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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics JOIN (Jurnal Online Informatika) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Justek : Jurnal Sains Dan Teknologi Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Elektronik Indonesian Journal of Electrical Engineering and Computer Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Jurnal Data Mining dan Sistem Informasi Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Informatika dan Ilmu Komputer Jurnal Media Borneo Jurnal Informatika: Jurnal Pengembangan IT
Nirwana Hendrastuty
Universitas Teknokrat Indonesia
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Analisis Sentimen Komentar Media Sosial Twitter Terhadap Tes CPNS dengan Algoritma Naive Bayes Nurhidayat, Rifki; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6148

Abstract

The Calon Pegawai Negeri Sipil (CPNS) is one of the most sought-after careers in Indonesia, with the number of applicants increasing every year. The CPNS selection process attracts public attention and triggers various opinions, both positive and negative, which are widely conveyed through social media such as Twitter. This research aims to analyze public sentiment towards the CPNS selection process using the Naive Bayes algorithm. The data used in this study consists of 5,599 comments on Twitter, with a composition of 5,269 negative sentiment data and 323 positive sentiment data. Tests were conducted using several data sharing ratios, namely 80:20, 70:30, 90:10, and 50:50. The results show that the 70:30 ratio provides the best accuracy, which is 95%. However, data imbalance causes the model to focus more on negative sentiment. To address this issue, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, which successfully improved the model's performance in classifying positive data, with precision and recall reaching 85-98%. After the application of SMOTE, the overall accuracy decreased slightly to 91% at 80:20, 70:30, and 90:10 ratios, but the model became more effective in detecting both sentiments. The results of this study provide insight into the public's views on CPNS selection and can be used by the government to improve the selection process in the future. With this approach, it is expected that government agencies can better understand public perceptions and optimize a more transparent and fair recruitment system.
Perbandingan Algoritma Naïve Bayes dan LSTM untuk Analisis Sentimen Terhadap Opini Masyarakat Tentang Sandwich Generation Ramadhan, Naufal Rizqi; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6385

Abstract

Sandwich Generation is a term for a group of people who have elderly parents and children, so they have to take care of both generations. Opinions about this phenomenon have elicited various responses on social media twitter, which requires in-depth analysis. This study identifies the problems of the lack of research comparing the performance of Naïve Bayes and LSTM algorithms in analyzing public opinion sentiment about the sandwich generation, the complexity of social media data analysis with the characteristics of informal language, abbreviations, and symbols that are difficult to analyze manually, the need to explore the algorithm's ability to classify sentiment, and determine the most accurate method to analyze public opinion sentiment. Sentiment analysis is used to evaluate opinions, feedback, and emotions by classifying texts into positive, negative, or neutral categories. The results obtained from this study are that the LSTM method has better performance when compared to Naive Bayes. The LSTM method produced an accuracy, precision and recall value of 91.85%. while the Naive Bayes method has an accuracy value of 83%, precision of 90% and recall of 82%.
COMPARISON OF NAÏVE BAYES CLASSIFIER, SUPPORT VECTOR MACHINE, RANDOM FOREST ALGORITHMS FOR PUBLIC SENTIMENT ANALYSIS OF KIP-K PROGRAM ON TWITTER Ali, Humaidi; Hendrastuty, Nirwana
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.2024.5.6.4030

Abstract

The Kartu Indonesia Pintar Kuliah (KIP-K) program has become a hot topic of conversation on social media Twitter, with various public sentiments regarding its implementation. The program is regulated through Minister of Education and Culture Regulation (Permendikbud) No. 10/2020, which notes an increase in the number of recipients from 552,706 in 2020 to 985,577 in 2024. However, controversy has arisen due to the alleged misuse of KIP-K funds by some influencers to support lavish lifestyles. This study aims to compare the performance of Naive Bayes, Support Vector Machine, and Random Forest algorithms in classifying public sentiment towards the KIP-K program. The research dataset was obtained from Twitter with a total of 6,842 tweets collected using crawling techniques in the time span of 2023 to 2024. The dataset was then processed through the preprocessing stage to produce clean data. The three algorithms were tested to evaluate the accuracy of each model in predicting public sentiment. The test results show that the Random Forest algorithm has the best performance with 100% accuracy, followed by Support Vector Machine with 99% accuracy, and Naive Bayes with 91% accuracy after optimization (SMOTE). Based on these findings, Random Forest proved to be the most effective algorithm in classifying sentiments related to the KIP-K program. It is hoped that the results of this research can help the management of the KIP-K program to be more targeted by providing a better understanding of public perception.
G2M weighting: a new approach based on multi-objective assessment data (case study of MOORA method in determining supplier performance evaluation) Hendrastuty, Nirwana; Setiawansyah, Setiawansyah; An’ars, M. Ghufroni; Rahmadianti, Fitrah Amalia; Saputra, Very Hendra; Rahman, Miftahur
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp403-416

