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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 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 Indonesian Journal of Innovation Multidisipliner Research Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Indonesian Journal of Innovation Multidisipliner Research Jurnal Komtika (Komputasi dan Informatika)
Elvia Budianita
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Klasifikasi Status Stunting Balita Dengan Metode Support Vector Machine Berbasis Web Adzhima, Fauzan; Budianita, Elvia; Nazir, Alwis; Syafria, Fadhilah
Jurnal Inovtek Polbeng Seri Informatika Vol 8, No 2 (2023)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v8i2.3641

Abstract

Orang tua harus memperhatikan anak mereka saat balita, karena di usia tersebut mereka rentan terhadap berbagai gangguan pertumbuhan dan perkembangan, salah satunya stunting. Stunting adalah gangguan pertumbuhan dan perkembangan yang disebabkan oleh kekurangan gizi dan ditandai dengan tinggi badan yang tidak memenuhi kriteria pertumbuhan normal anak seusianya. Untuk mencegah stunting, tenaga kesehatan atau kader posyandu mengukur antropometri tubuh anak-anak di posyandu. Data hasil pengukuran tubuh anak diproses secara manual, sehingga ada kemungkinan besar kesalahan pemrosesan karena kesalahan manusia (human error). Dengan mempelajari pola data pengukuran, data mining dapat mengatasi masalah dalam proses pengolahan data pengukuran. SVM merupakan salah satu metode data mining yang umum dipakai untuk permasalahan klasifikasi dengan kelebihannya yang dapat bekerja dengan menggunakan memori yang kecil serta dapat memisah data yang tidak dapat dipisahkan secara linier. Usia, jenis kelamin, Inisiasi Menyusui Dini (IMD), berat badan, dan tinggi badan adalah atribut yang digunakan untuk klasifikasi menggunakan algoritma SVM ini. Berdasarkan pengujian yang dilakukan, terdapat 1172 data dengan hasil rata-rata performa model terbaik menggunakan parameter γ = 0.01 dan akurasi 98.99%, sehingga model dapat digunakan untuk memprediksi data pengukuran baru secara akurat dan tindakan pencegahan stunting dapat segera dilakukan.
Pencarian adverse event yang timbul akibat penggunaan obat dexamethasone menggunakan algoritma apriori Nuradha Liza Utami; Alwis Nazir; Pizaini; Elvia Budianita; Fitri Insani
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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

Abstract

Inflammation is the body's response to infection, irritation, or injury characterized by redness, increased temperature, swelling, and pain. Dexamethasone is one of the drugs from the corticosteroid group that is commonly used, dexamethasone has a wide indication in medicine is often considered a drug that can save lives, causing many people to then buy dexamethasone drugs without medical indications and prescriptions assuming dexamethasone drugs can treat various diseases. The use of dexamethasone can result in side effects including decreased immunity, diabetes, hypertension, moon face, osteoporosis, and cataracts. In addition to frequent side effects, adverse events may also occur. This study aims to find the relationship of adverse events that arise as a result of using dexamethasone drugs, by applying the data mining technique of association rule method with apriori algorithm. The dataset used in the research is sourced from the FDA Adverse event Reporting System (FAERS) database which is managed using the KDD stages which include data selection, cleaning, transformation, and data mining. the results of the research are implemented into the apriori algorithm data mining system and tested using the lift ratio value. The rules generated in this study have a lift ratio value of more than 1, which means that the rules generated are valid and show the benefits of these rules.
Classification of Palm Oil Ripeness Level using DenseNet201 and Rotational Data Augmentation Nabyl Alfahrez Ramadhan Amril; Yanto, Febi; Elvia Budianita; Suwanto Sanjaya; Fadhilah Syafria
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.v4i6.1937

Abstract

Indonesia is a country in Southeast Asia with the largest palm oil production in the world. Based on Indonesian Central Statistics Agency data, in 2022 Indonesia produced 46,8 million Tons of Crude Palm Oil (CPO). To produce a high-quality oil, palm oil fruit must be harvested in an optimal condition. But, even a experienced and trained person found it difficult to identify whether the fruit is ripe or raw. In this research theres two type of classification which is ripe and raw, this is because palm oil milling factory only accept pure ripe palm oil fruit and not half ripe or almost ripe. The data that is used in this reseacrh was collected from two sources, the first source is from https://www.kaggle.com/datasets/ahmadfathan/kematangansawit and the second source was collected manually by going to palm oil plantation. The total of data that is used for this research is 1000 data and 1000 augmented data. Dense Convolutional Network (DenseNet) that is used in this research is a CNN architecture that was first introduced in 2017. Compared to DenseNet121 and DenseNet169, DenseNet201 is proven to have a higher level of accuracy. The 90:10 data scheme succeeded in getting the highest accuracy with a total accuracy of 97.50% with a learning rate of 0.001 and a dropout of 0.01
Deep Learning Menggunakan Algoritma Xception dan Augmentasi Flip Pada Klasifikasi Kematangan Sawit Masaugi, Fathan Fanrita; Yanto, Febi; Budianita, Elvia; Sanjaya, Suwanto; Syafria, Fadhilah
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.v4i6.1938

