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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) CESS (Journal of Computer Engineering, System and Science) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri INTEK: Informatika dan Teknologi Informasi Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Indonesian Journal of Multidisciplinary on Social and Technology SATIN - Sains dan Teknologi Informasi Journal of Engineering and Science Application INOVTEK Polbeng - Seri Informatika
Lola Oktavia
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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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.
Pemodelan Klasifikasi Untuk Menentukan Penyakit Diabetes dengan Faktor Penyebab Menggunakan Decision Tree C4.5 Pada Wanita Nining Nur Habibah; Alwis Nazir; Iwan Iskandar; Fadhilah Syafria; Lola Oktavia; Ihda Syurfi
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.v4i4.6202

Abstract

Diabetes is closely related to the pancreas, where the pancreas produces the natural hormone insulin, but its function is problematic which causes an increase in blood sugar levels in the body. Rising blood pressure can affect organ function in damaging the function of organs in a person's body such as the kidneys, heart and brain. Where makes a person have a history of diabetes. Diabetes that attacks adults can be prevented through exercise and a regular and healthy diet. According to the International Diabetes Federation (IDF) organization, it is estimated that at least 19.5 million Indonesian people between the ages of 20 and 79 will suffer from diabetes in 2021. China is in first place with diabetes with 140.9 million people. India is next in line with the number of people with diabetes of 74.2 million people. Therefore, early diagnosis is very important because it aims to reduce diabetes and diabetes complications in the future. It is necessary to collect data on patients with diabetes who are expected to be able to do prevention. Therefore applying classification techniques with data mining with the C4.5 algorithm. Where the classification can achieve better accuracy. Algorithm C4.5 is generally used in determining the nodes of a decision tree. Based on the test results, the accuracy is 76.67 percent, the precision is 72 percent, and the recall is 41.67 percent.
Sistem Pakar Diagnosa Gangguan Stress Pasca Trauma Menggunakan Metode Certainty Factor Marliana Safitri; Fitri Insani; Novi Yanti; Lola Oktavia
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.v4i4.6309

Abstract

Mental health disorder or commonly called Mental Health Disorder is a disturbing psychological behavior and is followed by traumatic events such as shock shell, war fatigue, accidents, victims of sexual violence, and the covid pandemic. Cases of post traumatic stress disorder data from Indonesian Psychiatric Association amounted to 80% of 182 examiners experiencing symptoms of post-traumatic stress due to exposure to covid, 46% experienced severe symptoms, 33% moderate, 2% mild and others did not show symptom. This study aims to diagnose post traumatic stress disorder using the assurance factor method with 35 symptom data and 3 levels of post traumatic stress disorder as a knowledge base. The certainty factor is a circulation management method and a decision-making strategy using the confidence factor in the system. Based on the research results of the expert system for diagnosing post traumatic stress disorder, the test results obtained an accuracy of 80%. The results of the accuracy of this expert system indicate that the expert system can potentially be used to diagnose post traumatic stress disorder.
IMPLEMENTASI K-MEANS CLUSTERING PADA DATA PENGELOMPOKAN PENDAFTARAN MAHASISWA BARU (STUDI KASUS UNIVERSITAS ABDURRAB Muhammad Hanif Abdurrohman; Elin Haerani; Fadhilah Syafria; Lola Oktavia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 1 (2024): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i1.4255

