Jurnal Komputer dan Teknologi

SISTEM INFORMASI E-LIBRARY MENGGUNAKAN METODE WEB ENGINEERING

Ihsan Alvindra — 2026-07-25

A digital library information system (E-Library) offers a solution to the challenges of library data management—previously handled manually—which often resulted in disorganized records and difficulties in retrieving information regarding books and members. This study aims to design and develop an E-Library information system for SMP Negeri Purwodadi using the Web Engineering method, encompassing user requirements analysis, system design, implementation, and testing. The Laravel framework serves as the core technology for building a robust and efficient backend system, while Black Box Testing ensures the system operates in accordance with user needs. The results demonstrate that the system enables students and staff to access book data, search collections, and manage member information digitally via a user-friendly interface. Testing confirms that the system meets functionality and reliability standards. This development fosters a more integrated, efficient, and secure library management process, thereby enhancing the quality of school library services and supporting digital transformation in education.

SISTEM PENDUKUNG KEPUTUSAN UNTUK PRIORITAS PENGADAAN OBAT DENGAN METODE PARETO ABC

Muhammad Furqon Huwaidy — 2026-07-25

Efficient drug procurement management is important to avoid overstocking and stockouts that affect the quality of pharmacy services. This study developed a web-based Decision Support System (DSS) using the Pareto ABC method to determine drug procurement priorities at Qita Sehat Pharmacy. This method categorizes drugs into A, B, and C based on usage and investment values, thereby supporting data-driven strategic decisions. The system was developed using the Waterfall SDLC model with PHP, HTML, Bootstrap, and MySQL. Key features include drug data management, Pareto calculations, and downloadable analysis reports. Testing was conducted through alpha testing (black box) and beta testing (saturated sampling). The results showed that the system ran stably with a user acceptance index of 79%, fulfilling the aspects of functionality, reliability, ease of use, efficiency, and portability.

RANCANG BANGUN SISTEM PAKAR PENENTUAN PROGRAM STUDI UNTUK CALON MAHASISWA BARU MENGGUNAKAN METODE FORWARD CHAINING

insan tri — 2026-07-25

The selection of study programs at ISTEK Widuri often becomes a challenge due to the absence of a structured decision support system. This research develops an expert system based on the forward chaining method to provide study program recommendations based on user input. The development stages include requirements analysis, rule formulation, interface design, implementation using PHP and MySQL, and testing using the black-box method. The system is capable of recommending four study programs: Communication Studies, Social Welfare, Informatics Engineering, and Information Systems. The testing results show that the system runs well and is able to provide appropriate recommendations. Therefore, this system can serve as an effective alternative in assisting prospective students in choosing their study program

SIDITA: SISTEM INFORMASI DINAS PERTANIAN TERINTEGRASI BERBASIS WEB

Beda Puspita Candra — 2026-07-25

Agricultural data management in Nganjuk Regency has traditionally been conducted manually using physical records and separate spreadsheets, leading to inefficiencies, data duplication, and delays in reporting. To address these challenges, this study developed SIDITA, a web-based integrated agricultural information system designed to centralize and streamline data management across multiple sectors including crops, livestock, farmer institutions, and agricultural aid distribution. The objective of this research was to provide a practical solution that enhances efficiency, accuracy, and accessibility of agricultural data for the local government. The system was developed using the CodeIgniter framework, MySQL database, and responsive interfaces with Bootstrap and Tailwind CSS, following the waterfall development methodology. Testing was conducted using black box methods to ensure functional reliability. The results demonstrate that SIDITA successfully integrates diverse agricultural data into a single platform, reduces redundancy, and improves the speed and accuracy of reporting. In conclusion, the implementation of SIDITA supports better decision-making processes in agricultural management and provides a scalable model for digital transformation in local government institutions