Abstract

Palm oil is an important commodity in Indonesia, especially as Indonesia is the highest palm oil exporting country in the world. Ripe palm fruit is marked by a change in color of the fruit from black to reddish yellow. Apart from that, immature palm fruit has a negative and significant effect on CPO production. The data collection process was carried out by directly taking pictures of palm fruit on oil palm plantations and data obtained from Kaggle. The total amount of data is 1000 images and 1000 data resulting from flip augmentation. The Xception algorithm is an algorithm in deep learning which stands for Extreme version of Inception. This combination was then proven to provide better accuracy in classifying images from a dataset. The optimizer used is the optimizer in TensorFlow, namely Adam (Adaptive Moment Estimation) using learning rate and dropout values. Images of mature and immature palm oil were classified using the Xception algorithm with augmented and without augmented data. In addition, experiments were carried out by changing the parameter values ??of learning rate to 0.1, 0.01, 0.001 and dropout to 0.1, 0.01, 0.001. It was found that the data division was (90;10) with the best accuracy reaching 95%. Test parameters carried out by trialling were proven to increase accuracy when compared to without using parameters and flip augmentation. The best accuracy of the Xception model is 95% on augmented data with a learning rate of 0.001 and a dropout of 0.1.
Implementasi VGG 16 dan Augmentasi Zoom Untuk Klasifikasi Kematangan Sawit Mazdavilaya, T Kaisyarendika; Yanto, Febi; Budianita, Elvia; Sanjaya, Suwanto; Syafria, Fadhilah
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.v4i6.1940

Abstract

Indonesia is a country that has very abundant palm oil plantations and makes palm oil one of the largest export commodities in Indonesia. Fruit maturity on oil palms has a significant influence on palm oil and kernel production. The level of ripeness in palm oil fruit can affect several contents in it, such as tocopherol content, yield and FFA. The classification will be divided into 2 classes, namely between ripe and immature fruit with data on 500 images of ripe fruit and 500 images of immature fruit, data taken from the Kaggle site and private gardens taken using a cellphone camera. The data that has been obtained is augmented which is useful for enriching the data to make it more abundant. Data augmentation uses zoom augmentation and makes the original 1000 data increase to 2000 data. The model used is VGG 16 which is part of deep learning. The existing dataset is then preprocessed, resized and rescaled, then divides the data into 3, namely train, test and valid data. After dividing the data, then carry out the classification process with VGG 16 and set the hyperparameters after that the model will learn with 20 epochs. The model will learn with 57 schemes to compare and find highest accuracy. After the model has finished learning, it is evaluated using a confusion matrix. The results obtained were that the 90:10 data division using data augmentation with a learning rate of 0.01 and a dropout of 0.001 obtained the best accuracy, reaching 93.8%.
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 Tulang Tengkorak Manusia Berdasarkan Jenis Kelamin Menggunakan Backpropagation Pada Antropologi Forensik Afrianty, Iis; Mhd. Kadarman; Elvia Budianita; Fadhilah Syafria
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.8235

Abstract

Klasifikasi tulang tengkorak berdasarkan jenis kelamin merupakan langkah utama pada antropologi forensik dalam mengidentifikasi profil sisa-sisa kerangka. Klasifikasi jenis kelamin bertujuan untuk menentukan apakah kerangka tertentu adalah milik laki-laki atau perempuan. Penelitian ini berfokus pada klasifikasi tulang tengkorak berdasarkan jenis kelamin dengan menggunakan teknik pembelajaran mesin tingkat lanjut, khususnya Backpropagation Neural Network (BPNN). Tujuan dari penelitian ini adalah untuk menunjukkan kinerja BPNN. Data yang digunakan dalam penelitian ini diperoleh dari Dr. William Howells, meliputi pengukuran kraniometri dari 2524 sampel tengkorak laki-laki dan perempuan, dengan 86 variabel seperti lebar bizygomatic dan panjang glabello-oksipital. Teknik BPNN digunakan karena kemampuannya untuk memodelkan hubungan yang kompleks dan tidak linier. Kinerja model ini dievaluasi dengan menggunakan metrik standar akurasi. Pembagian data latih dan data uji menggunakan k-fold cross-validation dengan k = 10. Penelitian ini menjalankan dua skenario uji, yaitu menggunakan satu hidden layer dan dua hidden layer. Untuk masing-masing model arsitektur menggunakan learning rate sebagai parameter uji, yaitu 0,1; 0,01; dan 0,001. Hasil penelitian menunjukkan bahwa pendekatan pembelajaran mesin dapat secara efektif membedakan antara tulang tengkorak laki-laki dan perempuan, dengan akurasi rata-rata 92,32% untuk satu hidden layer dan 90,74% untuk dua hidden layer. Hasil tersebut menunjukkan, model klasifikasi tulang tengkorak manusia berbasis gender dengan menggunakan jaringan syaraf tiruan backpropagation sangat disarankan sebagai teknik yang berhasil dalam mengklasifikasikan tulang tengkorak manusia.
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
Co-Authors Abdul Halim Adzhima, Fauzan Afrianti, Liza 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 Azhima, Mohd Baehaqi Berliana, Trisia Intan Boni Iqbal buhfi arides hanyodi Chely Aulia Misrun Damayanti, Elok Desra Rizki Riyandi Dicky Abimanyu Dinyah Fithara Dodi Efendi doli fancius silalahi Dwitama, Raja Zaidaan Putera 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 Fahrozi, Aqshol Al 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 Hasibuan, Ilham Habibi Ibnu Afdhal Ichsan Permana Putra Ihda Syurfi Ihlal Hanafi Harahap Iis Afrianty Iis Afrianty Ikhsanul Hamdi 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 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 Ulti Desi Arni, Ulti Desi Wahyuni, Ayu Sri 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