Abstract

Criteria weighting methods in decision support system (DSS) face various challenges and limitations that can affect their accuracy and reliability. One of the main challenges is subjectivity, this subjective assessment can reduce the objectivity and consistency of results. The main objective of the new weighting method grey geometric mean (G2M) weighting is to provide more objective and robust criteria weights under conditions of uncertainty and incomplete data. The new G2M weighting approach has a significant potential impact on the DSS field, it has the potential to generate more effective and efficient decisions, which can improve organizational performance, reduce risk and optimize outcomes. Pearson correlation test results of two sets of rankings generated by DSS methods namely grey relational analysis (GRA), simple additive weighting (SAW), multi-attributive ideal-real comparative analysis (MAIRCA), weighted product (WP), combined compromise solution (COCOSO), vlsekriterijumska optimizacija i kompromisno resenje (VIKOR), and a new additive ratio assessment (ARAS) that there is a strong positive correlation between the two methods using G2M weighting criteria. The high correlation value indicates that the rankings of the methods used tend to move together, giving confidence in the consistency and validity of the resulting ranking results. This gives confidence that both methods can be used simultaneously or interchangeably with consistent results. The use of G2M weighting in the DSS method used can support better decision-making by providing consistent information and validity of ranking results.
Perbandingan Algoritma NBC Dan SVM Untuk Melakukan Analisis Sentimen Terhadap PP NO.82 Tahun 2021 Rani, Arum Mustika; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6496

Abstract

Government Regulation (PP) No. 82/2021, which regulates the payment of pensions and allowances for Constitutional and Supreme Court Justices, has sparked public debate, especially after allegations of significant cuts to the Supreme Court's budget. This issue raises concerns regarding policy transparency, making it important to analyze public sentiment towards this PP. This study uses two sentiment analysis methods, namely Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM), to evaluate public opinion based on data from Twitter. The dataset consists of 2,719 tweets that have gone through preprocessing stages, such as cleansing, stemming, and using SMOTE techniques, with 70% data division for training and 30% for model testing. This study tests the performance of NBC and SVM through four scenarios: (1) without stemming and without SMOTE, (2) without stemming with SMOTE, (3) with stemming without SMOTE, and (4) with stemming and SMOTE. The results show that SVM has a more stable performance than NBC in all scenarios. In the scenario without stemming and without SMOTE, both models recorded 100% accuracy, but NBC failed to detect positive sentiment accurately. When SMOTE was applied without stemming, NBC's accuracy decreased to 97%, while SVM still achieved a perfect accuracy of 100%. In the scenario with stemming without SMOTE, NBC recorded 97% accuracy, while SVM reached 99%. With the application of SMOTE and stemming, NBC accuracy decreased to 95%, while SVM again recorded a perfect accuracy of 100%. This study concludes that SVM is the best method for sentiment analysis of PP No. 82 of 2021, especially in scenarios with stemming and SMOTE, providing important insights into public opinion and confirming the superiority of SVM in sentiment classification related to public policy.
Analisis Sentiment Terhadap Diabetes Menggunakan Algoritma Naïve Bayes, Random Forest, SVM Pada Media Sosial X Apriani, Linda; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6941

