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All Journal Scientific Journal of Informatics Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Information System Journal (INFOS) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Knowbase : International Journal of Knowledge in Database Indonesian Journal of Innovation Multidisipliner Research Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT
Elvia Budianita
UIN Suska Riau
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Penerapan Metode Backpropagation Neural Network untuk Klasifikasi Penyakit Stroke Azhima, Mohd; Afrianty, Iis; Budianita, Elvia; Gusti, Siska Kurnia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1956

Abstract

Stroke is a non-communicable disease that can occur suddenly due to local or global disruption of brain function. The early symptoms of stroke are often difficult to recognize, causing many sufferers not to realize or feel the signs, so the death rate is quite high. This research aims to determine the ability of the Backpropagation Neural Network (BPNN) method in classifying stroke. The dataset used consists of 4891 medical records with stroke and non-stroke classes which include ten relevant variables (gender, age, hypertension, history of heart disease, BMI, blood sugar levels, and so on). This research runs three scenarios with the BPNN architecture model [19:25:1], [19:29:1], and [19:35:1] using a certain combination of variables, namely the comparison of training and testing data (90:10, 80 :20, 70:30), and learning rate 0.1; 0.01; 0.001. Test results with the highest average accuracy level of 96.14% were achieved with an architectural model of [19:29:1], a learning rate of 0.001, and a training and testing data distribution of 80:20. Based on testing, it can be concluded that BPNN is considered capable of classifying stroke
Klasifikasi Penyakit Jantung Koroner Menggunakan Metode Backpropagation Neural Network (Studi Kasus: Rumah Sakit Ibnu Sina Pekanbaru) Ramadani, Repi; Budianita, Elvia
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2024: SNTIKI 16
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Penyakit Jantung Koroner (PJK) merupakan salah satu penyebab utama kematian tertinggi yang tidak menular. Dalam upaya mengatasi masalah ini, teknologi informasi dan data mining digunakan untuk analisis data medis, termasuk data Penyakit Jantung Koroner dengan menggunakan metode Backpropagation Neural Network. Penelitian ini bertujuan untuk menghasilkan model klasifikasi yang akurat dan efesien untuk mendukung diagnosis Penyakit Jantung Koroner. Jumlah data yang digunakan 500 data dari RS Ibnu Sina Pekanbaru dengan 9 atribut dan yang dilabeli menjadi dua kategori, 250 pasien “iya” (jantung koroner) penyakit jantung koroner dan 250 “tidak” (bukan jantung koroner). Dengan beberapa pembagian data hasil penelitian ini menunjukkan bahwa model yang dikembangkan mampu mengklasifikasikan data dengan tingkat akurasi tinggi yang mencapai 100% dengan presisi 100% dan recall 100%.
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"
Implementation of Feature Selection Information Gain in Support Vector Machine Method for Stroke Disease Classification Fitri, Anisa; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.116

Abstract

Stroke is a disease with a high mortality and disability rate that requires early detection. However, the main challenge in the classification process of this disease is data imbalance and the large number of irrelevant features in the dataset. This study proposes a combination of Support Vector Machine (SVM) method with Information Gain feature selection technique and data balancing using Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. The dataset used consists of 5,110 data with 10 variables and 1 label. Feature selection was performed with three threshold values (0.04; 0.01; and 0.0005), while SVM classification was tested on three different kernels: Linear, RBF, and Polynomial. Model evaluation was performed using Confusion Matrix and training and test data sharing using k-fold cross validation with k=10. The best results were obtained on the RBF kernel with Cost=100 and Gamma=5 parameters at an Information Gain threshold of 0.0005, with accuracy reaching 90.51%. These results show that the combination of techniques used aims to determine the variables that most affect SVM classification in detecting stroke disease
Implementation of XGBoost Ensemble and Support Vector Machine For Gender Classification of Skull Bones Ramadhani, Astrid; Afrianty, Iis; Budianita, Elvia; Gusti, Siska Kurnia
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.115

Abstract

Sex identification based on skull bones is an important step in forensic anthropology, especially in cases where unidentified human skeletons are found. Conventional methods such as DNA analysis are often used, but have limitations, especially when the bones are damaged, charred or decayed, making the analysis process difficult. This research applies XGBoost ensemble and Support Vector Machine for sex classification on skull bones. The purpose of this research is to handle complex data with many features and unbalanced data using the XGBoost ensemble method and Support Vector Machine (SVM). The data used consisted of 2,524 samples with 82 measurement features. Model performance was evaluated using accuracy, precision, recall, and F1 score metrics. The results showed that the combination of XGBoost and SVM methods, especially with the RBF kernel, was able to achieve accuracy of up to 91.52%. This finding proves that machine learning-based approaches can be an effective and reliable solution in supporting the forensic identification process
Diabetes Classification using Gain Ratio Feature Selection in Support Vector Machine Method Al Rasyid, Nabila; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.114