Abstract

Facing the complex dynamics of freshman enrollment, the k-means clustering method was introduced as the main approach. The focus is on Abdurrab University, where various attributes of prospective students are investigated, including gender, parental education, parental income, hometown, province, age, and choice of study program. With the k-means clustering algorithm, the purpose of the study is to uncover the underlying patterns of preferences and characteristics of new student groups. The results of this study provide in-depth insight into the factors that influence the decision to admit new students in the campus environment of Abdurrab University. In this study Davies-Bouldin Index (DBI) was used as a method to determine the optimal number of clusters, the lowest DBI value was 1.5 which occurred in 8 clusters. This shows that 8 clusters is the optimal number of clusters for data that has been transformed and is ready for k-means clustering. After carrying out the clustering process with the K-Means method which involves the formation of 8 clusters, to show patterns and insights from the clustering results, there are two ways used in this study, first make a heatmap of the correlation of features displayed, information can be obtained about the relationship between variables. The correlation value ranges from -0.4 to 1.0 where positive values indicate a positive correlation and negative values indicate a negative correlation. A positive correlation means that if the value of one variable increases, then the value of the other variable also tends to increase. Conversely, negative correlation means that if the value of one variable increases, then the value of the other variable tends to decrease.
Klasifikasi Sentimen Masyarakat Terhadap Revisi Undang-Undang Tentara Nasional Indonesia Menggunakan Naïve Bayes Classifier Abdul Haris Kurnia Sandi Harahap; Elin Haerani; Lola Oktavia; Okfalisa Okfalisa; Fitra Kurnia
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.615

Abstract

The revision of the Indonesian National Armed Forces Bill (RUU TNI) has become a hot topic in Indonesian public policy and has sparked controversy among the public due to its sudden emergence and lack of open planning process. This has raised concerns about the potential for military domination and the return of the dual function of the ABRI (Indonesian Armed Forces). The classification of public sentiment towards the RUU TNI is the focus of this study. Comments are categorized into two types of sentiment classes, namely positive and negative. The research stages include data collection, sentiment labeling, data cleaning, text normalization to lowercase letters, sentence or document segmentation into smaller parts, text data normalization, negation handling, stopword removal, and stemming, weighting using the TF-IDF technique, model classification development, and evaluation of the model's performance. The Naïve Bayes Classifier method classified 1,547 comment data points collected from two Instagram social media accounts. The Naïve Bayes Classifier model achieved an accuracy of 83.74%, precision of 81.17%, recall of 87.86%, and an F1-score of 84.38%. This study has limitations, including the limited amount of data collected. These include an imbalance in the amount of data between sentiment categories, data from only one social media platform, and the suboptimal identification of positive and negative sentiments. It is recommended that future research compare this method with other classification methods, expand the dataset, broaden the scope of data collection by involving various social media platforms over a wider time span, thereby providing a more comprehensive picture of public opinion, and test a wider range of algorithm combinations. This study can serve as an initial indicator for rapid policy evaluation, where positive or negative comments from the public on social media can provide important input in assessing the effectiveness of a policy.
Penerapan Support Vector Machine Dengan Smote Untuk Klasifikasi Sentimen Pada Data Ulasan Aplikasi Trading View Muhammad Badri; Elin Haerani; Fadhilah Syafria; Okfalisa Okfalisa; Lola Oktavia
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.v6i1.793

Abstract

In the digital era, user feedback on mobile applications serves as highly valuable information for developers to evaluate app performance. One popular application in the field of finance and investment is TradingView, widely used for technical analysis by traders. User feedback on this application reflects various user sentiments, including positive, negative, and neutral. However, the large volume of reviews and the unstructured nature of text data make manual analysis inefficient and prone to high subjective bias. Therefore, the use of automatic classification methods capable of processing text data with reasonable accuracy is required. This study aims to implement the “Support Vector Machine (SVM)” technique to classify user feedback on the TradingView application. To address the issue of imbalanced sentiment class distribution, the study also employs the “Synthetic Minority Over-sampling Technique (SMOTE)”. The study utilizes 10,000 reviews obtained via web scraping from the Google Play Store. The study workflow consists of text preprocessing, feature extraction using “Term Frequency-Inverse Document Frequency (TF-IDF)”, data balancing, SVM model training, and model evaluation. The evaluation results show that the application of SVM with SMOTE achieves an accuracy of approximately ±85.56% across data splits (70:30, 80:20, 90:10). In each scenario, the highest F1-score was achieved for the positive sentiment class, while the performance of minority classes (negative and neutral) improved after data balancing with SMOTE, with an average F1-score increase of 1.67% for the negative class and 10.67% for the neutral class. Without SMOTE, the average negative F1-score was ±57%, and the neutral class was undetected (0.00%). Furthermore, validation using K-Fold Cross Validation yielded an average accuracy of 89.20%, which increased to 95.10% after applying SMOTE. This improvement was consistent across all data proportions (70:30, 80:20, 90:10), with an average increase of 5.44%. These findings confirm that integrating SVM with SMOTE not only enhances classification performance on imbalanced data but also maintains model stability. Therefore, this study contributes to the advancement of automated sentiment classification systems, particularly for financial mobile app reviews, and can serve as a reference for future research in user review analysis on similar applications.
Penerapan Metode Support Vector Machine Untuk Analisis Sentimen Pada Komentar Bitcoin Di Aplikasi X Yaskur Bearly Fernandes; Elin Haerani; Fadhilah Syafria; Muhammad Fikry; Lola Oktavia
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.v6i1.928

