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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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KLASIFIKASI KEPUTUSAN PEMBELIAN SKINCARE DI TIKTOK SHOP BERDASARKAN FAKTOR INFLUENCER MENGGUNAKAN ALGORITMA DECISION TREE Intan Aidita Alfitrah; Dinna Yunika Herdiyanti; Allsela Meiriza
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7293

Abstract

The development of TikTok Shop as a social commerce platform has increased the role of influencers in shaping skincare purchase decisions. However, the complexity of influencer-related factors makes it difficult to systematically identify purchase decision patterns. This study aims to explore the use of the Decision Tree algorithm to classify skincare purchase decisions on TikTok Shop based on influencer factors. Data were collected from 124 university students through a questionnaire survey. The purchase decision variable was constructed as a binary variable (purchase and non-purchase) by aggregating three questionnaire indicators. The independent variables consisted of six influencer constructs, namely credibility, popularity, trust, attractiveness, content quality, and promotion intensity. Model evaluation was conducted using stratified 5-fold cross-validation to reduce the risk of overfitting. The results indicate that the Decision Tree model achieved an average accuracy of 72.6%, exceeding the baseline accuracy of 62.1%. The precision, recall, and F1-score were 81.1%, 74.2%, and 76.9%, respectively, indicating moderate and stable classification performance. Feature importance analysis shows that influencer attractiveness has the highest relative contribution to the classification process, followed by credibility and popularity. This study demonstrates that Decision Tree can be used as an exploratory tool, with result interpretations being non-causal.  
INDEKS RISIKO BENCANA PARIWISATA DAN KLASTER KABUPATEN KOTA JAWA BARAT BERBASIS DATA WISATA 2020–2024: TOURISM DISASTER RISK INDEX AND CLUSTERING OF WEST JAVA REGENCIES AND CITIES BASED ON TOURISM DATA (2020–2024) Ucu Nugraha; Sri Titi Handayani; Hernalom Sitorus; Bobi Kurniawan S; Adam Mukharil Bachtiar; Ednawati Rainarli; Hanhan Maulana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7294

Abstract

West Java Province is one of Indonesia’s leading tourism destinations and, at the same time, a region with a high incidence of disasters. However, available disaster risk information such as the Indonesian Disaster Risk Index and the West Java Provincial Disaster Risk Assessment remains broad in scope and has not explicitly integrated the tourism dimension. This study aims to develop a Tourism Disaster Risk Index at the regency/municipality level in West Java Province by utilizing data on the number of disaster events, the number of disaster victims during the 2020–2024 period, and the number of tourism destination objects, while also clustering regions to construct a disaster-based typology of tourism risk. The methods include: (1) aggregating five-year disaster event and victim data by regency/municipality; (2) calculating the total number of tourism destination objects (natural, cultural, and man-made); (3) applying min–max normalization to produce partial indices for events, victims, and tourism destination objects; (4) constructing a composite Tourism Disaster Risk Index using weights of 0.4:0.4:0.2, in which hazard (events) and impact (victims) are deliberately assigned greater weights than tourism exposure as a conceptual decision aligned with disaster risk frameworks that prioritize life safety and physical damage; and (5) applying the K-Means algorithm (k = 3) to perform clustering based on the partial indices. The results show that the Tourism Disaster Risk Index (0–100 scale) ranges from 0.76 to 56.70, with a mean of 12.27 and a median of 6.90. A total of 25 regencies/municipalities fall into the low tourism risk category, while Bogor Regency and Cianjur Regency are in the moderate category. The clustering yields three clusters: cluster 1 comprises 21 regencies/municipalities with relatively low tourism risk; cluster 2 includes five regencies (Bogor, Bandung, Garut, Majalengka, and Pangandaran) characterized by moderate risk and a high concentration of destinations; and cluster 3 consists solely of Cianjur Regency as an outlier with exceptionally high disaster impacts. These findings provide a quantitative foundation as well as a prototype decision-support tool for local governments and stakeholders to prioritize resources and design interventions for disaster-resilient tourism development in West Java Province.  
ANALISIS KOMPARATIF ARSITEKTUR DEEP LEARNING UNTUK PENGENALAN ISYARAT SIBI DINAMIS Uly Atmi Azizah; Muhamad Akrom
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7297

