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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) 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) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika: Jurnal Pengembangan IT
Fadhilah Syafria
UIN Suska Riau
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Pengenalan Suara Paru-Paru dengan MFCC sebagai Ekstraksi Ciri dan Backpropagation sebagai Classifier Syafria, Fadhilah; Buono, Agus; Silalahi, Bib Paruhum
Jurnal Ilmu Komputer dan Agri-Informatika Vol 3, No 1 (2014)
Publisher : Departemen Ilmu Komputer IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (568.648 KB)

Abstract

Paru-paru merupakan organ vital manusia yang berperan dalam proses pernapasan. Jika paru-paru mengalami gangguan maka sistem pernapasan manusia juga akan mengalami gangguan yang bisa menyebabkan kecacatan bahkan kematian. Untuk mengevaluasi keadaan paru-paru dapat dilakukan dengan mendengarkan suara pernapasan dengan menggunakan stateskop. Teknik ini dikenal dengan teknik auskultasi. Teknik ini paling sering digunakan namun memiliki beberapa kelemahan yaitu suara paru-paru berada pada frekuensu rendah, masalah kebisingan lingkungan, kepekaan telinga, hasil analisa yang subjektif, dan pola suara yang hampir mirip. Karena faktor-faktor di atas kesalahan diagnosa bisa terjadi jika proses auskultasi tidak dilakukan dengan benar. Dalam penelitian ini, akan dibuat pengenalan suara paru-paru normal dan abnormal menggunakan Mel Frequency Cepstrum koefisien (MFCC) sebagai ekstraksi ciri dan Backpropagation sebagai classifier. Suara paru-paru akan dihitung Coeffisient Ceptral nya sebagai penciri dari masing-masing suara untuk selanjutnya dikenali dengan menggunakan Backpropagation. Metode yang diusulkan memberikan akurasi 93.97% untuk data latih dan 92.66% untuk data uji.Kata kunci: Backpropagation, MFCC, pengenalan suara paru-paru
Penerapan Fuzzy Multi Criteria Decision Making untuk Diagnosa Awal Gangguan Jiwa dengan Metode Agregasi Wulandari, Fitri; Syafria, Fadhilah; Syafriandi, Muhammad
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 1, No 2 (2015): Desember 2015
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (659.94 KB) | DOI: 10.24014/coreit.v1i2.1232

Abstract

Penyakit Bipoalar Disorder merupakan penyakit psikologis dengan perubahan mood yang sangat ekstrim, yaitu berupa depresi dan mania. Dalam proses pendiagnosaan penyakit Bipolar Disorder masih sangat sulit dan jarang. Ini disebabkan karena banyak orang yang tidak menyadari bahwa dia telah telah terindikasi mengalami penyakit Bipolar Disorder, bahkan ada yang sudah merasakan telah terkena oleh penyakt ini, akan tetapi dia malu untuk berkonsultasi kepada psikolog atau dokter kejiwaan. Untuk menjawab mengatasi masalah tersebut maka timbullah bagaimana cara membangun suatu Sistem Informasi dalam benuk Sistem Pendukun Keputusan (SPK) dalam mendiagnosa kelas penyakit pasien Bipolar Disorder menggunakan metode Fuzzy Multi Criteria Decision Making yang berbasiskan komputersisasi yang lebih modern dan handal yang mudah di gunakan sehingga dapat mengatasi masalah dalam pendiagnosa penyakit Bipolar Disorder ini. Dalam penelitian penyakit Bipolar Disorder ini, kelas penyakit yang menjadi objek penelitian adalah kelas Mania, Hypomania, dan Depresi. Sistem yang dibangun hanya untuk Laboratorium Fakultas Psikolgi UIN Suska Riau. Dalam penelitian ini sistem berhasil dibangun dengan baik tanpa ada kesalahan atau Error System. Hasil pengujian di lapangan sistem ini cukup bisa dimengerti dan diterima oleh user dengan tingkat keberhasilan sebesar 83,34%. Dan dalam pengujian sistem dengan psikolog dengan menggunakan data dari sistem dan gejala yang sama menghasilkan yang sama.
Penerapan Learning Vector Quantization 3 (LVQ3) untuk Mengidentifikasi Citra Darah Acute Lymphoblastic Leukemia (ALL) dan Acute Myeloid Leukemia (AML) Putra, Fiqhri Mulianda; Syafria, Fadhilah
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 4, No 1 (2018): Juni 2018
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (722.516 KB) | DOI: 10.24014/coreit.v4i1.6124

