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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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SISTEM PAKAR BERBASIS WEB UNTUK MENDIAGNOSA PENYAKIT PHYTOPHTHORA PADA TANAMAN MERICA DENGAN METODE CERTAINTY FACTOR: A WEB-BASED EXPERT SYSTEM FOR DIAGNOSING PHYTOPHTHORA DISEASE IN PEPPER PLANTS USING THE CERTAINTY FACTOR METHOD Rayhana Bahar; 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.v11i2.7981

Abstract

Phytophthora  sp., is a soilborne pathogen that poses a major threat to the productivity of pepper (Piper nigrum L.) in Indonesia, with estimated yield losses reaching 10–30% in heavily infected plantations. This study designs and implements a web-based expert system using the Certainty Factor (CF) method to diagnose four clinical manifestations of Phytophthora sp. in pepper plants, comprising Stem Base Rot, Root Rot, Leaf Rot, and Fruit Rot. The knowledge base was constructed through structured interviews with a certified plant pathology expert and primary literature review, encompassing 15 clinical symptoms with 38 validated expert CF value pairs. Case study testing yielded the highest CF value for P01 (Stem Base Rot) at 1.0000 (100%), followed by P02 (Root Rot) at 0.9338 (93.38%). Functional validation through Black Box Testing confirmed all system modules operate in accordance with defined requirement specifications. The novelty of this study lies in three aspects: exclusive coverage of four Phytophthora sp. clinical manifestations in pepper within a single platform, integration of user confidence values as dynamic variables in CF computation, and direct presentation of treatment recommendations alongside the corresponding certainty percentage.
PENDEKATAN DEEP LEARNING UNTUK PEMANTAUAN AKTIVITAS BELAJAR MENGAJAR SISWA PADA LINGKUNGAN PEMBELAJARAN KELAS Ripka Nduru; Rina Andayani Rotua; Aldio Simamora; Edwin Todo Pardamean Sinaga; Amir Mahmud Husein
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.v11i2.7982

Abstract

This study aims to develop a student learning activity monitoring system based on deep learning and computer vision using YOLOv12n, DeepSORT, MediaPipe FaceMesh, and the Fuzzy Mamdani method. The study was conducted on eighth-grade students of SMP Swasta Deli Murni Suka Maju using classroom learning activity videos that had undergone data selection and preprocessing. The proposed system was designed to detect students, track their identities, and analyze their attention levels automatically and in real-time based on eye conditions and head position. YOLOv12n was employed for student detection, DeepSORT for identity tracking, MediaPipe FaceMesh for extracting facial features, including the Eye Aspect Ratio (EAR) and head position, while the Fuzzy Mamdani method was utilized to classify students' attention levels. System evaluation was performed by comparing the prediction results with manually annotated ground truth and was assessed using the Confusion Matrix, accuracy, precision, recall, and F1-score. The experimental results demonstrate that the proposed system is capable of performing multi-student detection, identity tracking, and automatic attention level analysis. The evaluation achieved an accuracy of 90.20%, indicating that the integration of YOLOv12n, DeepSORT, MediaPipe FaceMesh, and the Fuzzy Mamdani method provides reliable performance for classifying students' attention levels in a smart classroom environment.
EVALUASI PENGARUH FINE-TUNING DAN DATA AUGMENTATION TERHADAP KINERJA MOBILENETV2 DAN RESNET50 PADA KLASIFIKASI RAS KUCING MENGGUNAKAN TRANSFER LEARNING: EVALUATION OF THE EFFECTS OF FINE-TUNING AND DATA AUGMENTATION ON MOBILENETV2 AND RESNET50 PERFORMANCE IN CAT BREED CLASSIFICATION USING TRANSFER LEARNING Imam Munzagi; Yudie Irawan; Fajar Nugraha
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.v11i2.8004

Abstract

Manual identification of cat breeds often faces challenges due to the high similarity of visual features among breeds, potentially leading to errors in medical treatment and nutritional management. This study aims to compare the performance of the MobileNetV2 and ResNet50 architectures in classifying 13 cat breeds using a Transfer Learning approach. The CRISP-DM methodology was implemented through four experimental scenarios to evaluate the effects of Data Augmentation and Fine-Tuning, both individually and in combination. The results indicate that the combination of Data Augmentation and Fine-Tuning achieved the best performance. MobileNetV2 consistently outperformed ResNet50 across all scenarios, achieving the highest accuracy of 90,00% and an F1-Score of 90,02%, while ResNet50 achieved a maximum accuracy of only 39,69%. The 50,38% performance gap demonstrates that MobileNetV2 is more adaptive in extracting visual object features. The best-performing model was successfully implemented into an application prototype that is functional and user-friendly.
SPK PENENTUAN PRIORITAS PEMBANGUNAN INFRASTRUKTUR DESA MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS DAN WEIGHTED PRODUCT Anis Maghfiroh; Arif Setiawan; Eko Darmanto
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.v11i2.8005

