Garuda - Garba Rujukan Digital

p-Index From 2020 - 2025

5.488

P-Index

This Author published in this journals

All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan International Journal of New Media Technology Compiler JOURNAL OF SCIENCE AND APPLIED ENGINEERING IJID (International Journal on Informatics for Development) Informatika : Jurnal Informatika, Manajemen dan Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Cyber Security dan Forensik Digital (CSFD) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Health Science and Technology Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer

Muhammad Habibi

Universitas Jenderal Achmad Yani Yogyakarta

Author-ID : 1734466

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 35 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4

Analysis and visualization of BPJS on twitter using K-means clustering Andika Bayu Saputra; Puji Winar Cahyo; Muhammad Habibi; Adri Priadana
International Journal of Health Science and Technology Vol 3, No 3 (2022): April
Publisher : Universitas 'Aisyiyah Yogyakarta

Social security agency (BPJS) Health exists to provide national social security to meet the basic needs appropriate for all levels of society based on the principle of humanity. Originated from a change in the contribution premium policy, it is demanded by the organizers and health service providers to be able to provide safe, quality, affordable health facilities. But unfortunately, the government's efforts in realizing public welfare, especially in the field of health, are not fully supported by the community because of the ever- changing premium contribution policy and the health services they receive. The latest information developments related to BPJS on social media that can be easily accessed by the public. One of them is by using the Twitter platform as a place to exchange information using hashtags. The hashtag data can be processed and obtained information to be used as a tool for decision making. This study aims to analyze and visualize BPJS data on the Twitter platform using the K-Means clustering method. K-Means clustering method is a method of clustering data mining using the descriptive model concept. K- means method can use to explain the algorithm in determining an object into a specific cluster based on the nearest average.

Analysis of Deep Learning Approach Based on Convolution Neural Network (CNN) for Classification of Web Page Title and Description Text Aris Wahyu Murdiyanto; Muhammad Habibi
Compiler Vol 11, No 2 (2022): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

The volume of digital documents available online is growing exponentially due to the increasing use of the internet. Categorization of information obtained online is needed to make it easier for recipients of information to determine and filter which information is needed. Classification of web pages can be based on titles and descriptions, which are text data that can be done by utilizing deep learning technology for text classification. This study aimed to conduct data training and analysis experiments to determine the accuracy of the proposed deep learning architecture in classifying web page titles and descriptions. In this research, we proposed a Convolution Neural Network (CNN) architecture that generates few parameters. The training and evaluation set was conducted on the web page dataset provided by DMOZ. As a result, the proposed CNN architecture with the number of N (Dropout + 1D Convolution + ReLU activation) equal to 1 achieves the best validation accuracy. It achieves 79.51% with only generates 825,061 parameters. The proposed CNN architecture achieved outperformed performance on the accuracy of the five other technologies in the state-of-the-art.

Autoregressive Integrated Moving Average (ARIMA) Models For Forecasting Sales Of Jeans Products Jenny Meilila Azani Cahya Permata; Muhammad Habibi
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.7868

Purpose: To be able to compete with other companies, it is necessary to estimate and forecast jeans products that will be ordered according to consumer demand every month, so that there is no excess inventory and product shortage. If there is a shortage of goods, the consumer will be disappointed with the seller, and vice versa if the goods are overstocked, the quality will continue to decline to the detriment of the seller and the buyer, resulting in a shortage of materials.Methodology: To overcome the problem of selling jeans products, the ARIMA method is suitable to overcome the problem of forecasting the stock of jeans sales. ARIMA model is a model that completely ignores the independent variables in making forecasts. ARIMA uses past and present values of the dependent variable to produce accurate short-term forecasting.Results: The built forecasting has a MAPE accuracy rate of 17.05% so it can be said that predicting has good results according to the criteria. Forecasting results in the following year show that sales tend to increase from the previous year.Originality: This research was conducted using sales data of jeans products at company XYZ and using the ARIMA method which previous researchers have never done.

