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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Indonesia Symposium on Computing IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Kebidanan JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Jurnal Fisika dan Terapannya JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) International Journal of Clinical Inventions and Medical Sciences (IJCIMS) INFOKUM Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Journal of Innovation Research and Knowledge Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Electrician : Jurnal Rekayasa dan Teknologi Elektro Indonesian Counseling and Psychology Jurnal INFOTEL IJoICT (International Journal on Information and Communication Technology)
Sri Suryani Prasetyowati
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Hate Speech Classification in Tiktok Reviews using TF-IDF Feature Extraction, Differential Evolution Optimization, and Word2Vec Feature Expansion in a Classification System using Recurrent Neural Network (RNN) Fatha, Rizkialdy; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

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

Abstract

In the ever-evolving digital era, social media, especially platforms like TikTok, has become a primary channel for users to share opinions, experiences, and expressions. However, the increasing prevalence of hate speech in reviews on the Google Play Store for the TikTok app indicates the need for a sophisticated approach to identify and classify harmful content. This research is aimed to optimize the classification of hate speech in Google Play reviews of the TikTok app by integrating Term Frequency-Inverse Document Frequency (TF-IDF), Differential Evolution, and Word2Vec within a Recurrent Neural Network (RNN) model. The TF-IDF technique will be used to extract relevant features from a review, while Differential Evolution will be applied to efficiently optimize the model parameters. The use of Word2Vec will enhance the representation of words in the context of app reviews, whereas the RNN model will enable the recognition of temporal patterns in hate speech. The results of this research are expected to contribute significantly to the improvement of hate speech classification on digital platforms focused on app reviews.
Air Pollution Classification Prediction Model with Deep Neural Network based on Time-Based Feature Expansion and Temporal Spatial Analysis Muldani, Muhamad Dika; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5675

Abstract

− Air pollution is one of the most significant global challenges, with serious impacts on the health of living beings. In Indonesia, particularly in major cities such as Jakarta and Surabaya, the increase in the Air Quality Index (AQI) over the past few years indicates worsening air quality conditions. This decline in air quality is caused by increased industrial activities, motor vehicle emissions, and deforestation. Rising AQI levels pose severe health risks, including respiratory and cardiovascular diseases, and present major challenges for urban planning, public health management and environmental policy. Addressing this issue requires concerted efforts to implement sustainable practices, reduce emissions, and improve air quality management. The increasing air pollution level indicate the need for a more effective approach to identify and classify air quality index results with relevant success rates without using relatively expensive air quality index detection tools. This research aims to classify the air quality index using a Deep Neural Network model based on time-based feature expansion and spatial-temporal analysis. The Deep Neural Network model is used to extract complex patterns and hidden features in the data and help generate more accurate air pollution classifications. Meanwhile, time-based feature expansion is useful for extending the time representation in the data. The results of this research are expected to make a significant contribution in improving the global understanding of air pollution. By providing a cost-effective and efficient method for air quality monitoring, this study can lead to better pollution control measures. Furthermore, the insight gained from this research can help policymakers develop strategies to mitigate the adverse effects of air pollution on public health and the environment.
Application of Support Vector Machine and Kriging Interpolation for Rainfall Prediction in Java Island Purwanto, Brian Dimas; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

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Abstract

Rainfall is one of the crucial meteorological elements that can significantly impact human life. Accurate rainfall prediction is essential for effective natural resource planning and management across various regions, especially in Java Island, which is one of the most densely populated areas in Indonesia. This study aims to develop a rainfall distribution prediction model for Java Island using Support Vector Machine (SVM). The scenario developed involves time-based feature expansion implemented in SVM. This method is combined with Kriging interpolation to obtain the rainfall distribution classification on Java Island. The results show that the model's performance, exceeding 90%, is effective in predicting future rainfall distribution classifications on Java Island. The contribution of this research lies in providing insights into feature expansion techniques in machine learning to refine predictive models applied in meteorology and environmental management.
Classification of Public Sentiment on Fuel Price Increases Using CNN Maharani, Anak Agung Istri Arinta; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12609

