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Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia Siti Saadah; Kaenova Mahendra Auditama; Ananda Affan Fattahila; Fendi Irfan Amorokhman; Annisa Aditsania; Aniq Atiqi Rohmawati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic.

Analysis of Multi-Layer Perceptron and Long Short-Term Memory on Predicting Cocoa Futures Price Abbsumarmanali Firyabi Sakhtiyani; Siti Saadah; Gia Septiana Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4498

Predicting the price of Cocoa Futures is needed by farmers and also the government in determining policies. The uncertainty of price movements can affect farmers’ income and also foreign exchange savings because Indonesia is the largest cocoa-producing country in the world. In this study, we use the cocoa futures dataset to train using Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) to make a prediction of the cocoa futures price. In that way, this study resolves the uncertainties using the MLP method and also the LSTM, where these two methods produce a model using the input of data train and data test to predict the price of cocoa futures contracts and then be compared to see which one is the right one for the cocoa dataset. The dataset used is quoted from the Investing.com page taken from 2003 to 2021. The result of this study is the best model between MLP and LSTM model, where the LSTM can produce the best model using 50-50 Train to test data ratio, 128 batch size, and 64 Neurons on the hidden layer with evaluation metrics value in RMSE is 2.27, MAE is 32.11, and MAPE is 1.29 or 98.71% accuracy. This is because the LSTM model has logic gates in the layers that have an advantage on time series data using memory, where the LSTM model could memorize the output and use the output again as an input to achieve the best output.

Peningkatan Pemahaman Masyarakat Terhadap Nilai Cagar Budaya Berbasis Wisata Tematik Google Maps di Purwakarta Ratri Wulandari; Vika Haristianti; Idhar Resmadi; Djoko Murdowo; Annisa Aditsania; Aida Andrianawati; Rendy Pandita B; Wibisono Tegar GP; Aniq Atiqi R; Siti Saadah
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2023)
Publisher : Universitas Majalengka

Pada era Hindia Belanda, Kabupaten Purwakarta adalah ibukota Karesidenan Karawang, sehingga di Kabupaten Purwakarta terdapat kawasan pusat kota dengan alun-alun dan kelengkapan infrastruktur lainnya. Kelebihan ini belum disadari sebagai potensi oleh pemerintah daerah maupun masyarakat. Padahal, pemahaman terhadap potensi cagar budaya akan mendorong peningkatan Indeks Pembangunan Kebudayaan (IPK) daerah. Dari permasalahan tersebut, kegiatan pengabdian masyarakat ini menawarkan dua solusi yaitu, memberikan ilmu dan metode untuk peningkatan pemahaman masyarakat terhadap potensi cagar budaya daerah dan pengembangan peta wisata tematik berbasis Google Maps. Di dalamnya terdapat kegiatan terkait inventarisasi dan dokumentasi bangunan cagar budaya, termasuk pengetahuan teknologi untuk membangun media literasi cagar budaya. Metode yang digunakan melalui survey lapangan dan wawancara, penyusunan proposal, dan pencarian alternative solusi. Pada pelaksanaan kegiatan, metode yang digunakan adalah transfer pengetahuan melalui kegiatan workshop Adapun luaran kegiatan berupa infografis, peta tematik cagar budaya, dan website. Serta transfer pengetahuan dan metode kepada staf DISPORAPARBUD, dan masyarakat pecinta warisan budaya di bawah binaan DISPORAPARBUD Kabupaten.

Analisis dan Implementasi Jaringan Syaraf Tiruanâ€“Propagasi Balik Dalam Memprediksi Produksi dan Konsumsi Minyak Bumi, Gas Bumi, dan Batu Bara di Indonesia Anggit Nourislam; Jondri Jondri; Siti Saadah
eProceedings of Engineering Vol 1, No 1 (2014): Desember, 2014
Publisher : eProceedings of Engineering

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

ndonesia adalah salah satu negara penghasil sumber energi yang terbentuk dari fosil ataupun non fosil. Sumber energi yang tercipta dari fosil bukanlah sesuatu yang dapat dengan mudah didaur ulang yang mengakibat terjadinya krisis energi di masa mendatang. Kondisi krisis energi ini perlu diprediksi kapan terjadinya karena dapat mempengaruhi kondisi perekonomian Indonesia. Prediksi krisis energi fosil di masa mendatang dapat dilakukandengan melihat pola dari produksi dan konsumsi energi tersebut di Indonesia. Untuk mengetahui pola tersebut, dibutuhkan sebuah model yang cukup stabil terhadap perubahan karena naik turunnya produksi dan konsumsi bisa terjadi dengan cepat.Oleh sebab itu dibutuhkan algoritma jaringan syaraf tiruan yang merupakanmodel pembelajar an yang stabil terhadap perubahan pola dalam kurun waktu yang cepat. Model ini menghasilkan keluaran berupa nilai prediksi dari produksi dan konsumsi di masa mendatang yang nantinya dapat dikelompokkan apakah indeks tersebut tergolong krisis atau tidak. Kata Kunci: krisis energi, jaringan syaraf tiruan.

