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Jurnal Riset Informatika dan Teknologi Informasi (JRITI)
Published by Jejaring Penelitian dan Pengabdian Masyarakat
ISSN : 30248167     EISSN : 31098959     DOI : https://doi.org/10.58776/jriti.v3i1
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika dan Teknologi Informasi merupakan jurnal ilmiah yang diterbitkan oleh Jejaring Penelitian dan Pengabdian Masyarakat (JPPM) Banten. Jurnal ilmiah ini memuat hasil riset dosen, peneliti, mahasiswa dan masyarakat umum dibidang informatika dan teknologi informasi serta rumpun dan turunannya. Jurnal ini terbit tiga kali dalam setahun. Terbitan pertama di bulan Agustus 2023. Sedangkan untuk periode terbit adalah Agustus, Desember, dan April. Adapun bidang riset yang menjadi fokus jurnal ini (dengan tanpa bermaksud membatasi) adalah terkait dengan topik : data mining, data science, pembelajaran mesin (machine learning), kecerdasan buatan, sistem pakar, sistem informasi manajemen, sistem pendukung keputusan, cyber security, soft computing, logika samar (fuzzy logic), pengenalan pola, computer vission, pengolahan citra digital, software engineering, manajemen proyek, software testing, dan topik lain terkait informatika dan teknologi informasi yang relevan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 49 Documents
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Penerapan Algoritma K-Means Dalam Penggunaan Transportasi Online Surya, Aditya
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 2 (2025): Desember 2024 - Maret 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i2.137

Abstract

The rapid evolution of information technology over recent decades has revolutionized many aspects of human life, not least in transportation services. Online ride‑hailing platforms have simplified daily mobility—users no longer need to scour the streets or wait at stands, but simply place an order via a smartphone app. Beyond point‑to‑point travel, these platforms have expanded into parcel delivery and food ordering, creating a comprehensive on‑demand ecosystem for modern urban needs. As usage of online transportation diversifies, it becomes crucial for service providers to understand dominant usage patterns so they can tailor operational and marketing strategies effectively. One powerful approach to uncover these patterns is clustering, which groups historical usage data into segments based on similar characteristics. In this study, two primary clusters are defined: C1 for frequent users of online transport, and C2 for those who use it less often or sporadically. This segregation enables a clearer distinction between active and passive user behaviors, leading to more focused insights. Applying clustering to the online travel dataset involves several preprocessing steps—removing duplicate entries, normalizing numeric fields (such as booking frequency and total expenditure), and encoding categorical variables (like service type and booking time)—before running the algorithm. The resulting clusters are then analyzed to pinpoint the factors driving high or low usage intensity, such as average trip duration, driver response times, and preferences for additional services like food or parcel delivery. By understanding the profiles of these two clusters, the research offers data‑driven recommendations to online transport companies: design more precisely targeted promotional packages, optimize route planning and fleet allocation, and boost user retention strategies. Ultimately, this study seeks to answer the question, “What needs and habits most frequently emerge in online transportation usage?”—enabling continuous refinement of services in line with user preferences and fostering sustainable business growth. 
Memprediksi Tingkat Penjualan Peralatan Elektronik Menggunakan Metode Regresi Linear Berganda Melati, Puput
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 2 (2025): Desember 2024 - Maret 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i2.139

Abstract

In an era of fierce competition within the electronics industry, the ability to project market demand accurately has become a critical factor for companies when formulating marketing strategies, managing inventory, and making timely business decisions. This study develops a sales forecasting model for electronic equipment based on multiple linear regression, using historical sales data collected over several periods. The dataset comprises a dependent variable—sales realization—and two key independent variables: sales targets and the number of interested customers. The initial phase of the study involves data collection and cleansing to address missing values, duplicates, and anomalies. Numeric variables are normalized to ensure a uniform scale, and new features—such as squared terms and interaction variables—are created to enrich the model’s information. The dataset is then split into training and testing subsets in an 80:20 ratio to ensure the model’s generalizability. Regression coefficients are estimated using the ordinary least squares method, and model fit is evaluated using the coefficient of determination (R²), mean absolute error (MAE), and root mean squared error (RMSE). Analysis results indicate that the multiple linear regression model captures the relationships among variables effectively, as evidenced by an R² of 0.92—meaning 92% of the variance in sales realization can be explained by sales targets and customer interest. The MAE of 3.15 and RMSE of 4.27 suggest that prediction errors remain within acceptable bounds for business planning purposes. A t‑test on each coefficient yields p‑values below 0.05, confirming that both independent variables contribute significantly to sales realization. The final model is integrated into the company’s analytical framework as a quantitative tool for generating quarterly and annual sales forecasts. By using this predictive equation, management can simulate various scenarios involving changes in targets and levels of customer interest, allowing for more responsive strategic planning. Practical implications include optimized inventory control, precise scheduling of marketing campaigns, and targeted allocation of logistical resources. In conclusion, multiple linear regression proves to be an effective and reliable method for forecasting sales of electronic equipment and supports both operational and strategic decision‑making. This study opens avenues for further enhancement by incorporating external variables—such as seasonal promotions, competitor pricing, and macroeconomic factors—and by applying more advanced machine learning techniques to improve prediction accuracy in the future.
Implementasi Data Mining Penjualan Produk Kosmetik Pada PT. Habasa Natural Menggunakan Regresi Linear Sederhana Bahrul Saputra, Haikal
Jurnal Riset Informatika dan Teknologi Informasi Vol 3 No 1 (2025): Augustus - November 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v3i1.141

