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RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Published by Universitas Abdurrab
ISSN : 24772062     EISSN : 2502891X     DOI : https://doi.org/10.36341/rabit
Core Subject : Science, Engineering,
Computer Science & IT Engineering
This journal is called RABIT, where the name comes from two words namely, RAB which means Abdurrab University and IT which means information technology, it can be interpreted as a journal of this journal Journal of Informatics Engineering Study Program Pekanbaru Abdurrab University. This RABIT journal contains various sciences related to the world of computers especially information technology and information systems, namely, this journal is published twice a year where the initial publication is on January 10 while for the second issue which is on July 10.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 696 Documents
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ANALISIS SENTIMEN ULASAN GOOGLE MAPS UNTUK REKOMENDASI COFFEE SHOP DI KUDUS MENGGUNAKAN TF-IDF DAN LOGISTIC REGRESSION: SENTIMENT ANALYSIS OF GOOGLE MAPS REVIEWS FOR COFFEE SHOP RECOMMENDATIONS IN KUDUS USING TF-IDF AND LOGISTIC REGRESSION Diyas Aditya Adi Saputra; Noor Latifah; Soni Adiyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7952

Abstract

The rapid growth of the café industry in Kudus has given residents plenty of options for places to hang out. Customer reviews on Google Maps serve as a vital information source, as they contain customer opinions and satisfaction levels regarding a particular cafe. However, the sheer volume of review data makes manual analysis less effective. This study aims to analyze the sentiment of Google Maps reviews for 10 cafés in Kudus (2023–2025) using the TF-IDF method and a Logistic Regression algorithm based on K-Fold Cross-Validation. Research data was obtained through web scraping, comprising 3,393 Google Maps reviews. The research stages included data preprocessing (text normalization, tokenization, stopword removal), feature extraction using TF-IDF, splitting the data into 80% training and 20% testing sets, training the Logistic Regression model, and evaluating model performance using K-Fold Cross-Validation. The experimental results show that the TF-IDF-based Logistic Regression model is capable of classifying positive and negative reviews well, yielding an accuracy of approximately 86,68%, precision of 92,45%, recall of 91,71%, and an F1-score of 92,08%. This study is expected to help the public determine recommendations for the best coffee shops in Kudus based on objective customer opinions, as well as serve as a reference for business owners to improve service quality and customer satisfaction.
RANCANG BANGUN SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PEMANEN TERBAIK MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING BERBASIS WEB Jelita Anjelina Siburian; Ritna Wahyuni; Sri Lestari Rahayu
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7954

Abstract

Evaluation of oil palm harvesters' performance at PTPN IV Regional 4 Solok Selatan Plantation is still conducted manually, resulting in delays in reporting and calculation errors that affect the accuracy of determining the best harvester. This study aims to design and develop a web-based Decision Support System (DSS) implementing the Simple Additive Weighting (SAW) method to provide efficient, objective, and transparent performance evaluations based on harvest production, attendance level, and harvest quality/loose fruit criteria. This research employed a quantitative approach using a system development method. Data were collected through observations and interviews at the research site. The system was developed using the prototype model with PHP programming language, MySQL database, and Bootstrap 5.3 framework. The SAW method was implemented through value normalization, criteria weighting, and weighted summation processes to generate preference scores for each harvester. The developed system successfully automated the calculation of harvester rankings based on three main criteria in real time. Functional testing showed that all modules operated according to the design, including daily data input, monthly evaluation, SAW calculations, ranking graph visualization, and PDF report export features. The implementation of a web-based DSS using the SAW method effectively replaced the manual process in determining the best harvester, resulting in more accurate, consistent, and accountable evaluations. Future development is recommended to include integration with digital attendance systems and the implementation of automatic weighting methods to improve the objectivity of criteria weight determination.
PERANCANGAN WEBSITE INTERAKTIF PROVIT FARM VILLAGE SEBAGAI MEDIA INFORMASI DAN EDUKASI WISATA BERBASIS DIGITAL : **DESIGN OF AN INTERACTIVE WEBSITE FOR PROVIT FARM VILLAGE AS A DIGITAL-BASED TOURISM INFORMATION AND EDUCATIONAL MEDIA** Muhammad Rahmadhan Dwisyahputra; Azhari Shouni Barkah
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7956