PENGEMBANGAN APLIKASI MANAJEMEN ARSIP BERBASIS FRAMEWORK LARAVEL DENGAN METODE PROTOTYPING

Dian Yuthika Rizqi — 2026-07-25

The development of village archive management in Indonesia is still largely done manually, causing frequent damage, loss, and time-consuming searches. This study aims to develop a web-based village archive management application using the Laravel framework with the prototyping method. The background of this research is the suboptimal management of archives in Ngadas Village, Malang Regency. The objective is to build an application that can store, search, and secure village archives digitally. The method used is prototyping, consisting of four stages: communication, rapid design, user evaluation, and system refinement. Black-box testing and User Acceptance Test (UAT) were conducted with 5 village officials. The results showed that all functional features run as expected, and the user satisfaction level reached 86.4% (very feasible category). The conclusion is that the application effectively reduces archive search time from an average of 17 minutes to 12 seconds, improves data security, and fits the workflow of village apparatus

ANALISIS TREN CYBERSECURITY PADA INFRASTRUKTUR JARINGAN KOMPUTER MODERN: SYSTEMATIC LITERATURE REVIEW

Ekoputra Wahyu Prakasa — 2026-07-25

The development of modern computer network infrastructure has become increasingly complex due to digital transformation, cloud computing, and interconnected devices, leading to significant growth in cybersecurity risks. This condition requires a comprehensive understanding of cybersecurity trends and challenges to strengthen network security systems. This study aims to identify and analyze the main trends and challenges in cybersecurity within modern computer network infrastructure. The research method used is a Systematic Literature Review with a systematic selection process, including identification, screening, and evaluation of relevant scientific articles published between 2021 and 2025. The results indicate that the main trends include the adoption of artificial intelligence for threat detection, the implementation of zero trust architecture, enhanced security in cloud environments, and the rise of automated cyber attacks. Meanwhile, the challenges involve increasing attack complexity, vulnerabilities in network devices, limited human resources in cybersecurity, and the expansion of the attack surface due to modern technology integration. In conclusion, adaptive, proactive, and intelligent technology-based security approaches are essential to address the evolving cybersecurity threats in modern computer network infrastructure.

SISTEM PERAMALAN PENJUALAN KOPI BUBUK SELANGIT MENGGUNAKAN METODE WEIGHTED MOVING AVERAGE (WMA) MENGGUNAKAN DATA TIME SERIES BERBASIS FRAMEORK CI (CODEIGNITER)

Glen Jupiter — 2026-07-25

This study aims to forecast ground coffee sales using the Weighted Moving Average (WMA) method to support decision-making in business planning. The WMA method was selected because it assigns greater weight to recent historical data, thereby enabling a more responsive capture of changes in sales trends. The results indicate that ground coffee sales are projected to experience a stable upward trend in 2025. The model achieved a high level of accuracy, yielding a MAPE of 0.098%, an MAE of 54.463, and an RMSE of 70.683. Although discrepancies occurred in certain periods due to high sales volatility, the WMA method generally tracked actual data patterns effectively and produced realistic estimates. Consequently, the WMA method is a suitable tool for sales forecasting to support production planning, inventory control, and the formulation of more effective sales strategies

ANALISIS PERFORMA DAN KEAMANAN SISTEM BACKUP DAN RESTORE PADA PRIVATE CLOUD BERBASIS CASAOS DI LINGKUNGAN VIRTUAL

Lukmanul Hakim — 2026-07-25

Data loss caused by system failures and cyber threats requires a reliable data protection strategy for private cloud environments. This study aims to analyze the performance and security of a backup and restore system using Duplicati on a CasaOS-based private cloud in a virtual environment. An experimental method was employed by implementing CasaOS on Ubuntu Server running in VirtualBox, deploying Duplicati through Docker, resolving container network isolation, volume mapping, and privilege issues, and evaluating the performance of backup and restore processes using a 1.23 GB dataset. The results show that the proposed configuration successfully resolved network and permission constraints, enabling reliable backup and restore operations. The full backup process achieved an average throughput of 20.22 MB/s, while the incremental backup reached 87.42 MB/s, and the restore process was completed successfully without access failures. In conclusion, the integration of CasaOS, Docker, and Duplicati provides a secure, stable, and cost-effective backup and restore solution suitable for small- and medium-scale private cloud deployments.