Abstract

The government's policy of changing fuel prices is carried out every year. The public gave responses to this policy categorized as positive, negative, or neutral sentiments. The community's response was conveyed through tweets on the Twitter application. Based on the public's response to the policy, sentiment classification can be done using data mining classification techniques. Some research has been carried out on classification techniques using deep learning and machine learning methods. In general, deep learning methods get better results, and this research will be approached using the CNN method. The system stages start from crawling data, labeling, and preprocessing, which consists of cleaning, case folding, tokenization, normalization, removing stopwords and stemming, classification using CNN, and evaluation using 10-Cross Validation. The dataset used is 17.270. The results show that the developed classification system is relatively high, with the highest accuracy of 87%, 93% recall, 93% precision, and 90% F1 score. An in-depth analysis of the classification results and an understanding of sentiment toward rising fuel prices can also provide valuable insights.
Best Word2vec Architecture in Sentiment Classification of Fuel Price Increase Using CNN-BiLSTM Aqilla, Livia Naura; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12639

Abstract

The policy of increasing fuel prices has been carried out frequently in recent years, due to the instability of international price fluctuations. This study uses sentiment analysis to examine fuel price increases and their impact on public sentiment. Sentiment analysis is a data processing method to obtain information about an issue by recognizing and extracting emotions or opinions from existing texts. The method used is Word2vec Continuous Bag of Words (CBOW) and Skip-gram. Testing uses different vector dimensions in each architecture and uses a CNN-BiLSTM deep learning hybrid which performs better on sizable datasets for sentiment categorization. The results showed that the CBOW model with 300 vector dimensions produced the best performance with 87% accuracy, 87% recall, 89% precision and 88% F1 score.
The Performance of the Equal-Width and Equal-Frequency Discretization Methods on Data Features in Classification Process Putri, Pramaishella Ardiani Regita; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12730

Abstract

The classification process often needs help with suboptimal accuracy values, which can be attributed to various factors, including the dataset's wide range of attribute values. Discretization methods offer a solution to address these issues. This study aims to compare the effectiveness of Equal-Width and Equal-Frequency discretization methods in enhancing accuracy during the classification process using datasets with varying sizes. The research employs Naïve Bayes, Decision Tree, and Support Vector Machine as classification models, with three datasets utilized: Bandung City Traffic data (3804 records), Bandung City COVID-19 cases data (2718 records), and Bandung City Dengue Fever Disease Index data (150 records). Three experimental scenarios are executed to assess the impact of the two discretization methods on accuracy. The first scenario involves no discretization, the second employs Equal-Width, and the third applies Equal-Frequency discretization. Experimental results indicate significant accuracy improvements post-discretization. The Naïve Bayes model achieved 94% accuracy for the Traffic dataset, while the Decision Tree achieved 71% accuracy for the COVID-19 dataset and an impressive 98% for the Dengue Fever Disease dataset. These outcomes demonstrate that applying Equal-Width and Equal-Frequency discretization methods addresses the challenge of wide attribute value ranges in the classification process.
Performance of CART Time-Based Feature Expansion in Dengue Classification Index Rate Suhendar, Annisya Hayati; Rohmawati, Aniq Atiqi; Prasetyowati, Sri Suryani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13023

Abstract

This study proposes utilizing the machine learning technique CART to classify the spread of dengue hemorrhagic fever (DHF). To expand the features used, the CART classification model was developed based on data collected over the previous 2 to 4 years. The data sources included the Bandung City Health Office for the cases of DHF, the Bandung Meteorology, Climatology and Geophysics Agency for the climate data, the Bandung City Central Statistics Agency for population and educational history data. The top-performing CART classification model over the past 2, 3, and 4 years achieved accuracies of 93%, 93%, and 90%, respectively. The models that exhibited the highest accuracy values and optimal number of feature extensions were chosen as the best ones. CART is among several machine learning techniques that can effectively measure the most impactful features during the classification process. The meteorological parameters were found to be irrelevant in the classification process. This study reveals that the population size, male population proportion, and educational attainment levels are the most impactful features in the classification of DHF spread in Bandung City. The research provides valuable insights into the classification of DHF spread in Bandung City through feature expansion.
Handling Imbalance Dataset on Hoax Indonesian Political News Classification using IndoBERT and Random Sampling Fathin, Muhammad Ammar; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7099