Abstract

The development of digital technology in the tourism sector has increased the need for interactive and easily accessible information media. Provit Farm Village, as an agro-tourism-based educational destination, still faces limitations in delivering integrated digital information, making it difficult for potential visitors to obtain complete information regarding facilities, ticket prices, and tourism activities. This study aims to design an interactive website as a digital-based tourism information and education medium using the Design Thinking method, which consists of the empathize, define, ideate, prototype, and testing stages. Data collection was conducted through observation and interviews with the management to identify system requirements. The analysis results were used to design a website that provides information about the tourism profile, facilities, educational activities, ticket prices, galleries, Etafresh milk products, as well as reservation and product ordering features integrated with WhatsApp admin. System evaluation was carried out using alpha testing based on expert judgment involving representatives from Provit Farm Village and a website expert as evaluators, using a rating scale of 0–100. The testing results showed an average score of 90.2, which falls into the very good category. This indicates that the website meets user needs in terms of functionality, interface design, ease of use, and information clarity. Therefore, the website is effective as a medium for tourism information, education, and digital promotion.
ANALISIS HUBUNGAN ANTARA KEPUASAN USABILITY DAN SENTIMEN PENGGUNA: PENDEKATAN KOMBINASI SYSTEM USABILITY SCALE DAN ANALISIS SENTIMEN PADA APLIKASI SHOPEE I Gede Pageh Widiastra; Ni Putu Vega Nirmala Kanti; Ni Made Dwi Laksmi; Gede Indrawan; I Made Agus Oka Gunawan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7960

Abstract

The development of e-commerce in Indonesia has emphasized the importance of evaluating application quality, particularly in terms of usability and user perception. This study aims to analyze the relationship between usability and user sentiment in the Shopee application. The study employed a mixed methods approach with two types of data: primary data in the form of a SUS questionnaire from 100 Shopee user respondents, and secondary data in the form of scraped user reviews. Sentiment analysis was performed using the Naive Bayes algorithm, while the relationship between variables was evaluated using Pearson correlation. The results showed that the Shopee application obtained an average SUS score of 82, which is in the Good category. The sentiment analysis results indicated a dominance of positive sentiment compared to neutral and negative sentiment, with a classification accuracy level of 0.79. Furthermore, the Pearson correlation test obtained a coefficient value of 0.62 with a significance value (p-value) <0.05, indicating a positive and significant relationship between usability and user sentiment. These findings indicate that the better the level of application usability, the more positive user perceptions of the Shopee application. This study confirms that the SUS method and sentiment analysis can provide a more comprehensive assessment of application quality based on user experience.  
GLOBAL AIR POLLUTION PATTERN SEGMENTATION USING K-MEANS, DBSCAN, AND HIERARCHICAL CLUSTERING Muhamad Ja'far Shadiq; Devi Ajeng Efrilianda
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7962

