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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
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Design of a Palm Oil Harvest Recording Information System using Two-Factor Authentication Security Subastian, Jefry; Mansur, Mansur
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5917

Abstract

The palm oil harvest recording process in many farmer groups is still carried out manually, which may lead to recording errors, delays in reporting, and difficulties in verifying harvest data. This study aims to design and develop a web-based palm oil harvest recording information system that supports harvest data entry by farmers, harvest verification by agents, transportation monitoring by drivers, and the presentation of harvest information through a dashboard for the owner. The system is designed by integrating a Two-Factor Authentication (2FA) security mechanism using One-Time Password (OTP) verification as an additional authentication layer to enhance user access security. The software development method applied in this study is the System Development Life Cycle (SDLC) Waterfall model, which includes requirement analysis, system design, implementation, testing, and maintenance stages. Black-box testing results indicate that all main system features function properly according to user requirements. In addition, user acceptance testing using usability testing with the System Usability Scale (SUS) instrument obtained an average score of 72.5, which falls into the Good category. These results suggest that implementing OTP-based 2FA improves authentication security without reducing system usability. The developed system is able to enhance the efficiency, accuracy, and transparency of the palm oil harvest recording process and has the potential to be implemented in farmer groups or similar business units.
Optimizing Driver Drowsiness Detection: Evaluating CLAHE and AHE Enhancement Techniques Naufal, Muhammad; Al Azies, Harun; Al Zami, Farrikh; Brilianto, Rivaldo Mersis
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5206

Abstract

Driver drowsiness is a critical factor in road safety, and early detection can be key to preventing accidents. This research focuses on improving the accuracy of drowsiness detection by enhancing the contrast of driver facial images using image processing techniques. Specifically, the study explores the effectiveness of Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) in this context. The research utilizes the Drowsy Driver Detection (DDD) dataset, which includes facial images categorized into Drowsy and Non-Drowsy classes. AHE and CLAHE techniques are applied to preprocess these images, aiming to improve contrast and subsequently enhance drowsiness detection accuracy. Evaluation metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Signal-to-Noise Ratio (SNR) are employed to assess the quality of the processed images. The findings indicate that CLAHE performs better than AHE in terms of image enhancement. CLAHE achieves significantly lower MSE (93.90) compared to AHE (103.92), along with higher PSNR (28.41 for CLAHE vs. 27.97 for AHE) and SNR (0.49 for CLAHE vs. 0.04 for AHE) values. These results suggest that CLAHE effectively enhances contrast and improves image clarity. The success of CLAHE as a contrast enhancement technique highlights its potential application in real-time driver monitoring systems. In conclusion, this research underscores the importance of image preprocessing techniques like CLAHE in advancing driver safety technologies, emphasizing their potential to enhance the performance of drowsiness detection systems in practical driving scenarios.
Mobile Banking Acceptance: A Modified UTAUT 2 Model with Trust and Risk Integration Hidayah, Nur Aeni; Putra, Adam Kusuma; Arham, Zainul; Putra, Syopiansyah Jaya; Waspodo, Bayu
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5831

Abstract

This study examines the factors influencing the acceptance of the JakOne Mobile mobile banking application developed by Bank DKI using a modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, extended with the external variables of perceived trust and perceived risk. Although the application has a high number of downloads, it faces significant challenges related to user satisfaction. This is reflected in the sharp polarization of ratings, where one-star reviews account for 32.17% of total reviews, indicating fundamental issues in a substantial portion of the user experience. This research employed a quantitative approach, with data collected from 450 respondents in the Jabodetabek region through an online questionnaire. Data analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to test 13 proposed hypotheses. The main results show that performance expectancy and facilitating conditions have a positive and significant effect on behavioral intention. Interestingly, this study found that price value, hedonic motivation, and habit have a significant negative effect on behavioral intention. Furthermore, trust was proven to effectively reduce perceived risk, while effort expectancy and social influence showed no significant influence. The practical implications of these findings suggest that Bank DKI should prioritize improving the technical performance of the application and develop communication strategies focused on building user trust, rather than emphasizing low cost or entertainment-related aspects.
Aceh Province Tourism Destination Recommendation System using Content based Filtering Method Maulana, Ariefhan; Rohman, Arif Nur; Pristyanto, Yoga
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5500

