cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kab. indragiri hilir,
Riau
INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 920 Documents
 77   78   79   80   81 
Perancangan Sistem Monitoring Operasional Alat Berat di Pertambangan PT. Pamapersada Nusantara Berbasis Android Taufiq, Aldi Muhammad; Hertina, Siti; Gunawan, Hendra
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5264

Abstract

PT Pamapersada Nusantara (PAMA), a company operating in the mining sector, faces challenges in monitoring the performance of heavy equipment, which is still conducted manually. This method is prone to recording errors and disrupts the smooth flow of production processes. The issues include inaccurate logging of activities for Shovel and Haul Truck units, discrepancies in operator status during production, and the lack of real-time operational location information. This study aims to improve efficiency in identifying the status of heavy equipment—such as standby, delay, and down—through direct reporting by operators. The development methodology employed is Agile Scrum, with the work process divided into sprints to allow for continuous evaluation and refinement based on user needs. The outcome of this study is an Android-based application called MineTrack, which enables real-time monitoring of heavy equipment operations by utilizing GPS technology for location tracking and SQLite for data management. In conclusion, MineTrack enhances the accuracy of operational monitoring, supports more effective decision-making in heavy equipment management within the mining industry, and contributes to increased productivity while reducing the risk of operational errors.
Deep Learning Approach for Music Genre Classification using Multi-Feature Audio Representations Asanah, Nurul; Pratama, Irfan
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5369

Abstract

Automatic music genre classification is critical for enhancing user experience in streaming platforms and recommendation systems. This study proposes a Convolutional Neural Network (CNN)-based approach using the GTZAN dataset, which contains ten music genres. The original 30-second audio tracks were segmented into overlapping 3-second chunks, then preprocessed and converted into three feature representations: Mel-Spectrogram, Chroma, and Spectral Contrast. CNN model consisting of four convolutional layers with increasing filters (32–256). The model was trained over 13 epochs using the Adam optimizer. The proposed model achieved 91% accuracy, outperforming previous approaches based on single-feature extraction. The integration of diverse spectral and harmonic features enabled the model to better distinguish between similar genres and improved its generalization. This method offers practical value for real-time music classification, automatic tagging, and intelligent audio indexing in music streaming services and digital libraries.
Optimizing Sentiment Analysis of Digital Wayang Viewer Comments using SMOTE and the Naïve Bayes Algorithm hardiyanti, mawar; Fajarlestari, Maria Karmelia
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5002

Abstract

Wayang performances are an integral part of Indonesia’s rich cultural heritage. This traditional art form has been deeply rooted in Indonesian society for centuries, evolving through live performances and, more recently, through rapid digital adaptations—including presentations on online platforms such as YouTube. In the digital age, YouTube has become a leading platform for video sharing, allowing audiences to enjoy wayang performances without being physically present. However, data from the Central Bureau of Statistics on Socio-Cultural Affairs indicates a decline in interest among younger generations in traditional arts such as wayang. This highlights the need for innovative and relevant approaches to reintroduce this cultural heritage to them. Sentiment analysis based on viewer comments offers an effective way to identify audience opinions—whether positive, negative, or neutral. Comment data were collected using web scraping techniques with Selenium WebDriver, enabling efficient data extraction. The collected data then underwent preprocessing, including case folding, tokenization, and stopword removal, to prepare it for classification. The Naïve Bayes algorithm was employed to categorize comments into positive, negative, or neutral sentiments. Preliminary results revealed that 51.6% of comments were positive, 42.3% neutral, and 6.0% negative. Model evaluation using K-fold cross-validation yielded an accuracy of 0.98 ± 0.01, a precision of 0.99 ± 0.01, and a recall of 0.72 ± 0.11 without applying SMOTE. After applying SMOTE, recall improved to 0.80 ± 0.05. This study contributes to the development of more accurate sentiment analysis models in the context of social media and underscores the importance of techniques like SMOTE in addressing class imbalance issues.
Association Rule Analysis for Sales Strategy Optimization with Apriori Algorithm Method Amelia, Avril Firda; Rochmoeljati, Rochmoeljati
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5288

Abstract

PT XYZ is a manufacturing company that produces various types of industrial valves. Despite having entered the export market, the company continues to experience monthly fluctuations in order volumes due to the suboptimal use of data in formulating effective sales strategies. This study aims to identify association rules using the Apriori algorithm as the basis for sales recommendations. The analysis was conducted on transaction data from January 2024 to February 2025, using a minimum support threshold of 20% and a minimum confidence threshold of 65%, determined through exploratory analysis. The results yielded 14 first-level (L1) rules, 21 second-level (L2) rules, and 8 third-level (L3) rules, indicating associations between products that can inform cross-selling schemes and product sampling strategies. These patterns were used to design sales strategies, such as cross-selling and product bundling, to increase the average value per transaction, as well as product sampling to introduce less popular items. GV and CV products showed the strongest association, with a support value of 43%, a confidence level of 77%, and a lift value of 1.5—indicating a strong potential for increased sales when these products are offered together. These personalized strategy recommendations are expected to improve customer loyalty, expand market reach, and drive sustainable growth in the company’s sales volume.
Exploring Blockchain and AI in Digital Banking: A Literature Review on Transactions Enhancement, Fraud Detection, and Financial Inclusion Pramudito, Dendy; Na’am, Jufriadif; Ernawan, Ferda
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5231

