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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Computing Science and Applied Mathematics INTECOMS: Journal of Information Technology and Computer Science Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Jurnal Ilmu Teknik dan Komputer Journal of Computer Science and Engineering (JCSE) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sistem Informasi Dan E-Bisnis Jurnal Ilmu Komputer Dan Informatika Dedikasi Nusantara: Jurnal Pengabdian Masyarakat Pendidikan Dasar Sinergi: Jurnal Pengabdian Kepada Masyarakat JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) JCOSIS (Journal Computer Science and Information Systems)
Ayumi, Vina
Universitas Mercu Buana
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Model Extreme Gradient Boosting Berbasis Term Frequency (TFXGBoost) Untuk Klasifikasi Laporan Pengaduan Masyarakat Vina Ayumi; Desi Ramayanti; Handrie Noprisson; Yuwan Jumaryadi; Umniy Salamah
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 1 (2023): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i1.6089

Abstract

Various algorithms and machine learning techniques are being applied to improve the efficiency and effectiveness of the process of automatically classifying complaint reports from the public in Indonesia. One machine learning algorithm that has recently gained benchmarks in the state of the art of various problems in machine learning is eXtreme Gradient Boosting (XGBoost). This study aims to develop an extreme gradient boosting model based on term frequency (TFXGBoost) to predict whether a text is classified as a complaint or not a complaint based on the data studied. Based on the experimental results, TFXGBoost achieved 92.79% accuracy with eta / learning rate hyperparameters of 0.5, gamma of 0, and max_depth of 3 and the computation time required to adjust the hyperparameters was 13870.012468 seconds.
Jakarta Smart City: Pengembangan Model Smart Mobility Prediction Mengunakan GHMM-ARIMA Vina Ayumi
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 1 (2023): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i1.6090

Abstract

The Jakarta government has used digital infrastructure, such as online platforms and software applications, to implement these elements. However, there is still room for improvement in maximizing benefits for the city and its residents. One area that needs to be optimized is the development of smart mobility prediction models and improving the performance of existing models. In this study, Gaussian hidden markov model (GMM) and autoregressive integrated moving average (ARIMA) algorithms were used for predictable mobility monitoring to decipher congestion in Jakarta. The parameters used in detecting stay points are a time threshold of 20 minutes, and a distance threshold of 200 meters. The evaluation results showed that the ARIMA model test obtained a root mean square error (RMSE) value of 162,766, showing a fairly high error. The evaluation results for prediction using GHMM predicting mobility to support the Jakarta Smart City program on the test data were calculated using the accuracy model and RMSE model. The performance of GHMM gets an accuracy of 76.90% and RMSE of 1,641. The evaluation value of GHMM can be said to be good enough to model mobility data.
Pengaruh Tuning Parameter dan Cross Validation Pada Klasifikasi Teks Komplain Bahasa Indonesia Menggunakan Algoritma Support Vector Machine Vina Ayumi; Desi Ramayanti; Handrie Noprisson; Anita Ratnasari; Umniy Salamah
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6117

Abstract

Text classification aims to group text data, for example, to find some information from a large social media text dataset so that it can be used by the data owner. Manual text classification is time-consuming and difficult, so some researchers try to research text classification automatically. This study attempts to classify Indonesian text datasets using the SVM algorithm. The research was conducted in two stages, namely the first experiment without cross validation parameters and parameter tuning, then the second experiment was carried out with cross validation parameters and parameter tuning. Experiments without cross validation parameters and parameter tuning for support vector machines (SVM) obtained 89.47% accuracy with precision and recall values of 0.90 and 0.89 respectively. The second experiment used cross validation with k-5 and k-10 and tuning parameters with C constant and gamma values. Cross validation results with k-10 obtained the best accuracy with a value of 96.48% with a computation time of 40.118 seconds. Next, kernel functions in tuning parameters namely sigmoid, linear and radial basis functions are analyzed and it is found that sigmoid kernel functions achieve the best accuracy and computational time.
Perancangan Aplikasi Web Untuk Deteksi Motif Batik Indonesia Berbasis Image Processing dan Machine Learning Vina Ayumi; Ida Nurhaida; Wachyu Hari Haji
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6240

Abstract

Machine learning batik motif detection is important because it helps identify, classify, and find batik by motif and area. The diversity of batik motifs in Indonesia poses a challenge to society, as many motifs have similarities in pattern or color, leading to errors in identification. Researchers have used machine learning techniques to address this problem. Machine learning models with image preocessing techniques such as torch techniques, log gabor, gray level co-occurrence matrix (GLCM) techniques have been used to identify batik motifs with high accuracy. This application will be developed using the web information system development methodology (WISDM) methodology. These advances in machine learning of batik motif detection contribute to preserving Indonesian culture and heritage. The best results were obtained from the combination of gabor, log gabor, GLCM features with retrieval rate quality reaching 84.54% in motif detection.
Evaluasi Usability pada Portal Basis Data Tanaman Obat Indonesia Menggunakan Metode System Usability Scale (SUS) Wachyu Hari Haji; Anita Ratnasari; Vina Ayumi; Handrie Noprisson; Nur Ani
JSAI (Journal Scientific and Applied Informatics) Vol 6 No 3 (2023): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v6i3.6263

