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All Journal Rang Teknik Journal JURNAL TEKNOLOGI DAN OPEN SOURCE JOURNAL OF SCIENCE AND SOCIAL RESEARCH Ensiklopedia of Journal JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Algoritma Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat
Anita Anita
Fakultas Teknologi Dan Ilmu Komputer, Universitas Prima Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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PENERAPAN DATA MINING UNTUK KLASIFIKASI BERITA HOAX MENGGUNAKAN ALGORITMA NAIVE BAYES Saut P Tamba; Agusteti Laia; Yudika Kristian Butar-butar; Anita Anita
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.922

Abstract

The research aims to develop a classification model that is effective in identifying hoax news. The rapid development of information technology has had a significant impact on the dissemination of information, especially in the context of the spread of hoaxes via the internet. Hoaxes, or fake news, can cause misperceptions and negative impacts on various aspects of people's lives. To classify hoax news, this research was carried out using the Naive Bayes algorithm. The data used comes from various sources and involves stages of data collection, data analysis, and preprocessing processes. Modeling uses the Naive Bayes Algorithm, which applies the law of probability, to calculate the confidence or probability that a news item falls into the fraud category. The data preprocessing process includes tokenization, case transformation, stopwords filter, and tokens filter (by Length), which aims to improve the quality of the analyzed data. Model evaluation was carried out using cross-validation, confusion matrix, and classification report methods. The evaluation results show that the model accuracy is 73.91%, with a deviation of 1.04%. The results of this research can be used to classify hoax news properly. This model can be used as an initial reference in developing more complex prediction models.
PELATIHAN PEMBELAJARAN AKTIF ABAD -21 YANG TERINTEGRASI PADA PROFIL PELAJAR PANCASILA BAGI GURU GURU SD IT PERMATA BERBASIS TEKNOLOGI DAN SAINTIFIK Palma Juanta; Muhardi Saputra; Anita Anita; Siti Aisyah; Sumita Wardani
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol 3, No 2 (2023): Desember 2023
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v3i2.1534

Abstract

Abstract: Learning is a process of interaction between students and educators in a learning environment that takes place in an educational manner, so that students can develop their attitudes, knowledge and skills to achieve the goals that have been set. The development of technology every year makes it difficult for teachers to keep up with developments so that almost every teacher still does not understand how to use Microsoft PowerPoint and Excel. Teachers only use the application as a tool. Therefore, there is a need for training for teachers to make learning plans, recap student learning outcomes, or create interesting teaching materials for students that are technologically and scientifically based so that they can improve professional competence, especially in the appropriate use of technology in the learning process.Keywords: teachers; technology; scienceAbstrak: Pembelajaran adalah proses interaksi antara peserta didik dan pendidik pada suatu lingkungan belajar yang berlangsung secara edukatif, agar peserta didik dapat membangun sikap, pengetahuan dan keterampilannya untuk mencapai tujuan yang telah ditetapkan. Berkembangnya teknologi setiap tahunnya membuat para guru kesulitan dalam mengikuti perkembangannya sehingga hampir setiap guru masih kurang paham menggunakan microsoft powerpoint dan excel. Guru hanya menggunakan aplikasi tersebut sebatas pada tools. Maka itu perlu adanya pelatihan bagi guru untuk membuat rencana pembelajaran, merekap hasil belajar peserta didik, atau membuat bahan ajar yang menarik bagi peserta didik yang berbasis teknologi dan saintifik sehingga dapat meningkatkan kompetensi profesional terutama dalam pemanfaatan teknologi pada proses pembelajaran secara tepat.Kata kunci: guru-guru; teknologi; saintifik
Penerapan Data Mining Untuk Prediksi Kelulusan Siswa Sekolah Dasar Menggunakan Algoritma Naïve Bayes Classifier Aurike Wijaya; Anita; Marchelina Chistina Manurung; Yosef Dwi Santosa Sitanggang
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3448