Abstract

Communication is a fundamental right that often becomes a barrier for the deaf community when interacting with the general public. This limitation restricts equal access to education and information. In Indonesia, the Indonesian Sign Language System (SIBI) is the formal standard taught in Special Schools (SLB), yet the implementation of automatic translators for dynamic signs remains suboptimal. Previous studies have often focused on static signs, which are unable to capture temporal gestures. To address this, this study compares three spatio-temporal deep learning architectures for dynamic SIBI sign recognition. The three models compared include the landmark-based Stacked Bi-LSTM using MediaPipe, as well as the visual-based (appearance-based) ResNet50+Bi-LSTM and MobileNetV2+Bi-LSTM. This study utilizes a private dataset consisting of 989 videos covering 11 sign classes. The dataset was partitioned into 60% training, 20% validation, and 20% testing data using a stratified split. The landmark-based Stacked Bi-LSTM achieved the highest accuracy of 98.48%, outperforming ResNet50+Bi-LSTM (97.98%) and MobileNetV2+Bi-LSTM (95.96%). This model also proved to be the most efficient, with 19 times fewer parameters than ResNet50 and 2 times fewer than MobileNetV2, as well as an inference time 53 times faster than ResNet50. The Stacked Bi-LSTM is demonstrated to be the optimal architecture for SIBI recognition, offering the highest accuracy and best efficiency.
ANALISIS PENGARUH VARIASI POSTUR DAN MOBILITAS TERHADAP AKURASI ESTIMASI TINGGI BADAN PADA KURSI RODA CERDAS MENGGUNAKAN PENDEKATAN CHUMLEA Fajri Albar Amri; Wahid MIftahul Ashari; Jeki Kuswanto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7316

Abstract

This study evaluates the accuracy of height estimation on a sensor-based smart wheelchair system, focusing on user posture and mobility variables. Precise height measurement is crucial in medical applications, particularly for pharmacological dose determination and nutritional status evaluation, where non-standard posture or user movement poses a high risk of causing data bias. Testing was conducted using the Chumlea method as a validation standard for the ultrasonic sensors integrated into the wheelchair. The experiment involved four specific conditions: normal/static, non-normal/static, normal/dynamic, and non-normal/dynamic postures, involving 20 subjects (10 men, 10 women). Data analysis utilized Mean Absolute Error (MAE) and ANOVA tests to assess the significance of accuracy differences between conditions. The results indicate that testing conditions significantly affect height estimation accuracy. In the normal/static condition, the average MAE was 0.65 cm for men and 1.12 cm for women. In contrast, in the non-normal/dynamic condition, MAE increased sharply to 2.93 cm for men and 2.88 cm for women, indicating a significant decrease in accuracy. The ANOVA statistical test confirmed a significant difference (p < 0.001) in sensor accuracy among the different testing conditions . These findings conclude that non-ideal posture and dynamic mobility simultaneously exacerbate sensor inaccuracies. This research contributes to the development of smart wheelchair technology by providing insights into how external factors influence sensor performance for more accurate medical decision-making.
IMPLEMENTASI MODEL YOLO11n BERBASIS TRANSFER LEARNING UNTUK DETEKSI SAMPAH DAUR ULANG Valent Tio Inkiriwang; Medi Hermanto Tinambunan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7323