Abstract

The rapid adoption of the internet in Indonesia, with over 200 million active users as of January 2022, has dramatically transformed information dissemination, particularly through social media and online platforms. These platforms, while democratizing information sharing, have also become hotbeds for the spread of misinformation and hoaxes, significantly impacting the political landscape, as seen in the Jakarta gubernatorial election from late 2016 to April 2017. Research by the Indonesian Telematics Society (MASTEL) revealed a high prevalence of hoax content, predominantly socio-political, underscoring the critical need to address this misinformation and hoaxes challenge. This research delves into the challenge of detecting hoaxes in Indonesian political news, particularly focusing on the classification of news as factual or hoax in the presence of class imbalances within datasets. The dataset exhibits a significant class imbalance with 6,947 articles identified as hoaxes and 20,945 as non-hoaxes, Utilizing the IndoBERT model, a specialized variant of the BERT framework pre-trained on the Indonesian language, the study aims to assess its effectiveness in discerning between factual and hoax news. This involves fine-tuning IndoBERT for specific text classification tasks and exploring the impact of various resampling techniques, such as Random Over Sampling and Random Under Sampling, to address class imbalances since the dataset, significantly imbalanced with 6,947 articles labeled as hoaxes and 20,945 as non-hoaxes, necessitated these approaches. The study's findings demonstrate the IndoBERT model's consistent accuracy across different resampling methods like Random Over Sampling (ROS) and Random Under Sampling (RUS), highlighting its effectiveness in handling imbalanced datasets produce the accuracy of hoax detection with the 98.2% accuracy, 97.5% Recall, 97.8% F1-score, and 97.2% Precision. This is particularly relevant for tasks like misinformation detection, where data imbalance is common. The success of IndoBERT, a language-specific BERT model, in text classification for the Indonesian language contributes to the understanding of BERT-based models in diverse linguistic contexts.
Analisis Setting Over Current Relay Satu Fasa Ke Tanah Sistem 20 Kv Pada Recloser B 21 Penyulang Salju Gardu Induk Teluk Betung Berbasis Etap Son, Jeckson; Yenni Afrida; Sri Suryani
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 18 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v18n2.2683

Abstract

Gangguan jaringan sistem 20 kV selalu berpotensi terjadi dan penyebab yang paling sering menyebabkan gangguan adalah gangguan hubung singkat 3 fasa, 2 fasa, 2 fasa ke tanah dan 1 fasa ketanah. Dalam mengamankan jaringan dari gangguan-gangguan tersebut maka dibutuhkan peralatan proteksi pada jaringan sistem 20 kV yang sensitif, selektif, cepat, andal dan sederhana. Relai yang paling sering bekerja pada recloser B 21 adalah Over Current Relay (OCR) satu fasa ke tanah. Perhitungan dan Analisa waktu kerja OCR satu fasa ke tanah pada recloser B 21 penyulang Salju dilakukan pada outgoing dan incoming serta waktu kerja recloser. Setelah data-data tersebut didapatkan, dilanjutkan dengan menganalisa koordinasi setting OCR satu fasa ketanah antara recloser B 21 dengan outgoing penyulang Salju mengguankan software Etap. Teknik pengambilan data dilakukan dengan metode studi pustaka, literatur, wawancara, dan pemodelan sistem. Hasil perhitungan arus gangguan hubung singkat satu fasa tanah pada 1% jaringan atau 1.88 km sebesar 288.396 A, sedangkan arus gangguan hubung singkat satu fasa ke tanah yang mempunyai nilai minimum terletak pada 100% jaringan atau 18.8 km sebesar 202.65 A. Hasil perhitungan pada OCR satu fasa ke tanah incoming tms = 0.26, t = 0.7 detik, outgoing tms = 0.1, t = 0.3 detik dan recloser tms = 0.062, t = 0.2 detik dan setting eksisting dari PLN adalah incoming tms = 0.37, t = 1 detik, outgoing tms = 0.047, t = 0.15 detik dan recloser tms = 0.03, t = 0.09 detik. Dari hasil pengamatan data yang diperoleh, direkomendasikan untuk mempertimbangkan resetting pada recloser B 21, outgoing penyulang Salju dan incoming agar menaikkan setting relai sehingga ketika merasakan arus pick up relai tidak langsung trip.
WORD EMBEDDING OPTIMIZATION IN SENTIMENT ANALYSIS OF REVIEWS ON MYTELKOMSEL APP USING LONG SHORT-TERM MEMORY AND SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUE Haziq, Muhammad Raffif; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2498