Abstract

Women’s lives are generally inseparable from the use of cosmetics, which not only serve to enhance appearance but also to maintain skin and body health, making them one of the basic needs with ever-increasing demand. PT. Habasa Natural, as a producer and seller of natural cosmetics, experiences daily growth in sales transactions, resulting in an ever-expanding volume of stored data. However, most of this data is merely archived without being optimally utilized, even though it contains valuable insights such as consumer purchasing patterns, the most popular products, and relationships between products that are often bought together. By properly leveraging sales data, the company can develop more effective marketing strategies, such as creating product bundles, offering special promotions, or arranging strategic product placement, enabling data-driven business decisions to improve operational efficiency, competitiveness, and customer satisfaction.  
Penerapan Regresi linier Berganda untuk Menganalisis Jumlah Kecelakaan Lalu Lintas Nurhasanah, Gita Aprilia
Jurnal Riset Informatika dan Teknologi Informasi Vol 3 No 1 (2025): Augustus - November 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v3i1.142

Abstract

Traffic accidents are a serious problem that can have negative impacts on both society and the economy of a region. This study aims to analyze the factors contributing to traffic accidents and identify possible prevention measures. The research method involves collecting traffic accident data from various sources, including police reports, witness statements, and medical records. The analysis of accident causes covers aspects such as speeding, non-compliance with traffic regulations, road conditions, and vehicle conditions. The results show that inappropriate speed, the use of mobile phones while driving, and violations of traffic rules are the dominant factors in traffic accidents. In addition, poor road infrastructure conditions and lack of vehicle maintenance also contribute to the high rate of accidents. This study contributes to a deeper understanding of the factors causing traffic accidents and provides insights into prevention strategies that can be adopted by the government and relevant stakeholders. By implementing these measures, it is expected that the number of traffic accidents can be reduced, thereby improving road safety for all road users. Multiple-unit vehicles, such as buses and large trucks, play an important role in mass transportation and logistics. However, traffic accidents involving these vehicles can have serious impacts on road safety and human life. This study aims to analyze the factors contributing to traffic accidents involving multiple-unit vehicles and identify prevention strategies that can be implemented..  
Implementasi Webview Menggunakan Android Pada Website Cinema Kampus Nurani M, Muhammad Ikmal; Amirulloh, Alif; Mahdi, Arif
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 2 (2025): Desember 2024 - Maret 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i2.152

Abstract

In the midst of today's rapid technological developments, everyone can easily access various things, one of which is information. Everyone can access various kinds of information, including information related to a particular film channel or TV program. This information is spread very widely on web browsers, just by searching on the web browser, information related to the film you are looking for can be found. However, this is still considered a hassle for some people. For this reason, a WebView application was created from a cinema website. WebView is a UI widget that helps integrate web applications into the native context of Android applications. It provides a powerful mechanism for two-way interaction between the native-end (Java) and web-end (JavaScript) of Android applications. This application aims to make it easier for people to find information related to films or TV content and improve the user's experience in watching films or content. Just by installing and opening this application on a smartphone, someone can easily find out the film or content they are looking for, without having to bother opening the film website URL link. And from the black box testing results, 100% results were obtained which were in line with what was expected.
Implementasi Open Source Enterprise Resource Planing (ERP) ODOO Pada PMB Universitas Bhayangkara Jakarta Raya iskandar, rudi; Syahroni, Muhammad; Yunizar Pratama Yusuf, Ajif
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 2 (2025): Desember 2024 - Maret 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i2.153

Abstract

Currently, Information and Communication Technology is developing rapidly, including in the field of education. In general, technology is used to process data, which includes processing, obtaining, compiling, storing, and manipulating data in various ways and procedures to produce high-quality information. Universities must continue to improve their services to meet the needs of the academic community in order to continue to exist. The rapid development of technology and information in the field of education also triggers fierce competition, especially when accepting new students every academic year. Universitas Bhayangkara Jakarta Raya (Ubhara Jaya), one of the private universities in Bekasi City, routinely carries out the new student admission process (PMB) at the beginning of each academic year. This process is managed by a marketing team that must handle many applicants, remind prospective students about the entrance exam time, and manage the re-registration and payment process. All of this requires an adequate system to meet the needs of universities and applicants. Management also needs reports that can be used to determine promotional strategies based on the results of new student admissions. With the implementation of Odoo, it is expected to increase efficiency in data processing, analysis, reporting, and evaluation of new student admissions. 
Identifikasi Pola Pengerjaan Alat Berat Menggunakan Algoritma K-Prototype Clustering Muslih, Ahmad
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 2 (2025): Desember 2024 - Maret 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i2.154