ANALISIS SENTIMEN ULASAN SHOPEE DI PLAY STORE DENGAN NAIVE BAYES

Muh Arfah Wahlil Pratama — 2026-07-24

This study evaluates user sentiments towards the Shopee application on the Google Play Store using a dataset of 1,000 crawled reviews. Preprocessing methods including cleaning, normalization, stopword filtering, and stemming were conducted, followed by feature weighting using TF-IDF. The classification model based on the Naive Bayes Classifier yielded an overall accuracy of 84.5%, with a precision of 85.2%, recall of 83.8%, and F1-score of 84.5%. The analysis suggests that shipping promotions are the main drivers of positive sentiment, while post-update app performance drops and payment errors trigger negative reviews.

ANALISIS TINGKAT PEMAHAMAN MAHASISWA ILMU KOMPUTER PADA JENIS PARAGRAF

Tasya Aulia Fitri — 2026-07-25

This study aims to determine the level of understanding of students from the Computer Science Study Program at the State Islamic University of North Sumatra (UINSU) regarding types of paragraphs in the Indonesian language. The research employed a quantitative approach using questionnaires and descriptive analysis. The results show that persuasive (40.5%) and argumentative (35.1%) paragraphs are the most difficult for students to understand, while expository, descriptive, and narrative paragraphs are relatively easier. These findings indicate that non-language students experience difficulties with paragraph types that require reasoning and systematic argument construction, highlighting the need for more effective Indonesian language learning strategies

KOMPARASI PERFORMA ALGORITMA K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI CITRA TEKSTUR TENUN

budiman baso — 2026-07-24

The diversity of woven fabrics on Timor Island makes it difficult to distinguish between types of woven fabrics and their origins. Each region on Timor Island has its own woven fabric motifs that represent local culture. There are Timor woven motifs that look similar but have different types. Each motif and process of making weaving on Timor Island can describe the type and origin of the weaving. To distinguish Timor woven fabrics, it can be seen from the style of motifs or textures contained in Timor woven fabrics. Therefore, the pattern recognition of Timor woven motifs with the concept of classification is implemented using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classification algorithms based on texture feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). This study aims to compare the performance of the two classification algorithms. Several parameters are used to configure the KNN and SVM algorithms to determine the performance gap between the two algorithms. The experimental architecture was carried out on 500 images of Timor weaving with 4 motifs; the image dataset will be divided into 75% training data and 25% testing data using the hold-out validation method. From the experimental results, the KNN algorithm using Euclidean distance with 1 Neighbor obtained the best performance, with an Accuracy rate of 88.73%, Precision of 89.41%, Recall of 88.73%, and F1-Score of 89.07%.

ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI PINTEREST PADA GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER

Muh Arfah Wahlil Pratama — 2026-07-24

The rapid growth of user reviews on the Google Play Store offers valuable insights for app developers. This study conducts sentiment analysis on Pinterest user reviews using the Naive Bayes Classifier algorithm. Data was collected via web scraping using the `google-play-scraper` library within the Google Colaboratory environment. The dataset, consisting of Indonesian-language reviews, underwent preprocessing steps including case folding, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, and the model was evaluated using a confusion matrix with an 80:20 train-test split. The results indicate that the Naive Bayes model achieved an accuracy of 84.00%, a precision of 84.00%, and a recall of 77.78%. The sentiment distribution reveals a predominance of positive reviews, reflecting overall user satisfaction with the Pinterest app. This research contributes to the understanding of public opinion regarding visual-based applications and validates the effectiveness of Google Colab as an integrated platform for Indonesian-language sentiment analysis

PREDIKSI HARGA SAHAM BANK NEGARA INDONESIA (BNI) BERDASARKAN HISTORICAL STOCK PRICE MENGGUNAKAN METODE GATED RECURRENT UNIT (GRU)