Abstract

One important aspect in improving public services and community welfare is village infrastructure development. However, in Desa Piji, the process of determining development priorities has still been conducted manually through village deliberation meetings, making it vulnerable to subjectivity. This study aims to develop a Decision Support System (DSS) for determining village infrastructure development priorities using the Analytical Hierarchy Process (AHP) and Weighted Product (WP) methods. The AHP method was employed to determine the weight of each criterion through pairwise comparison matrices and consistency testing, while the WP method was used to calculate preference values and rank the development alternatives. The criteria used in this study include the level of infrastructure damage, the number of affected residents, development costs, and socio-economic impact. The consistency test results showed a Consistency Ratio (CR) value of 0.0389, which is less than 0.1, indicating that the criterion weights were consistent. The highest criterion weight was obtained for the level of infrastructure damage at 0.5117 (51.17%), followed by socio-economic impact at 0.2378, the number of affected residents at 0.1725, and development costs at 0.0780. From 21 processed development alternatives, alternative A019, namely the Construction of the KUD Block Drainage Channel, obtained the highest preference value of 0.056459244, making it the top priority. The developed system is capable of assisting the government of Desa Piji in determining infrastructure development priorities in a more objective, structured, and transparent manner.
IMPLEMENTASI CONTENT-BASED FILTERING UNTUK REKOMENDASI PRODUK PADA WEBSITE E-COMMERCE: THE APPLICATION OF CONTENT-BASED FILTERING FOR PRODUCT RECOMMENDATIONS ON E-COMMERCE WEBSITES Devan Rizky Saputra Zebua; Ranggi Praharaningtyas Aji; Nandang Hermanto
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.v11i2.8006

Abstract

The growth of e-commerce in the fashion sector has driven the need for systems that can help users discover products more efficiently amid an abundance of available choices. This study aims to implement a smart recommendation system based on content-based filtering on the Dinara Konveksi e-commerce website using the TF-IDF (Term Frequency–Inverse Document Frequency) method and cosine similarity.The dataset consists of 35 active products registered in the Dinara Konveksi e-commerce system. The data used consists of product attributes including name, category, description, variant colors, and sizes, which are combined into text documents and processed through preprocessing steps comprising case folding, tokenization, and stopword removal. Feature weighting is performed using TF-IDF to generate a vector representation for each product, while cosine similarity is used to measure the degree of similarity between products. The system produces two types of recommendations: similar product recommendations displayed on product detail pages, and personalized recommendations tailored to users' purchase history, shopping cart, and product view history. The implementation uses a two-tier architecture consisting of a Python script with scikit-learn for batch computation and a PHP service as the runtime interface. Evaluation results show a Precision@4 of 0.75, Recall@4 of 0.60, F1-Score of 0.67, and a Hit Rate of 1.00. The developed system is capable of providing relevant and responsive product recommendations while supporting a more personalized shopping experience for users of the Dinara Konveksi website.
PENGELOMPOKAN BAHAN BAKU BERDASARKAN TINGKAT PENGGUNAAN BERBASIS ALGORITMA CLUSTERING PADA SELARAS COFFEE & SPACE Bayu Samudro Fadhilah; Muhammad Arifin; Rhoedy Setiawan
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.v11i2.8008

Abstract

Inventory management in the food and beverage business requires a measurable approach to reduce the risk of stock shortages and excess inventory. Selaras Coffee & Space has operational kitchen raw material data that can be utilized to identify usage patterns more objectively. This study aims to group raw materials based on usage levels by comparing K-Means, Hierarchical Clustering, and K-Medoids algorithms. The data were obtained from kitchen raw material stock opname and purchase order records for February 2026, using stock_fisik, min_stock, and qty_po as clustering attributes. The research stages included data collection, preprocessing, unique item aggregation, Min-Max normalization, clustering algorithm implementation, evaluation using Sum of Squared Errors (SSE) and Silhouette Score, and implementation of the results into a web-based system. The initial dataset consisted of 3,080 rows and was aggregated into 110 unique items. The evaluation results showed that K-Means and Hierarchical Clustering achieved an SSE value of 4.630818 and a Silhouette Score of 0.781801, indicating a strong cluster structure. K-Medoids obtained an SSE value of 11.022485 and a Silhouette Score of 0.470763. K-Means was selected as the best algorithm because it achieved optimal evaluation performance and is simpler to implement in the system. The clustering results showed that 6 items were categorized as High Usage, 7 items as Medium Usage, and 97 items as Low Usage. The results can assist management in understanding raw material usage levels as a basis for more effective inventory control.  
PERANCANGAN SISTEM INFORMASI PENJUALAN BERBASIS WEB PADA PETERNAKAN PASARIBU CHICKEN AIMAS Dian Agata Sesilia Pasaribu Dian; Sahiruddin Sahiruddin; Dian Nitari Ribanor Sabarudin
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.v11i2.8012

