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Redaktur Jurnal RABIT Teknik Informatika Universitas Abdurrab: Gedung Universitas Abdurrab Pekanbaru Jl. Riau Ujung No. 73 Pekanbaru Riau - Indonesia
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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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PERANCANGAN UI/UX PADA APLIKASI VOUCHER BERBASIS WEBSITE MENGGUNAKAN METODE DESIGN THINKING (STUDI KASUS: KLINIK HIDUP BARU) Abdi Duta Makarios Zega; Penidas Fiodinggo Tanaem
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.v11i2.7801

Abstract

Hidup Baru Clinic has adopt the use of vouchers as one of the methods in good selling and services. The requirement about transaction and operational about record sales and redeems encourage the clinic to design an application. Therefore the clinic necessary to design UI/UX based on website that answer the challenges dan users needs. A popular method such as Design Thinking is utilized to discover innovations and creative solutions to solve problems and meet user requirements. This iterative method, produce a prototype of the voucher application which, after being tested using the System Usability Scale (SUS) by 6 main users of the application, achieved an average score of 92.92 out of 100. This result indicate that the designed application falls within grade A, with an adjective rating of “excellent”, and an acceptability range of “acceptable”.
PENGEMBANGAN SISTEM INFORMASI MONITORING DAN FORECASTING PASOKAN SERTA KEBUTUHAN BERAS BERBASIS WEB DI DINAS PANGAN KOTA TOMOHON : DEVELOPMENT OF A WEB-BASED RICE SUPPLY AND DEMAND MONITORING AND FORECASTING INFORMATION SYSTEM AT THE TOMOHON CITY FOOD SERVICE Kristofel Santa
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.v11i2.7804

Abstract

The management of rice supply and demand data is a crucial aspect of maintaining regional food security stability. However, at the Tomohon City Food Security Agency, data recording and processing are still conducted semi-manually, leading to potential information delays. This study aims to develop a web-based information system for monitoring and forecasting rice supply and demand to support effective data-driven decision-making. The system development follows the Waterfall model, while the forecasting method utilizes the AutoRegressive Integrated Moving Average (ARIMA) applied to monthly historical data from 2023 to 2025. The system features data management, trend visualization, and an automatic prediction module. The results demonstrate that the system provides real-time information and generates highly accurate predictions, with a Mean Absolute Percentage Error (MAPE) of 6.84%. This confirms that the ARIMA method is highly effective for short-term forecasting in the regional food sector.
ANALISIS PENGGUNAAN MEDIA WAYGROUND SEBAGAI EVALUASI UJIAN TENGAH SEMESTER (UTS) DI SEKOLAH MENENGAH KEJURUAN MUHAMMADIYAH SALAWATI : AN ANALYSIS OF THE USE OF WAYGROUND AS A TOOL FOR MID-SEMESTER EXAM EVALUATION AT MUHAMMADIYAH SALAWATI VOCATIONAL HIGH SCHOOL kiki hndayani; Kiki Handayani
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.v11i2.7806

Abstract

This study aims to analyze the use of Wayground as an evaluation medium in the Midterm Examination (UTS) at SMK Muhammadiyah Salawati. The approach used is qualitative descriptive with data collection through documentation, observations, and interviews. The study involved 1 teachers and 30 students from the Computer and Network Engineering department. The findings of the study reveal that the use of Wayground successfully increased student engagement and motivation during exams, while also making it easier for teachers to create questions and carry out assessments efficiently. The platform provides automatic feedback that accelerates the evaluation process. However, technical challenges such as internet connectivity stability and students’ device readiness remain obstacles that need to be addressed. Overall, the use of Wayground has proven effective in improving efficiency and interactivity during evaluations, although improvements in technical infrastructure are still required.  
KLASIFIKASI CYBERBULLYING PADA TEKS MEDIA SOSIAL BERBAHASA INDONESIA MENGGUNAKAN MODEL BIDIRECTIONAL LSTM Finuri Zamzahariro; Jafar Fakhrurozi
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.v11i2.7808

Abstract

This study aims to classify cyberbullying text on Indonesian social media using an enhanced Bidirectional Long Short-Term Memory (BiLSTM) model integrated with 300-dimensional FastText pre-trained embeddings. The key challenges addressed include the high prevalence of informal language, slang, abbreviations, and writing variations that complicate automatic text analysis, as well as the semantic limitations of random embeddings. The proposed method comprises a preprocessing pipeline including text cleaning, slang normalization with toxic word preservation, tokenization, and padding, followed by a BiLSTM model built using the Functional API with dual pooling (GlobalMaxPooling + GlobalAveragePooling), L2 regularization, SpatialDropout1D, BatchNormalization, and class weight balancing. The dataset consists of 2,109 Indonesian comments collected from TikTok and Instagram, with a nearly balanced class distribution (1,058 Non-CB and 1,051 CB). Experimental results demonstrate that the proposed model achieves improved performance over the baseline, with a significant reduction in the overfitting gap between training and testing accuracy. This study demonstrates that combining FastText pre-trained embeddings with an optimized BiLSTM architecture is an effective and resource-efficient approach for Indonesian cyberbullying text classification.
ANALISIS PERFORMA LIGHTWEIGHT CRYPTOGRAPHY PADA ESP32: STUDI KOMPARATIF ASCON-128, TINYJAMBU, DAN GIFT-COFB UNTUK KEAMANAN DATA SENSOR IoT Muhammad Baso Adrian Ibrahim; Dwi Prianto; Imelda Imelda
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.v11i2.7823