HUBUNGAN PENGETAHUAN DAN SIKAP DENGAN PARTISIPASI MASYARAKAT DALAM MELAKSANAKAN VAKSINASI COVID-19 DI DESA CINTANAGARA KECAMATAN CIGEDUG KABUPATEN GARUT Siti Saadah; Sri Maywati; Neni Neni
Jurnal Kesehatan Komunitas Indonesia Vol 19, No 1 (2023)
Publisher : Fakultas Ilmu Kesehatan Universitas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jkki.v19i1.6859

The World Health Organization (WHO) in China reported a case of pneumonia of unknown etiology in Wuhan, Hubei Province, China. On January 7, 2020, China identified the case as a new type of coronavirus. Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. The village that had the lowest percentage of COVID-19 vaccination achieved in the working area of the Sukahurip Health Center was Cintanagara Village with the percentage of first dose of COVID-19 vaccination achieved 66.95%, second dose 24.46%, and third dose 0.90%. The purpose of this study was to determine the relationship between knowledge and attitudes with community participation in carrying out the COVID-19 vaccination in Cintanagara Village, Cigedug District, Garut Regency. This research method is a quantitative type with a cross sectional approach involving 363 respondents with a chi square test analysis. The results showed that there was a relationship between knowledge and community participation in carrying out the COVID-19 vaccination (p-value=0.0440.05) and there was no relationship between attitudes and community participation in carrying out the COVID-19 vaccination (p-value=0.3950.05). Suggestions for respondents are that they can understand well information related to COVID-19 vaccination, and actively seek information about COVID-19 vaccination in the mass media or other sources. This information is expected to increase respondents' knowledge about COVID-19 vaccination.

Deteksi Risiko Kredit dalam Peer-to-Peer Lending Menggunakan CatBoost Fadhlurrahman Akbar Nasution; Siti Saadah; Prasti Eko Yunanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5139

P2P lending (Peer-to-peer lending) is widely used by private borrowers, small businesses, and MSMEs because P2P lending allows individuals and businesses to be able to lend money directly from lenders without the stringent requirements and criteria of traditional banks and financial institutions. However, P2P lending has a credit risk problem characterized by a high failure rate for borrowers to repay their loans. Therefore, a system was necessary to detect credit risk to minimize the risk of P2P lending. In this study, a system had been built using the CatBoost method; the dataset used was taken from the Bondora loan dataset. To measure the performance of the CatBoost algorithm, an evaluation matrix was performed using ROC (Receiver Operating Characteristics) curves and AUC (Area Under Curve) was performed. The experiment consists of three scenarios, of which the best result regards Scenario 2 with a data splitting of 90:10. It was caused by the result of AUC value 0.80329 compared to scenario 1 with a data split of 80:20 with the AUC value around 0.789583, and scenario 3 with a data split of 70:30 with the AUC value around 0.781066, respectively.

Peningkatan Pemahaman Masyarakat Terhadap Nilai Cagar Budaya Berbasis Wisata Tematik Google Maps di Purwakarta Ratri Wulandari; Vika Haristianti; Idhar Resmadi; Djoko Murdowo; Annisa Aditsania; Aida Andrianawati; Rendy Pandita B; Wibisono Tegar GP; Aniq Atiqi R; Siti Saadah
BERNAS: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jb.v4i1.4018