Abstract

Leukemia merupakan kanker yang terjadi pada sel darah manusia.  Salah satu cara mengenali penyakit leukemia dengan menggunakan teknik pengolahan citra dan metode jaringan syaraf tiruan. Penelitian ini membangun sebuah sistem untuk mengidentifikasi citra darah leukemia jenis Acute Lymphoblastic Leukemia (ALL) dan  Acute Myeloid Leukemia (AML) dengan konsep pengolahan citra yakni ekstraksi ciri warna Hue, Saturation, Value (HSV) dan ekstraksi ciri tekstur Gray Level Co-Occurence Matrix (GLCM) serta klasifikasi Learning Vector Quantization 3 (LVQ3). Data citra pada penelitian terdiri dari 100 data citra leukemia. Pengujian  identifikasi dilakukan terhadap pembagian data latih dan data uji yang berbeda. Sistem mampu mengenali citra ALL dan AML dengan akurasi tertinggi sebesar 100% pada pembagian data latih 90% dan data uji 10% dengan learning rate 0,01; 0,05; 0,09 dan window 0,2; 0,4 dan akurasi rendah sebesar 70% pada pembagian data latih 50% dan data uji 50% dengan learning rate 0,01; 0,05; 0,09 dan window 0,4. Dengan demikian dapat disimpulkan penelitian menggunakan  metode HSV dan GLCM serta LVQ3 mampu mengimplementasikan sebuah sistem identifikasi citra darah leukemia.
Data Warehouse Design For Sales Transactions on CV. Sumber Tirta Anugerah Syaputra, Muhammad Dwiky; Nazir, Alwis; Gusti, Siska Kurnia; Sanjaya, Suwanto; Syafria, Fadhilah
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 2 (2022): December 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (644.133 KB) | DOI: 10.24014/coreit.v8i2.19800

Abstract

Many data warehouses are implemented in companies engaged in retail, CV. Sumber Tirta Anugerah is one of the paint product retail companies that has not implemented it yet. As time goes by, the sales transaction data is getting more and more difficult to process because it is still stored in Microsoft Excel. This is a serious problem in utilizing historical data to assist in making a decision. It is difficult to store sales data because the data is quite large and a lot. Based on the above problems, a data warehouse design is needed for sales transaction data. This data warehouse design uses Kimball's nine-steps method and star schema. To perform the ETL process (extract, transform, and load) using Pentaho software. In this data warehouse design, Tableau software is used to visualize the processed data into a graph and dashboard report. The result of this research is a data warehouse design using nine steps and a star schema which gets a transformation response time of 4048 MS. 
Penerapan Neural Network dengan Menggunakan Algoritma Backpropagation pada Prediksi Putusan Perceraian Zulastri, Zulastri; Afrianty, Iis; Budianita, Elvia; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2437