Abstract

Increasing air pollution levels worldwide have raised serious concern regarding environmental sustainability and public health, making systematic analysis of pollution patterns increasingly important. This study analyzes global air quality patterns using three unsupervised clustering algorithms, K-Means, DBSCAN, and Hierarchical Clustering, applied to a dataset of 23,463 records from 175 countries, utilizing four major pollutant indicators: CO, Ozone, NO₂, and PM2.5 AQI values. Prior to clustering, outlier handling was evaluated by comparing three approaches: original data, IQR, and Z-Score methods, with the original data selected based on the highest silhouette score of 0.5696. Cluster configuration analysis indicated that four clusters provided the most interpretable segmentation structure. K-Means clustering produced four interpretable air quality groups, namely Clean, Moderate, Unhealthy, and Hazardous, with PM2.5 identified as the most dominant pollutant based on its strong correlation with the overall AQI Value (r = 0.985). Among the three algorithms, Hierarchical Clustering achieved the highest Silhouette Score (0.6341), followed by DBSCAN (0.6161) and K-Means (0.5696). Despite this, K-Means was selected as the primary algorithm due to its scalability, computational efficiency, and interpretability. A stability test conducted on the K-Means model across 20 trials confirmed consistent clustering performance, with a mean Silhouette Score of 0.5491 and a standard deviation of 0.0193. External validation against EPA AQI categories yielded an ARI of 0.2010 and NMI of 0.2410, confirming partial alignment with internationally recognized standards. The findings demonstrate that unsupervised clustering provides an effective framework for identifying global air quality patterns and supporting evidence-based environmental policy decisions.
IDENTIFIKASI SEL DARAH ABNORMAL PADA PENYAKIT POLYCYTHEMIA VERA MENGGUNAKAN DEEP LEARNING: IDENTIFICATION OF ABNORMAL BLOOD CELLS IN POLYCYTEMIA VERA USING DEEP LEARNING Valentino Tinambunan; Aldo Hia Aldo; Rianto Sahputra Berutu; Nurmala Sari; Saut Dohot Siregar
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7964

Abstract

Polycythemia Vera is a blood disorder characterized by an excessive number of red blood cells. This study aims to identify abnormal blood cells using a deep learning method based on convolutional neural networks (CNN). The dataset used consists of 1,200 blood cell images, including 600 normal images and 600 abnormal images. The steps in the study include data pre-processing, training a convolutional neural network model, model testing, and implementing a web-based application using Streamlit. The research findings show that the CNN model can classify blood cell images very effectively, with a training accuracy of 98.33%, a validation accuracy of 97.92%, and a testing accuracy of 100% with a loss value of 0.0136. In addition, the application created is able to classify blood cell images quickly and automatically. Based on these results, the CNN method is proven to be effective in identifying abnormal blood cells in cases of Polycythemia Vera.
SISTEM INFORMASI MANAJEMEN GUDANG PERKEBUNAN KELAPA SAWIT DENGAN PENDEKATAN HYBRID MENINGKATKAN EFISIENSI DAN AKURASI INVENTORI Ready Perdana; Ratu Mutiara Siregar; Andi Prayogi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7966

Abstract

Warehouse management in oil palm plantations still faces challenges including manual recording that is prone to errors, inventory data discrepancies, and limited internet connectivity in remote areas. This study aims to design and develop a Warehouse Management Information System using a hybrid approach that supports offline and online operations, implement a data synchronization mechanism between local storage and the main database, and improve operational efficiency and inventory accuracy in oil palm plantation environments. The Research and Development (R&D) method with a Prototype approach was employed. The system was developed using Flutter as the frontend framework, Laravel as the backend and API, SQLite as the local device database, and MySQL as the main server database. Testing was conducted using the Blackbox Testing method with 46 test scenarios covering eight functional modules. The results show a 100% success rate across all functional test scenarios. The system operates in hybrid mode, automatically synchronizes data via a push-pull mechanism using UUID as an idempotency key, and provides inventory management features including item master data, goods receipt, goods issuance, stock opname, multi-level approval, digital reporting (PDF and Excel), and activity logs. The system has been proven to improve operational efficiency by eliminating redundant recording processes and enabling remote digital approvals, and to enhance inventory accuracy through automatic input validation, duplicate data prevention, and a structured audit trail.  
SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI KARIR UNTUK SISWA SMA MENGGUNAKAN METODE PROFILE MATCHING BERBASIS TEORI HOLLAND Aditya Yoga Saputra; Agus Sidiq Purnomo
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7967

