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All Journal Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic IJoICT (International Journal on Information and Communication Technology) Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Faktor Exacta Jurnal Komtika (Komputasi dan Informatika) Jurnal Cendikia JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Teknologi Terpadu Jurnal ICT : Information Communication & Technology Competitive JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat Journal of Students‘ Research in Computer Science (JSRCS) Jitu: Jurnal Informatika Utama Competitive Journal of Digital Business and Innovation Management Journal of Computer Science Contributions (Jucosco) Journal of Informatics and Information Security Jurnal Riset Informatika dan Teknologi Informasi (JRITI) Jurnal Komtika (Komputasi dan Informatika) Jurnal Bersama Pengabdian Kepada Masyarakat Journal of Intelligent Systems for Community Development (JISCoDe)
Prima Dina Atika
Fakultas Ilmu Komputer; Universitas Bhayangkara Jakarta Raya
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Perangkat Lunak Kriptografi Algoritma Rivest Shamir Adleman (RSA) Untuk Mengenkripsi Data Password Pada Aplikasi Login Sugiyatno Sugiyatno; Prima Dina Atika
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 7 No. 1 (2019): Maret 2019
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v7i1.1663

Abstract

Abstract Nowadays, information is a basic need for everyone, but the information obtained is not guaranteed right. One solution to data security techniques is cryptography. With cryptography, messages or documents are safe and cannot be read by unauthorized parties. This article discusses about the implementation of the Rivest Shamir Adleman (RSA) Algorithm software in the login functiion. This function has two menus, namely login and registration. The purpose of this paper is to encrypt password data that goes into the database, so that data is protected from abuse by irresponsible parties. The encryption process occurs when a user saves data during registration. The workings of the encryption process are as follows, first take the recipient's public key, e, and modulus n, then declare plaintext m to be blocks m1, m2, m3, ... so that each block represents the value in interval [0, n- 1], then each block m, encrypted into ci blocks with formulas (ci = mic mod n). Each ciphertext c block is decrypted back into a mi block with a formula (mi = cid mod n). Keywords: Enkripsi, Rivest Shamir Adleman (RSA), Plainteks, Cipherteks Abstrak Saat ini informasi menjadi kebutuhan pokok setiap orang, namun informasi yang didapatkan belum tentu terjamin. Salah satu solusi teknik pengamanan data adalah menggunakan kriptografi. Dengan kriptografi, pesan atau dokumen aman dan tidak dapat dibaca oleh pihak yang tidak berhak Artikel ini akan membahas implementasi perangkat lunak Algoritma Rivest Shamir Adleman (RSA) dalam fungsi login. Fungsi ini memiliki dua menu yaitu login dan registrasi. Proses enkripsi terjadi pada saat user menyimpan data ketika registrasi. Tujuan dari penulisan ini untuk mengenkripsi data password yang masuk ke dalam database, sehingga data terhindar dari penyalahgunaan oleh pihak yang tidak bertanggung jawab. Cara kerja proses enkripsi sebagai berikut, pertama mengambil kunci publik penerima pesan, e, dan modulus n, lalu nyatakan plainteks m menjadi blok-blok m1, m2, m3,…, sedemikian sehingga setiap blok merepresentasikan nilai di dalam selang [0, n-1], kemudian setiap blok m, dienkripsi menjadi blok ci dengan rumus (ci=mic mod n). Setiap blok cipherteks c, didekripsi kembali menjadi blok mi dengan rumus (mi=cid mod n). Kata kunci: Enkripsi, Rivest Shamir Adleman (RSA), Plainteks, Cipherteks
Android-Based Shortest Path Finding Using A-Star (A*) Algorithm in Bekasi City Herlawati Herlawati; Prima Dina Atika; Ajif Yunizar Pratama Yusuf; Fata Nidaul Khasanah; Endang Retnoningsih; Beno Aditya Sanusi; Gedhe Hilman Wakhid
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 9 No. 2 (2021): September 2021
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v9i2.3227