Abstract

Diabetes is one of the chronic diseases that has received widespread attention in society, especially on social media X. This is due to the increasing number of sufferers every year. Based on data from the World Health Organization (WHO), in 2021 it is estimated that 537 million people aged 20-79 years are living with diabetes, an increase from the 2019 estimate of 463 million people. In addition, around 1.3 million deaths are caused by diabetes, with 4 percent of them occurring before the age of 70. This condition occurs due to high blood sugar levels that interfere with the body's metabolic functions, making it difficult for the body to process sugar optimally. This study aims to compare the performance of Naïve Bayes, Random Forest, and Support Vector Machine (SVM) algorithms in sentiment analysis related to diabetes. The research data was obtained from the Twitter platform with a total of 8,401 tweets collected using crawling techniques using certain keywords in the time span of 2024 to 2025. The data then went through a pre-processing stage to produce clean data. Tests were conducted to evaluate the accuracy of each model in predicting public sentiment. The test results show that the SVM algorithm provides the best performance with 85% accuracy, followed by Random Forest with 82% accuracy, and Naïve Bayes with 74% accuracy before the application of Synthetic Minority Oversampling Technique (SMOTE). After optimization using SMOTE, the SVM algorithm still showed the best performance with 96% accuracy, followed by Random Forest with 95% accuracy, and Naïve Bayes with 85% accuracy. Based on these results, SVM proved to be the most effective algorithm in classifying sentiment related to diabetes. It is hoped that the results of this research can contribute to efforts to manage diabetes through a better understanding of public perceptions.
Perbandingan Metode Naive Bayes, Random Forest dan SVM Untuk Analisis Sentimen Pada Twitter Tentang Kenaikan Gaji Guru Yuniar, Eny; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6970

Abstract

The increase in teacher salaries has become a highly debated issue within the community, with various opinions being expressed through social media, particularly Twitter. This study aims to analyze public sentiment regarding the teacher salary increase policy using three machine learning algorithms: Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The data used consists of 6010 tweets collected on the topic, which were processed into 5531 data points after cleaning and preprocessing. This study evaluates the performance of each algorithm using accuracy, precision, recall, and F1-score metrics. The results show that SVM achieved the highest accuracy (86%) before applying the SMOTE technique, followed by Random Forest (85%) and Naïve Bayes (84%). After applying SMOTE to address data imbalance, Random Forest showed a significant performance improvement, with accuracy reaching 99%, followed by SVM (98%) and Naïve Bayes (89%). These results indicate that the SMOTE technique can effectively improve model performance, particularly in handling the imbalance between positive, negative, and neutral sentiment data. This study provides new insights into how the public responds to the teacher salary increase policy, while also introducing the use of SMOTE to enhance accuracy in sentiment analysis on social media.
Analisis Sentimen: Perbandingan Performa Algoritma Naive Bayes, Support Vector Machine, Random Forest, dan K-Nearest Neighbor Dalam Pemecatan Shin Tae Yong pada Media X Prasatya, Agung; Hendrastuty, Nirwana
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6987

Abstract

The dismissal of Shin Tae Yong as the coach of the Indonesian national team has triggered a variety of reactions, ranging from disappointment to relief, among Indonesian football fans. Factors such as unsatisfactory match results and internal conflicts within the team, as well as pressure from fans and the media, were the main reasons for this decision. Although this change opens up opportunities for a new coach to improve the performance of the Indonesian national team, it also raises controversy and debate. This study aims to compare the performance of Naïve Bayes, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms in analyzing sentiment related to this dismissal. The research data were obtained from the Twitter platform with a total of 4,345 tweets collected using crawling techniques. The data then underwent pre-processing stages to produce clean data. Testing was conducted to evaluate the accuracy of each model in predicting public sentiment. The test results showed that the SVM algorithm performed best with an accuracy of 78%, followed by Random Forest with an accuracy of 77%, and Naïve Bayes with an accuracy of 63% and KNN 74% before the application of Synthetic Minority Oversampling Technique (SMOTE). After optimization using SMOTE, the SVM algorithm still showed the best performance with an accuracy of 80%, followed by Random Forest with an accuracy of 79%, and Naïve Bayes and KNN with an accuracy of 72%. Based on these results, SVM proved to be the most effective algorithm in classifying sentiment related to the dismissal of Shin Tae Yong. It is hoped that the results of this study can contribute to understanding public opinion regarding the decision to dismiss Shin Tae Yong as coach of the Indonesian national team.
Implementasi Agile Scrum dalam Rancang Bangun Sistem Rekrutmen Karyawan di Perusahaan X Surakarta Paradhita, Astrid Noviana; Pusparisti, Myrtana; Hendrastuty, Nirwana
Justek : Jurnal Sains dan Teknologi Vol 8, No 2 (2025): Juni
Publisher : Unversitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/justek.v8i2.30578