Abstract

The goal of this project is to create a web-based sales information system for Pasaribu Chicken Aimas Farm in order to solve issues with manual sales management, making transaction recording less effective. The developed system is expected to support transaction processing, customer data management, product stock management, and sales reporting in a faster, more accurate, and integrated manner. This investigation employed the prototyping approach as its methodology, which consists of the stages of requirements gathering, prototype design, prototype evaluation, system coding, system testing, also implementation. The Black Box Testing approach was used to test the system, which achieved a success rate of 100%, indicating every system feature operates in accordance with user specifications. In addition, the level of system usability was measured using the System Usability Scale (SUS) method and obtained an average score of 83.75, which falls into the Good category. The results indicate that the developed web-based sales information system is capable of improving the effectiveness of sales management at Pasaribu Chicken Aimas Farm and providing convenience for users in conducting transactions and managing data.
PERANCANGAN APLIKASI INVENTORY BARANG BERBASIS WEBSITE SEBAGAI PENCATATAN BARANG PADA CV LANGGENG JAYA LESTARI: DESIGN OF A WEB-BASED INVENTORY APPLICATION FOR GOODS RECORDING AT CV LANGGENG JAYA LESTARI Nurika Sefira Lestari; Imam Tahyudin; Dinar Mustofa
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.v11i2.8013

Abstract

This study aims to design and develop a web-based inventory system for CV Langgeng Jaya Lestari using the Design Thinking method, which consists of empathize, define, ideate, prototype, and test stages. The system was developed to improve manual inventory management into a more computerized and efficient process. The main features include recording incoming goods, outgoing goods, stock monitoring, and integrated inventory data management through a single dashboard. Data collection was carried out through observation and interviews with the company to identify system requirements. The results show that the inventory website improves efficiency and accuracy in managing stock data. System testing was conducted using alpha testing based on expert judgment involving the company leader of CV Langgeng Jaya Lestari and a website expert. The evaluation resulted in an average score of 85.14 out of 100, categorized as very good. This indicates that the system meets user requirements and is feasible to use, although further improvements are still needed for future development and optimization..
ANALISIS PENERIMAAN PENGGUNA SP4N–LAPOR! DI DINAS KOMUNIKASI DAN INFORMATIKA PROVINSI SULAWESI TENGAH MENGGUNAKAN TECHNOLOGY ACCEPTANCE MODEL (TAM) Natalia Warani; Deny Wiria Nugraha
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.v11i2.8016

Abstract

SP4N-LAPOR Public Complaint System! It is an important medium to convey people's complaints and aspirations to the government digitally. To ensure the impact of the ease of use and usefulness of the system on the intention to use the service, it is still necessary to assess the level of acceptance of the system's users. This study used a TAM approach to test the acceptance of SP4N-LAPOR! users. Questionnaires were distributed to SP4N-LAPOR! users in Central Sulawesi Province to collect research data. Validity tests, reliability tests, descriptive analysis, and simple linear regression are used in the analysis. With a contribution of the determination coefficient value (R²) of 20.2%, the findings show that the Perceived Ease of Use (PEOU) has a positive and significant influence on the Perceived Usefulness (PU). Furthermore, the Perceived Usefulness (PU) had a positive influence on Behavioral Intention (BI) 39.3% and the Perceived Ease of Use (PEOU) had a positive influence on Behavioral Intention (BI) 26.7%. These findings show that user acceptance of SP4N-LAPOR! It is greatly influenced by the perceived ease of use and perceived usefulness of the system.
KLASTERING LAGU BERBASIS AKTIVITAS PENDENGAR MENGGUNAKAN ALGORITMA SELF ORGANIZING MAP BERDASARKAN FITUR AUDIO SPOTIFY: ACTIVITY BASED SONG CLUSTERING USING THE SELF ORGANIZING MAP ALGORITHM BASED ON SPOTIFY AUDIO FEATURES Ridho Agiel Syahputra Siallagan; Muhammad Arifin; Pratomo Setiaji
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.v11i2.8017

Abstract

This study aims to cluster songs based on listener activities using the Self Organizing Map (SOM) algorithm with Spotify audio features. The dataset used in this study was obtained from Kaggle with a total of 20,718 songs. The variables used include Danceability, Energy, Valence, and Acousticness. The research stages consist of data pre-processing, searching for the best SOM parameters using Grid Search, SOM clustering, clustering evaluation using Silhouette Score and Davies-Bouldin Index (DBI), activity labeling, and implementation of a Streamlit-based web dashboard. The results show that the best SOM parameters were obtained using a 2x3 grid, 3000 iterations, sigma 1.0, and learning rate 0.5 with a Silhouette Score of 0.2348 and a DBI value of 1.2894. The Silhouette Score indicates that the separation between clusters is moderate but not perfect, which is reasonable because music data often have continuous and overlapping audio characteristics. Meanwhile, the DBI value indicates that the similarity between clusters is still relatively high, although the clusters can still be interpreted based on their dominant audio characteristics. The clustering process produced six activity clusters, namely Workout, Dancing, Gaming, Sleeping, Studying, and Hanging Out. The Gaming cluster became the largest cluster with 31.35% of the data, while the Workout cluster became the smallest with 9.53%. The web dashboard implementation successfully visualized clustering results, music activity distributions, cluster characteristics, and music recommendations based on listener activities. The study concludes that the SOM algorithm is capable of clustering songs based on Spotify audio feature similarities at a moderate level and can be implemented in activity-based music recommendation systems.

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