Abstract

Recyclable waste management in Indonesia is often hindered by the inefficiency of manual sorting and lack of public awareness. This study aims to implement the YOLO11n model based on transfer learning to detect six types of recyclable waste and analyze the effect of layer freezing strategies on model accuracy and efficiency. An experimental method was employed utilizing the Recycle Trash Dataset of 2,462 images divided into three training scenarios: Full Transfer Learning, Partial Backbone Freezing (5 layers), and Full Backbone Freezing (10 layers). The results indicated that the Partial Backbone Freezing strategy delivered the best performance with an mAP50 of 0.840 and Precision of 0.823, showing statistical significance ($p<0.05$). The model demonstrated superior performance on the Metal class (mAP50 0.970) but faced significant challenges with the Organic class (Recall 0.496) due to high shape variation and the Plastic class due to material transparency. Testing on Raspberry Pi 5 showed an average inference time of 261 ms with a model size of 5.9 MB. In conclusion, the Partial Backbone Freezing technique applied to YOLO11n has proven to be the most effective in balancing generic feature extraction and semantic adaptation, making it suitable for implementation on resource-constrained edge devices.
ANALISIS PENGELOMPOKAN POLA PENJUALAN PRODUK UMKM MENGGUNAKAN ALGORITMA K-MEANS Putri Diana; Achmad Solichin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7325

Abstract

Developments in machine learning technology offer significant opportunities for Micro, Small, and Medium Enterprises (MSMEs), particularly those operating in the Warung Madura sector, to analyze sales patterns in greater depth through data processing. This article aims to evaluate the sales patterns of MSME Warung Madura products by utilizing the K-Means Clustering algorithm. The data used in this study include several key parameters, namely product price, sales volume, and profit, taken from sales transaction records at one MSME Warung Madura. The analysis was carried out through a process that includes pre-processing, information normalization, and the application of the K-Means algorithm with a value of k = 3 to group products based on similarities in their sales characteristics. The findings of this study indicate the formation of three product categories, namely (1) products with affordable prices and small margins, (2) premium products with high sales levels and large profits, and (3) products with stable sales performance. The results of this clustering provide a clear product map, allowing business owners to allocate resources (stock, promotions) more strategically based on cluster characteristics. Thus, the application of machine learning using the K-Means algorithm can provide valuable insights to support the digitalization process and increase efficiency in managing the Warung Madura MSME business.
PERBANDINGAN VGG16, VGG19, MOBILENET DAN MOBILENETV2 UNTUK KLASIFIKASI BERAS BERDASARKAN CITRA Nur Nafiiyah; Dwi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7338

Abstract

Rice is the main staple food for the majority of the Indonesian population; therefore, rice quality plays an important role in maintaining national food security. However, rice quality assessment in Indonesia is still largely conducted manually, making it subjective and inconsistent. Consequently, the application of image processing techniques and deep learning algorithms is required to achieve a more objective and accurate classification process. This study aims to compare the performance of four Convolutional Neural Network (CNN) architectures, namely VGG16, VGG19, MobileNet, and MobileNetV2, in classifying five types of rice: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The dataset used consists of 4,000 images that were processed directly without additional preprocessing stages. The experimental results show that MobileNet achieved the highest accuracy of 99% with a training time of 756 seconds. Meanwhile, MobileNetV2 and VGG16 achieved accuracies of 98% with training times of 728 seconds and 1,502 seconds, respectively, while VGG19 produced the lowest accuracy of 97% with a training time of 1,038 seconds. Based on these results, it can be concluded that the MobileNet architecture demonstrates the best performance in classifying the five rice varieties in the dataset used.
SENTIMENT ANALYSIS OF BRIMO APPLICATION USER REVIEWS USING NAÏVE BAYES AND LONG SHORT-TERM MEMORY Muhammad Alif Ilmansyah; Anik Vega Vitianingsih; Anastasia Lidya Maukar; Seftin Fitri Ana Wati; Arizia Aulia Aziiza
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7341

