cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dentik Karyaningsih
Contact Email
jurnaljriti@gmail.com
Phone
+628121871795
Journal Mail Official
harsiti@yahoo.com
Editorial Address
http://ejurnal.jejaringppm.org/index.php/jriti/editorialteamjriti
Location
Kota serang,
Banten
INDONESIA
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 7 Documents
 1 
Search results for , issue "Vol 2 No 3 (2025): April - Juli 2025" : 7 Documents clear
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%.
Klasifikasi Penentuan Siswa Berprestasi Menggunakan Algoritma Naïve Bayes Classifier DI PT.Yes Study Education Group Indonesia Laksono, Novan Ponco; Syaaifullah, Achmad Akbar; Yusuf, Ajif Yunizar Pratama
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.v2i3.158

Abstract

PT. Yes Study Education Group Indonesia is an overseas education consultancy founded by international alumni and based in Toronto, Canada, with experience helping thousands of students from various parts of the world to achieve their dream of studying abroad. However, it is not easy to study abroad because there are several factors and documents that must be prepared, such as passports, visas, and English test certificates like the Test Of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS). To achieve optimal results, good learning outcomes are required; furthermore, of course, learning outcomes are indicators of student achievement, so an algorithm is needed to determine student performance, with the aim of serving as a supporting tool in evaluating the learning process and outcomes using the naïve bayes classifier algorithm with a trial dataset of 200 student names along with their respective scores, from which 80 test records were obtained. From these calculations, the Gaussian NB model with a 50:50 split validation yielded an accuracy of 73%, scenario 2 with a 60:40 ratio yielded 75% accuracy, scenario 3 with a 70:30 ratio yielded 76.6% accuracy, scenario 4 with an 80:20 ratio yielded 82.2% accuracy, and scenario 5 with a 90:10 ratio yielded 85% accuracy.
Optimalisasi Rute Terpendek Pengiriman Hewan Qurban Dengan Penerapan Algoritma Djikstra Pada UD Ridho, Bekasi Ridho Al-Rafiq, Muhammad; Handayani, Dwipa Handayani
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.v2i3.159

Abstract

This research aims to optimize the shortest route for sending sacrificial animals to UD Ridho in Bekasi by applying the Djikstra algorithm. In the sacrificial animal delivery industry, efficient route selection is very important to ensure fast delivery times and low operational costs. Djikstra's algorithm, which is known as one of the most popular graph algorithms for finding the shortest path, is used in this study to calculate the best delivery route. This research began with modeling a delivery route network involving main delivery points in Bekasi. Data regarding the distance between points is collected and entered into the system for analysis using the Djikstra algorithm. The results of implementing this algorithm are compared with the delivery routes currently used by UD Ridho. The research results show that using the Djikstra algorithm can reduce the total distance traveled and delivery time significantly compared to the previously used route method. Thus, applying the Djikstra algorithm to the sacrificial animal delivery route at UD Ridho can increase operational efficiency and reduce delivery costs.
Analisis Pengaruh Usia Terhadap Kemenangan Petarung Gladiator Dengan Metode Regresi Linier Sederhana Maulana, Firdan
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.v2i3.177

Abstract

Gladiators were fighters in ancient Rome who participated in arena battles as a form of public entertainment, often held in the Colosseum in Rome. The battles could take place between gladiators or against wild animals. Gladiators usually came from various backgrounds, such as prisoners of war, slaves, or even volunteers. The life of a gladiator was filled with risks, but those who survived could gain fame, wealth, and freedom. This study aims to analyze the effect of age on gladiator fighters' victories using a simple linear regression method, where the battle is influenced by various factors, including age. The data used includes the age and victories of gladiator fighters, which were analyzed to show a correlation between the gladiators' age and the number of victories obtained. This study provides specific insights into the physical characteristics that affect success in battle, as well as offering scientific factors for researchers regarding factors that influence fighting performance.
Pemilihan Siswa Terbaik Menggunakan Simple Addictive Weighting pada SMK Assalam Depok Kusmanto, Tria Hadi; Hatmoko, Bondan Dwi
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.v2i3.202

Abstract

This research aims to develop a Best Student Selection System using the Simple Additive Weighting (SAW) method. Problem identification was carried out through observation and interviews with individuals authorized to manage and understand the best student selection process at SMK Assalam. A literature review was conducted to gather information about the SAW method and its application in schools. The alternative data collected included ten students selected as candidates for the selection process, which was then normalized to convert the data into a relative and comparative scale across criteria. The decision support system implemented using the SAW method was evaluated by collecting implementation data and analyzing the results. The evaluation results indicate that the system can provide useful information to support decision-making in selecting the best student.

Page 1 of 1 | Total Record : 7

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 2 No 2 (2025): Desember 2024 - Maret 2025 Vol 3 No 1 (2025): Augustus - November 2025 Vol 2 No 3 (2025): April - Juli 2025 Vol 1 No 2 (2024): Desember 2023 - Maret 2024 Vol 2 No 1 (2024): Agustus - November 2024 Vol 1 No 3 (2024): April - Juli 2024 Vol 1 No 1 (2023): Agustus - November 2023