Abstract

Telkomsel is one of the internet service provider companies that has a mobile-based application called MyTelkomsel which functions to facilitate users in conducting online services independently. Users of the application certainly have their own responses about the application, so that users can provide responses to the application. Therefore, sentiment analysis can be one of the solutions to find out public sentiment towards the application. In this research, the author builds a system for sentiment analysis using word embedding Word2vec, GloVe, FastText to get word representation in vector form with classification using Long Short-Term Memory (LSTM) combined with Synthetic Minority Over-sampling Technique (SMOTE) which can handle data imbalance. The data used comes from user reviews of the MyTelkomsel application found on the Google Play Store. This study compares the performance of several word embedding in LSTM and LSTM-SMOTE classifiers. The results showed the results show that the performance of three-word embedding on the LSTM model is superior compared to the LSTM-SMOTE model. Overall, it was found that the combination of FastText and LSTM gave the best performance compared to the other five combinations with an accuracy value of 89.11%.
Co-Authors Abduh Salam Adhe Akram Azhari Adhitya Aldira Hardy Aditya Andar Rahim Aditya Faudzan Aditya Firman Ihsan Aditya Gumilar Afrida, Yenni Akmal Muhamad Faza Aniq A. Rohmawati Aniq Atiqi Rohmawati Aqilla, Livia Naura arief rahman Arnasli Yahya Asdar, Meisya Putri Indrayani Asramanggala, Muhammad Sulthon Aufa, Rizki Nabil Azmi Aulia Rahman Bakara, Stella Maris Chamadani Faisal Amri Christina Natalia Claudia Mei Serin Sitio Damar, Muhammad Dede Tarwidi Derwin Prabangkara Diyas Puspandari Ekaputra, Muhammad Novario Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Erna Sri Sugesti Erwin Budi Setiawan Fairuz, Mitha Putrianty Fatha, Rizkialdy Fathin, Muhammad Ammar Fatri Nurul Inayah Gede Astawa Pradika Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hari Yudha Fanani Hawa, Iqlima Putri Haziq, Muhammad Raffif Hilda Fahlena Hilman Rama Pratama I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Ichwanul Muslim Karo Karo Indra Kusuma Yoga Indri Octavellia Wulanissa Irfani Adri Maulana Ita Susanti Jauzy, Muhammad Abdurrahman Al Jeckson, Jeckson Juniardi Nur Fadila Karno Karno Kartika Putri Septiyani Kasim, Jamila Lesmana, Aditya Lie Othman Linda Salma Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mardha Al Nazhfi Ali Marissa Aflah Syahran Marissa Aflah Syahran Mitha Putrianty Fairuz Muh. Ilham Muh. Kiki Adi Panggayuh Muhammad Alauddin Angka Kurniawan Muhammad Damar Muhammad Gentur WitjaksonoKurniawan Muhammad Gentur WitjaksonoKurniawan Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Novario Ekaputra Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Nazhrin Nazarudin Achmad Nenny Lisbeth Minarno Ni Made Dwipadini Puspitarini Nur Fadila, Juniardi Nuraena Ramdani Nurul Aliyah Nurul Fajar Riani Oktovina Suka Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Purwanto, Brian Dimas Putra, Ihsanudin Pradana Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafika Salis Rahim, Husnul Fahma Rahmanda, Rayhan Fadhil Ratna Rian Febrian Umbara Ridha Novia Ridho Isral Essa Rifaldy, Fadil Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Shofura Shofura Sinaga, Astria M P Siti Rahmah Madusari Siti Uswah Hasanah Sri Harini Sri Harini Sri Harini Suhendar, Annisya Hayati Tuti Budiawati Willie Japaries Winico Fazry Wira Abner Sigalingging Yulian Sibaroni Yuliant Sibaroni Zaidan, Muhammad Naufal