Abstract

This paper explores the integration of Blockchain and Artificial Intelligence (AI) in the realm of digital banking, focusing on enhancing transaction efficiency, preventing fraud, and promoting financial inclusion. Utilizing a literature review methodology, this study synthesizes existing research to identify the synergistic effects of these two transformative technologies. Blockchain offers a decentralized, secure framework for transactions, while AI enhances data analysis and decision-making capabilities. The findings reveal that the combined application of Blockchain and AI can significantly streamline banking operations, reduce the incidence of fraud through advanced predictive analytics, and extend financial services to underserved populations. A comparison followed on case studies of successful digital banks that e taken advantage of AI and Blockchain technologies. In order to validate the results, industry experts and banking professionals were interviewed qualitatively to find out on the one hand where the opportunities lie and on the other where the challenges are when doing this implementation. Furthermore, the research highlights the challenges and limitations of implementing these technologies, including regulatory hurdles and the need for robust cybersecurity measures. By addressing these issues, financial institutions can leverage Blockchain and AI to create a more secure, efficient, and inclusive banking environment. This study not only fills a critical research gap but also provides practical recommendations for banking practitioners and policymakers. Ultimately, the integration of Blockchain and AI is poised to redefine digital banking, ensuring that technological advancements contribute to a more equitable financial landscape.
Comparative Analysis of Linear Regression and Neural Network Algorithms for Stock Price Prediction Wibowo, Eldrianto Christian; Cahyono, Ariya Dwika
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5325

Abstract

This study compares the performance of Linear Regression and Neural Network algorithms in predicting stock prices using historical data from PT Bank Central Asia Tbk (BBCA) for the period from January 1, 2019, to February 17, 2025. The dataset includes daily open, high, low, and close prices, as well as trading volume. Linear Regression is employed as a conventional statistical approach, while Neural Networks are applied as a machine learning method based on deep learning. Performance evaluation is conducted using three error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The experimental results show that the Linear Regression model consistently produces more accurate predictions with lower error values compared to the Neural Network. Although Neural Networks are more flexible in capturing non-linear patterns, Linear Regression demonstrates greater stability under market conditions present in the observed data period.
Web-based Inventory Information System using Agile Scrum Method at CV Tunggal Putra Jaya Widaningsih, Nona; Windiyanti, Noni; Rukhviyanti, Novi
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5253

Abstract

CV Tunggal Putra Jaya is a building materials store that still manages inventory manually using Microsoft Excel. This approach leads to data inaccuracies, difficulties in real-time stock monitoring, and risks of overstocking or stock shortages. This research aims to design a web-based inventory system to address these issues, with key features such as real-time stock monitoring, order and pre-order creation, and automatic financial report generation. The system is developed using the Agile Scrum method with a sprint duration of 14 days to ensure flexibility to changes in requirements. The implementation results show an efficiency increase of up to 92.73%, marked by a reduction in invoice creation time from 5–10 minutes to 1 minute, stock monitoring from 30 minutes to 1 minute, and financial report generation from 10 minutes to 1 minute. The system has also successfully passed Black Box testing for all features, and user validation indicates that this system can significantly reduce working time.
Assessing Academic Information System Performance Through Sentiment Analysis Thuraya, Zafira; Ibrahim, Ali; Utama, Yadi; Indah, Dwi Rosa
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5130

Abstract

The Academic Information System (SIMAK) at Sriwijaya University plays a crucial role in facilitating student academic activities; however, it faces several technical issues that affect user satisfaction, including server outages and challenges in data access. This dissatisfaction serves as a vital metric for evaluating the system's effectiveness. This study aims to analyze student sentiment regarding SIMAK utilizing the Naïve Bayes method. A total of 92 tweets were gathered from Twitter through web scraping, which were then categorized into manually labeled training and test datasets for model validation. The data underwent processing that included text cleaning and the application of Term Frequency-Inverse Document Frequency (TF-IDF) to assess the significance of words within a collection of documents. The evaluation results indicated that the model achieved an accuracy of 65%, with a precision of 63% for negative sentiment and a recall of 100%. In contrast, positive sentiment exhibited a low precision of 12.5% and an F1-score of 22.2%, highlighting difficulties in identifying positive sentiment due to data imbalance. The model demonstrated greater effectiveness in identifying user grievances, particularly concerning server disruptions, data delays, and challenges in completing Study Plan Cards and accessing grades. These findings provide valuable insights for SIMAK maintainers to enhance system reliability and user experience. Future research should aim to broaden data coverage and explore alternative analytical methods to yield more representative outcomes.
Architectural Design of Referral Patient Data Security using Advanced Encryption Standard Romli, Moh. Ali; Zakariyah, Muhammad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.4864