Abstract

Tourists often experience difficulties in finding tourist destinations in Aceh Province that match their content preferences and are geographically close to their location. This study aims to develop a tourism destination recommendation system in Aceh Province using a Content-Based Filtering approach with the Cosine Similarity algorithm and the Haversine Formula. The dataset consists of 119 tourist destinations, including attributes such as destination name, destination description, and geographical coordinates (latitude and longitude). The research process began with text data preprocessing, which included case folding, punctuation removal, tokenization, duplicate word removal, stopword removal, and stemming. Next, the similarity between destinations was calculated using the Cosine Similarity algorithm based on tourism content descriptions, while the Haversine Formula was applied to measure the geographical distance between the user’s location and the tourist destinations. The results indicate that the developed system is able to provide relevant tourism destination recommendations by simultaneously considering content relevance and geographical proximity. Therefore, the system can assist tourists in selecting destinations that best match their preferences.
Word Embedding Features to Improve Machine Learning Performance in Sentiment Analysis of the Honor of Kings Game Harris, Abdul; Nugroho, Agus; Novianto, Yudi; Jasmir, Jasmir; Fatma, Dhea
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5850

Abstract

The rapid growth of social media has encouraged an increasing number of studies on sentiment analysis to better understand public perceptions and opinions. This study aims to evaluate the performance of three machine learning algorithms—Naïve Bayes, K-Nearest Neighbor (KNN), and Random Forest—in classifying user review sentiments toward the game Honor of Kings. The dataset was collected from the Google Play Store, consisting of 900 reviews. The data then underwent preprocessing steps including cleaning, case folding, tokenization, stopword removal, stemming, and sentiment labeling into positive and negative classes. Furthermore, three word embedding techniques were applied, namely Word2Vec, GloVe, and FastText, each of which was tested across the three machine learning algorithms. The experimental results indicate that the use of word embedding features significantly improves classification accuracy compared to models without embedding features. KNN combined with FastText achieved the best performance, reaching an accuracy of 87.55%, while Random Forest combined with FastText produced the lowest accuracy. FastText demonstrated superior performance due to its ability to represent words through subword information, making it more effective in handling rare vocabulary and large-scale datasets. This study confirms that combining machine learning classification methods with word embedding features plays a crucial role in improving sentiment analysis performance. Future research may focus on hyperparameter optimization, the application of more advanced preprocessing techniques, and dataset expansion to develop more robust models with better generalization capability.
Detecting Chili Ripeness Using YOLOv11 Pratama, Andika Ramadian; Fajriani, Alfiah; Rifai, Sitti Najmia
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.6122

Abstract

This study aims to develop a deep learning-based chili ripeness detection system using the YOLOv11 model. Chili ripeness is classified into three categories: unripe, semi-ripe, and ripe. The dataset consists of 150 original images, which were expanded to 300 images to increase data variation. Model training was conducted using the Roboflow platform, while accuracy testing was performed in Google Colab through an image upload-based processing method. The experimental results show that the model achieved an accuracy of 93.94%, with a precision of 94.21%, recall of 93.94%, and an F1-score of 93.94% on the test dataset. This system is expected to support the automation of chili sorting based on ripeness levels.
Implementation of Role-Based Access Control (RBAC) in a Drug Stock Management Information System Purba, Dea Agustina; Hidayasari, Nurmi
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5958

Abstract

Role-Based Access Control (RBAC) is a role-based access management mechanism that restricts user privileges according to their authority within an information system. The implementation of this mechanism is particularly important in pharmacy drug stock management systems, especially at Apotek Pratama dr. Moris, which still relies on manual stock recording. This condition often results in data discrepancies, delays in monitoring expired medications, difficulties in report generation, and the absence of clear access restrictions for each user. This study focuses on developing a web-based drug stock management information system with RBAC as the primary mechanism for user authorization and security. The system was developed using the Waterfall methodology, which includes requirement analysis, system design, implementation, and testing. The application was built using Laravel framework version 11, MySQL database, and the Laravel Spatie Permission package for managing roles and permissions. Black Box Testing results indicate that all functional test scenarios were executed successfully, achieving a 100% success rate. User acceptance testing, conducted using the System Usability Scale (SUS), yielded an average score of 76, categorized as Good. The findings demonstrate that the implementation of RBAC effectively restricts user access based on roles, enhances data security, and improves the accuracy and efficiency of drug stock management compared to the previous manual system.
Web-based Educational Payment Information System using Role-based Access Control Security Romadhan, Hidayatur; Mansur, Mansur
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5920