Abstract

Government regulations that heavily influence graduation decisions often lead to data imbalances that obscure the effectiveness of machine learning models in the education sector. This study evaluates the performance of the Naïve Bayes algorithm and compares it with Decision Tree and K-NN on a dataset of 385 students from SD Negeri 067053 Medan Deli, which exhibits extreme label imbalance (with the “Pass” class dominating at 88%). Model evaluation was conducted using Stratified 10-Fold Cross Validation. Test results show that Naïve Bayes achieved a high accuracy of 94.04% and proved to be the most robust in identifying the minority class with a Recall of 91.11%, outperforming other comparison algorithms that suffered from overfitting. However, this high accuracy masked an administrative bias, where the precision of Naïve Bayes in predicting the “Fail” class plummeted to 68.33%. This study confirms that accuracy metrics alone can be misleading on imbalanced data, making the application of resampling techniques during the data preprocessing stage absolutely necessary to address bias in educational data mining implementations.
Klasifikasi Sentimen Ulasan GoFood di Google Play Store dengan Metode Naive Bayes Dwi Diva Teresia Situngkir; Anita; Dheo Laurenz Purba
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3479

Abstract

GoFood is a food delivery feature within the Gojek application that has received numerous user reviews on the Google Play Store. The high volume of reviews creates a need for efficient automated sentiment analysis. This study aims to classify the sentiment of GoFood reviews using the Multinomial Naive Bayes method with TF-IDF weighting. A total of 1,649 Indonesian-language reviews were collected through web scraping from the Google Play Store, then processed through preprocessing and sentiment labeling stages, with an 80 percent training data and 20 percent testing data split. The evaluation results show an accuracy of 78.14 percent, with negative sentiment precision of 0.76 and recall of 1.00, as well as positive sentiment precision of 0.96. The low positive recall was caused by data imbalance and the absence of data balancing techniques such as SMOTE. The scientific contribution of this study is the provision of a sentiment map based on Multinomial Naive Bayes and TF-IDF as a reference for GoFood service evaluation and the development of Indonesian-language text sentiment analysis.
Implementation of Grid Search Optimization Algorithm and Adaptive Response Rate Exponential Smoothing In Product Sales Prediction Franklin Fong; Randy Ciptady; Anita Anita
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4437

Abstract

Effective inventory management is one of the keys to a company's success, especially in the retail and distribution sectors that are highly dependent on product availability according to market demand. One common problem faced in inventory management is deadstock, which is a condition where a product is not sold for a long time, causing a buildup of goods and financial losses. This problem is generally caused by inaccuracy in predicting product sales needs. This study aims to overcome this problem by implementing the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm combined with the Grid Search optimization method to improve the accuracy of sales predictions. By utilizing the Sales Data Analysis dataset from Kaggle, the algorithm is implemented in a web-based system using Python and Flask. The results showed that the combination of Grid Search and ARRES was able to significantly increase prediction accuracy, as indicated by a decrease in the MAPE value from 2.845% (ARRES only ) to 0.877% (Grid Search + ARRES). This proves that the proposed method can help companies manage stock more efficiently, reduce the risk of deadstock, and increase the effectiveness of product sales planning
Co-Authors Adelin Yoseva Simanjuntak Agusteti Laia Annita Carolina Br Barus Aurike Wijaya Christine Erniati Panjaitan Dheo Laurenz Purba Dwi Diva Teresia Situngkir Elvi Yolanda Tumanggor Evta Indra Franklin Fong Frengki Arjuna Herwin Simbolon Indra S eptian Salomo Simatupang Jeffer Oliando Josua Silaban Krisna Ferdinan Leo Simanjuntak Marchelina Chistina Manurung Marlince NK Nababan Muhardi Saputra Oktafiani Br Ginting Palma Juanta Randy Ciptady Rismadayanti Waruwu Rizky Prayoga Saut P Tamba Saut Parsaoran Tamba Siti Aisyah Sri Chairunnisa Sri Wahyuni Sri Wahyuni Tarigan Sumita Wardani Yosef Dwi Santosa Sitanggang Yudika Kristian Butar-butar