Abstract

Previous research discussed valuable recommendations for the development of an Indonesian medicinal plant database portal. However, previous research has not discussed usability evaluation on the Indonesian medicinal plant database portal. One usability evaluation technique that is quite popular is the system usability scale (SUS). This study aims to analyze the portal database of medicinal plants using the usability scale (SUS) system to find out the next portal improvement. The SUS method allows researchers to collect data from users through surveys and calculate usability scores, providing recommendations for improving the design and functionality of web-based systems. From the experimental results in the form of calculation results using SUS measurement, it is known that the implementation of the medicinal plant database portal received an assessment of 72.14. This value if interpreted using the measurement level of the final value of SUS can be said that the implementation of the medicinal plant database portal can be accepted (acceptable) with a good category (good).
MODEL OF INDONESIAN CYBERBULLYING TEXT DETECTION USING MODIFIED LONG SHORT-TERM MEMORY Mariana Purba; Paisal Paisal; Cahyo Pambudi Darmo; Handrie Noprisson; Vina Ayumi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5239

Abstract

Cyberbullying, in its essence, refers to the deliberate act of exploiting technological tools to inflict harm upon others. Typically, this offensive conduct is perpetuated repeatedly, as the perpetrator takes solace in concealing their true identity, thereby avoiding direct exposure to the victim's reactions. It is worth noting that the actions of the cyberbully and the responses of the individual being cyberbullied share an undeniable interconnection. The main objective of this study was to identify and analyze Instagram comments that contain bullying words using a model of WLSTML2 which is an optimization of a long short-term memory network with word-embedding and L2 regularization. This experiment using dataset with negative labels as many as 400 data and positive as many as 400 data. In this study, a comparison of 70% training data and 30% testing data was used. Based on experimental results, the WLSTMDR model obtained 100% accuracy at the training stage and 80% accuracy at the testing stage. The WLSTML2 model received an accuracy of 99.25% at the training stage and an accuracy of 83% at the testing stage. The WLSTML1 model obtained an accuracy of 97.01% at the training stage and an accuracy of 80% at the testing stage. Based on the experimental results, the WLSTML2 model gets the best accuracy at the training and testing stages. At the testing stage of 132 data, it was found that the positive label data predicted to be correct was 56 data and the negative label data that was predicted to be correct was 53 data.
Hospital quality classification based on quality indicator data during the COVID-19 pandemic Nurhaida, Ida; Dhamanti, Inge; Ayumi, Vina; Yakub, Fitri; Tjahjono, Benny
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4365-4375

Abstract

This research aim is to propose a machine learning approach to automatically evaluate or categories hospital quality status using quality indicator data. This research was divided into six stages: data collection, pre-processing, feature engineering, data training, data testing, and evaluation. In 2020, we collected 5,542 data values for quality indicators from 658 Indonesian hospitals. However, we analyzed data from only 275 hospitals due to inadequate submission. We employed methods of machine learning such as decision tree (DT), gaussian naïve Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) for research archive purposes. Logistic regression achieved a 70% accuracy rate, SVM a 68% accuracy rate, and neural network a 59.34% of accuracy. Moreover, K-nearest neighbors achieved a 54% of accuracy and decision tree a 41% accuracy. Gaussian-NB achieved a 32% accuracy rate. The linear discriminant analysis achieved the highest accuracy with 71%. It can be concluded that linear discriminant analysis is the algorithm suitable for hospital quality data in this research.
Aircraft Recognition in Remote Sensing Images Based on Artificial Neural Networks Abrar, Muhammad Fauzan; Ayumi, Vina
Journal of Computer Science and Engineering (JCSE) Vol 4, No 2: August (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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

Abstract

Computer Vision (CV) is a field of Artificial Intelligence (AI) that enables computers and systems to obtain data from images, recordings and other visual information sources. Image Recognition, a subcategory of Computer Vision, addresses a bunch of strategies for perceiving and taking apart pictures to engage the automation of a specific task. It is fit for perceiving places, people, objects and various types of parts inside an image, and reaching deductions from them by analyzing them. With these kinds of utilities it is a no-brainer that Computer Vision has its use cases in the military world. Computer Vision can be immensely useful for Intelligence, Surveillance and Reconnaissance (ISR) work. This paper provides on how Computer Vision might be used in ISR work.  This paper utilises Artificial Neural Network (ANN) such as Convolutional Neural Network (CNN) and Residual Neural Network (ResNet) for demonstration purposes. In the end, the ResNet model managed to edge out the CNN model with a final validation accuracy of 90.9% compared to a validation accuracy of 86% on the CNN model. With this, Computer Vision can help enhance the efficiency of human operators in image and video data related work.
POJOK TAMAN BACA BERBASIS TEKNOLOGI UNTUK MENINGKATKAN MINAT BACA ANAK Putra, Erwin Dwika; Mariana Purba; Marissa utami; Vina Ayumi; Hadiguna Setiawan; Wachyu Hari Haji
JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan) Vol. 3 No. 2 (2023): Oktober
Publisher : Lembaga Penelitian Pengabdian Masyarakat Penerbitan dan Percetakan Indonesian Scholar Khiar Wafi (LPPMPP IKHAFI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54650/jpmtt.v3i2.532