Customer Experience Analysis Skincare Products Through Social Media Data Using Topic Modeling and Sentiment Analysis Muhammad Habibi; Kartikadyota Kusumaningtyas
JOURNAL OF SCIENCE AND APPLIED ENGINEERING Vol 6, No 1 (2023): JSAE
Publisher : Widyagama University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jsae.v6i1.4169

Currently, skin care products (skincare) are popular among the public. Both men and women are interested in buying skin care products. Moreover, there are many brands of skin care products that are divided into several types of facial and body care, such as moisturizers, toners, cleansers, and masks. Therefore, many consumers take the time to find information, for example, in terms of price, quality, and brand for decision-making. A lot of useful information is in the form of Twitter messages known as tweets which are sent from people who use skin care products because Twitter is one of the online social media where users can share their opinions and experiences. However, consumers still have to spend a lot of time searching, reading, and understanding the comprehensive collection of tweets before buying skin care products.The purpose of this study is to analyze customer experience, analyzing automated tweets about skin care products. Tweets about skin care products will be subjected to a topic modeling process to find out what topics are being discussed. In addition, the topics that have been obtained will be subject to sentiment analysis in the form of positive and negative messages for skin care products. Consumers who are app users don’t waste time reading and analyzing large amounts of data manually and they can decide to buy skin care products more easily.The results of this study obtained 14 topics of discussion related to skincare. Meanwhile, the sentiment analysis results of 14 topics resulted in more positive sentiment class tweets overall. It related the category topic that has the number of tweets to the importance of skincare. In addition, categories related to ingredients for skincare products from nature, namely fruits and spices, are the topics that have the second highest number of tweets. The results of the analysis of tweets related to user experience on Twitter, it was found that users prefer skincare products that use ingredients from nature.

KLASIFIKASI SITUASI BENCANA ALAM BANJIR MENGGUNAKAN SUPPORT VECTOR MACHINE BERDASARKAN DATA TWITTER Ramses Caniago; Muhammad Habibi
I N F O R M A T I K A Vol 15, No 1 (2023): MEI, 2023
Publisher : STMIK DUMAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36723/juri.v15i1.333

Bencana alam banjir sering terjadi di Indonesia karena banyaknya pulau dan iklim tropisnya. Setiap harinya tweet masyarakat mengenai banjir di Twitter bertambah banyak dan dapat mencapai ribuan hanya dalam beberapa hari saja. Tujuan dari penelitian ini adalah membuat suatu model klasifikasi untuk melakukan klasifikasi situasi bencana alam banjir berdasarkan data Twitter. Penelitian ini menggunakan algoritma Support Vector Machine (SVM) yang merupakan salah satu metode dari data mining untuk melakukan klasifikasi. Tahapan yang dilakukan dalam penelitian ini, diantaranya, pengumpulan data, pelabelan manual membagi data ke dalam tiga jenis situasi yaitu ringan, sedang, dan berat. Pembobotan menggunakan TF-IDF dan dilakukan proses training untuk menghasilkan sebuah model. Hasil pengujian model dengan confusion matrix dan K-fold cross validation menghasilkan nilai akurasi sebesar 90,61% dan nilai F1-score sebesar 90,64%. Hasil klasifikasi tweet terkait data banjir menunjukkan bahwa sebanyak 67,40% tweet masuk ke dalam kategori ringan, 19,79% tweet kategori sedang, dan 12,81% tweet kategori berat.Â Kata kunci : Banjir, Data Mining, Klasifikasi, SVM , TwitterDue to its numerous islands and warm environment, Indonesia frequently experiences flood natural disasters. Tweets about floods on Twitter grow every day and can reach thousands in a matter of days. This study's objective is to develop a classification model for categorizing flood-related natural catastrophe events using data from Twitter. The Support Vector Machine (SVM) algorithm, a data mining technique for categorizing, is used in this work. Data gathering, manual labeling, and segmenting the data into three categoriesâ€”mild, moderate, and severeâ€”were all steps taken in this study. A training process is carried out to create a model before weighting is applied. The accuracy value and F1-score obtained by evaluating the model using the confusion matrix and K-fold cross-validationÂ are 90.61% and 90.64%, respectively. 67.40% of the tweets classified as having flood-related data fell into the light category, followed by 19.79% of tweets classified as medium tweets, and 12.81% of tweets classified as heavy tweets.Keywords: Classification, Data Mining, Flood, SVM, Twitter

