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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 40 Documents
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Search results for , issue "Vol 14, No 1 (2025): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
Analysis of Public Perception on Domestic Violence Cases using Support Vector Machine Algorithm Husnah, Mirdatul; Hidayat, Rahmat
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4724

Abstract

Domestic violence is currently a case that is easily exposed by the public. People can easily find many cases through social media. The latest case was experienced by a social media influencer, Cut Intan. This case has attracted public attention and is widely discussed on several social media, one of which is on the X app. With this phenomenon, an analysis of public sentiment towards domestic violence cases that occur in Indonesia is needed. The analysis was conducted using the Support Vector Machine algorithm, a classification algorithm that can classify values into certain classes and has a good level of accuracy. Experiments on analyzing public sentiment towards domestic violence cases using the SVM algorithm resulted in an accuracy score of 95%. The precision score for negative sentiment is 94%, neutral sentiment is 100%, and positive sentiment is 100%. The recall result for negative sentiment is 100%, neutral sentiment is 67%, and positive sentiment has a value of 77%. The results of the f-1 score on negative sentiment are 97%, 80% neutral sentiment, and 87% positive sentiment. While the percentage of community sentiment obtained is 84.40% having negative sentiment, 8.24% having positive sentiment, and 7.36% having neutral sentiment.
Classification of Thyroid Class using ID3 Algorithm and Artificial Neural Network (ANN) Henisaniyya, Nabila; Pertiwi, Citra; Desiani, Anita; Amran, Ali; Arhami, Muhammad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.3440

Abstract

Thyroid disease refers to a range of conditions or issues affecting the thyroid gland. This gland, located below the Adam’s apple, is responsible for coordinating various metabolic processes in the body, making its function essential. Early detection of thyroid symptoms is crucial as an initial step in planning the necessary treatments to prevent more severe thyroid-related health risks. One commonly applied method for early detection involves classification using a data mining approach. Among the algorithms frequently used for classification are the ID3 algorithm and Artificial Neural Networks (ANN). This study aims to obtain the best classification results for detecting thyroid disease by comparing these two algorithms. The accuracy results for percentage split testing were 88% for ID3 and 90% for ANN. Meanwhile, the accuracy values for K-Fold cross-validation were 93% for the ID3 algorithm and 95% for the ANN algorithm. Additionally, the overall average precision and recall values for both algorithms were above 75% for percentage split testing and above 90% for K-Fold cross-validation. The results indicate that ANN achieved higher percentages compared to ID3. Based on the accuracy, precision, and recall values obtained from both algorithms, it can be concluded that the ANN algorithm performs better than ID3 in classifying thyroid disease.
Designing an Information System Architecture for the East Java Community Eye Hospital Pharmacy using TOGAF ADM 9.2 Approach Hutagalung, Novi Monica; Sudianto, Yupit; Kusumawati, Aris; Fajria, Asruliasani
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4932

Abstract

The East Java Community Eye Hospital (RSMM Jatim) is a Type B specialty hospital owned by the Provincial Government of East Java. RSMM Jatim envisions becoming a nationally recognized eye hospital. To achieve this vision, one critical requirement is providing high-quality pharmaceutical services. However, the hospital’s pharmacy department currently faces several challenges in supporting its business processes, such as long queues in pharmacy services, inaccuracies in drug inventory data, and lack of system integration. Although RSMM Jatim has implemented the Medify application to manage its Hospital Management Information System (HMIS), its development has not been fully optimized to support all business processes in the pharmacy department. Therefore, this study aims to design an information system architecture using the Enterprise Architecture (EA) approach with The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM) 9.2. The research focuses on Phase A: Architecture Vision, Phase B: Business Architecture, and Phase C: Information System Architecture. The study involves remapping the pharmacy department's business processes and designing an integrated information system to manage pharmacy queue registration, pharmaceutical supply requests, inventory management, and the handling of expired medications. The research outputs include deliverables for each phase, such as the Principle Catalog, Requirement Catalog, Value Chain Diagram, Functional Decomposition Diagram, Goal/Objective/Requirement, Business Architecture Solutions, Data Entity/Component Catalog, and Application Portfolio Catalog Targeting. This architecture design is expected to serve as a guide for RSMM Jatim in developing an integrated information system for its pharmacy department, ultimately improving service quality and operational efficiency.
Gamified AI Analysis as Learning Media for Islamic Education on Students’ Learning Outcomes Margareta, Siska; Sesmiarni, Zulfani; Zakir, Supratman
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4865