Abstract

In the age of digital transformation, the development of digital banking platforms such as BRImo by Bank Rakyat Indonesia (BRI) continues to evolve to improve customer experience. However, many users still express dissatisfaction through online reviews, especially on platforms such as the Play Store and Twitter (X). This study conducts a systematic and fair comparison between a traditional machine learning approach (Naïve Bayes) and a deep learning approach (Long Short-Term Memory) for sentiment classification under identical dataset conditions. User reviews were collected using web scraping and crawling techniques, followed by text preprocessing, lexicon-based labeling, and appropriate feature representations for each model. The results indicate that both algorithms classify sentiments into three categories: positive, negative, and neutral. The Naïve Bayes model achieved an accuracy of 89%, with macro-average precision, recall, and F1-score of 0.88, 0.58, and 0.59, respectively. Meanwhile, the LSTM model achieved an accuracy of 85%, with macro-average precision, recall, and F1-score of 0.59, 0.63, and 0.60. The findings reveal that Naïve Bayes demonstrates more stable performance on short and highly imbalanced user review data, while LSTM shows limited improvement for minority classes despite its contextual modeling capability. These results highlight the importance of dataset characteristics and evaluation metrics beyond accuracy in sentiment analysis tasks. This research provides practical insights for BRImo development teams and contributes to the understanding of model behavior under real-world sentiment data imbalance.  
SISTEM CHATBOT KESEHATAN MENTAL BERBASIS LLM DENGAN DETEKSI EMOSI DAN RETRIEVAL AUGMENTED GENERATION Dhia Tawakalna; Junta Zeniarja
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7347

Abstract

Mental health challenges in Indonesia highlight the urgent need for accessible, safe, and responsible early psychological support systems. Recent advances in Large Language Models (LLMs) enable the development of mental health chatbots capable of generating empathetic and context-aware responses. This study proposes and evaluates a hybrid LLM-based mental health chatbot architecture that integrates parameter-efficient fine-tuning (QLoRA), short-term user profiling through emotion detection, and Retrieval-Augmented Generation (RAG) to improve response quality, relevance, and safety. The research methodology is designed to be modular and reproducible, encompassing Indonesian mental health dialogue preprocessing, QLoRA-based LLM fine-tuning, IndoBERT-based emotion recognition, and a FAISS-powered RAG framework using sentence embeddings. The contribution of each component is systematically assessed through a staged ablation study, while response quality is evaluated using BLEU, ROUGE-L, METEOR, and CIDEr metrics, complemented by qualitative analysis and safety stress testing. Results indicate that although BLEU scores remain relatively low—consistent with open-domain dialogue systems—higher METEOR and CIDEr scores demonstrate strong semantic alignment and informational relevance. Furthermore, the system consistently identifies user emotions, rejects high-risk requests, and produces non-diagnostic responses aligned with AI safety principles. These findings demonstrate that the proposed hybrid LLM–RAG architecture is effective as a context-aware, safe, and responsible early-stage mental health support system, without replacing professional clinical services.
IMPLEMENTASI SISTEM ENTERPRISE RESOURCE PLANNING MODUL POINT OF SALE MENGGUNAKAN METODE QUICKSTART Salsabila Aulia Choirunisa; Nuur Wachid Abdul Majid
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7356

Abstract

This research aims to document and evaluate the implementation of an Enterprise Resource Planning (ERP) system based on Odoo, specifically the Point of Sale (POS) module, at Rumah Buket by Rere, a small business operating in the creative retail sector of flower bouquet sales. Prior to system implementation, transaction recording, inventory management, and raw material purchasing were conducted manually, which led to potential recording errors and delays in information availability. This research adopts a case study approach using the QuickStart implementation method, which consists of the Kick-Off Call, Analysis, and Configuration stages, to align the ERP system with the specific business needs of the SME. System evaluation was carried out through Black Box Testing to ensure that system functionalities operated as expected, as well as User Acceptance Test (UAT) designed based on the Technology Acceptance Model (TAM) to assess user acceptance. The testing results indicate that all main system functions operated properly. Furthermore, the UAT results show a high level of user acceptance, with Perceived Ease of Use (PEOU) reaching 88% and Perceived Usefulness (PU) reaching 89%, indicating that the system is considered easy to use and provides tangible benefits in supporting daily operational activities. The implementation of the Odoo POS module successfully integrated sales processes, inventory management, and purchase recording in a more structured and efficient manner compared to the previous manual approach. This study contributes practical documentation of ERP implementation and evaluation using the QuickStart method in the context of creative retail SMEs and may serve as a reference for similar businesses planning to adopt ERP systems to improve operational efficiency and data accuracy.

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