Abstract

This research aims to identify work patterns for heavy equipment, namely dozers, dump trucks and excavators, using the K-Prototype Clustering algorithm. This algorithm was chosen because of its ability to handle data that has a combination of numeric and categorical attributes, which are often found in heavy equipment operational data. By applying K-Prototype Clustering, we can group heavy equipment usage data into several representative clusters. The results show that heavy equipment usage patterns can be grouped effectively, allowing the identification of clusters with similar operational characteristics. This cluster helps in optimizing heavy equipment allocation, planning preventive maintenance, and improving overall operational efficiency. Implementation of clustering results in operational practice shows the potential for reducing idle time and increasing machine productivity. This research concludes that the use of the K-Prototype Clustering algorithm is an effective method for identifying heavy equipment work patterns. Strategic recommendations resulting from clustering can be applied to improve operational efficiency and effectiveness in the construction and mining industries.
Analisis Sentimen Terhadap Bullying Di Indonesia Pada Twitter Menggunakan Naïve Bayes dan SVM AlHakim, Abdu Malik; Leonardo D.P, Harun; Putri, Alifia Nursyahrani
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 3 (2025): April - Juli 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i3.155

Abstract

Bullying has become a serious social problem in Indonesia. In the past few years, bullying cases are increasing, especially among children and adolescents. Bullying can occur anywhere, including at home, work, community, social media, and school, but it is most common in educational settings. Twitter or "X" is the most used social media in Indonesia, often a place for people to express their opinions on bullying. This research aims to analyze sentiment towards bullying in Indonesia through comments or tweets collected from Twitter using Naïve Bayes and Support Vector Machine (SVM) methods. From the analysis of 330 tweets, the Naïve Bayes method showed an accuracy of 77.27%, while the SVM method showed an accuracy of 72.72%.
Segmentasi Pelanggan Berbasis RFM dengan Algoritma K-Means pada Data Transaksi Online Retail Darma Oktavian, Vedly Vedliyan; Ramadhan , Ridho; Fadhilla, Daffa Rayhan
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 3 (2025): April - Juli 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i3.156

Abstract

This research focuses on customer segmentation using the RFM (Recency, Frequency, Monetary) model and the K-Means algorithm on online retail transaction data. Customer segmentation is the process of categorizing customers into different groups based on their transactional behavior patterns. The RFM model allows us to evaluate customers based on three critical dimensions: how recently a customer made their last purchase (Recency), how often a customer makes purchases (Frequency), and the total monetary value generated by the customer (Monetary). By combining RFM data and the K-Means algorithm, we can divide customers into homogeneous segments. This analysis provides deep insights into the characteristics and value of each customer segment, enabling companies to develop more targeted and effective marketing strategies. The segmentation results are expected to assist companies in enhancing customer retention, maximizing customer lifetime value,and improving the effectiveness of marketing campaigns.
Analisa Sentimen terhadap Twitter Pemilu 2024 menggunakan Perbandingan Algoritma Naïve Baiyes rahmaddyan, reyhan tri; Damara, Rian; Pratama Yusuf, Ajif Yunizar; Munandar, Tb Ai
Jurnal Riset Informatika dan Teknologi Informasi Vol 2 No 3 (2025): April - Juli 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat (JPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/jriti.v2i3.157

Abstract

In the digital era, sentiment analysis has become essential for understanding public opinion on various issues, including general elections. In the context of the 2024 General Election (Pemilu), this study aims to analyze sentiments expressed on the Twitter platform regarding the event. A primary classification algorithm, Naïve Bayes, was used to classify sentiments into positive, negative, and neutral categories and compare its performance. Twitter data was collected using a crawling technique during the 2024 election campaign period and used as the dataset. The data was then processed to remove noise and underwent text preprocessing, including tokenization, stemming, and stop word removal. Subsequently, the Naïve Bayes algorithm was applied to classify the sentiment of the collected tweets. Naïve Bayes, with its probabilistic approach and feature independence assumption, offers a fast and straightforward solution for classification tasks. The analysis results show that the algorithm was able to classify sentiments effectively. In tests using a separate test set, Naïve Bayes achieved an accuracy of approximately 82%. However, this algorithm has strengths and weaknesses that must be considered in the context of sentiment analysis on Twitter related to the 2024 election. For example, Naïve Bayes is more efficient in terms of time and resources. The study concludes that although Naïve Bayes produced accurate results, selecting the best algorithm depends on specific analysis needs, such as processing speed and resource availability. Further research is recommended to explore hybrid methods and deep learning techniques to enhance the accuracy and efficiency of sentiment analysis on social media platforms. The processed data consisted of 1,500 tweets. This study shows that the classification of Twitter data using the Naïve Bayes algorithm achieved an accuracy of 80%.

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