Abstract

This research investigates the effectiveness of a gamified AI learning medium, specifically the “Marbel Shalat” game, on the learning outcomes of second-grade students in Islamic Religious Education (PAI) at SDIT. Employing a quantitative research design with a One-Group Pretest-Posttest approach, the study involved a sample of 36 students of SDIT Tahfizh Daarul Huda Sariak Laweh and SDIT Al-Kautsar Bukittinggi. Data were collected through pre-tests and post-tests, alongside observations and questionnaires to assess the impact of the gamified learning medium. The pre-test results indicated that only 15 students (41.67%) achieved mastery of the material, while the post-test results revealed a significant improvement, with 33 students (91.61%) reaching mastery. Statistical analysis confirmed the reliability and validity of the assessment instruments, demonstrating a very strong positive correlation (r = 0.860) between the pre-test and post-test scores. Furthermore, a t-test indicated a statistically significant difference in learning outcomes before and after the intervention (t = 9.83). The findings suggest that the “Marbel Shalat” game effectively enhances student learning in PAI, highlighting the potential of gamified AI as a valuable educational tool for improving student engagement and understanding of complex subjects.
Implementation of a Web-Based Waste Collection Data System Using QR Code Scanning Romadhon, Ibrahim Aji Fajar; Rohman, Arif Nur; Pristyanto, Yoga
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4782

Abstract

The suboptimal management of waste collection in urban areas significantly impacts environmental quality and public health. Yogyakarta City, which generates 644.69 tons of waste annually, can only manage 583.80 tons per year. Various initiatives have been implemented to improve waste management, yet challenges persist, such as limited temporary disposal sites, irregular waste collection schedules, and the absence of an effective and efficient system to assist waste collection officers in recording and tracking waste collection for each household.This study aims to develop a web-based Waste Collection Data System using QR Code Scanning, employing the waterfall method, which consists of the following stages: requirement analysis, design, development, testing, and maintenance. The system enables waste collection officers to log waste collection activities by scanning a QR code at each household and allows residents to access information regarding waste collection status, mandatory fees, collection schedules, and waste processing. The testing results demonstrate that all features function effectively as intended. The implementation of this system is expected to enhance the efficiency of waste collection data management, improve environmental quality, and increase community satisfaction in Yogyakarta City.
Sentiment Analysis of Sunscreen Product Reviews using Naive Bayes Classifier Algorithm Zahirma, Mutia; Rumini, Rumini
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4454

Abstract

The advancement of information and communication technology has transformed consumer interactions with products and brands, especially in the beauty industry. This study focuses on sentiment analysis of sunscreen product reviews using the Naive Bayes Classifier method. Review data for the Wardah UV Shield Essential Sunscreen Gel SPF 35 PA+++ were collected through web scraping from the Femaledaily website, resulting in 1,451 data entries. The data were labeled as positive or negative based on ratings and then processed through data cleaning, case folding, stopword removal, and tokenization. The processed data were converted into numerical representations using TF-IDF. The Naive Bayes Classifier model built for this study achieved an accuracy of 79%, precision of 67%, recall of 64%, and an F1-score of 65%. A word cloud visualization highlighted frequently occurring words in both positive and negative reviews. This study demonstrates that the Naive Bayes Classifier method is effective for classifying sentiments in sunscreen product reviews. Although this method is easy to implement and understand, it has limitations due to the assumption of word independence and the imbalance between positive and negative reviews. Future research is expected to expand the dataset and explore other sentiment analysis methods to improve accuracy.
Comparison of Support Vector Machine and Naïve Bayes Algorithms on Date Fruit Type Classification based on Hue Saturation Value Image Br. Keliat, Mia Risa; Ikhsan, Muhammad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4978