Abstract

Getting information on routes can be he main problem for visitors. For example in determining the route to a proper place for eating and how to find the closest route to a mall. Based on the existing problems, this study proposes an application for finding information about places that visitors want to go based on the closest route. Algorithm A-Star (A*) was implemented that uses the distance estimation by finding the closest path to the destination using a heuristic function as a basis to select from several alternatives effectively. The result showed that an android application can give the information about the location of places to visit for eating and malls by calculating the distance from the starting point to the end point.
Web-Based Recommender System for High School Major Decision Using Forward Chaining Ira Wardani; Prima Dina Atika; Herlawati Herlawati
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 10 No. 1 (2022): March 2022
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v10i1.4402

Abstract

Education is a learning process for students to develop their potential skills. One of the vocational schools is the State Vocational High School (SMK) 05 Bekasi, that educates students to have high expertise in industrial fields. However, not every student can choose the right major, there are students who choose majors based on the wishes of their parents, and do not know their real potential and abilities, so that the abilities of students are not in accordance with the majors they have chosen. One way that can be used to help choose the right major is to take a preference test that is assessed by a psychologist or the Counseling Guidance section. This method is quite effective, but it takes time especially for large numbers of student. Therefore, the researcher created a website-based recommendation system to identify majors using the Forward Chaining method. The purpose of the research is to make a recommendation system to determine the majors that students will choose. The application are developed using the System Development Life Cycle.
Nutritional Status Classification of Toddlers Using K-Nearest Neighbor Algorithm Nur Amanda Pratiwi; Prima Dina Atika; Herlawati Herlawati
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 10 No. 2 (2022): September 2022
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v10i2.5604

Abstract

Nutrition is important for the balance of the human body. Knowing the nutritional status is very important to realize the good and the quality human resources. Posyandu Parkit is one of the many Integrated Healthcare Center (posyandu) in Indonesia that provides health services for the community, one of which is monitoring and nutritional development of toddlers. However, the Posyandu Parkit in determining the nutritional status of toddlers is done manually; this method uses measurement parameters based on body weight (BB/U) which are less specific in showing the nutritional status of the toddler by matching manually with a reference standard table available in a healthy card (KMS). The purpose of this research is to produce a website that can determine the nutritional status of toddlers quickly and accurately. The system is designed using the K-Nearest Neighbor method which is a classification method. The K-Nearest Neighbor process is carried out by calculating the distance between the test data and the training data using the Euclidean distance formula, before sorting from the closest distance to the k-th order, then nutritional status is determined. The results of this study are a website that can determine the nutritional status of toddlers by applying the K-Nearest Neighbor algorithm with BB/U, TB/U, and BB/TB accuracies were 93.75%, 87.5%, and 93.75 %, respectively.
Sentiment Analysis of Application Reviews using the K-Nearest Neighbors (KNN) Algorithm Damar Wijati; Prima Dina Atika; Siti Setiawati; Rasim Rasim
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 1 (2024): March 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i1.9490

Abstract

Product reviews play a crucial role in evaluating user satisfaction and overall performance. Vidio, one of the over-the-top (OTT) media platforms, offers a wide range of entertainment content, including movies, TV shows, sports events, music shows, lifestyle programs, and more, accessible through its application. Users have the opportunity to provide reviews and feedback on their experience with the Vidio application. Therefore, this research was conducted to analyze user sentiment towards the Vidio application on the Google Play Store platform using the K-Nearest Neighbors (KNN) method. Data for sentiment analysis were randomly selected from the Vidio application based on the most relevant reviews. A total of 3,000 data were analyzed, with 2,238 data in the negative class, 508 data in the neutral class, and 254 data in the positive class. This research used the K-Nearest Neighbors (KNN) method for classifying reviews based on negative, neutral, and positive classes, and the Multiclass Confusion Matrix for model evaluation. With a data split of 70% for training data 30% for testing data, and several n_neighbors of 10 data, the results in an accuracy of 81.6%, precision of 79%, recall of 81.6%, and F1-Score of 77%.
Comparative Study of Logistic Regression, Neural Network, and Deep Learning in Predicting Hypertension Risk Prima Dina Atika
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11646

