cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Salamun
Contact Email
salamun@univrab.ac.id
Phone
-
Journal Mail Official
Jurnal.ti@univrab.com
Editorial Address
Redaktur Jurnal RABIT Teknik Informatika Universitas Abdurrab: Gedung Universitas Abdurrab Pekanbaru Jl. Riau Ujung No. 73 Pekanbaru Riau - Indonesia
Location
Kota pekanbaru,
Riau
INDONESIA
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
 52   53   54   55   56 
- KLASIFIKASI MULTI-LABEL GENRE FILM BERDASARKAN FITUR VISUAL POSTER MENGGUNAKAN CNN BERBASIS EFFICIENTNET: - Shyalenn Cerolin Kolibonso; Dhani Ariatmanto
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.v11i2.7883

Abstract

In the digital content era, movie posters serve as rich visual media conveying crucial semantic information rather than mere promotional tools. This research aims to implement EfficientNet, a modern convolutional neural network (CNN) architecture, to automate multi-label movie genre classification based solely on poster images. The study utilized the Kaggle Movie Poster Dataset and evaluated the performance of EfficientNet-B3 against classical CNN baselines, namely VGG16, ResNet50, and InceptionV3. To address the inherent challenge of class imbalance, the methodology incorporated comprehensive data preprocessing, class-weighting, and dynamic threshold-tuning. Experimental results demonstrated that EfficientNet-B3 significantly outperformed the baseline models, achieving the highest F1 Macro score (0.3200) and AUC-ROC (0.7676), while maintaining the lowest Hamming Loss (0.1269). In conclusion, EfficientNet's compound scaling approach provides a robust and highly effective feature extraction mechanism that successfully mitigates majority bias in imbalanced datasets. This study contributes to the advancement of intelligent visual classification systems, particularly within creative industries where supporting metadata is limited.  
PENGEMBANGAN SISTEM DETEKSI INFORMASI PALSU PADA MEDIA ONLINE MENGGUNAKAN ALGORITMA BERT: DEVELOPMENT OF A FAKE INFORMATION DETECTION SYSTEM ON ONLINE MEDIA USING THE BERT ALGORITHM Ferdy Agustian; Safitri Jaya
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.v11i2.7885

Abstract

The spread of fake news through digital media platforms has become a serious problem affecting social, political, and public health conditions in Indonesia. The speed of information dissemination has exceeded the capacity of existing manual verification mechanisms. This study aims to develop an automated fake news detection system for Indonesian-language news using a fine-tuning approach on two BERT variants, namely IndoBERT and multilingual BERT (mBERT), with a multi-field input strategy that combines news headlines and narratives into a single sequence using the [SEP] separator token. The dataset consists of 22,400 balanced samples collected from TurnBackHoax.id, Antaranews, Kompas, and Detik, and is divided into training, validation, and testing sets using a 70:15:15 ratio. Both models were trained using identical hyperparameter configurations on the Kaggle platform with dual T4 GPU acceleration. Evaluation results on the test set show that mBERT achieved an accuracy of 99.79% and an F1-score of 0.9979, slightly outperforming IndoBERT which achieved an accuracy of 99.67% and an F1-score of 0.9967. The system was implemented as a web application using FastAPI and HTML/JavaScript, featuring real-time prediction, automated article scraping from seven news websites, and LIME-based explainability visualization integrated directly into the user interface. Black-box testing achieved a pass rate of 95.5%, while white-box testing achieved 100%, confirming that the system functions according to the designed specifications.  
SISTEM INFORMASI RE-PENDAYAGUNAAN PERANGKAT DESA PURNA TUGAS DENGAN METODE KEY PERFORMANCE INDICATOR (KPI) sri; Fajar Nugraha, S.Kom., M.Kom; Noor Latifah, S.Kom., M.Kom
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.v11i2.7890

