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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 1,011 Documents
 86   87   88   89   90 
Sentiment Analysis of MyBCA Application User Reviews using Naive Bayes, Random Forest, and Decision Tree Akbar, Muhammad Rizky Mawandhyka; 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.v14i5.5472

Abstract

In today’s era of globalization, rapid technological advancements are driving innovation across various sectors, including the banking industry. One of the key digital innovations in banking is mobile banking (m-banking), which allows customers to perform transactions via smartphones. This study aims to analyze the sentiment of user reviews on the MyBCA application using three classification methods: Naive Bayes, Random Forest, and Decision Tree. A total of 5,000 user reviews were collected from the Google Play Store through web scraping techniques. The data was preprocessed using the TF-IDF weighting method and processed with Python programming language and the Scikit-Learn library. The dataset was split into 90% training data and 10% testing data. This study also applies the ISO 9126 standard for multi-label classification to assess software quality based on Usability, Efficiency, Functionality, Reliability, and Maintainability. Evaluation results indicate that Random Forest achieved the highest accuracy at 94.09%, outperforming Naive Bayes (81.77%) and Decision Tree (82.38%). This research contributes to the development of a sentiment-based evaluation method for mobile banking applications, integrating user feedback analysis with ISO 9126 quality standards, and offers a useful reference for improving digital banking services.
Smart Clove Oil Distillation System using IoT and Ultrasonic Sensors As'ad, Ihwana; Rahmatullah, Andi Muhammad; Abdullah, Syahrul Mubarak; Fahmi, Fahmi; Pakka, Hariani Ma'tang; Andiyan, Andiyan
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.v14i5.5232

Abstract

Traditional clove essential oil distillation remains inefficient due to manual labor dependency, imprecise oil-water separation, and inconsistent product quality. Addressing these limitations, this study aims to design and develop a smart, Internet of Things (IoT)-based system named AquaClove to automate and optimize the distillation process. The system integrates an ESP32 microcontroller, ultrasonic sensors, and solenoid valves, enabling precise fluid level detection and automated oil-water separation. Using the Arduino IoT Cloud, the system supports real-time monitoring and remote control, enhancing operational transparency and scalability. results indicate that the system achieved a 32% reduction in total distillation time (from 4.2 to 2.9 hours), 66.7% reduction in labor requirements (from 3 to 1 personnel), and 66.7% reduction in oil loss per 10-liter batch (from 0.6 L to 0.2 L). The ultrasonic sensors consistently detected liquid levels with an average measurement deviation of less than ±2 mm, while solenoid valves responded within 0.8 seconds of command input. These outcomes demonstrate significant improvements in process efficiency, separation precision, and system responsiveness. Furthermore, the modular container design promotes gravity-assisted separation, enhancing energy efficiency and reducing mechanical complexity. The remote monitoring feature allows users to access real-time data on fluid levels and system status, ensuring reliable operation with minimal manual supervision. AquaClove thus demonstrates how integrating ultrasonic sensing and IoT technologies can modernize traditional agricultural processes. This study contributes a scalable and sustainable solution to the essential oil industry, particularly in small- and medium-scale clove oil production.
Simple Selection Index (SSI) Method in Electric Vehicle Selection for Logistics Companies Bisono, Hadi Hikmadyo; Utami, Ema
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.v14i5.5434

Abstract

The rapid development of electric vehicles (EVs) has encouraged various industrial sectors, including logistics, to transition from fossil fuel-based vehicles to more environmentally friendly solutions. While EVs offer advantages such as energy efficiency, reduced carbon emissions, and lower operating costs, selecting the right electric vehicle for a logistics company is not a straightforward task. The main challenge lies in the wide variety of available models, each with different technical and operational specifications. This complexity increases as companies must consider multiple criteria such as price, payload capacity, vehicle width, battery capacity, and cargo volume. Therefore, a systematic approach is needed to support decision-making. One commonly used approach is the Multi-Criteria Decision-Making (MCDM) method. This study introduces the Simple Selection Index (SSI) method, a newly developed MCDM approach designed as a simplified version of the Preference Selection Index (PSI) method. The novelty of SSI lies in its ability to eliminate complex steps such as the calculation of preference variation values and preference deviation scores, making the ranking process more concise and easier to apply—without compromising the accuracy of the results. The study aims to evaluate the performance of the SSI method in selecting the most suitable electric vehicle by directly comparing its results with those of the PSI method, using a dataset comprising four vehicle alternatives and five key criteria: price, payload, width, battery capacity, and cargo volume. The findings show that the SSI method produces an identical ranking to the PSI method, with EV-4 as the top recommendation and EV-1 as the second-best alternative. With its more efficient process, the SSI method holds strong potential for application in fast and straightforward multi-criteria decision-making scenarios.
Developing Web-Based Patient Reservation and Data Management System using Rapid Application Design Subiyakto, A'ang; Yudhanta, Satya; Aini, Qurrotul
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.v14i5.5110