Abstract

Hypertension is a major risk factor for cardiovascular diseases, and early detection is crucial for effective management. This study compares the predictive performance of three modeling techniques—Logistic Regression (LR), Neural Network (NN), and Deep Learning (DL)—in estimating the risk of hypertension. The dataset, obtained from Kaggle, consists of demographic and clinical variables with binary labels indicating the presence or absence of hypertension. Each model was trained and evaluated using RapidMiner, with performance assessed through accuracy and Root Mean Squared Error (RMSE). The results indicate that the Neural Network outperformed both Deep Learning and Logistic Regression, achieving the highest accuracy (99.88%) and the lowest RMSE (0.124). These findings suggest that shallow neural networks can provide reliable and efficient predictions for hypertension risk, sometimes even surpassing more complex deep learning architectures.  
A Comparative Study of Machine Learning-Based Student Dropout Risk Prediction Prima Dina Atika
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12299

Abstract

Student dropout is a critical issue in higher education, affecting both institutional performance and student success. This study aims to develop a classification model for predicting student dropout risk and to compare the performance of several machine learning algorithms. A quantitative experimental approach was employed using a dataset that integrates academic records and Learning Management System (LMS) activity. The dataset exhibits imbalanced characteristics, with approximately 20% of instances belonging to the dropout class. The classification algorithms evaluated in this study include Naïve Bayes, Decision Tree, Random Forest, and K-Nearest Neighbor (KNN). Model performance was assessed using Accuracy, Precision, Recall, and ROC-AUC metrics to ensure a comprehensive evaluation. The results indicate that Naïve Bayes achieved the best performance with an accuracy of 86.40% and a ROC-AUC value of 0.934, followed by Random Forest with a ROC-AUC of 0.907. All models demonstrated high recall values (above 90%), indicating strong capability in identifying students at risk of dropout. These findings highlight the importance of selecting appropriate algorithms and evaluation metrics when dealing with imbalanced datasets. This study contributes by utilizing a more realistic dataset with noise and imbalance, as well as integrating academic and behavioral data to improve prediction performance. The proposed approach can support early intervention strategies to reduce student dropout rates in higher education.
Exploring Customer Perceptions through Sentiment Analysis of Google Reviews at Rainbow Alamanda: SVM vs Naive Bayes Algorithm Farizal Salman; Prima Dina Atika; Rafika Sari
Journal of Digital Business and Innovation Management Vol. 5 No. 1 (2026): June 2026
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jdbim.v5i1.73320

Abstract

As one of the popular family tourist destinations, Rainbow Alamanda Park has received thousands of reviews from visitors on the Google Review platform. These reviews reflect public perceptions of the quality of services and facilities offered, making it important to analyze them systematically. This study aims to analyze the sentiment of visitor reviews on Google Review regarding Rainbow Alamanda using two machine learning algorithms: Naive Bayes and Support Vector Machine (SVM), and to compare the performance of both methods. The research process follows the SEMMA approach (Sample, Explore, Modify, Model, Assess), utilizing a dataset of 2,394 reviews collected through web scraping techniques. The evaluation results show that the Naive Bayes method performed best with a training-to-testing data ratio of 70:30, achieving an accuracy of 86.32%, precision of 86.83%, recall of 85.81%, and an F1-score of 86.08%. Meanwhile, the SVM method with an RBF kernel (C=10, γ=0.1) achieved higher performance, with an accuracy of 88.44%, precision of 90.27%, recall of 88.31%, and an F1-score of 89.28%.
Analisis Clustering K-Means untuk Pemetaan Tingkat Pengangguran Terbuka di Provinsi-Provinsi Indonesia Tahun 2013-2023 Alif Izzuddin Ramadhan; Prima Dina Atika; Khairunnisa Fadhilla Ramdhania
Journal of Students‘ Research in Computer Science Vol. 5 No. 2 (2024): November 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/wbpydb62