Abstract

Dates are a popular fruit in Indonesia, especially during the month of Ramadan. With the increase in consumption, an automated system is needed to classify different types of dates to avoid misidentification. This study compares two classification algorithms: Support Vector Machine (SVM) and Naïve Bayes. The research compares the performance of the Support Vector Machine (SVM) and Gaussian Naïve Bayes algorithms in classifying date fruits based on Hue, Saturation, Value (HSV) images. The dataset consists of 200 images of dates from four types: Ajwa Dates, Sukari Dates, Golden Valley Dates, and Deglet Nour Dates, with a 70% training data and 30% testing data split. The images were captured using a high-resolution smartphone camera under controlled lighting conditions to ensure consistent image quality. The segmentation process includes converting from RGB (original), grayscale, binary, complement operation, filling holes, and conversion to the HSV color space. MATLAB tools were used to implement the algorithms and evaluate model performance. The results show that Gaussian Naïve Bayes outperforms the SVM with a higher accuracy of 80.00%, precision of 79.74%, recall of 78.46%, and F1-score of 79.09%. In contrast, the SVM with a linear kernel only achieved an accuracy of 66.67%, precision of 52.49%, recall of 65.00%, and F1-score of 58.08%. Evaluation showed that neither model suffered from overfitting. Based on the GUI analysis, Naïve Bayes proved superior in classifying the types of dates. This study makes a significant contribution to the development of an automated image-based system for classifying agricultural products.
Public Sentiment Analysis on TikTok about Tapera Policy using Random Forest Classifier Muhandhis, Isnaini; Ritonga, Alven Safik
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4878

Abstract

At the beginning of 2024, the Tapera policy proposed by the government sparked widespread public debate, resulting in both pros and cons. To improve the quality of public services, it is crucial for the government to evaluate policies to align with the needs and expectations of the community. This study aims to analyze public sentiment on the social media platform TikTok regarding the Tapera policy. Comment data was collected from several TikTok videos discussing the Tapera policy with high view counts. These videos received various responses in the form of comments, expressing positive, neutral, and negative sentiments about Tapera. A total of 5,036 comments were successfully scraped. The Random Forest Classifier was used for sentiment classification. This method was chosen for its ability to maintain high predictive accuracy, minimize overfitting, and perform effectively in classification tasks. The study results showed that negative sentiment dominated TikTok users' opinions, accounting for 82%, followed by neutral sentiment at 10% and positive sentiment at 8%. Many expressed disapproval for various reasons, including concerns about potential corruption, the ineffectiveness of contributions due to inflation, and the policy being burdensome amid a sluggish economy. Neutral sentiment was dominated by questions related to Tapera, such as the amount of Tapera deductions and whether participation is mandatory for those who already own a house. Positive sentiments expressed support for the Tapera policy and willingness to pay the contributions. However, the proportion of supporters of this program was significantly smaller than those opposing it. The training results of the classification model using the Random Forest Classifier achieved an accuracy of 89%. The highest F1-score for detecting negative sentiment was 94%, while the F1-score for detecting neutral sentiment was 17% and for positive sentiment, it was 32%. This disparity is due to the dataset composition being dominated by negative sentiment. The proportion of sentiment significantly influences the training of the classification model. A balanced proportion for each sentiment would enable the model to better learn and recognize the words frequently associated with each sentiment.
Implementation of ISO 31000:2018 Framework in Risk Management Analysis of e-Poin Application Putri, Nandini Nurmalita; Rudianto, Christ
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4822

Abstract

The e-Poin application is a platform developed by Satya Wacana Christian University to facilitate the submission of activity proposals and financial accountability reports. This application is utilized by work units and student organizations responsible for carrying out activities annually. Ensuring the smooth implementation of this application is crucial, as it directly impacts processes ranging from proposal validation to fund disbursement. Therefore, risk management for the e-Poin application using the ISO 31000:2018 framework is essential to minimize potential risks that could threaten its performance. The research identified 15 potential risks, comprising 13 risks categorized as medium-level and 2 risks categorized as low-level.
Quality of Service on Virtual Local Area Network (VLAN) in Campus Network Yunika, Indira Salsa; Ichsan, Ichwan Nul
Sistemasi: Jurnal Sistem Informasi Vol 14, No 1 (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.v14i1.4725

Abstract

This study explores the implementation of Quality of Service (QoS) on Virtual Local Area Network (VLAN) within the campus network of Universitas Pendidikan Indonesia, Purwakarta Campus. In the era of technology, computer networks serve as critical infrastructure for educational institutions, supporting learning and communication activities. This research employs an experimental method using Cisco Packet Tracer 8.2.1 to design and analyze QoS on VLAN for Faculty, Students, and Administration, each connected to the server via trunking. The QoS testing results show that communication between devices within the same VLAN was successful, while communication between different VLAN failed. Measurements of latency, delay, and packet loss indicate stable performance, with FTP throughput varying between uploads and downloads. These findings highlight that the implementation of VLANs can enhance security and efficiency within campus networks.

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