Abstract

The high divorce rate has a negative impact on couples who will file for divorce and also has an extreme impact on children such as psychological disorders of children. The magnitude of the impact of divorce, it is necessary to predict the divorce decision. In this study, the application of the backpropagation method to predict divorce decisions was carried out. The data used is data on divorce decisions from the Pekanbaru Religious Court from 2020 - 2021 totaling 779. The dataset obtained is not balanced with 724 accepted classes and 55 rejected classes, balancing is done by reducing excess classes. The parameters used in this study build 3 architectural models [6-7-1], [6-9-1], [6-12-1], learning rate (0.01, 0.03, 0.09), max epoch and data sharing (70:30), (80:20), (90:10). The results of this study indicate that the best architectural model is in the network architecture [6-9-1] learning rate 0.09 epoch 300 dataset distribution 80% training data and 20% test data the accuracy value is 80% and the Mean Squared Error (MSE) is 0.1402. In this study, the backpropagation method was successful in predicting divorce decisions.
Klasifikasi American Sign Language Menggunakan Convolutional Neural Network Israldi, Tino; Haerani, Elin; Sanjaya, Suwanto; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2570

Abstract

Communicating is a necessity for all groups or individual because each individual should communicate with their surroundings. Communicating can also make us get information so that it can be used as a reference to be able to adapt. Verbal language used by speaking out loud is a way of communicating with individuals, but not all individuals can communicate with it, especially there are some individuals who have hearing limitations. Because of these limitations, another program that can be used is through sign language. Language requirements are languages that are usually used by people with disabilities in terms of hearing or speaking and sign language also has a fairly well-known sign language standard, namely the American Sign Language (ASL) standard. Unlike languages in the world, sign language is also often of little interest to most people because people's interest in sign language is still lacking so that most people are unable to understand their language. Sign language has many types, one of which is sign language by using hands to form letters and numbers. In overcoming these problems, the solution is to create a system that can be used to recognize sign language, the system developed is a system that used machine learning technology. This study will propose an ASL classification approach through data preprocessing and a convolutional neural network model. The proposed model can classify ASL hand posture images to be translated into the alphabet. The result of this study is an model with accuracy of 99.8% obtained from the process of merging preprocessing data and the convolutional neural network model.
Implementasi Metode Learning Vector Quantization (LVQ) Untuk Klasifikasi Keluarga Beresiko Stunting Aziz, Abdul; Insani, Fitri; Jasril, Jasril; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3478

Abstract

Stunting is a condition where a child's height is too short compared to children of the same age. This condition affects the health of toddlers in the short and long term, such as suboptimal body posture in adulthood, decreased reproductive health, and decreased learning capacity, resulting in suboptimal performance in school. One of the causes of stunting is a lack of nutrition, basic health facilities, and poor parenting practices. However, the current data collection and classification of families at risk of stunting still use Microsoft Excel, which is ineffective in processing large data. Therefore, the LVQ method, which is an improvement of the Vector Quantization method, is used to accelerate the classification process. In this study, 5 parameters were tested, and the optimal result was achieved by using 7 input neurons, Chebychev distance as the distance measure, a learning rate of 0.1, 7 epochs, and 30% of training data. With these parameters, an accuracy of 99.38% was obtained. Based on these results, the LVQ method can help improve accuracy in classifying families at risk of stunting
Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology Harni, Yulia; Afrianty, Iis; Sanjaya, Suwanto; Abdillah, Rahmad; Yanto, Febi; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3625

Abstract

One approach to handling large of data dimensions is feature selection. Effective feature selection techniques produce the essential features and can improve classification algorithms. The accuracy performance results can measure the accuracy of the method used in the classification process. This research uses the Learning Vector Quantization (LVQ) 1 method combined with Gain Ratio feature selection. The data used is male and female skull bone measurement data totaling 2524. The highest accuracy results are obtained by LVQ 1, which uses a Gain Ratio with a threshold of 0.01 with a learning rate = 0.1, which is 92.01%, and the default threshold weka(-1.7976931348623157E308) with a learning rate = 0.1, which is 92.19%. In comparison, previous research that did not use gain ratio or that did not use GR only had the best results of 91.39% with a learning rate = 0.1, 0.4, 0.7, 0.9. This shows that LVQ 1 using the Gain Ratio can be recommended to improve the performance of the Skull dataset compared to LVQ 1 without Gain Ratio.
Klasifikasi Kematangan Buah Mangga Menggunakan Pendekatan Deep Learning Dengan Arsitektur DenseNet-121 dan Augmentasi Data Permata, Rizkiya Indah; Yanto, Febi; Budianita, Elvia; Iskandar, Iwan; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5381

