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Sentimen Analisis Masyarakat Indonesia Terhadap Presiden Rusia Pada Komentar Media Berita Online Ihud Hafid; Windu Gata; Khairunisa Hilyati; Valianda Farradillah Hakim; Sri Rahayu
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 7 No 1 (2023): JANUARY-MARCH 2023
Publisher : KITA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v7i1.698

Russia's invasion of Ukraine was criticized by various parties, including from Indonesia. The attitude shown by the Indonesian government is not the same as the response of the Indonesian people based on various comments on online news media pages. Comments by online news readers are used as an assessment of the Russian President who is involved in the conflict between Russia and Ukraine in the form of sentiment analysis. This study succeeded in obtaining data as many as 352 comments from one of the online news media, the data had previously gone through the cleansing stage to eliminate duplication. To get basic information on comments, Text mining and Text Pre-Processing become an important part of the process. The algorithm used in this research is the Naive Bayes (NB) and Support Vector Machine (SVM) classification algorithm which is optimized using Particle Swarm Optimization (PSO). The two algorithms were tested and gave the result that PSO-based SVM got the best accuracy, which was 79.90% and AUC 0.901.

Grouping Data in Predicting Infant Mortality Using K-Means and Decision Tree Ridwansyah Ridwansyah; Verry Riyanto; Abdul Hamid; Sri Rahayu; Jajang Jaya Purnama
Paradigma Vol. 24 No. 2 (2022): September 2022 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Death is something that we cannot avoid where, when and how death comes. The high infant mortality rate is the main thing and the Indonesian government must prioritize, one of the government's efforts to reduce infant mortality is by conducting a surveillance program, namely PWS KIA where the program is uniting the health of mothers and babies in the local area, basically there are several infant deaths that have causes from the time of pregnancy, accidents, disasters, diseases or because it is destiny from God, for that research is carried out in classifying infant mortality data. For grouping infant mortality data, a K-Means method is needed to analyze data by carrying out a data modeling process without supervision or also known as unsupervised learning. In showing the centroid in the early stages of the k-means algorithm, it is very influential on the results of the cluster carried out on the infant mortality dataset. taken from data.go.id with different centroid results. The results of the clustering model pattern that can be trusted by the government or the Health department to prevent infant mortality. From the clustering results, four labels are tested again using the decision tree algorithm.

Pendekatan Algoritma Klasifikasi Machine Learning untuk Deteksi Penyakit Demensia Muhammad Iqbal; Hendri Mahmud Nawawi; Muhammad Rezki; Abdul Hamid; Sri Rahayu
Computer Science (CO-SCIENCE) Vol. 3 No. 2 (2023): Juli 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v3i2.1987

Early detection of dementia through the use of machine learning classification algorithms is important for providing appropriate interventions to patients. In this context, this study aims to compare the performance of several machine learning classification algorithms in detecting dementia using the attribute selection method. In the early stages, patient data including medical history, cognitive test results, and other attributes were collected as input, an attribute selection algorithm was used to select the most informative attribute subset in detecting dementia. The subset of attributes used as input for training machine learning classification models, several classification algorithms such as Extra Trees (ET), Linear Discriminant Analysis (LDA), Random Forest (RF) and Ridge. In this study, accuracy is used as the main metric to compare algorithm performance. The evaluation results show that the Random Forest (RF) algorithm produces the best performance with an accuracy of 91.56%. The Extra Trees (ET) algorithm has an almost comparable accuracy of 91.44%, while Ridge and Linear Discriminant Analysis (LDA) have an accuracy of 90.44% respectively. In the context of dementia detection, the performance of the Random Forest algorithm with the attribute selection method proved to be the best with an accuracy of 91.56%. These results indicate that the developed model is capable of recognizing complex patterns and relationships between features that are relevant to dementia status. The use of the attribute selection method also contributes to increasing the accuracy and efficiency of the classification algorithm.

Analisis Sentimen AicoGPT (Generative Pre-trained Transformer) Menggunakan TF-IDF Sri Rahayu; Jajang Jaya Purnama; Abdul Hamid; Nina Kurnia Hikmawati
Jurnal Buana Informatika Vol. 14 No. 02 (2023): Jurnal Buana Informatika, Volume 14, Nomor 2, Oktober 2023
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v14i02.7039

Peran artificial intelligence memudahkan mencari informasi yang tepat dan akurat bahkan penyelesaian masalah dengan model yang kompleks. Salah satu terobosan berbasis AI adalah ChatGPT oleh OpenAI pada tahun 2020, dilanjutkan dengan versi terbaru pada tahun 2023 yaitu GPT–3. Sejak saat itu, beberapa teknologi AI serupa versi mobile mulai bermunculan, salah satunya AicoGPT. Namun, kinerja dari aplikasi serupa ini belum dapat diandalkan sehingga masih perlu menganalisis tanggapan para penggunanya, apakah akan sama menakjubkannya atau tidak. Dari permasalahan tersebut, penelitian ini dibuat dengan tujuan untuk menganalisis 1443 data ulasan para pengguna aplikasi AicoGPT di Google Playstore dengan teknik analisis sentimen menggunakan TFIDF dan perbandingan klasifikasi LR dan SVM. Dari kedua ujicoba tersebut, menghasilkan akurasi terbaik dengan Algoritma SVM, yaitu sebesar 92%. Sedangkan LR menghasilkan akurasi sebesar 89%. Dari penelitian ini, dapat disimpulkan secara singkat bahwa metode TF-IDF dengan klasifikasi SVM, cocok digunakan untuk melakukan analisis sentimen dari dataset yang diteliti.

Grouping Data in Predicting Infant Mortality Using K-Means and Decision Tree Ridwansyah Ridwansyah; Verry Riyanto; Abdul Hamid; Sri Rahayu; Jajang Jaya Purnama
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 2 (2022): September 2022 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i2.1399

Death is something that we cannot avoid where, when and how death comes. The high infant mortality rate is the main thing and the Indonesian government must prioritize, one of the government's efforts to reduce infant mortality is by conducting a surveillance program, namely PWS KIA where the program is uniting the health of mothers and babies in the local area, basically there are several infant deaths that have causes from the time of pregnancy, accidents, disasters, diseases or because it is destiny from God, for that research is carried out in classifying infant mortality data. For grouping infant mortality data, a K-Means method is needed to analyze data by carrying out a data modeling process without supervision or also known as unsupervised learning. In showing the centroid in the early stages of the k-means algorithm, it is very influential on the results of the cluster carried out on the infant mortality dataset. taken from data.go.id with different centroid results. The results of the clustering model pattern that can be trusted by the government or the Health department to prevent infant mortality. From the clustering results, four labels are tested again using the decision tree algorithm.

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