Abstract

The management of Sumbangan Pembinaan Pendidikan (SPP) payments in Islamic boarding schools still faces issues related to delayed recording, data inaccuracies, and weak access control over financial information. This study aims to design and implement a web-based SPP payment information system that applies Role-Based Access Control (RBAC) to improve administrative order and data security. The system was developed using the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The application was built using the Laravel framework with RBAC implemented at the middleware level to manage user access based on defined roles, namely Super Admin, Treasurer, Student Guardian, and Principal. System testing was conducted using the Black-Box Testing method to validate core functionalities, including user authentication, billing management, payment verification, report generation, and role-based access restrictions. The test results indicate that all system functions operate as expected and that the RBAC mechanism effectively prevents unauthorized access to sensitive features and data. Overall, the implemented system supports more structured payment administration, improves data accuracy, and enhances security and accountability in managing financial transactions within the pesantren environment.
WhatsApp Hybrid Chatbot Architecture Rasa-DeepSeek: Design and Performance Evaluation Had, Iqbaluddin Syam; Utomo, Fandy Setyo; Karyono, Giat; Kinding, Dwi Putriana Nuramanah
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5791

Abstract

This study designed and evaluated a hybrid chatbot for a domain-specific application by addressing two main issues: limited NLU coverage and the variability of latency and cost when all queries are routed directly to an LLM. The proposed solution integrates a deterministic Rasa-based pipeline with a DeepSeek fallback mechanism. In this architecture, Rasa handles NLU processing, rules, stories, and context storage for mk and jk, while the LLM is only invoked when the NLU confidence score falls below a defined threshold. The methodology includes end-to-end implementation through a Node.js bridge connected to Rasa, functional testing to validate the intent–entity–action flow, and performance testing using load (stress) testing across two access paths: the Rasa REST endpoint and the Node-to-Rasa bridge. Meanwhile, the LLM pipeline was profiled separately through instrumented action calls. The results indicate that domain-specific conversations were successfully answered using curated knowledge, and both deterministic access paths met the service level objective (SLO), achieving a median latency of approximately 32 milliseconds with no observed errors. This study contributes by demonstrating that a hybrid chatbot architecture separating deterministic and generative pipelines can maintain SLO compliance in domain-specific settings. In addition, it highlights limitations of LLMs in understanding domain ontologies, reinforcing the need for semantic guardrails.
Comparative Analysis of Deep Learning Architectures for Indonesian Spice Image Classification Putri, Fatma Meylinda; Ghozali, Muhammad Imam; Sugiharto, Wibowo Harry
SISTEMASI Vol 15, No 2 (2026): 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.v15i2.5946

Abstract

Spices are an important commodity in Indonesia; however, visual identification remains challenging due to the similarity in appearance among different types of spices. This study develops an image classification system for Indonesian spices using transfer learning by comparing four Convolutional Neural Network (CNN) architectures: VGG16, ResNet50, EfficientNetB0, and MobileNetV2. The Indonesian Spices dataset consists of 31 classes with a total of 6,510 images, which were stratified and divided into training, validation, and testing sets. The training process was conducted in two stages: head-layer training and fine-tuning, with the application of regularization techniques such as dropout, batch normalization, and L2 regularization. The results show that ResNet50 achieved the best performance with a test accuracy of 95.80%, followed by VGG16 with 95.70%. EfficientNetB0 provided an optimal balance between accuracy (94.17%) and the fastest inference time (5.51 ms), while MobileNetV2 achieved an inference time of 6.07 ms with an accuracy of 92.63%, making it suitable for mobile devices. This study demonstrates the effectiveness of transfer learning for Indonesian spice image classification.

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