Abstract

Village government plays a crucial role in public services, necessitating effective human resource management, including the empowerment of retired village officials. However, the ongoing process in Mijen District, Demak Regency, is still manual, resulting in inefficiency, unintegration, and a tendency towards subjectivity. This study aims to develop a web-based Re-Employment Information System for Retired Village Officials by implementing the Key Performance Indicator (KPI) method as a measuring tool for performance assessment and reassignment eligibility. Data collection methods were conducted through observation, interviews, and literature studies while system development uses the Software Development Life Cycle (SDLC) with the Waterfall model. The KPI indicators used include attendance, job responsibility, understanding of village legal products, information technology capabilities, length of service, integrity, and social involvement. The research results are a web-based information system capable of managing village official data, conducting structured performance assessments, and generating objective reassignment recommendations based on KPI values. This system is also equipped with data management features, evaluation processes, and presentation of assessment results reports. With this system, it is hoped that it can increase efficiency, transparency, and objectivity in the process of empowering retired village officials and assist village and sub-district governments in making more precise and measurable decisions.  
IMPLEMENTASI METODE AHP PADA SELEKSI POHON INDUK UNGGUL KELAPA SAWIT : IMPLEMENTATION OF THE AHP METHOD IN THE SELECTION OF SUPERIOR OIL PALM PARENT TREES NUSAIBAH WALIATUL AMIROH
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.v11i2.7895

Abstract

The selection of superior oil palm mother trees is an important stage in producing high-quality seeds and improving plantation productivity. The process of selecting mother trees at PT ASD Bakrie Oil Palm Seed Indonesia is still carried out manually based on observations and assessments by experts, which can lead to subjectivity, inconsistency, and a relatively long decision-making process. This study aims to develop a web-based decision support system using the Analytic Hierarchy Process (AHP) method to support the selection process objectively, systematically, and measurably. The research methods used include observation, interviews, and literature studies. The Analytic Hierarchy Process (AHP) method was implemented through hierarchical structuring, pairwise comparison, matrix normalization, priority weight calculation, consistency testing, and alternative ranking processes. The criteria used consist of Fresh Fruit Bunch (FFB) productivity, Oil Extraction Rate (OER), pest and disease resistance, tree morphology and health, environmental adaptability, and seed price. This study used six superior mother tree alternatives, namely Themba, Spring, Ovane, Supreme, Tanza MR Gano, and Premium. The results showed that the developed system was able to perform automatic calculations and generate recommendations for the best superior mother trees based on the highest ranking values, where the Themba alternative obtained the highest value of 0,303. In addition, the developed system improved efficiency, objectivity, and accuracy in the decision-making process for selecting superior oil palm mother trees.
SISTEM PENDUKUNG KEPUTUSAN KELAYAKAN PENERIMA BLT-DD MENGGUNAKAN METODE AHP DAN SAW BERBASIS WEB Riska Aprilia -; Riska Aprilia Putri Nabilla
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.v11i2.7901

Abstract

The problem of poverty in rural areas requires the distribution of social assistance to be carried out precisely on target, one of which is through the Village Fund Direct Cash Assistance (BLT-DD) program. However, the process of determining BLT-DD recipients in Undaan Lor Undaan Kudus Village is still done manually through village deliberations, so that the resulting decisions are often influenced by subjective assessments and have the potential to be less targeted. This study aims to build a web-based Decision Support System using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods to make the process of determining aid recipients more objective, transparent, and structured. The AHP method is used to determine the weight of each criterion through a pairwise comparison process and consistency testing, while the SAW method is used to calculate the final score and determine the ranking of potential aid recipients. The criteria used include age, employment status, health condition, family status, ownership of other social assistance, economic condition, disability, and type of income. The results show that the Consistency Ratio (CR) value is 0.0348 or less than 0.1, so the criteria weighting is declared consistent and can be used in the next calculation process. From the 20 alternative data sets processed, a more objective and measurable ranking of aid recipients was obtained. The NC alternative received the highest preference score of 0.7818, making it the top priority for aid recipients, followed by RU with a score of 0.6728 and M with a score of 0.6316. The developed system can assist village governments in determining BLT-DD recipients more quickly, accurately, objectively, and transparently, allowing aid distribution to better align with the needs of those in need.
MONITORING KELEMBABAN TANAH BERBASIS IOT MENGGUNAKAN ESP32 PADA KELOMPOK TANI SEKAR HANDAYANI KABUPATEN SORONG: ESP32-BASED IOT SOIL MOISTURE MONITORING SYSTEM FOR THE SEKAR HANDAYANI FARMER GROUP IN SORONG REGENCY Haerunnisa; Indri Anugrah Ramadhani; Muhamad Ali Kasri
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.v11i2.7909