Abstract

This study analyzes unemployment rates in Indonesian provinces using data from the Central Statistics Agency (BPS) for the period 2013-2023 and the K-Means clustering algorithm. The aim is to group regions based on the Open Unemployment Rate (TPT). Two main clusters were produced: one with a high unemployment rate (cluster 0) and one with a low unemployment rate (cluster 1). Cluster 0 consists of 12 provinces, while cluster 1 consists of 22 provinces. The model evaluation shows a Davies-Bouldin Index score of 0.7041, indicating good clustering quality. The clustering results are visualized in the form of a map for easy interpretation. This research is expected to help policymakers design more effective policies in reducing unemployment in Indonesia, provide deep insights into regional differences in terms of unemployment, and support targeted decision-making.
Analisis Sentimen Masyarakat Terhadap PHK di Indonesia Pada Twitter Menggunakan Naïve Bayes dan Support Vector Machine (SVM) Abdu Malik AlHakim; Prima Dina Atika; Herlawati Herlawati
Journal of Students‘ Research in Computer Science Vol. 6 No. 1 (2025): Mei 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/96sfw544

Abstract

The phenomenon of layoffs in Indonesia has led to various public opinions, especially on social media. This research aims to analyze public sentiment on the layoff issue using data from Twitter, and compare the performance of two text classification algorithms, namely Naïve Bayes and Support Vector Machine. The Knowledge Discovery in Databases approach is used as the research framework, which includes the stages of data selection, text cleaning, transformation, classification, and evaluation. A total of 3,458 tweets were collected and processed through the pre-processing stage, then classified into positive and negative sentiments. Performance assessment was conducted with three scenarios of training and test data sharing: 80:20, 70:30, and 90:10. The results showed that Support Vector Machine gave the highest accuracy of 84.93% in the 90:10 scenario, compared to Naïve Bayes with 82.61% accuracy in the same scenario. Visualization through wordcloud was also used to strengthen the interpretation of dominant words in public opinion. The findings show that classification algorithms can be utilized to understand public perceptions of employment issues and support social data-based decision-making. This research can be further developed by expanding data coverage and evaluating more complex methods to improve classification accuracy.
Co-Authors .S.T., M.Kom., Suhadi Abdu Malik AlHakim Afzil Ramadian Ahmad Fathurrozi Ahmad Fathurrozi Aida Fitriyani, Aida Aidah Afifah Hafshah Ajif Yunizar Pratama Yusuf Alif Izzuddin Ramadhan Almajid, Nafis Alviansyah, Mohammad Anita Setyowati Srie Gunarti Beno Aditya Sanusi Dadan Irwan Damar Wijati Dani Yusuf Dwipa Handayani Ekawati, Inna Endang Retnoningsih Fadia Amelia Putri Faisal Adi Saputra Farizal Salman Fata Nidaul Khasanah Fitria Nurapriani Galih Apriansha Pradana Gedhe Hilman Wakhid Gilby Lionska Wenas Haryono Haryono Hendharsetiawan, Andy Achmad Herlawati Herlawati Indah Dwijayanthi Nirmala Ira Wardani Ismaniah Ismaniah Joni Warta Julianto Khairunnisa Fadhilla Ramdhania Kusmara, Hadi Lestari, Panca Indah Maimunah Malikus Sumadyo Mamang Jhulianawati Mega Wahyu Rhamadani Mohamad Diandra Ferdiansyah Mugiarso Mugiarso Muhamad Galih Muhammad Akmal Dzulfiqar Muhammad Riky Sudrajat Muhammad Yazid Mukhlis Nabila Ramadhani Sari Nur Amanda Pratiwi Priatna , Wowon Prihatin, Sandy Satyo RAFIKA SARI Rafika Sari Rahmadya Trias Handayanto Rasim Rasim Rasyid Darusman Resty Nandya Retna Ayu Puspitasari Retno Nugroho Whidhiasih Rian Wijaya Ridwan Nurfauzi Ridwan Ridwan Rifky Putra Wijaya Rizky Alfiansyah Robertoi . Samuel, Federick Dedi Sandy Satyo Prihatin Sari , Rafika Septia, Dwi Yoga Septyo Saputro Seta Samsiana Seta Samsiana Shofa Shofia Siti Setiawati SITI SETIAWATI Sri Rejeki Sri Rejeki Sugeng Murdowo Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sugiyatno Syahbaniar Rofiah Tambun, Jerisman Jhon Wesli Tedi Ramadhan Tyastuti Sri Lestari Tyastuti Sri Lestari Yusuf, Ajif Yunizar Pratama Zaki Nur Fauzan