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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Journal of Information Systems and Informatics Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Journal of Informatics, Information System, Software Engineering and Applications (INISTA) INOVTEK Polbeng - Seri Informatika Jurnal Nasional Komputasi dan Teknologi Informasi
Muhammad Arifin
Muria Kudus University
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Other

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Classification of Package Delivery Duration Using Decision Tree Algorithm Nava Azahra; Febriana Permatasari; Nalendra Cahaya Heraditya; Wahyu Dhani Prayoga; Muhammad Arifin
Journal of INISTA Vol 8 No 2 (2026): May 2026
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v8i2.2122

Abstract

This study aims to classify package delivery duration based on the difference between shipping time and delivery completion time using the Decision Tree algorithm. The dataset used in this research was obtained from PT Idenative and consists of historical logistics data containing various shipment attributes such as number of items, destination location, delivery status, and time-related variables. Data preprocessing was conducted through cleaning, transformation, and categorization, where delivery duration was classified into four categories: Fast, Normal, Slow, and Very Slow. The classification model was developed using the Decision Tree algorithm due to its interpretability and ability to handle both categorical and numerical data. The dataset was divided into training and testing sets with an 80:20 ratio, and model performance was evaluated using confusion matrix, accuracy, precision, recall, and F1-score metrics. The results show that the model achieved an accuracy of 40.72%, with a macro precision of 0.62, recall of 0.35, and F1-score of 0.34, indicating moderate performance. The model faces challenges in distinguishing between similar classes, particularly Normal and Slow categories. Feature importance analysis reveals that the number of items and destination location are key factors influencing delivery duration. This study demonstrates that the Decision Tree algorithm can be applied to classify delivery duration in the logistics domain while providing interpretable insights for operational decision-making. However, further improvements are required, such as applying ensemble methods and data balancing techniques to enhance model performance.
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.  
Expert System for Detecting Academic Burnout Levels among University Students Using the Certainty Factor Method Zahrotul Khoiriyah; Arif Setiawan; Muhammad Arifin
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/mdwyhb15

Abstract

Academic burnout is a condition of physical, emotional, and mental exhaustion resulting from prolonged academic demands and may negatively affect students' learning and well-being. This study aimed to develop a web-based expert system to support the preliminary identification of academic burnout among university students using the Certainty Factor method. The system knowledge was constructed through a literature review and a structured knowledge acquisition process involving a psychology expert before being represented in the knowledge base. The system was developed using the Waterfall model with PHP, the CodeIgniter framework, and MySQL. Functional evaluation using black-box testing showed that all system features operated as expected. In addition, User Acceptance Testing (UAT) involving 102 students of Universitas Muria Kudus achieved an overall acceptance score of 83.92%, indicating a Very Good level of user acceptance. The developed system was able to classify consultation results into mild, moderate, and severe academic burnout categories based on the implemented Certainty Factor inference process. Therefore, the proposed expert system may serve as a supporting tool for the preliminary identification of academic burnout. Further validation using standardized psychological instruments or professional psychological assessments is recommended in future studies.
Co-Authors Ahmad Zidan Nur Rizqi Alif Fadhillah Laili Putri Arif Setiawan Bayu Samudro Fadhilah Diana Laily Fithri Era Wulandari Fajar Nugraha Farhan Wildan Darmawan Febriana Permatasari Intan Firdausatul Salsabilla Intan Hayati Khilal Arlisna Rahmadani Mohammad Maulana Afriza Muhamad Davin Tanzila Ramdani Nalendra Cahaya Heraditya Nanda Lutfi Rizqiyanto Nava Azahra Rhoedy Setiawan Salma Elsa Widyadhana Soni Adiyono Supriyono Supriyono Syahrul Bagus Andreyan Wahyu Dhani Prayoga Zahrotul Khoiriyah