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Decision Support System Design for Informatics Student Final Projects Using C4.5 Algorithm Rafika Sari; Hasan Fatoni; Khairunnisa Fadhilla Ramdhania
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5954

Academic consultation activities between students and academic supervisors are necessary to help students carry out academic activities. Based on the transcript of grades obtained, many students do not choose the appropriate final project/thesis specialization fields based on their academic abilities, resulting in a lot of inconsistencies between the course grades and the final project specialization fields. The purpose of this research is to minimize the subjectivity aspect of students in choosing their final project academic supervisors and minimize the inconsistencies between the course grades and the final project specialization fields. The method used in this research is classification data mining using the Decision Tree and C4.5 Algorithm methods, with the attributes involved being courses, course grades, and specialization courses. The C4.5 Decision Tree algorithm is used to transform data (tables) into a tree model and then convert the tree model into rules. The implementation of the C4.5 Decision Tree algorithm in the specialization field decision support system has been successfully carried out, with an accuracy rate of 70% from the total calculation data. The data used in this research is a sample data from several senior students in the Informatics program at Ubhara-Jaya. The results of the research decision support system can be used as a good recommendation for the Informatics program and senior students to direct their final project research. It is expected that further research will use more sample data so that the accuracy rate will be better and can be implemented in website or mobile-based applications.

Adaptasi Teknologi Untuk Mendukung Penguatan Kemampuan Literasi dan Numerasi Siswa Melalui Aplikasi AKM-Kelas Berbasis Desktop dan Android Rafika Sari; Ajif Yunizar Pratama Yusuf; Khairunnisa Fadhilla Ramdhania; Muhammad Ganang Martyana; Illa Nur’aini; Syifa Rahmadhani; Renilda Filiandini; Reghita Suryani Putri
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 10 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

The Minimum Competency Assessment (AKM) is an assessment of the basic competencies that all students need in order to be able to develop their own capacity and participate positively in society. The aim of AKM is to measure competency at the individual student level, which is expected for all students to reach a proficient or proficient level of competence. The minimum competency assessment since 2021 has been carried out online using the AKM-Kelas application provided by the Pusmenjar Kemdikbud. The challenge that later arises in implementing this is the ability of the school community to operate the application, apart from facilities and infrastructure, some schools do not yet have computer laboratory facilities. Based on this, the Kampus Mengajar (KM) program from the Merdeka Belajar Kampus Merdeka (MBKM) will work together to socialize and assist in using the AKM-Class application. In line with this, the participants in the Kampus Mengajar Batch 4 (KM-4) program at SDN 02 Jatireja designed a mentoring program for using the AKM-Kelas application in collaboration with the community service program (Pengabdian kepada Masyarakat - PkM) from Informatics lecturers at Bhayangkara University Jakarta Raya. Through this mentoring program, teachers and grade 5 elementary school students are the main targets as training participants. The main focus of this training is how to install desktop-based applications for teachers as proctors and install Android-based applications for students as AKM participants. This training program is carried out in stages over three consecutive months. The results of this training show an increase in competence that must be owned by teachers and students in the aspects of: AKM knowledge, literacy, numeracy.

Aktualisasi Masyarakat Desa Sukamekar Bekasi Dalam Kondisi Pandemi Covid-19 Melalui Program KKN Mahasiswa Rafika Sari; Ratna Sari; Safarin Novarizal
Journal Of Computer Science Contributions (JUCOSCO) Vol. 1 No. 2 (2021): Juli 2021
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jucosco.v1i2.691

Kegiatan Kuliah Kerja Nyata (KKN) adalah suatu bentuk pendidikan dengan cara memberikan pengalaman empiris kepada mahasiswa untuk hidup ditengah-tengah masyarakat, dan secara langsung mengajarkan kepada mahasiswa cara identifikasi berbagai masalah sosial di masyarakat. KKN bagi mahasiswa diharapkan dapat menjadi suatu pengalaman belajar yang baru untuk menambah pengetahuan, kemampuan, dan kesadaran hidup bermasyarakat. Bagi masyarakat, kehadiran mahasiswa diharapkan mampu memberikan motivasi dan inovasi dalam bidang sosial kemasyarakatan. Bertepatan dengan kondisi pandemi Covid-19 yang masing berlangsung, termasuk di Desa Suka-Mekar Kabupaten Bekasi, maka kegiatan KKN yang dilakukan oleh mahasiswa Universitas Bhayangkara Jakarta Raya dapat menjadi sarana sosialisasi tentang penerapan Protokol Kesehatan (Prokes) kepada masyarakat setempat. Disamping itu, pemberian beberapa macam pelatihan seperti pembuatan masker home made, pelatihan komputer bagi para pelajar dan berkontribusi dalam pembenahan administrasi ke arah sistem informasi digital juga telah dilaksanakan di kantor desa Suka Mekar. Dengan adanya kegiatan KKN ini dapat menumbuhkan kembali kesadaran masyarakat dalam menjaga kesehatan dan tetap produktif di masa pandemi Covid-19 agar dapat meningkatkan kualitas hidup masyarakat.