Abstract

A private infant and toddler care clinic in the South Tangerang area, Omah Bayi, faces problems in managing reservations and patient data. During this time, reservations were recorded in a book and scheduling was done verbally. This causes inefficiency and increases the possibility of data recording errors. To solve this problem, a web-based reservation system was developed using the rapid application development (RAD) method. This method involves requirements analysis, design, implementation, and system testing, with the support of technologies such as PHP and MySQL. The designed system effectively shortens the turnaround time of the reservation process and patient data management. Test results show that reservation recording time is reduced from 10 minutes to two minutes (80% faster), and monthly report preparation time is reduced from two hours to 10 minutes (92% faster). This research aimed to create a digital solution that will not only improve the clinic's operational efficiency and prevent data duplication but will also make services more accessible to patients. It is expected that the implementation of this system will be the first step towards the clinic's digital transformation and will have a positive impact on the development of healthcare services in the future.
Combining SVM and Naive Bayes Models using a Soft Voting Approach for Sentiment Analysis of Tong Tji Tea House Saputra, Fendi Pradana; Suria, Ozzi
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.v14i5.5481

Abstract

In today’s digital technology and social media era, people are increasingly influenced to actively share reviews of restaurant services, expressing a wide range of customer opinions and perceptions. This study aims to analyze sentiment in Indonesian-language review texts using three machine learning models: Support Vector Machine (SVM), Naive Bayes (NB), and a combination of both through an Ensemble Soft Voting Classifier approach. The research focuses on user reviews of the Tong Tji Tea House, collected from the Google Maps platform, with sentiment data distributed as follows: positive (2,676 entries), neutral (670 entries), and negative (251 entries). The class imbalance poses a significant challenge in developing an optimal classification model. To address this, parameter optimization was carried out using the Grid Search method. The SVM model with a linear kernel and C=10 parameter achieved an accuracy of 0.9289 and a positive F1-score of 0.9289. The NB model recorded an accuracy of 0.8340 with an F1-score of 0.9102. Meanwhile, the Ensemble model with a soft voting approach and a 4:1 weight ratio (SVM:NB) demonstrated the best performance, achieving an accuracy of 0.9344 and a positive F1-score of 0.9750. These results indicate that the Ensemble method effectively enhances model accuracy and robustness in handling imbalanced data.
User Satisfaction Analysis of University of Jember's UC3 using EUCS Approach Fadhil, Martiana Kholila; Darmawan, Muhammad Riza
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.v14i5.5249

Abstract

The UC3 service is the University of Jember's integrated digital platform, facilitating academic services including diploma applications, service complaints, and campus problem reporting. Despite being crucial for digital academic administration, the UC3 application encounters technical obstacles such as frequent server downtime, less intuitive interfaces, and slow response times. This study aims to evaluate user satisfaction levels with the UC3 application to provide insights for service improvement and benefit optimization for users. A quantitative methodology was adopted using the End User Computing Satisfaction (EUCS) method, incorporating five variables: content, accuracy, format, ease of use, and timeliness. Data were collected through questionnaires distributed to 100 student respondents and analyzed using IBM SPSS version 27. The instrument test results showed an average validity score of 0.807 and reliability of 0.892 across all EUCS variables. Classical assumption testing confirmed normally distributed data (0.137, p > 0.05), with tolerance values of 0.699 > 0.1 for multicollinearity and no heteroscedasticity detected. Hypothesis testing revealed that accuracy and timeliness had partially significant effects on user satisfaction based on the T-test, while the F-test demonstrated that all EUCS variables collectively had significant influence on satisfaction levels (sig 0.001 < 0.05).The results indicate that UC3 users demonstrate relatively high satisfaction levels, as all EUCS variables significantly influence their experience both individually and collectively, providing a foundation for targeted system improvements.
A Hybrid Internet of Behavior Algorithm for Predicting IoT Data of Plant Growing using LSTM and NB Models Ahmad, Khansaa Yaseen; Abdullah, Omar Muayad
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.v14i5.5354