Abstract

Social media has become a primary medium for users to express opinions, including those related to Bitcoin, whose fluctuating value often triggers diverse public responses. The large volume of unstructured comments makes manual sentiment analysis inefficient, thereby necessitating an automated approach based on machine learning. This study aims to classify positive and negative sentiments in Bitcoin-related comments on the X platform using the Support Vector Machine (SVM) algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) feature weighting. The dataset consists of 1,750 Indonesian-language comments labeled by three annotators. The data were processed through several preprocessing stages, including case folding, text cleaning, tokenization, stopword removal, and stemming. Model evaluation was conducted using four data split ratios, namely 90:10, 80:20, 70:30, and 60:40. The experimental results indicate that the 90:10 ratio achieved the best performance, with an accuracy of 72.57%, precision of 0.75, recall of 0.73, and an F1-score of 0.67. The SVM model demonstrates strong performance in identifying positive sentiments; however, it is less effective in detecting negative sentiments due to class imbalance in the dataset. As an additional experiment, testing was performed using a balanced dataset obtained through an undersampling process and several SVM kernel types for comparison. The results show that using a balanced dataset leads to more evenly distributed classification performance across sentiment classes, while the linear kernel provides the most stable performance compared to other kernels. Overall, SVM with TF-IDF weighting proves to be an effective approach for sentiment analysis of Bitcoin-related comments on social media.
Analisis Tingkat Kualitas Computerized Maintenance Management System (CMMS) Menggunakan COBIT 5 Fahrul Al Hidayat; Novriyanto Novriyanto; Muhammad Irsyad; Lola Oktavia
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 6 No. 2 (2024): Jurnal Ekonomi Manajemen Sistem Informasi (November - Desember 2024)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v6i2.3451

Abstract

PT Perkebunan Nusantara V Pekanbaru adalah sebuah Badan Usaha Milik Negara (BUMN) yang mengelola perkebunan karet dan kelapa sawit. Dalam pemanfaaatan teknologi untuk meningkatkan kualitasnya, PTPN V menggunakan Computerized Maintenance Management System (CMMS) sebagai sistem pengawasan untuk pemeliharaan. Selain itu, CMMS juga memiliki kemampuan untuk meningkatkan kondisi peralatan dan hasil produksinya. Hingga saat ini, belum ada pengukuran tingkat kualitas untuk mengevaluasi kualitas keseluruhan dari aktivitas bisnis yang menghasilkan solusi TI pada CMMS. Penelitian ini bertujuan untuk meninjau sejauh mana perkembangan tingkat manajemen kualitas dari yang direncanakan dengan yang sudah direalisasikan sekaligus pengusulan suatu rekomendasi perbaikan dalam CMMS pada PTPN V Pekanbaru menggunakan framework COBIT 5 domain APO11 tentang manage quality. Melakukan pengisian kusioner kepada 5 orang responden, kemudian mewawancarai salah satu responden yang terkait dengan CMMS, diperoleh hasil tingkat kemampuan keseluruhan (capability level) dari subdomain APO11 pada CMMS di PTPN V Pekanbaru sebesar 4,45 atau berada di level 4 (Predictable Process). Hal ini menunjukkan bahwa PTPN V Pekanbaru telah mampu menghasilkan proses secara berkelanjutan dan melakukan perbaikan secara konsisten untuk masa depan. Namun, berdasarkan keadaan yang ingin untuk dicapai dan keadaan sekarang, CMMS yang digunakan pada PTPN Pekanbaru harus melakukan pengembangan secara konsisten dan berkelanjutan untuk mencapai keadaan yang diinginkan.
Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbor (K-NN) Untuk Klasifikasi Penyakit Gagal Jantung Firman Zahri; Fitri Insani; Jasril Jasril; Lola Oktavia
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6480