Abstract

This study aims to design and implement an Internet of Things (IoT)-based soil moisture monitoring system using an ESP32 microcontroller in the Sekar Handayani Farmers Group in Sorong Regency. The main problem faced by farmers is the lack of accurate and regular soil moisture monitoring, which results in inaccurate watering and decreased crop productivity. The developed system utilizes a soil moisture sensor to measure soil moisture levels in real-time, then the data is processed by the ESP32 and sent via a Wi-Fi network to a cloud platform to be displayed on the monitoring dashboard. In addition, the system is equipped with a relay module and a water pump that allows the watering process to be carried out automatically based on soil conditions. The research method used is Research and Development (R&D), which includes the stages of needs analysis, system design, implementation, testing using the Blackbox Testing method, and system performance evaluation. The test results show that the system is able to read soil moisture conditions well, send data in real-time, and control the water pump automatically according to soil conditions (dry or wet). Based on the results of the user questionnaire using the Likert scale method, an average value of 88.7% was obtained, which is included in the very good category. Thus, this IoT-based soil moisture monitoring system using the ESP32 has proven effective in helping farmers monitor soil conditions, improving water efficiency, saving labor, and potentially improving the quality and productivity of agricultural produce.
KLASIFIKASI KADAR C-ORGANIK TANAH BERBASIS CITRA DIGITAL MENGGUNAKAN SUPPORT VECTOR MACHINE Pamitta Maulina Sijabat; Andi Prayogi; Muhammad Akbar Syahbana Pane
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.v11i2.7911

Abstract

Soil organic carbon is an important indicator of soil fertility and health, particularly in oil palm plantations. Conventional laboratory methods for organic carbon measurement are costly, time-consuming, and less practical for routine monitoring. This study aims to develop a digital image-based classification model for organic carbon levels using the Support Vector Machine (SVM) algorithm. A total of 96 soil images from the ITSI practice plantation were collected from three soil layers up to a depth of 60 cm. Feature extraction was performed using HSV Color Moment and Gray Level Co-occurrence Matrix (GLCM), producing 15 features per image. The SVM model with RBF kernel was optimized using GridSearchCV, while class imbalance was handled using SMOTE. Experimental results showed an overall accuracy of 60%, precision of 68.57%, recall of 60%, and F1-score of 58.53%. The highest accuracy was obtained in the 0–20 cm and 20–40 cm layers at 57.14%, while the 40–60 cm layer achieved the lowest accuracy at 42.86%. The results indicate that deeper soil layers have more similar visual characteristics, making classification more difficult. This study demonstrates the potential of combining SVM and digital image processing as a low-cost and environmentally friendly alternative for organic carbon monitoring in oil palm plantations.
SISTEM MONITORING SUHU DAN KELEMBAPAN BERBASIS IoT TERINTEGRASI APLIKASI MOBILE DI RUANG SERVER PTPN IV: IoT-BASED TEMPERATURE AND HUMIDITY MONITORING SYSTEM INTEGRATED WITH A MOBILE APPLICATION IN THE PTPN IV SERVER ROOM Ahmad Billy Sutanto; Raden Aris Sugianto; Ritna Wahyuni; 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.v11i2.7912

Abstract

A server room requires stable environmental conditions to maintain the performance of electronic devices, as excessive temperature and uncontrolled humidity can cause hardware damage. Manual monitoring implemented in the server room of PTPN IV Regional I still has limitations, such as delayed detection and the absence of digital data recording. This study aims to design and implement an Internet of Things (IoT)-based temperature and humidity monitoring system using a DHT22 sensor and ESP32 microcontroller. Data are transmitted in real-time to Firebase Realtime Database and displayed through a Flutter-based mobile application called ServGuard. The system is also equipped with remote Air Conditioner (AC) control features using an IR Transmitter. This research applied a quantitative approach through observation, direct measurement, and system testing. The results showed an average temperature reading difference of 0.23°C and humidity difference of 1.08% RH compared to standard measuring instruments. The alarm system was automatically activated when the temperature exceeded the predefined threshold, and all AC control commands were successfully executed. The ServGuard application and Firebase operated stably in real-time data synchronization. The developed system proved to be more effective than manual monitoring methods and has the potential for further development through artificial intelligence integration.  
DETEKSI DINI KATARAK BERBASIS CITRA MATA MENGGUNAKAN METODE DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK (CNN): EARLY DETECTION OF CATARACT BASED ON EYE IMAGES USING DEEP LEARNING METHOD: CONVOLUTIONAL NEURAL NETWORK (CNN) Anindya Novia Ramadani; Rujianto Eko Saputro
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.v11i2.7914