Forecasting Inventory Demand Under Volatile Sales Patterns Using the Prophet Algorithm Rafika Sari; Ratna Salkiawati; Nur`aini Puji Lestari; Aida Fitriyani
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2032

Inventory availability is a critical factor for companies to maintain operational continuity and customer satisfaction. However, many organizations still face challenges in forecasting demand, particularly when sales patterns are highly volatile and irregular. Although the Prophet forecasting algorithm has been widely used for time-series prediction, its behavior and robustness under unstable sales patterns remain insufficiently examined in practical inventory contexts. This study aims to evaluate the ability of the Prophet algorithm to forecast inventory demand using historical sales data characterized by fluctuating patterns. A quantitative time-series forecasting approach was applied using one year of secondary sales data obtained from PT XYZ. The data were cleaned to address missing values and aggregated into weekly time intervals to reduce noise. Five products with the highest transaction frequency were selected as case studies. Forecasting performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that Prophet is capable of generating reasonably accurate forecasts even under volatile demand conditions. The evaluation results indicate RMSE values ranging from 5.41 to 52.78 and MAPE values ranging from 5% to 23.46% across the five analyzed products. These findings provide empirical evidence that the Prophet algorithm can maintain forecasting robustness despite irregular demand patterns. However, the absence of comparisons with alternative forecasting models limits the strength of conclusions regarding its relative performance. This study contributes by providing empirical insight into the application of Prophet for inventory forecasting under volatile sales conditions and offers practical implications for improving inventory planning in data-driven decision-making environments.

Team-Teaching-Based Course Scheduling Using Genetic Algorithm Rafika Sari; Khairunnisa Fadhilla Ramdhania; Rakhmat Purnomo
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.v10i1.4416

Scheduling problems occur in various fields, e.g., education, health institutions, transportation, sports, etc. Main scheduling problems in education is course scheduling which creates schedules for students and lecturers. In this study, course scheduling allocates the lecturers in the form of team teaching and courses into the class and a certain time to even out the workload of lecturers per day and a group of students per day in one week without breaking the constraint. The method used in this research is a genetic algorithm where Universitas Bhayangkara Jakarta Raya as the case study. The genetic algorithm process is done by getting several candidate solutions that undergo a process of selection, mutation, and crossing over to produce chromosomes with the best fitness values. The objective function in this research is minimizing the average variance of the workload of lecturers and students per day in one week. The parameters used in genetic algorithm are determined based on the Design of Experiments mechanism (DOE). The optimal parameter values used to run the program are as: population size = 50, with probability of crossing over = 0.4 and probability of mutation = 0.008. The results of scheduling with genetic algorithms show that the value of the workload variance lecturers and students by considering team teaching is better than actual scheduling. The application of the genetic algorithm method results in a decrease in the standard value deviation of the workload of lecturers and a group of students in one week is 0.114 (3.68%) and 3.11 (55.7%). In addition, course scheduling uses a genetic algorithm with consider team teaching better than genetic algorithm without considering team teaching because there is no class schedule that clashes in real conditions.

Decision Support System Design for Informatics Student Final Projects Using C4.5 Algorithm Rafika Sari; Hasan Fatoni; Khairunnisa Fadhilla Ramdhania
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 11 No. 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5954

Academic consultation activities between students and academic supervisors are necessary to help students carry out academic activities. Based on the transcript of grades obtained, many students do not choose the appropriate final project/thesis specialization fields based on their academic abilities, resulting in a lot of inconsistencies between the course grades and the final project specialization fields. The purpose of this research is to minimize the subjectivity aspect of students in choosing their final project academic supervisors and minimize the inconsistencies between the course grades and the final project specialization fields. The method used in this research is classification data mining using the Decision Tree and C4.5 Algorithm methods, with the attributes involved being courses, course grades, and specialization courses. The C4.5 Decision Tree algorithm is used to transform data (tables) into a tree model and then convert the tree model into rules. The implementation of the C4.5 Decision Tree algorithm in the specialization field decision support system has been successfully carried out, with an accuracy rate of 70% from the total calculation data. The data used in this research is a sample data from several senior students in the Informatics program at Ubhara-Jaya. The results of the research decision support system can be used as a good recommendation for the Informatics program and senior students to direct their final project research. It is expected that further research will use more sample data so that the accuracy rate will be better and can be implemented in website or mobile-based applications.