Abstract

The Internet of Things (IoT) has become the cornerstone of modern digital infrastructure, connecting billions of devices with limited computational resources. Data security on IoT devices presents a critical challenge given the constraints in memory, power, and processing capabilities. This study presents a comprehensive comparative analysis of three algorithms from the NIST LWC Standardization process—ASCON-128 (official NIST standard, 2023), TinyJAMBU, and GIFT-COFB—implemented on the ESP32 microcontroller based on the Xtensa LX6 dual-core 240 MHz architecture. Testing was conducted with 10,000 iterations across four primary metrics: encryption throughput (kbps), operational latency (µs), memory consumption (RAM and Flash), and energy efficiency (nJ/operation). Experimental results demonstrate that ASCON-128 achieves the highest throughput of 7,035.10 kbps with the lowest energy consumption per operation of 9.6 nJ/operation, making it the optimal solution for IoT devices that offers the best balance between cryptographic security and performance. TinyJAMBU demonstrates a significant advantage in RAM efficiency, requiring only 4 bytes of stack memory, making it the preferred choice for ultra-constrained IoT devices. Meanwhile, GIFT-COFB offers superior resistance to side-channel attacks among the three evaluated algorithms. This research produces algorithm selection criteria based on resource profiles and device security requirements as a technical reference for IoT system developers in making decisions on measurable and accountable cryptography implementation.
OPTIMASI KLASIFIKASI RISIKO SLEEP APNEA MENGGUNAKAN TREE FEATURE IMPORTANCE PADA MODEL MACHINE LEARNING Desi Intan Padila; Rohmat Indra Borman
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.v11i2.7824

Abstract

Sleep apnea is a serious sleep disorder with high global prevalence and is associated with complications such as cardiovascular disease and reduced quality of life, while conventional diagnostic methods remain limited in terms of cost and time efficiency. Therefore, this study aims to classify the risk of sleep apnea and analyze the impact of feature selection on classification model performance. The proposed method employs a quantitative experimental approach using three machine learning algorithms, namely Logistic Regression, Decision Tree, and Random Forest, combined with a Tree Feature Importance-based feature selection technique. The dataset used is the Sleep Health and Lifestyle Dataset obtained from Kaggle, consisting of 374 samples and 13 features related to sleep health and lifestyle factors. The data are split into 80% training and 20% testing sets using stratified sampling and evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) metrics. The results indicate that Logistic Regression achieves the most consistent performance with an accuracy of 0.933 and the highest AUC of 0.975 on the Top 10 feature subset, while Random Forest demonstrates strong discriminative capability with an AUC of up to 0.960 on the Top 5 subset. Additionally, Diastolic BP, Systolic BP, BMI Category, and Quality of Sleep are identified as the most influential features. These findings suggest that feature selection effectively preserves model performance while reducing data complexity, leading to a more efficient approach for sleep apnea risk classification.  
EVALUASI KEMATANGAN TATA KELOLA INFRASTRUKTUR TI MENGGUNAKAN COBIT 2019 DAN AHP UNTUK ROADMAP TRANSFORMASI DIGITAL (STUDI KASUS: STMIK PONTIANAK): EVALUATION OF IT INFRASTRUCTURE GOVERNANCE MATURITY USING COBIT 2019 AND AHP FOR A DIGITAL TRANSFORMATION ROADMAP (CASE STUDY: STMIK PONTIANAK) Viorel Andriy Zico; Robert Marco
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.v11i2.7831

Abstract

Digital transformation is crucial for an institution so it requires the support of good technology governance, so that all operations can run effectively. STMIK Pontianak is a university that wants to carry out digital transformation, addressing the problems of unintegrated information systems, limited infrastructure capacity, suboptimal system change control, and immature knowledge documentation. The purpose of this study is to illuminate the maturity level of IT infrastructure governance using the COBIT 2019 framework, then the results are explained again using the MCDM method, namely AHP, and the results of the AHP will be used to create a digital transformation roadmap, which is used as a guide so that STMIK Pontianak can carry out digital transformation in a directed manner. This study uses a descriptive approach, and from the results of the initial analysis and based on initial data received from interviews and questionnaires, as well as identification of design factors, the capability level of STMIK Pontianak is at level 1.57, which is included in the repeatable stage. The processes with the lowest capabilities consist of BAI04 Managing Availability and Capacity, BAI06 Managing Change, and BAI08 Managing Knowledge. The AHP analysis indicates that the main focus for improvement is BAI04 with a weighting value of 0.521, followed by BAI06 with a weighting value of 0.312 and BAI08 with a weighting value of 0.167. Based on these findings, the digital transformation plan is structured in three phases: a short phase to increase infrastructure capacity, a medium phase to control change and system integration, and a long phase for knowledge management and continuous improvement. This study proves that the use of a combination of COBIT 2019 and AHP can be an effective strategic foundation to support digital transformation in higher education institutions.  
ANALISIS PERFORMA MODEL YOLOV5 PADA DETEKSI PENYAKIT DAUN TANAMAN PISANG BERBASIS DEEP LEARNING: PERFORMANCE ANALYSIS OF THE YOLOV5 MODEL FOR DEEP LEARNING-BASED DETECTION OF BANANA LEAF DISEASES Aditya Rezky; Lilis Nur Hayati; Sugiarti Sugiarti
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.v11i2.7832