Abstract

Kegiatan membaca agar dapat meningkatkan minat baca harus dilakukan sedini mungkin. Perlunya pembentukan kebiasaan akan membaca atau budaya membaca akan menjadi lebih baik apabila dimulai sejak dini dengan kegeiatan sesederhana mungkin. Literasi juga dapat diartikan sebagai kemampuan dalam melakukan kegiatan baca, tulis, berhitung dan berbicara serta kemampuan dalam mencari dan menggunakan sebuah informasi. Dengan bantuan teknologi gadget seperti tablet PC maka model pembalajaran yang akan dikembangkan pada pojok baca ini nantinya akan terdapat beberapa games edukasi, tetapi pada pojok baca ini juga terdapat beberapa buku bacaan agar anak-anak dapat menyukai membaca dari beberapa buku. Dengan adanya program Pojok Baca Berbasis Teknologi ini telah berhasil meningkat minat baca anak-anak serta dapat meningkatkan kemajuan berfikir dan kreatifitas anak-anak dilingkungan yang menjadi objek penelitian. Serta dapat disimpulkan pula dengan adanya pojok digital ini dapat mendorong pemikiran positif anak-anak dalam memanfaatkan gadget serta alat-alat teknologi lainnya. Kata Kunci: Pojok, Baca, Digital
Komparasi Algoritma Regresi Linear dan Algoritma C 4.5 Untuk Memprediksi Penjualan Sayur Mayur (PT. Kebun Sayur Segar) Naufal, Muhamad Fahri; Ayumi, Vina
Jurnal Ilmu Teknik dan Komputer Vol 6, No 2 (2022)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/jitkom.v6i2.004

Abstract

Sayur-mayur adalah makanan pokok selain nasi yang sering dikonsumsi sehari-hari. Sayur-mayur juga makanan sehat yang mengandung kadar air tinggi, vitamin dan serat yang banyak sehingga baik untuk tubuh. Adapun contoh sayur yang sering di konsumsi sehari-hari seperti Bayam yang mengandung vitamin A, B, C dan E, ada juga Wortel yang merupakan sayuran yang baik bagi mata karena mengandung vitamin A, dan masih banyak lagi sayur yang banyak mengandung vitamin dan baik untuk tubuh. PT. Kebun Sayur Segar menanam sendiri sayur-mayur yang di jual sehingga kondisi sayur sampainya diprodusen masih segar, namun sering kali ada beberapa sayur yang layu dan busuk dikarenakan stok terlalu banyak. Hal ini mengakibatkan kerugian untuk PT. Kebun Sayur Segar. Oleh karena itu dalam penelitian ini peneliti ingin melakukan prediksi terhadap penjualan sayuran pada PT. Kebun Sayur Segar untuk dapat memprediksi terhadap stok sayur kedepannya untuk dapat mengurangi kerugian oleh karena itu dibutuhkan prediksi dengan menggunakan algoritma regresi linear dan algoritma C4.5 untuk memprediksi. Hasil dari penelitian ini disimpulkan hasil pengujian algoritma regresi linear dan algoritma C4.5 mendapatkan nilai standar.
Co-Authors Abd Rahman Abrar, Muhammad Fauzan Angga, Tyas Dwi Anita Ratnasari Anzalna, Akbarakah Bagus Priambodo Cahyo Pambudi Darmo Desi Ramayanti Desmon, Johanes Devi Fitrianah Dhamanti, Inge Dianing Asri, Sri ferdi Fitri Yakub Hadiguna Setiawan Handrie Noprison Hong Wei Ida Farida Ida Nurhaida Ida Nurhaida Ida Nurhaida Inge Handriani, Inge Iryani, Lemi Jumaryadi, Yuwan Laksono Trisnantoro Mailana, Siska Mariana Purba Mariana Purba Naufal, Muhamad Fahri Nengsiana, Nengsiana Noprisson, Handrie Nur Ani Purba, Mariana Putra, Erwin Dwika Remmy A. M. Zen Salamah, Umniy Sarwati Rahayu Sri Dianing Asri Syamsiar, Syamsiar Tjahjono, Benny Uus Rusmawan, Uus Wachyu Hari Haji Wachyu Hari Haji Wahyu Adianto Wina Firly, Tiara Yogga Herlambang, Helwy