Abstract

Cataract is one of the leading causes of visual impairment that requires early detection to prevent more severe conditions. This study aims to develop an image classification model based on Convolutional Neural Network (CNN) using the EfficientNetB0 architecture with a transfer learning approach and progressive training strategy to distinguish between normal and cataract eye images. The dataset used was obtained from Kaggle, consisting of two classes, namely normal and cataract eye images, which were then processed through preprocessing stages including resizing, normalization, and data augmentation. The dataset was divided into training and validation data with a ratio of 80:20. The model was trained in two stages, where Stage 1 involved freezing the entire EfficientNetB0 base model, and Stage 2 applied fine-tuning on selected layers to improve performance. The experimental results show that the model achieved a validation accuracy of 87.60% with a loss value of 0.3742. Evaluation using precision, recall, and F1-score indicates that the model performs relatively balanced, but shows better performance in recognizing normal eye images compared to cataract images. This is reflected in the higher recall value for the normal class and the relatively high false negative rate in the cataract class. In conclusion, the proposed CNN model based on EfficientNetB0 is capable of classifying cataract and normal eye images; however, further improvements in dataset size, model architecture, and training strategy are required to achieve better performance for reliable medical-based early detection systems.
IMPLEMENTASI YOLOV11 UNTUK SISTEM DETEKSI OTOMATIS SENSUS KELAPA SAWIT : IMPLEMENTATION OF YOLOV11 FOR AUTOMATIC DETECTION SYSTEM OF OIL PALM CENSUS Zia; Raden Aris Sugianto; Ritna Wahyuni; Ratu Mutiara 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.v11i2.7915

Abstract

Oil palm tree census plays a crucial role in plantation management, influencing production planning and managerial decision-making. Manual counting using drone imagery in QGIS is time-consuming and prone to human error. This study aims to design and implement an automatic oil palm tree census detection system using the YOLOv11 algorithm integrated with QGIS. A quantitative approach with system development methodology was employed. Drone imagery was processed in Agisoft Metashape to produce orthomosaic GeoTIFF images, then annotated via Roboflow and trained using YOLOv11 for 100 epochs. Detection was performed using SAHI (Sliced Aided Hyper Inference) with 640×640 pixel tiles (25% overlap) and DBSCAN deduplication. The system successfully detected 4,886 oil palm trees in Block P14402 Sei Baleh Estate PT BSP (±24 Ha). Model evaluation yielded Precision 0.96, Recall 0.95, mAP@IoU=0.5 of 0.98, and F1-Score 0.955. Black Box Testing across 9 functional scenarios all passed. The automated system proved significantly more efficient than the manual method, supporting digitalization of oil palm plantation management.

Page 54 of 70 | Total Record : 696

 52   53   54   55   56 

Filter by Year

2016 2026


Reset
Filter By Issues
All Issue Vol 11 No 2 (2026): Juli Vol 11 No 1 (2026): Januari Vol 10 No 2 (2025): Juli Vol 10 No 1 (2025): Januari Vol 9 No 2 (2024): Juli Vol 9 No 1 (2024): Januari Vol 8 No 2 (2023): Juli Vol 8 No 1 (2023): Januari Vol 7 No 2 (2022): Juli Vol 7 No 1 (2022): Januari Vol 6 No 2 (2021): Juli Vol 6 No 1 (2021): Januari Vol 5 No 2 (2020): Juli Vol 5 No 1 (2020): Januari Vol 4 No 2 (2019): Juli Vol 4 No 1 (2019): Januari Vol 3 No 2 (2018): Juli Vol 3 No 2 (2018): Juli Vol 3 No 1 (2018): Januari Vol 3 No 1 (2018): Januari Vol 2 No 2 (2017): Terbitan Ke-4 Bulan Juli 2017 Vol 2 No 2 (2017): Juli 2017 Vol 2 No 1 (2017): Januari 2017 Vol 1 No 2 (2016): Juli 2016 Vol 1 No 1 (2016): Januari 2016 More Issue