Abstract

The MyPertamina application is a requirement for buying subsidized fuel oil (BBM), namely pertalite and diesel, the goal is that subsidized (BBM) purchases are right on target. The MyPertamina application has received many ratings and comments from the public, both positive and negative, with these comments and ratings expected to help the government as a benchmark in implementing a program. Therefore, this research aims to assess the MyPertamina application by grouping sentiment classes 90:10, 80:20 and 70:30. In this study, the method used is Fasttext and Support Vector Machine (SVM) to review the MyPertamina application. This research uses 8000 data, the data is grouped into three portions of data, with portions of 90:10, 80:20 and 70:30. The best SVM model was obtained with a data portion of 90:10 with a total of 7200 training data and 800 testing data, obtained 80% accuracy, 50% recall and 84% precision without undersampling. Meanwhile, if the amount of data is balanced (undersampling) with the number of positive data 1325, neutral 1325 and negative 1325, that is, with the benchmark of the lowest data value from the sentiment class, an accuracy of 67% is obtained, recall is 69% and precision is 57%. The highest number of sentiment classes from the 90:10 portion of the data is negative, namely 4300, neutral 1575 and positive 1325, because many users found reviews of the MyPertamina application, namely "after updating the MyPertamina application the bugs are getting worse".
Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier M Ikhsan Maulana; Elvia Budianita; Muhammad Fikry; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5854

Abstract

Battle Royale games are games that mix adventure and survival elements with last man standing game modes. One of the most popular battle royale games is the Sausage Man game. The number of complaints such as bugs, cheaters, and FPS which continues to decrease makes the game annoying. The solution is that developers must improve and improve game security so that users feel comfortable playing the game. There are many opinions or reviews from users regarding problems in the game, sentiment analysis will be carried out on the Sausage Man application review data on the Google play store as a process to produce categorization of opinions through reviews. The purpose of the researcher is to carry out a sentiment analysis to see positive, neutral or negative opinions from Sausage Man game users. The stages carried out in this study were data collection using web scraping, data labeling, text preprocessing, document weighting, classification, and evaluation. The results of data labeling using the VADER Lexicon obtained 1089 reviews (36.3%) for positive sentiment, 912 reviews for neutral sentiment (30.4%), and 999 reviews for negative sentiment (33.3%). Classification using the Naïve Bayes Classifier. Evaluation using the Confusion Matrix by dividing 90% training data and 10% test data produces an accuracy of 75%, 79% precision, and 75% recall. For the division of 80% training data 20% of the test data produces an accuracy of 73%, 76% precision and 73% recall. Positive sentences are found more often, but the accuracy is still below 80%.
Penerapan Data Mining untuk Menentukan Penyebab Kematian di Indonesia Menggunakan Metode Clustering K-Means Lili Rahmawati; Alwis Nazir; Fadhilah Syafria; Elvia Budianita; Lola Oktavia; Ihda Syurfi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5912

Abstract

Death in medical science is studied in a scientific discipline called tanatology. death is not only experienced by elderly people, but also can be experienced by young people, teenagers, or even babies. Death can be caused by various factors, namely, due to illness, old age, accidents, and so on. Based on information provided by the World Health Organization (WHO), there are five highest causes of death including ischemic heart disease, Alzheimer's, stroke, respiratory disorders, neonatal conditions. In this study, k-means is used to group causes of death in Indonesia based on the number of deaths that occur to determine the cases of death that have the most impact on the high mortality rate in Indonesia. Knowing what these death cases are will provide early preparation in anticipating the causes of death in Indonesia. The purpose of this study was to classify mortality rates based on the number of causes of death which were included in the low, medium, and high clusters by applying the K-Means method. In this study the authors used the K-Means clustering algorithm to classify death rates in data on causes of death in Indonesia from 2017-2021. The results of this study formed 3 clusters which were evaluated using the Davies Bouldin Index (DBI) in Rapidminer with a value of 0.259. Clustering results from a total of 21 cases obtained high, medium and low clusters. This cluster grouping was obtained according to the number of deaths per case, namely the first cluster (C0) was low with 17 cases, the second cluster (C1) was moderate with 3 cases and the third cluster (C2) was high with 1 case.
Analisis Sentimen Tanggapan Masyarakat Terhadap Calon Presiden 2024 Ridwan Kamil Menggunakan Metode Naive Bayes Classifier Neni Sari Putri Juana; Elin Haerani; Fadhilah Syafria; Elvia Budianita
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6168