Pada era Hindia Belanda, Kabupaten Purwakarta adalah ibukota Karesidenan Karawang, sehingga di Kabupaten Purwakarta terdapat kawasan pusat kota dengan alun-alun dan kelengkapan infrastruktur lainnya. Kelebihan ini belum disadari sebagai potensi oleh pemerintah daerah maupun masyarakat. Padahal, pemahaman terhadap potensi cagar budaya akan mendorong peningkatan Indeks Pembangunan Kebudayaan (IPK) daerah. Dari permasalahan tersebut, kegiatan pengabdian masyarakat ini menawarkan dua solusi yaitu, memberikan ilmu dan metode untuk peningkatan pemahaman masyarakat terhadap potensi cagar budaya daerah dan pengembangan peta wisata tematik berbasis Google Maps. Di dalamnya terdapat kegiatan terkait inventarisasi dan dokumentasi bangunan cagar budaya, termasuk pengetahuan teknologi untuk membangun media literasi cagar budaya. Metode yang digunakan melalui survey lapangan dan wawancara, penyusunan proposal, dan pencarian alternative solusi. Pada pelaksanaan kegiatan, metode yang digunakan adalah transfer pengetahuan melalui kegiatan workshop Adapun luaran kegiatan berupa infografis, peta tematik cagar budaya, dan website. Serta transfer pengetahuan dan metode kepada staf DISPORAPARBUD, dan masyarakat pecinta warisan budaya di bawah binaan DISPORAPARBUD Kabupaten.

Effect of Macroprudential Loan to Value (LTV) Policy using the Support Vector Regression (SVR) Approach Saadah, Siti; Purnomo, Muhammad Ridaffa
Telematika Vol 15, No 2: August (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i2.1882

Macroprudential policy has a goal to confine the risk and price from crises systemic, especially in managing financial stability amidst the COVID-19 pandemic. One of its instruments is Loan to Value (LTV). Ratio of LTV is a ratio between value of credit or cost that can gave from Bank Conventional or Syariah towards collateral value as property. This study aims in getting to know about its influence on citizen to take Kredit Kepemilikan Rumah (KPR). Based on the data from Central Bank Indonesia (BI) would be found about the increasing ratio of LTV yoy. The data set in this study derived from five bank with the data range being from 2014 to 2020. According to the characteristic data that will be used, thus one of the algorithm machine learning that is Support Vector Regression (SVR) was chosen as an approach to observe this trend. By using this method, the result indicated which bank that had been influenced by LTV ratio. Category of the bank who got impact are the bank that had the reverse influence between credit value of home ownership, they are Foreign Bank, Mixed Bank, Bank Persero, Bank Swasta, and Bank Perkreditan Rakyat.

Kmeans-SMOTE Integration for Handling Imbalance Data in Classifying Financial Distress Companies using SVM and Naïve Bayes Maulana, Didit Johar; Siti Saadah; Prasti Eko Yunanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5140

Imbalanced data presents significant challenges in machine learning, leading to biased classification outcomes that favor the majority class. This issue is especially pronounced in the classification of financial distress, where data imbalance is common due to the scarcity of such instances in real-world datasets. This study aims to mitigate data imbalance in financial distress companies using the Kmeans-SMOTE method by combining Kmeans clustering and the synthetic minority oversampling technique (SMOTE). Various classification approaches, including Nave Bayes and support vector machine (SVM), are implemented on a Kaggle financial distress data set to evaluate the effectiveness of Kmeans-SMOTE. Experimental results show that SVM outperforms Nave Bayes with impressive accuracy (99.1%), f1-score (99.1%), area under precision recall (AUPRC) (99.1%), and geometric mean (Gmean) (98.1%). On the basis of these results, Kmeans-SMOTE can balance the data effectively, leading to a quite significant improvement in performance.

ANALISA PENGGUNAAN CONTENT VIDEO DAN LIVE STREAMING TIKTOK DALAM MEMPENGARUHI MINAT BELI GEN Z Siti Saadah; Zakiyya Tunnufus; Maesaroh; Mukti, Muhi
The Asia Pacific Journal Of Management Studies Vol 12 No 1 (2025)
Publisher : Universitas La Tansa Mashiro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55171/apjms.v12i1.1392

The number of people interested in the TikTok application is increasing, and the majority of its users are Generation Z. This can be utilized by business owners to market their products. Thus, business owners must be more creative in increasing Gen Z's purchasing interest so that they are interested in the video and live streaming content used. This study aims to analyze the use of TikTok video and livestreaming content in influencing Gen Z's purchasing interest. The data used in this research is primary data obtained from the results of distributing questionnaires to 50 respondents. This study used a quantitative method with a purposive sampling technique. Data analysis used multiple linear regression analysis assisted by SPSS Software Version 20. In this study, several problems were found related to service quality and price, both of which have the potential to increase customer satisfaction. The results partially video content variable has no effect on purchase intention variable. The live streaming variable partially influences buying interest. Simultaneously having video content and live streaming results together influences buying interest of gen Z.

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