Implementation of Cosine Similarity in an Automatic Classifier for Comments Muhammad Habibi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 3 No. 2 (2018): September 2018
Publisher : UIN Sunan Kalijaga Yogyakarta

Classification of text with a large amount is needed to extract the information contained in it. Student comments containing suggestions and criticisms about the lecturer and the lecture process on the learning evaluation system are not well classified, resulting in a difficult assessment process. So from that, we need a classification model that can classify comments automatically into classification categories. The method used is the Cosine Similarity method, which is a method for calculating similarities between two objects expressed in two vectors. The data used in this study were 1,630 comment data with several different categories. The test in this study uses k-fold cross-validation with k = 10. The results showed that the percentage accuracy of the classification model was 80.87%.

Journal Classification Based on Abstract Using Cosine Similarity and Support Vector Machine Muhammad Habibi; Puji Winar Cahyo
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 4 No. 3 (2020): Januari 2020
Publisher : UIN Sunan Kalijaga Yogyakarta

One of the problems related to journal publishing is the process of categorizing entry into journals according to the field of science. A large number of journal documents included in a journal editorial makes it difficult to categorize so that the process of plotting to reviewers requires a long process. The review process in a journal must be done planning according to the expertise of the reviewer, to produce a quality journal. This study aims to create a classification model that can classify journals automatically using the Cosine Similarity algorithm and Support Vector Machine in the classification process and using the TF-IDF weighting method. The object of this research is abstract in scientific journals. The journals will be classified according to the reviewer's field of expertise. Based on the experimental results, the Support Vector Machine method produces better performance accuracy than the Cosine Similarity method. The results of the calculation of the value of precision, recall, and f-score are known that the Support Vector Machine method produces better amounts, in line with the accuracy value.

Analisis Sentimen Berdasarkan Topik Terkait Wabah Covid-19 di Twitter Menggunakan Latent Dirichlet Allocation (LDA) dan Naive Bayes Classifier (NBC) Pangky Putra Aziztiya; Muhammad Habibi; Netania Indi Kusumaningtyas
Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v15i2.1098

In 2020 WHO determined that the Corona Virus (COVID-19) was a pandemic. The global spread of the COVID-19 outbreak has made Twitter one of the most widely used tools to publish and find information. This study aims to form a modeling of topics related to the COVID-19 outbreak on the Twitter social media platform and analyze positive and negative sentiments in each topic that has been obtained by combining the two Latent Dirichlet Allocation (LDA) and Naïve Bayes Classification (NBC) methods. Beginning with modeling the topic using the Latent Dirichlet Allocation so that the topics that have been obtained will be searched for the sentiment value of each topic using the Naïve Bayes Classifier method. This study succeeded in combining the two methods with a fairly good accuracy of 89%. In topic modeling, 5 ideal topics were obtained and it can be seen that the most discussed topic is booster vaccination. The results of the classification using NBC show that the topic of booster vaccination has more negative sentiments than positive sentiments.

ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER DENGAN STUDI KASUS KARTU PRAKERJA Iqbal Hadi Subekti; Muhammad Habibi; Aris Wahyudi Murdiyanto; Alfun Roehatul Jannah
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1101

Kartu Prakerja is one of the government's flagship programs in providing training to the workforce. In its implementation there is a lot of information scattered, especially on social media Twitter both in the pros and cons of Kartu Prakerja program. Based on information in the form of tweets that have not been analyzed in depth, it is necessary to analyze sentiment on the Kartu Prakerja in order to obtain appropriate information based on the opinions of netizen s on Twitter. This study discusses sentiment analysis of tweet data with the keyword “Kartu Prakerja” which uses data as many as 6658 tweet data taken in the period May 27 - August 5, 2021. This research uses the Naive Bayes Classification method which has several stages, namely data retrieval, data preprocessing, manual labeling, data training and testing. The solution offered in this study is to create an analysis model that can be used to perform sentiment analysis about Kartu Prakerja on Twitter. Based on the results of this study obtained that the calculation of accuracy obtained a value of 86% for training data and 87% for data testing. This study concluded that the Kartu Prakerja has a positive sentiment by Twitter netizens based on the results of Classification that discusses many positive sentiments such as the benefits, effectiveness and addition of the Kartu Prakerja budget.

Analisis Sentimen dan Klasifikasi Terhadap Tren “UU ITE” di Media Sosial Twitter Risky Setyadi Putra; Muhammad Habibi; Aris Wahyu Murdiyanto; Nafisa Alfi Sa'diya
Jurnal Teknomatika Vol 14 No 2 (2021): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v14i2.1116

Undang-undang Informasi and Transaksi Elektronik abbreviated UU ITE is a law that regulates information and electronic transactions, or information technology in general. This study discusses sentiment analysis from tweet data with keywords “UU ITE” Who uses as much data 7.407 tweet data and re-tweets taken in the period July 21 - August 16, 2021, with details 914 data that has been manually labeled and 6,493 data labeled using Predicting that the data was taken using authentication on the Twitter API and executed using the Python library. This research uses methods Support Vector Machine because it has several advantages including It is capable of handling the classification of two classes, and its implementation is relatively easy. For the support vector machine stage, namely data retrieval, preprocessing data, manual labeling, data training and testing. As for the solution offered in this research is to create an analysis model that can be used to conduct sentiment analysis about the ITE Law on social media Twitter. This research was successful using the Support Vector Machine method to create a sentiment analysis model with an accuracy of 81.20% for data Training and 87% for data testing. This study provides results that UU ITE have negative sentiments by netizens on social media Twitter based on on the results of classification and calculations on the model and tweet data and the number of Negative discussions.

Co-Authors A. Sinay, Stani ABIYOGA BAGUS MUSTRIYANTO Adri Priadana Alfirna Rizqi Lahitani Alfun Roehatul Jannah Alfun Roehatul Jannah Aris Wahyu Murdiyanto dayat subekti Dedy Hariyadi Elvika Fit Ari Shanti, Elvika Fit Ari Endah Puji Astuti, Endah Puji Fajar Syahruddin Fresia Nandela, Emilia I Gede Iwan Sudipa Ika Arfiani Iqbal Hadi Subekti Irmma Dwijayanti Jenny Meilila Azani Cahya Permata Jullev Atmadji, Ery Setiyawan Kharisma Kristi, Ria Kusumaningtyas, Kartikadyota Lahitani, Alfirna Rizqi M. Wahyu Indriyanto Muhammad Erwan Syah Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Nasron, Ulil Amri Netania Indi Kusumaningtyas Nur Azizah Firdausa Oktaviana, Dela Pangky Putra Aziztiya Puji Winar Cahyo Purnamaningsih, Nurâ€™Aini Purwatiningsih, Agustina Ramses Caniago Rifanny Br Girsang, Ribka Rifqi Ma’arif, Muhammad Risky Setyadi Putra Rizky Fauzi Akbar Rizky Fauzi Akbar Rizqi Lahitani, Alfirna Rukmi, Dwi Kartika Saputra, Andika Bayu Sujono Riyadi Sumiyarini, Retno Suwarno Tegar Agustian Tri Sunarsih Ulil Amri Nasron Wahyuningsiam, Astr

Title Search

Found 35 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 35 Documents
Search