Abstract

Banana leaf disease (Musa spp.) poses a critical threat to tropical agricultural productivity in Indonesia, with harvest loss estimates reaching 30–50% during rainy seasons due to undetected pathogen infections. Limited manual diagnostic capacity among farmers produces disease misidentification and delayed control interventions, particularly at early infection stages when inter-class visual symptom similarity remains high. This study proposes an automated banana leaf disease detection system leveraging deep learning through the You Only Look Once version 5 (YOLOv5) architecture as a digital image-based diagnostic solution. The dataset comprises 9,684 images across eight banana leaf disease classes, curated via Roboflow with 87% training (8,460 images), 8% validation (820 images), and 4% testing (404 images) splits. Preprocessing includes 2×2 tiling, auto-orientation, and 512×512 pixel stretch resizing. Data augmentation applies horizontal and vertical flip alongside 0–25% zoom crop, generating three outputs per training image. Model performance evaluation employs precision, recall, F1-score, and mean Average Precision (mAP@0.5) metrics. Results demonstrate YOLOv5 capability in accurately detecting and classifying banana leaf diseases under Indonesian tropical field imaging conditions.
SISTEM REKOMENDASI WISATA BOGOR MENGGUNAKAN N-GRAM DAN INDOBERT Panji Ihsanudin Fajri; Arif Nur Rohman; Ika Nur Fajri
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.v11i2.7835

Abstract

Bogor Regency has significant tourism potential; however, available tourism information is mostly static and does not support preference-based recommendations. This study aims to develop a tourism recommendation system for Bogor Regency using a content-based filtering approach by integrating N-Gram, TF-IDF, and IndoBERT methods. The tourism destination dataset was collected through web scraping from online tourism sources and processed using text preprocessing techniques. Feature extraction was performed using N-Gram and TF-IDF to capture lexical similarity, while IndoBERT was trained using an Unsupervised SimCSE approach to generate contextual semantic representations. Destination similarity was calculated using cosine similarity, and system performance was evaluated using Precision, Recall, and F1-Score under Top-3, Top-5, and Top-10 scenarios. The experimental results show that the N-Gram and TF-IDF approach achieved the highest Precision of 63.95% in the Top-3 scenario and an F1-Score of 16.71% in the Top-10 scenario, indicating strong category consistency. Meanwhile, IndoBERT provided more context-aware recommendations with lower Precision, demonstrating its ability to capture semantic similarity beyond keyword matching. These findings indicate that lexical and semantic approaches complement each other and can be effectively combined to support more flexible and adaptive tourism recommendation systems.
PREDIKSI TINGKAT KEPARAHAN KLAIM KOMPENSASI K3 MENGGUNAKAN MULTIPLE LINEAR REGRESSION DENGAN KOREKSI HETEROSKEDASTISITAS: PREDICTION OF SEVERITY LEVEL OF OCCUPATIONAL SAFETY AND HEALTH (OSH) COMPENSATION CLAIMS USING MULTIPLE LINEAR REGRESSION WITH HETEROSCEDASTICITY CORRECTION Aqid Fahri Hafin; Herman; Abdul Fadlil
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.v11i2.7836

Abstract

The identification of determinants influencing the severity of occupational injuries (serious claims) is crucial for formulating precise and evidence-based Occupational Health and Safety (OHS) intervention strategies. This study aims to identify and quantify the most significant ergonomic risk factors and workplace hazard exposures affecting claim severity using 200 job-type observations from the O*NET-ANZSCO dataset published by Safe Work Australia. The analytical method employed is Multiple Linear Regression (MLR) with a staged validation approach. Since the initial regression model using the original data violated the assumptions of normality (Shapiro-Wilk < 0.001) and heteroskedasticity (Breusch-Pagan = 0.025), this study applies a Log-Linear model transformation and Robust Standard Errors (HC3) estimator to produce estimates that meet the Best Linear Unbiased Estimator (BLUE) criteria. The results indicate that the final model (Log-Lin HC3) is statistically significant simultaneously (Prob(F-statistic) = 0.0003) with an Adjusted R-squared of 0.801, meaning that 80.1% of the variation in claim severity can be explained by the model. Partially, four key risk factors are identified as significant: Exposed to Disease or Infections (p = 0.001), Spend Time Making Repetitive Motions (p = 0.006), Spend Time Bending or Twisting the Body (p = 0.008), and Exposed to Radiation (p = 0.035). These findings indicate that mitigating injury severity should prioritize these specific hazard exposures and ergonomic risk factors.

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