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All Journal Jurnal Teknologi Sistem Informasi dan Aplikasi KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Informatics and Data Science
Kurnia Gusti, Siska
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

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Aplikasi Pengawasan Antar-Jemput Anak Sekolah Menggunakan API (Application Program Interface) Whatsapp Sardi, Hajra; Jasril, Jasril; Irsyad, Muhammad; Kurnia Gusti, Siska
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1898

Abstract

Alfatih School Indonesia is the first Islamic school with a combination of Indonesia and Turkey which was founded on August 15 2019. This school always ensures that its students are safe from going to school to leaving school, but currently the school only carries out supervision by waiting and ensuring that only parents students who are allowed to pick up students. This method is less effective because there is no accurate documentation or records, besides that many crimes occur, such as kidnapping, which means that security must be improved. In this research, an Android-based school children pick-up and drop-off monitoring application was built using the WhatsApp API with the QR Code feature. The application that was built is equipped with the main feature of Whatsapp Information, namely a feature that allows parents to receive detailed information about the person who picked up their child via Whatsapp messages sent to the parents' Whatsapp number by the application automatically. The type of WhatsApp that will be used is the WhatsApp API (Application Program Interface). The message will be sent after the school confirms it using a scan of the QR Code that each parent has. To ensure security the generated QR Code will always be different and can only be created through the application. The application built can also validate invalid QR-Code
Implementation of Feature Selection Information Gain in Support Vector Machine Method for Stroke Disease Classification Fitri, Anisa; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.116

Abstract

Stroke is a disease with a high mortality and disability rate that requires early detection. However, the main challenge in the classification process of this disease is data imbalance and the large number of irrelevant features in the dataset. This study proposes a combination of Support Vector Machine (SVM) method with Information Gain feature selection technique and data balancing using Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. The dataset used consists of 5,110 data with 10 variables and 1 label. Feature selection was performed with three threshold values (0.04; 0.01; and 0.0005), while SVM classification was tested on three different kernels: Linear, RBF, and Polynomial. Model evaluation was performed using Confusion Matrix and training and test data sharing using k-fold cross validation with k=10. The best results were obtained on the RBF kernel with Cost=100 and Gamma=5 parameters at an Information Gain threshold of 0.0005, with accuracy reaching 90.51%. These results show that the combination of techniques used aims to determine the variables that most affect SVM classification in detecting stroke disease
Diabetes Classification using Gain Ratio Feature Selection in Support Vector Machine Method Al Rasyid, Nabila; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.114

Abstract

Diabetes is a major cause of many chronic diseases such as visual impairment, stroke and kidney failure. Early detection especially in groups that have a high risk of developing diabetes needs to be done to prevent problems that have a wide impact. Indonesia is ranked seventh in the world with a prevalence of 10.7% of the total number of people with diabetes. This research aims to determine the attributes in the diabetes dataset that most affect the classification and apply the Support Vector Machine method for diabetes classification. For the determination process, Gain Ratio feature selection technique is applied. The dataset used consists of 768 data with 8 attributes. In this classification process, 3 SVM kernels (Linear, Polynomial, and RBF) are used with three possible data divisions using the ratio (70:30; 80:20; 90:10). Before applying feature selection, there were 8 attributes used and achieved the highest accuracy of 94.81% at a ratio of 80:20 using the RBF kernel with a combination of two parameters namely C = 100, Gamma = 3 and C = 100, Gamma = Scale.  Feature selection parameters in the form of thresholds used include 0.02; 0.03; and 0.05. After applying feature selection, the attribute that produces the highest accuracy uses 6 attributes. The highest accuracy after applying feature selection reached 95.45% at a threshold of 0.02 with a ratio of 80:20 using the RBF kernel with parameters C = 100 and Gamma = Scale. The results showed that there was an increase in accuracy after applying feature selection
Penerapan Algortitma C4.5 untuk Klasifikasi Sentimen Masyarakat terhadap #RUUKUHP pada Twitter Vusuvangat, Imam; Kurnia Gusti, Siska; Syafira, Fadhilah; Novriyanto, Novriyanto; Insani, Fitri
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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

Abstract

Social media, especially Twitter, has developed into an important tool for people to share their opinions and feelings widely. Users often use hashtags to share messages related to certain topics. Some of the issues that lead to the need for sentiment analysis of the Draft Criminal Code are social impact, Public disapproval, Potential legal uncertainty, Potential abuse, Support and criticism. By conducting a sentiment analysis of the draft Penal Code, the government and policymakers can better understand the views of the public, identify possible problems and address them, and make necessary improvements or clarifications to the draft law. This can help ensure that the draft Penal Code has greater public support and adheres to good legal principles. The classification of public responses to this hashtag provides a significant snapshot of public attitudes and perspectives. This study aims to classify public sentiment towards the RUUKUHP hashtag on the Twitter platform using the C4.5 algorithm. This study uses a collection of tweets with the hashtag RUUKUHP which are manually categorized into two and three sentiment categories, namely positive, negative and positive, negative and neutral. In this study, data preprocessing is carried out before training the model which includes removing links, special characters, removing stopwords, and word tokenization. Furthermore, this research uses text representation methods such as TF-IDF to extract features from the tweet text and convert them into numerical vectors used by the C4.5 algorithm. After training the classification model using the C4.5 algorithm with the classified dataset, it evaluates the performance of the model with the metrics of accuracy, recall, precision, and F1 score. Experimental results using 2 categories of Negative and Positive show that the model applied with the C4.5 algorithm achieved an accuracy of 96.6% with a recall of 96.6%, a percision of 97.1% and an F1 score of 96.8. And experiments using 3 categories of Negative, Positive and Neutral achieved an accuracy of 67%, a recall of 67%, a precision of 65%, and an F1 score of 66%. Thus it can be concluded that the results of the RUUKUHP hashtag sentiment classification with 2 class predictions are more relevant than 3 sentiment class predictions with a value reaching 96.6%.
Co-Authors Al Rasyid, Nabila Elvia Budianita Fitri Insani Fitri, Anisa Iis Afrianty Jasril Jasril Muhammad Irsyad Novriyanto Novriyanto Sardi, Hajra Syafira, Fadhilah Vusuvangat, Imam