Abstract

Mango is a seasonal fruit in Indonesia. In lowland areas and hot climates, this mango plant can grow abundantly. People who use mangoes generally focus more on the characteristics of the fruit which require a more precise classification to be more certain. Traditional classifications sometimes fail to properly articulate maturity criteria. This research classifies mango ripeness using a deep learning approach with densenet-121 architecture, parameters, learning rate, dropout, and data augmentation. Augmentation is the process of changing or modifying an image in such a way that the computer will detect that the image has been changed is the same picture. The original dataset was 895 data, after being augmented it became 1790 data consisting of three classes, namely ripe mango, young mango, and rotten mango. The test compares the original data and the original data added with augmentation. Accuracy using original data is 95.95%. Meanwhile, using original data combined with augmentation gets an accuracy of 99.73%
Klasifikasi Tulang Tengkorak Berdasarkan Jenis Kelamin dalam Antropologi Forensik Menggunakan Metode Support Vector Machine Rahayu, Siti Sri; Afrianty, Iis; Budianita, Elvia; Syafria, Fadhilah
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4046

Abstract

Classification of skull bones by sex is part of human biological profile identification in forensic anthropology that aims to determine whether the skeleton belongs to a male or female. The most popular method for determining sex from bones is DNA analysis. However, under some conditions such as burnt, damaged, or very dry skeletal remains, DNA analysis cannot provide accurate results. So forensic anthropology is developing by utilizing the help of machine learning technology. This research shows the performance of Support Vector Machine in classifying skull bones based on gender. The skull parameter data used is data collected by Dr. William Howells from craniometric measurements consisting of male and female data with a total of 2524 data and 82 features, namely bizygomatic breadth, glabello-occipital lenght and others.  In building the skull bone classification model, the Support Vector Machine kernels used are linear, RBF, and polynomial. Based on the test results, the best accuracy was obtained in each kernel function, namely the linear kernel obtained the best accuracy of 88.14% with C = 2. For the RBF kernel, the best accuracy was 91.30% at C = 2, γ = 'auto'. For the polynomial kernel, the best accuracy was 88.14% at C = 1 and 2, γ = 1 and 2, d = 1. The evaluation results show that the Support Vector Machine model with the RBF kernel has proven to be the optimal choice in skull bone classification compared to other kernels, based on accuracy, precision, recall, and CrossValidation measurements reaching values above 90%. These results indicate that the skull bone classification model based on gender using Support Vector Machine is recommended in forensic anthropology.
Co-Authors Abdul Aziz Adzhima, Fauzan Afriyanti, Liza Agus Buono Agustina, Auliyah Aprima, Muhammad Dzaky Bib Paruhum Silalahi Che Hussin, Ab Razak Dede Fadillah Elin Haerani Elvia Budianita Faska, Ridho Mahardika Fauzan Adzim Febi Yanto, Febi Fitri Insani Fitri Wulandari Gusti, Siska Kurnia Guswanti, Widya Harni, Yulia Iis Afrianty Ikhsan, Tomi Israldi, Tino Iwan Iskandar Jasril Jasril Khair, Nada Tsawaabul Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Muhammad Fahri Muhammad Syafriandi, Muhammad Muhammad Yusuf Fadhillah Mulyono, Makmur Muslimin, Al’hadiid Nazir, Alwis Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini, Pizaini Putra, Fiqhri Mulianda Putri, Widya Maulida Rahmad Abdillah Rahmadhani, R. Ramadhan, Aweldri Ramadhani, Siti Siti Sri Rahayu Suwanto Sanjaya Syaputra, Muhammad Dwiky Zabihullah, Fayat Zulastri, Zulastri