Abstract

Reaction to public facts about the election of the presidential candidate Ridwan Kamil, which will later be obtained, the data is taken from Twitter based on these problems, it is necessary to do sentiment analysis research. Based on the results of this study, the classification process for the Naïve Bayes Classifier has 3 scenarios for dividing training data and test data, namely with 90%:10% training data, the test data produces an accuary value of 85.43%, a recall value of 100.00%, and a precision of 85.33%. For training data 80%: 20% of the test data produces an accuracy value of 86.38%, a recall of 100.00% and a precision value of 86.38% and for data on the distribution of training data 70%: 30% of the test data produces an accuary value of 84.29 %, 100.00% recall and 84.29% precision. From the tweet data that has been used, there are 1262 positive comments and 242 negative comments. These results prove that the Naïve Bayes classifier is very good for conducting sentiment analysis on Twitter comments about the 2024 presidential candidate Ridwan Kamil. The naïve Bayes classifier process gets the highest accuracy value of 86.38% by dividing the training data 80%:20% test data.
Clustering Data Persediaan Barang Menggunakan Metode Elbow dan DBSCAN Trisia Intan Berliana; Elvia Budianita; Alwis Nazir; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7089

Abstract

In the world of business and inventory management, efficient inventory management is very important. If a company does not have inventory, it is impossible to fulfill consumer desires. Managing inventory requires careful inventory management and good data analysis. Challenges in inventory involve unpredictable fluctuations in demand, making it difficult to determine optimal inventory levels. Product diversification with various characteristics is also an obstacle, hindering grouping and formulating inventory management strategies. The lack of clear product segmentation adds to the inhibiting factor, making it difficult to identify groups of similar goods. Inefficient stockpiling can be detrimental to the business as a whole, so implementing clustering is necessary to optimize inventory strategies based on product characteristics. By analyzing product groups, companies can develop more efficient and effective inventory management strategies. This research uses a clustering method using the elbow method and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The elbow method is used to determine the most optimal EPS and Minpts values. The aim of this research is to group goods inventory data using the attributes Initial quantity (initial stock), quantity sold (stock sold), and quantity available (available product stock). So that grouped data can make it easier for companies to optimize the inventory of the most sold goods. and fans. Based on the elbow and DBSCAN test results, 144 clusters and 0 noise data were obtained, with cluster 2 being the product with the largest number of sales and inventory. The DBSCAN method which was tested without using elbows obtained cluster 3 results and 959 noise data.
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 Fakhri Fakhri 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 Irfan Arifin Irfan Jamal Matondang Isra Almahsa, Muhammad Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril jasril jasril jasril Jeki Dwi Arisandi Lestari Handayani Lestari Handayani Lili Rahmawati lis Afrianty Liza Afrianti Lola Oktavia M Fikry M Ikhsan Maulana M Ridho Alfani M ridwan Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita 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 Nada Tsawaabul Khair 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 Herfin 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 Salmiyati Salmiyati Sephia Pratista Shir Li Wang Silfia Silfia Siti Ramadhani Siti Sri Rahayu Surya Agustian Suwanto Sanjaya Syahputra, Armadani Trisia Intan Berliana Ulti Desi Arni, Ulti Desi Ummy Agustina Putri Widodo Prijodiprodjo Wiranti, Lusi Diah Yeni Fariati Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra Zabihullah, Fayat Zulastri, Zulastri Zulkarnain Zulkarnain