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All Journal TEKNIK INFORMATIKA Syntax Jurnal Informatika Jurnal Ilmu Komputer dan Agri-Informatika SITEKIN: Jurnal Sains, Teknologi dan Industri CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer bit-Tech Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer 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) Sisfo: Jurnal Ilmiah Sistem Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Indonesian Journal of Multidisciplinary on Social and Technology Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Fadhilah Syafria
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Journal : building of informatics technology and science

 1 
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%
Perbandingan Kinerja Random forest dan SVM Pada Klasifikasi Tingkat Kekumuhan Permukiman Menggunakan SMOTE Nurika Dwi Wahyuni; Fadhilah Syafria; Novi Yanti; Surya Agustian
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10101

Abstract

Classifying slum levels is essential for a structured, data-driven analysis of settlement conditions. This study compares the performance of Random forest and Support vector machine (SVM) in classifying slum levels in Pekanbaru City across two scenarios with and without SMOTE using slum indicator scoring data. Its contributions include analyzing SMOTE's impact on model performance and evaluating the top 10 features against the full feature set. The dataset comprises 992 RT-level records from Disperkim Pekanbaru City (2020, 2021, and 2023) featuring 16 slum indicator scores based on PUPR Ministerial Regulation No. 14/2018, categorized into three classes: Non-Slum, Low Slum, and Moderate Slum. Following the KDD process (selection, preprocessing, transformation, data mining, evaluation, and analysis), the data was split 80:20 using stratified sampling and evaluated based on accuracy, precision, recall, F1-score, and confusion matrix. Results show that the Linear SVM without SMOTE achieved perfect evaluation metrics (1.0000); however, this is interpreted cautiously as the class labels derive from strict regulatory scoring rules, making class boundaries inherently linear. Random forest saw its F1-score rise from 0.9660 to 0.9700 after SMOTE, while the most significant improvement occurred in SVM RBF, jumping from 0.9214 to 0.9779. Testing the top 10 features led to a decreased F1-score across models, indicating that utilizing all 16 features remains optimal for this dataset.
Co-Authors Abdul Aziz Abdullah, Said Noor Abdussalam Al Masykur Adrian Maulana Adzhima, Fauzan Agung Syaiful Rahman Agus Buono Agustina, Auliyah Ahmad Paisal Aji Pangestu Adek Akbar, Lionita Asa Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Aminuyati Andre Suarisman Aprima, Muhammad Dzaky Ariq At-Thariq Putra Baehaqi Bib Paruhum Silalahi Boni Iqbal Che Hussin, Ab Razak Darmila Dede Fadillah Deny Ardianto Devi Julisca Sari Dina Septiawati Dodi Efendi Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elin Hearani Ellin Haerani Elvia Budianita Fakhri Fakhri Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Nur Salisah Febi Yanto Felian Nabila Ferdian Arya Dinata Fitra Lestari Fitri Insani Fitri Insani Fitri Wulandari Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Hafez Almirza Hafsyah Hara Novina Putri Harni, Yulia Hertati Ibnu Afdhal Ihda Syurfi Iis Afrianty Iis Afrianty Ikhsan, Tomi Ikhsanul Hamdi Indrizal, Habibi Putra Inggih Permana Irma Sanela Ismail Marzuki Ismail Marzuki Ismar Puadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Jauhari, Najwa Karina Julita Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati Liza Afriyanti Lola Oktavia Lola Oktavia Lola Oktavia Oktavia M Fikry M. Afif Rizky A. Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Matondang, Irfan Jamal Maulana Junihardi Mawadda Warohma Mazdavilaya, T Kaisyarendika Mhd. Kadarman Mori Hovipah Mori Hovipah Morina Lisa Pura Muhammad Affandes Muhammad Alvin Muhammad Fahri Muhammad Farid Audi Rahman Simatupang Muhammad Fikry Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Ichsanul Bukhari Muhammad Irsyad Muhammad Syafriandi, Muhammad Muhammad Taufiq Muhammad Yusril Haffandi Muhammad Yusuf Fadhillah Mulyono, Makmur Muslimin, Al’hadiid Nabyl Alfahrez Ramadhan Amril Nailatul Fadhilah Nazir, Alwis Nazruddin Safaat H Negara, Benny Sukma Neni Sari Putri Juana Nesdi Evrilyan Rozanda Nining Nur Habibah Novi Yanti Novriyanto Novriyanto Nurainun Nurainun Nurika Dwi Wahyuni Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini Puspa Melani Almahmuda Putra, Fiqhri Mulianda Putri Mardatillah Putri, Widya Maulida R. Rahmadhani Rahmad Abdillah Rahmad Kurniawan Raja Sultan Firsky Ramadhan, Aweldri Ramadhan, Muhammad Ilham Ramadhani Herfin Ramadhani, Siti Reski Mai Candra Reski Mai Candra Reski Mai Candra Reski Mei Candra Restu Kharrisa Andini Riska Yuliana Roni Salambue Said Nanda Saputra Salmiyati Salmiyati Satria Bumartaduri Silfia Silfia Siti Ramadhani Siti Ramadhani Siti Sri Rahayu Surya Agustian Suswantia Andriani Suwanto Sanjaya Syaputra, Muhammad Dwiky Teddie Darmizal Teddie Darmizal Ummy Agustina Putri Vitriani, Yelvi Wulandari, Fitri Yaskur Bearly Fernandes Yusra, Yusra Yusril Hidayat Zabihullah, Fayat Zulastri, Zulastri