Abstract

Diabetes is a major cause of many chronic diseases such as visual impairment, stroke and kidney failure. Early detection especially in groups that have a high risk of developing diabetes needs to be done to prevent problems that have a wide impact. Indonesia is ranked seventh in the world with a prevalence of 10.7% of the total number of people with diabetes. This research aims to determine the attributes in the diabetes dataset that most affect the classification and apply the Support Vector Machine method for diabetes classification. For the determination process, Gain Ratio feature selection technique is applied. The dataset used consists of 768 data with 8 attributes. In this classification process, 3 SVM kernels (Linear, Polynomial, and RBF) are used with three possible data divisions using the ratio (70:30; 80:20; 90:10). Before applying feature selection, there were 8 attributes used and achieved the highest accuracy of 94.81% at a ratio of 80:20 using the RBF kernel with a combination of two parameters namely C = 100, Gamma = 3 and C = 100, Gamma = Scale.  Feature selection parameters in the form of thresholds used include 0.02; 0.03; and 0.05. After applying feature selection, the attribute that produces the highest accuracy uses 6 attributes. The highest accuracy after applying feature selection reached 95.45% at a threshold of 0.02 with a ratio of 80:20 using the RBF kernel with parameters C = 100 and Gamma = Scale. The results showed that there was an increase in accuracy after applying feature selection
KLASIFIKASI PENYAKIT GINJAL KRONIS MENGGUNAKAN INFORMATION GAIN DAN LVQ Putri, Widya Maulida; Budianita, Elvia; Syafria, Fadhilah; Afrianty, Iis
Journal of Information System Management (JOISM) Vol. 7 No. 1 (2025): Juni
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2025v7i1.2102

Abstract

Penyakit Ginjal Kronis (PGK) terjadi ketika fungsi ginjal menurun secara bertahap selama lebih dari tiga bulan tanpa penyebab yang jelas. Penelitian ini bertujuan mengklasifikasikan PGK dengan menggunakan seleksi fitur Information Gain dan Learning Vector Quantization (LVQ). Dataset yang digunakan terdiri dari 1659 data dengan 53 atribut. Proses penelitian meliputi preprocessing data, penerapan SMOTE Oversampling, seleksi fitur Information Gain, dan penerapan model LVQ. Pengujian menghasilkan akurasi tertinggi sebesar 93,37% tanpa seleksi fitur, serta 36 fitur terpilih dengan threshold 0,3 setelah seleksi fitur. Learning rate digunakan antara 0,1 hingga 0,9, min learning rate 0,001, dan pengurangan alpha 0,1. Penggunaan SMOTE dan LVQ meningkatkan nilai presisi, recall, dan f1 score, tetapi akurasi menurun menjadi 84,59%. Hasil ini menunjukkan bahwa metode LVQ efektif dalam klasifikasi penyakit ginjal kronis, membantu ahli identifikasi penyakit ginjal kronis menggunakan data mining dan Jaringan Syaraf Tiruan.
PENGARUH TEKNIK PENYEIMBANGAN DATA PADA KLASIFIKASI PENYAKIT NAFLD DENGAN ALGORITMA SVM Faska, Ridho Mahardika; Gusti, Siska Kurnia; Budianita, Elvia; Syafria, Fadhilah
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5849

Abstract

Non-Alcoholic Fatty Liver Disease (NAFLD) merupakan penyakit hati kronis yang prevalensinya terus meningkat secara global, termasuk di Indonesia, dengan faktor risiko utama seperti obesitas, diabetes melitus, dan dislipidemia. Deteksi dini NAFLD menjadi tantangan penting karena metode konvensional seperti biopsi hati dan pencitraan memiliki keterbatasan dalam hal biaya, risiko invasif, dan kepraktisan. Penelitian ini bertujuan untuk mengembangkan model klasifikasi NAFLD menggunakan algoritma Support Vector Machine (SVM) dengan memanfaatkan dataset dari Kaggle yang terdiri dari 10 variabel dan 17.549 data. Untuk mengatasi masalah ketidakseimbangan kelas, diterapkan teknik oversampling seperti SMOTE, ADASYN, dan Random Oversampling (ROS) untuk melihat performa akurasi. Hasil penelitian menunjukkan bahwa SMOTE memberikan performa terbaik dengan akurasi tertinggi mencapai 78,70% pada kernel RBF, ROS dengan akurasi 78,18% dan ADASYN dengan akurasi 76,86%. Penelitian ini menyimpulkan bahwa pemilihan teknik oversampling data dan parameter yang tepat sangat penting dalam meningkatkan efektivitas model untuk menangani data tidak seimbang, sehingga dapat berkontribusi pada pengembangan metode deteksi NAFLD yang lebih efisien dan non-invasif.
Penerapan Information Gain Untuk Seleksi Fitur Pada Klasifikasi Jenis Kelamin Tulang Tengkorak Menggunakan Backpropagation Khair, Nada Tsawaabul; Afrianty, Iis; Syafria, Fadhilah; Budianita, Elvia; Gusti, Siska 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.v5i4.637