Abstract

The researches that compare the accuracy between classical statistical prediction procedures and deep learning algorithms represent an important and modern field. The prediction accuracy of the plant growing is considered as an important factor in the field of smart agricultural technologies. This research proposes a hybrid Internet of Behaviors (IoB) technique that linking between time-series predicting and the classification models to estimate the plant growing behaviors using real environmental data. The proposed algorithm includes ML algorithms, especially Recurrents Neural Networks (RNN) and Long Short-Term Memory (LSTM), used for predicting the plant growing depending on sensor data. To improve the prediction accuracy, the outputs of the LSTM system were used as inputs to the Naïve Bayes algorithm. The dataset is collected from the Kaggle website using Internet of Things (IoT) sensor readings depending on the factors that affecting the plant growing. The obtained results stated that the proposed hybrid algorithm enhanced the prediction accuracy compared to using LSTM alone. Additionally, the using of Naïve Bayes algorithm added more reliable to the process of examining the growing behavior, making the proposed system more practical and provide the rapidity in task performing.
Static Analysis-based Detection of Android Malware using Machine Learning Algorithms Saied, Omar Emad; Thanoon, Karam Hatim
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.v14i5.5498

Abstract

The rapid growth of Android applications has led to increased security threats, making malware detection a critical concern in cybersecurity. This research proposes a static analysis-based technique that employs machine learning for Android malware detection. The proposed method utilizes three classification algorithms: Support Vector Machine (SVM), Random Forest, and Decision Tree. The tool extracts static permission features from APK files to evaluate their effectiveness. The dataset consists of 400 Android applications (200 benign and 200 malicious), which were analyzed using the three machine learning models. Their performance was evaluated and compared using accuracy , precision, recall, and F1-score. The Random Forest model achieved the highest accuracy. The results demonstrate that static analysis combined with a robust classification model can effectively identify malicious applications with a high degree of accuracy. Although the tool is reliable in detecting Android malware, it has limitations in handling obfuscated and dynamic threats. Future research could focus on integrating dynamic analysis techniques to improve detection accuracy and enhance resistance to evasion techniques
Multi-Platform System Development for Violence Complaint Services using the CodeIgniter Framework Rohmah, Putri Anjilis; Setiaji, Pratomo; Muzid, Syafiul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5528

Abstract

Violence against women and children remains a prevalent social issue in Indonesia, including in Kudus Regency. The lack of fast, secure, and easily accessible reporting facilities is one of the factors contributing to the low reporting rate, leaving many cases unaddressed. This study aims to design and develop a web-based violence complaint information system using the CodeIgniter framework with a Model-View-Controller (MVC) architecture to ensure a more structured, secure, and efficient system. The development method follows the waterfall model, consisting of requirements analysis, design, implementation, integration, and system testing. The system provides key features such as an online reporting form, automated notifications to officers, real-time report status tracking, and case progress recording by authorized personnel. Black-box testing conducted by one reporter and three staff members of the Kudus Social Service (Dinas Sosial P3AP2KB) on six main features across four different scenarios resulted in a total of 96 test cases, achieving a functional success rate of 98.9%. One failure was identified in file upload validation, where the system still allowed unsupported file formats. Nevertheless, all other features functioned properly, and the system was proven responsive across devices. This reliability supports faster reporting and case handling, enabling victims to report more easily while allowing relevant institutions to respond quickly, accurately, and transparently.
User Review Automation: Detecting Actionable Complaints on Gojek in the Play Store using the LSTM Method Ramadhani, Indira Nailah; Sari, Winda Kurnia; Tania, Ken Ditha
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (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.v14i6.5708

Abstract

This study aims to develop an automatic complaint detector for Gojek app reviews using Long Short Term Memory (LSTM). The dataset consists of 225,002 user reviews on the Google Play Store. The purpose of this study itself is to facilitate the service team in understanding the shortcomings of the application complained by users. Automatic complaint detection will facilitate the service team to take action to resolve the problems experienced by users. Therefore, the review data provided by users is properly processed using LSTM to create an effective and efficient detection system. Processing is carried out using three different data sharing ratios, namely 90:10, 80:20, and 70:30 to ensure that the system is stable and effective. The accuracy results of the three data sharing ratios reached above 90%, thus proving that the system is able to detect complaints well. A pre-built dashboard is used to visualize the results of the system built using LSTM to facilitate monitoring the classification results. This system is expected to facilitate companies in detecting all user complaints and finding solutions to improve services to provide comfort for users.

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