Abstract

High school students often face challenges in choosing a career due to guidance processes that remain conventional, inefficient, and impersonal. To provide a more objective and scientifically sound career recommendation solution, this study focuses on the design and implementation of a Decision Support System (DSS) built on a web-based platform. The system integrates Holland’s personality theory (RIASEC) with the Profile Matching method to compare students’ interest scores against the ideal profiles of six career alternatives. The algorithm’s operation begins by measuring the distance between a student’s actual profile and the career profiles established as ideal standards. This distance value is then converted into systematically determined weight scores. The final stage involves aggregating the Core Factor and Secondary Factor scores, with a 60% weight for the Core Factor and 40% for the Secondary Factor, which then yields a final score as the basis for ranking. The system was implemented using Laravel, React, and PostgreSQL, and tested on 32 students. Validation was conducted by comparing the system’s recommendations with expert psychological assessments. Test results showed a 78.13% level of agreement, where the majority of recommendations aligned with the students’ dominant personality characteristics. These findings demonstrate that the integration of computational approaches and career psychology theory can enhance the efficiency, transparency, and accuracy of the guidance process, while also serving as a strategic tool for counselors in data-driven student potential mapping.
RANCANG BANGUN ROLE BASED ACCESS CONTROL (RBAC) PADA SISTEM DONASI BERBASIS WEB UNTUK PANTI ASUHAN dzaki diana; Dzaki Mutammadien Illiyin; Moh. Aminollah Hamzah; Anwari; Fathorrozi Ariyanto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7975

Abstract

The rapid advancement of information technology has driven digital transformation across various sectors of life, including donation management in non-profit institutions such as orphanages. At Al-Muthi Orphanage, recording practices still rely on manual Microsoft Excel, leaving significant gaps in data security and accountability, primarily due to the absence of access restrictions between users. This research aims to develop and implement an online donation platform by adopting the Role Based Access Control (RBAC) framework, which regulates access rights into three user categories: Admin, Caregiver, and Donor. The Waterfall method was chosen as the development approach, covering system requirements analysis, design using UML and ERD, coding utilizing PHP Native and MySQL, as well as testing phases. The resulting platform presents various innovations including horizontal RBAC (access restrictions between users with equal authority), layered verification mechanisms for financial transactions, state-based lock system (data locking after verification with privileged access for Admin), account lifetime, proportional transparency, donation schemes without login requirements, public activity galleries, and password recovery facilities. Based on test results, the system successfully achieved all targets for vertical RBAC (100%), horizontal RBAC (100%), state-based lock system (100%), account lifetime (100%), and donor privacy protection (100%), while cross-check verification reached 80%. The usability assessment using the SUS method yielded a score of 81.00 classified as the Excellent category, and donor trust level reached 4.3 out of 5, or Very High. Overall, the integration of RBAC into the donation information system has proven effective in strengthening data security and donation management accountability at Al-Muthi Orphanage.
SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KESIAPAN PSIKOLOGIS CALON PEKERJA MIGRAN INDONESIA DENGAN AHP DAN TOPSIS: SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KESIAPAN PSIKOLOGIS CALON PEKERJA MIGRAN INDONESIA DENGAN AHP DAN TOPSIS Rizal Rio Andrian; Agus Sidiq Purnomo
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7978

Abstract

The selection of psychological readiness for Indonesian Migrant Worker Candidates (CPMI) at placement institutions often faces subjectivity constraints that risk mental unpreparedness in destination countries. This study aims to implement a Decision Support System (DSS) using a hybrid Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to enhance selection objectivity. The system development methodology follows the Turban model encompassing intelligence, design, choice, and implementation phases. The research employs eight psychological criteria involving three experts for criteria weighting and 40 alternative CPMI candidates for ranking. AHP weighting results indicate Work Motivation (0.206) and Discipline (0.203) as the most dominant parameters with validated Consistency Ratio ≤ 0.1. TOPSIS implementation produces preference values ranging from 0.134 to 0.882, classified into Ready, Sufficiently Ready, and Needs Strengthening categories. Sensitivity analysis demonstrates that the hybrid model aggregation is more robust indicated by the high stability of top-ranking candidates compared to individual expert preference scenarios and Equal Weight methods. In conclusion, this system successfully transforms qualitative assessments into transparent and accurate quantitative indicators to support managerial decision-making in the CPMI selection process.

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