Abstract

Forensic anthropology and skull analysis play a crucial role in the biological identification of individuals, including sex determination. This study aims to improve the accuracy of gender classification based on skull structure by combining the Information Gain feature selection method with the Backpropagation algorithm. The dataset used is the craniometric data compiled by William W. Howells, consisting of 2,524 samples with 85 measurement features. The preprocessing stage includes data selection, data cleaning, and normalization. Feature selection was conducted using the Information Gain method with three threshold values: 0.01, 0.05, and 0.1, resulting in 79, 46, and 38 selected features, respectively. The model was evaluated using the K-Fold Cross Validation method with K=10 and K=20. The highest accuracy of 93.91% was achieved at the 0.01 threshold using the Backpropagation architecture [79:119:1], a learning rate of 0.01, and K=20. These results demonstrate that feature selection using Information Gain enhances the performance of the Backpropagation model by eliminating irrelevant features and minimizing the risk of overfitting.
PENERAPAN METODE INFORMATION GAIN DAN LEARNING VECTOR QUANTIZATION 3 PADA KLASIFIKASI PENYAKIT GINJAL Aprima, Muhammad Dzaky; Budianita, Elvia; Syafria, Fadhilah; Afrianty, Iis
Information System Journal Vol. 8 No. 01 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i01.2117

Abstract

Penyakit Ginjal Kronis (PGK) adalah penyakit yang ditunjukkan dengan turunnya fungsi ginjal yang disebabkan oleh penumpukan sisa metabolik dan berakibat tidak berfungsinya ginjal. Prediksi penyakit ini dengan data mining berperan penting dalam upaya pencegahan penyakit ini. Penelitian ini menerapkan seleksi fitur information gain pada metode Learning Vector Quantization 3 (LVQ3) dalam mengklasifikasikan penyakit ginjal kronis. Pengujian dilakukan 5 skenario pengujian dengan jumlah data sebanyak 1659 data dan 53 atribut. Seleksi fitur menerapkan information gain dengan threshold 0,3 dengan 36 fitur terpilih dan 0,7 dengan 33 fitur terpilih. Model diuji dengan kombinasi parameter learning rate dan window serta dievaluasi menggunakan akurasi, presisi, recall, dan F1-Score. Hasil akurasi tertinggi diperoleh tanpa menerapkan seleksi fitur sebesar 92,77%. Setelah seleksi fitur, akurasi menurun menjadi 86,45%. Kombinasi SMOTE dan seleksi fitur pada threshold 0,3 menurunkan akurasi hingga 81,64%. Hasil penelitian berhasil menerapkan LVQ 3 dalam klasifikasi penyakit ginjal kronis.
Co-Authors Adzhima, Fauzan Agustina, Auliyah Akhyar, Amany Al Rasyid, Nabila Alfarabi.B, Alif Aprilia, Tasya Aprima, Muhammad Dzaky Azhima, Mohd Elin Haerani Fadhilah Syafria Fahrozi, Aqshol Al Faska, Ridho Mahardika Febi Yanto, Febi Fitri Insani Fitri, Anisa Gusti, Siska Kurnia Guswanti, Widya Habibi, M. Ilham Iis Afrianty Indah Wulandari Iwan Iskandar Jasril Jasril Khair, Nada Tsawaabul Kurnia Gusti, Siska Lestari Handayani Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Nazir, Alwis Permata, Rizkiya Indah Putri, Widya Maulida Ramadani, Repi Ramadhani, Astrid Ramadhani, Siti Siti Sri Rahayu Suwanto Sanjaya Syahputra, Armadani Ulti Desi Arni, Ulti Desi Yusra, Yusra Zabihullah, Fayat Zulastri, Zulastri