Abstract

A condition known as heart failure, where the heart is unable to pump enough blood to meet the body's needs for oxygen and nutrients, should not be taken lightly. This can result in a number of symptoms, such as fatigue, fluid retention, and dyspnea. The World Heart Federation estimates that up to 1.8 million people in Southeast Asia suffered from heart failure in 2014. For prompt and efficient treatment, heart failure is a medical problem that needs to be identified. This disease has the potential to worsen if not treated immediately. Several machine learning methods can be used to help diagnose and categorize this disease. One of them is the popular algorithm, namely Naive Bayes and K-Nearest Neighbors. Naive Bayes is a simple but very efficient probability-based machine learning algorithm, especially in classification applications. K-Nearest Neighbors is comparing the data to be predicted with a number of its closest data in a feature space based on a certain distance, such as Euclidean distance, Manhattan, or others. This study was conducted using Confusion Matrix to evaluate and compare the Naive Bayes and K-Nearest Neighbor algorithms in the categorization of heart failure disease by collecting data totaling 918 heart failure patient data from kaggle. Based on the research findings, the K-Nearest Neighbor method achieved an accuracy score of 76%, while the Naive Bayes approach achieved 90% accuracy using a ratio of 80:20.
Application of ADASYN Technique in Classification of Stroke Disease using Backpropagation Neural Network said rizki zikrillah aulia; okfalisa okfalisa; elin haerani; lola oktavia
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/jdhv9s39

Abstract

The high prevalence of stroke in Indonesia and the challenge of imbalanced medical record data are major obstacles to the development of an accurate early detection system. This research aims to build a reliable stroke classification model by applying the ADASYN (Adaptive Synthetic Sampling) oversampling technique to address class imbalance before the data is processed using the Backpropagation Neural Network (BPNN) algorithm. The ADASYN technique is applied with the goal of reducing the bias that arises from the imbalanced data distribution between the majority and minority classes. Testing was conducted through various data splitting scenarios (70:30, 80:20, 90:10) and hyperparameter variations to find the optimal configuration. The best results were obtained with the 90:10 data split scheme, using an architecture of 29 neurons and a learning rate of 0.01, which successfully achieved peak performance with an accuracy of 90.46% and an F1-score of 91.03%. This study demonstrates that the combination of ADASYN and BPNN is a highly effective approach for producing a stroke prediction model that is not only accurate but also sensitive to the minority class, thus having great potential as an early detection support tool in the healthcare sector.
Co-Authors Abdillah, Rahmad Abdul Haris Kurnia Sandi Harahap Alwis Nazir Alwis Nazir Alwis Nazir Eka Suryani Indra Septiawati Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Emir Ramon Fadhilah Syafria Fahrul Al Hidayat Febby Kurniawan Firman Zahri Fitra Kurnia Fitra Kurnia Fitri Insani Heri Susanto Ihda Syurfi Irma Sanela Iwan Iskandar Jasril Jasril Lili Rahmawati Marliana Safitri Muhammad Affandes Muhammad Badri Muhammad Fikry Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Irsyad Muhammad Yusril Haffandi Nining Nur Habibah Novendri Wahyudy Novi Yanti Novriyanto Novriyanto Nurainun Nurainun Okfalisa Okfalisa Okfalisa Okfalisa Okfalisa Okfalisa Pizaini Pizaini Raja Sultan Firsky Reski Mai Candra Rizuan Rizuan Said Nanda Saputra said rizki zikrillah aulia Saktioto Saktioto Sofiah Sola Huddin Suwanto Sanjaya Tiara Dwi Arista Tiara Ramayanti Yaskur Bearly Fernandes Yusra Yusra Yusra Yusra, Yusra