Abstract

Electronic medical record can manage various kinds of patient data in digital form. Patient data security is a priority that must be met by healthcare provider for referral process. One of the medical data exchange standards that is widely used is Health Level Seven (HL7) standard. The absence of security in the HL7 standard makes patient data vulnerable to digital attacks, information security disturbances, and can even disrupt the patient's own psyche. This study aims to create an architectural design as well as a prototype of a patient data security system that uses HL7 standard, by utilizing the Advanced Encryption Standard (AES) as a cryptographic algorithm. Architectural design for data exchange, can change HL7 data from plain text and unauthenticated data transmission to data with secure and protected protocols. The research method starts from requirements analysis and finished with making system prototypes and model evolution. The system that has been developed is deployed into a SaaS model on cloud computing. The SaaS architecture for securing patient referral data has been adapted to the stakeholders involved (users), the medical data exchange standard used (HL7 standard), workflow and data exchange processes, and the data security technique itself (AES).
Security of School Financial Transaction Applications with the Implementation of Two Factor Authentication Method Hardiansyah, Novi; Aulia, Rima; Hadriani, Angelina
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.4862

Abstract

Financial transactions over the internet often become targets of cyber attacks that can harm users in conducting transactions due to negligence from both the system and human error. This research will focus on the security level of the SIKOLAH financial application, where every school financial transaction will be conducted online. In this study, the Two Factor Authentication (2FA) method will be implemented, ensuring that each user has official access with data in the database registered in the application. The results of implementing this method have successfully validated user data through verified email and WhatsApp numbers to send OTP codes to the access holder's smartphone via the official WhatsApp channel of the application manager. Avoiding server phishing actions also limits the OTP code delivery time to no more than 300 seconds to send the OTP code to users, thereby reducing the risk of data interception by cybercrime.

Page 79 of 92 | Total Record : 920

 77   78   79   80   81 

Filter by Year

2013 2025


Reset
Filter By Issues
All Issue Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (2025): Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi Vol 12, No 1 (2023): Sistemasi: Jurnal Sistem Informasi Vol 11, No 3 (2022): Sistemasi: Jurnal Sistem Informasi Vol 11, No 2 (2022): Sistemasi: Jurnal Sistem Informasi Vol 11, No 1 (2022): Sistemasi: Jurnal Sistem Informasi Vol 10, No 3 (2021): Sistemasi: Jurnal Sistem Informasi Vol 10, No 2 (2021): Sistemasi: Jurnal Sistem Informasi Vol 10, No 1 (2021): Sistemasi: Jurnal Sistem Informasi Vol 9, No 3 (2020): Sistemasi: Jurnal Sistem Informasi Vol 9, No 2 (2020): Sistemasi: Jurnal Sistem Informasi Vol 9, No 1 (2020): Sistemasi: Jurnal Sistem Informasi Vol 8, No 3 (2019): Sistemasi: Jurnal Sistem Informasi Vol 8, No 2 (2019): Sistemasi: Jurnal Sistem Informasi Vol 8, No 1 (2019): Sistemasi Vol 8, No 1 (2019): Sistemasi: Jurnal Sistem Informasi Vol 7, No 3 (2018): Sistemasi: Jurnal Sistem Informasi Vol 7, No 2 (2018): Sistemasi: Jurnal Sistem Informasi Vol 7, No 2 (2018): SISTEMASI Vol 7, No 1 (2018): Sistemasi: Jurnal Sistem Informasi Vol 6, No 3 (2017): Sistemasi: Jurnal Sistem Informasi Vol 6, No 2 (2017): Sistemasi: Jurnal Sistem Informasi Vol 6, No 1 (2017): Sistemasi: Jurnal Sistem Informasi Vol 5, No 3 (2016): Sistemasi: Jurnal Sistem Informasi Vol 5, No 2 (2016): sistemasi Vol 5, No 2 (2016): Sistemasi: Jurnal Sistem Informasi Vol 5, No 1 (2016): Sistemasi: Jurnal Sistem Informasi Vol 4, No 3 (2015): Sistemasi: Jurnal Sistem Informasi Vol 4, No 2 (2015): Sistemasi: Jurnal Sistem Informasi Vol 4, No 1 (2015): Sistemasi: Jurnal Sistem Informasi Vol 3, No 4 (2014): SISTEMASI: Jurnal Sistem Informasi Vol 3, No 3 (2014): Sistemasi: Jurnal Sistem Informasi Vol 3, No 2 (2014): Sistemasi: Jurnal Sistem Informasi Vol 3, No 1 (2014): Sistemasi: Jurnal Sistem Informasi Vol 2, No 4 (2013): Sistemasi: Jurnal Sistem Informasi Vol 2, No 3 (2013): Sistemasi: Jurnal Sistem Informasi Vol 2, No 2 (2013): Sistemasi:Jurnal Sistem Informasi Vol 2, No 1 (2013): Sistemasi: Jurnal Sistem Informasi More Issue