Abstract

Abstract:  Technology can optimize the company management process by helping related parties provide the necessary considerations. Company X, one of the state-owned companies in Surakarta, found obstacles in the new employee recruitment process, such as long selection times, high operational costs, and concerns about fraud and bias that occurred during the selection process. Therefore, this study was conducted to design a new employee recruitment system at Company X to improve efficiency, effectiveness, and transparency during the recruitment process. The system was designed by implementing the Agile Scrum method using Laravel. The test results showed that the development process could be completed in 80 effective working days with minimal errors. The test results showed that all components could interact with each other so that data exchange could run well according to needs.Abstrak: Teknologi mampu mengoptimasi proses pengelolaan perusahaan dengan membantu pihak terkait untuk memberikan pertimbangan yang dibutuhkan. Perusahaan X, salah satu perusahaan BUMN di Surakarta mendapati kendala dalam proses rekrutmen karyawan baru seperti waktu seleksi yang panjang, biaya operasional yang besar, hingga kekhawatiran akan kecurangan dan bias yang terjadi selama proses seleksi. Oleh sebab itu, penelitian ini dilakukan untuk merancang sistem rekrutmen karyawan baru di Perusahaan X untuk meningkatkan efisiensi, efektivitas, dan transparansi selama proses rekrutmen. Sistem dirancang dengan menerapkan metode Agile Scrum manggunakan laravel. Hasil pengujian menunjukkan bahwa proses pengembangan dapat dilakukan dalam 80 hari kerja efektif dengan minimal eror. Hasil pengujian menunjukkan bahwa seluruh komponen dapat saling berinteraksi sehingga pertukaran data dapat berjalan dengan baik sesuai kebutuhan.        
Prediksi Stok Barang di Toko Eko Helm Menggunakan Metode Time series Analysis Fadillah, Betran Dwi; Hendrastuty, Nirwana
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8584

Abstract

Eko Helm Store located in South Lampung, faces challenges in managing helmet inventory, particularly in determining the optimal stock levels for two categories: affordable and premium helmets. This study aims to forecast helmet stock requirements for the year 2024 using the ARIMA method. Weekly sales data from January to December 2024 were analyzed through stationarity testing using the Augmented Dickey-Fuller (ADF) test and differencing, followed by parameter identification based on ACF and PACF plots. The best-fitting models were identified as ARIMA(2,1,0) for premium helmets, with a Mean Squared Error (MSE) of 24.5101 and an Akaike Information Criterion (AIC) of 249.4062, and ARIMA(1,1,0) for affordable helmets, with an MSE of 32.6102 and an AIC of 250.5381. ARIMA was selected due to its ability to capture trends and seasonal fluctuations more effectively than methods such as moving average or exponential smoothing. The forecasting results estimate a stock requirement of 112 units for affordable helmets and 64 units for premium helmets over the next four weeks. The ARIMA model is integrated into an automated forecasting system that runs scheduled scripts without manual intervention. This system supports timely and precise inventory procurement decisions.
Co-Authors Ade Dwi Putra aditya hidayat Akmal Junaidi Algho Niyu Behainksa Ali, Humaidi Andre Wijaya Anton Mahendra An’ars, M. Ghufroni Apriani, Linda Ari Sulistiyawati aryadi, bagas putra Azhari SN Bagus Bramantyo Chris Mario Damayanti Damayanti Damayanti, Damayanti Dedi Darwis Dharma, Faisal Dimas Elang Setyoko Ely Kurniawan Erliyan Redy Susanto Fadillah, Betran Dwi Fitrah Amalia Fitrah Amalia Rahmadianti Hayurian, Laisha Amilna Heni Sulistiani Heru Setiawan Intan Permata Sari Isnain, Auliya Rahman Iwan Jaya Bakti Jumaryadi, Yuwan Lili Andraini Lumbanraja, Favorisen R M Ghufroni An'ars M. Ghufroni An'ars M. Ghufroni An'ars M. Ghufroni An’ars M. Tova Maulida Waya Inayah Meutia Kartika Arisandi Muhammad Pajar Kharisma Putra Muhammad Tahir Noviana, Astrid Nugroho Susanto, Gregorius Nurhidayat, Rifki Paradhita, Astrid Noviana Prasatya, Agung Pusparisti, Myrtana Rahmadianti, Fitrah Amalia Rahman, Miftahur Ramadhan, Naufal Rizqi Rani, Arum Mustika Samuel Hutagalung Setiawansyah Setiawansyah Setyawati Setyawati Shabrina Shabrina, Shabrina Styawati Styawati Sumanto, Sumanto Susanto, Erliyan Redy Sutyarso Sutyarso Very Hendra Saputra Wahyono Wahyono Wahyudi, Agung Deni Wang, Junhai Wayan Darlin Wisnu Mukti Dwiyudho Putro Wita Kurnia Yunanda, Vinsensius Dendi Yuniar, Eny