Determining Sales Patterns Using the Apriori Algorithm: A Case Study of Unlocked Cafe's Website Applications Rafika Sari; Nur Helmy; Allan Desi Alexander
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.v12i1.8908

In the business world, the sales process is the key to a company's success. Business processes will involve a lot of transaction data which will increase over time. This accumulation of data will not provide meaningful information if it is not processed and utilized properly. Correct decision making is obtained from accurate and informative data. This research was conducted to analyze, simulate and digitize data in the form of a website-based system which can be used as recommendations in making business decisions. The application of the Apriori algorithm supports system development in determining sales patterns by providing an overview of sales patterns for products of interest so that sales association rules are obtained. This research dataset was taken from transactions at the Unlocked Café & Coffee shop. The result of this research is an application designed according to the needs of business owners in the form of selecting itemsets by applying the Apriori algorithm to produce output in the form of product stock reference data and product sales patterns.

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

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 Sentimen Ulasan Customer Kopi TMLST Menggunakan Algoritma Naïve Bayes Dhiya Azizah Hamidah; Ratna Salkiawati; Rafika Sari
Journal of Students‘ Research in Computer Science Vol. 5 No. 1 (2024): Mei 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/mrm89y71

The rapid development of Coffeeshop is currently influenced by advances in internet technology, the existence of online food applications and websites, such as Shopeefood, and Google Maps, can help people place online orders that have no time limit. However, there are problems that arise over time such as, in collecting feedback from customers the more review data available on Google Maps and online food applications, namely Shopeefood. Therefore, a solution is needed that can help TMLST Coffee to collect, process, and analyze feedback from customers on online food applications such as Shopeefood and Google Maps in a better and more structured manner. In this study, retrieving and collecting customer review data was carried out using web scrapping techniques taken through online food applications, namely Shopeefood and Google Maps, but collecting review data was also carried out by distributing questionnaires via google forms filled out by TMLST Coffee customers. Furthermore, the method used in this research is Naïve Bayes which aims as a classification method and is able to classify customer comments into positif or negatif. And review data processing is done using the Cross-Industry Standard Process for Data Mining (CRIPS-DM) method. The CRIPS-DM stage involves the research and implementation process of the stages that have been carried out previously. The results of this study produce a high level of accuracy in predicting positif and negatif sentiment, with an accuracy of 0.82 or 82%. In addition, it produces a positif recall of 0.76 or 76% and a negatif recall of 0.89 or 89%. indicating that the model has a good ability to identify correctly. With the evaluation results of the model used, it gives an indication that Naïve Bayes can be an effective choice in conducting sentiment analysis on TMLST Coffee review data.

Analisis Klasterisasi Pelanggan Layanan Pijat Menggunakan Algoritma K-Means Clustering Pada Griya Sehat Faza Depok Wildanul Jannah; Adi Muhajirin; Rafika Sari
Journal of Informatic and Information Security Vol. 7 No. 1 (2026): Juni 2026
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/prvjnr35

Customer segmentation is a crucial element in strengthening data-driven marketing strategies, especially for Micro, Small, and Medium Enterprises (MSMEs) operating in the service sector. Customer data management at Griya Sehat Faza Depok is still conducted conventionally without adopting an analytical approach to identify consumer profiles. This limitation hinders the optimization of marketing strategies and efforts to maintain customer loyalty. This study groups home massage service customers using the K-Means Clustering algorithm. The CRISP-DM (Cross Industry Standard Process for Data Mining) framework, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment, is used in this study. Customer transaction data from January to December 2025 serves as the dataset for analysis. Five variables are used in the clustering process: visit frequency, treatment type, total expenditure, service duration, and transportation costs representing customer distance. Data preprocessing stages include data cleaning, categorical data encoding, aggregation, and normalization using the Min-Max method. The optimal number of clusters is determined using the Elbow Method, while the quality of clustering results is evaluated using the Davies-Bouldin Index (DBI). The analysis results show the formation of four customer groups with a DBI value of 0.647, indicating good clustering quality. Each group exhibits distinct behavioral characteristics, enabling the identification of high-value customers, loyal customers, regular customers, and low-value customers. These findings offer practical recommendations for developing targeted marketing strategies, improving customer retention, and supporting more efficient therapist allocation at Griya Sehat Faza Depok.

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