Arina Kanuri — 2026-07-25

The volatile nature of stock price movements necessitates a prediction method capable of accurately modeling time-series data patterns. This study aims to predict the stock price of Bank Negara Indonesia (Persero) Tbk (BNI) using the Gated Recurrent Unit (GRU) method, based on historical data from the 2020–2025 period. The dataset includes closing prices, trading volume, moving averages (MA_7 and MA_21), volatility, and high-low ratios. The research process encompasses preprocessing, feature engineering, Min-Max normalization, the creation of sequential data with a time step of 30, and the splitting of data into training and testing sets. The GRU model is trained using the Adam optimizer, incorporating ReduceLROnPlateau and Early Stopping mechanisms to enhance training stability and mitigate the risk of overfitting. Model performance is evaluated using MSE, RMSE, MAE, R², and MAPE metrics. The results demonstrate that the GRU model yields effective predictions, achieving a MAPE of 3.91%, an RMSE of 211.21, and an R² of 0.746. Stock price predictions for the upcoming 10 days indicate a trend of gradual price decline. These findings suggest that the GRU method is effective for predicting BNI stock prices based on historical data and holds potential for supporting investment decision-making, while acknowledging external factors that influence market conditions

DAMPAK PENGGUNAAN MBANGKING PADA DIGITAL NATIVE MENGGUNAKAN METODE TECHNOLOGY ACCEPTANCE MODEL (TAM)

maksal mina — 2026-07-25

The rapid development of information technology has accelerated the transformation of banking services into digital-based systems, one of which is mobile banking (Mbanking). This service enables users to conduct financial transactions efficiently without physical interaction with bank offices. This study aims to analyze the effect of perceived usefulness, perceived ease of use, and perceived risk on the intention to use Mbanking among digital native generations using the Technology Acceptance Model (TAM) approach. This research employs a quantitative method with data collected through a Likert-scale questionnaire. The population consists of Generation Z and Millennials in Banda Aceh who use Mbanking services, with a total sample of 70 respondents selected using purposive sampling techniques. Data analysis was conducted using multiple linear regression with the assistance of SmartPLS software. The results indicate that perceived usefulness and perceived ease of use have a positive and significant effect on the intention to use Mbanking. In addition, perceived risk also shows a significant influence, indicating that security and trust are crucial factors in adopting digital banking services. Simultaneously, all independent variables significantly affect the intention to use Mbanking. These findings suggest that improving service benefits, ease of use, and system security can enhance users’ interest and sustained use of Mbanking, particularly among digital native groups

ANALISIS PENGARUH SELEKSI FITUR TERHADAP KINERJA RANDOM FOREST PADA KLASIFIKASI KUALITAS UDARA

onesimus Harefa — 2026-07-25

To reduce the impact of pollution on human health and the environment, air quality is one of the important indicators that must be monitored. The purpose of this study was to evaluate the effect of feature selection on the performance of the Random Forest algorithm on air quality classification. The data is processed through the stages of preprocessing, feature selection, model formation, and evaluation using 10-Fold Cross Validation. This data is used as an experimental method with a quantitative approach using Orange Data Mining. The results of the feature selection resulted in five main attributes: PM10, CO, NO₂, SO₂, and O₃. The Random Forest model yielded an accuracy of 76.6%, an accuracy of 0.764, a recall of 0.766, an F1 score of 0.764, a Matthews correlation coefficient of 0.689, and an area under the curve of 0.948. The results show that although feature selection succeeded in simplifying the model by reducing the number of attributes, the use of all attributes has not been able to improve the performance of Random Forest.

ENHANCED RBAC DENGAN PENDEKATAN HIERARCHICAL DAN DOCUMENT LIFECYCLE PADA SISTEM MANAJEMEN DOKUMEN

Wildan Jazuli — 2026-07-29

Access rights management in Document Management Systems (DMS) has become a critical challenge in organizations with complex and dynamic structures. Conventional Role-Based Access Control (RBAC) models have limitations in addressing access requirements that consider organizational hierarchy and changes in document status throughout its lifecycle. This study proposes an Enhanced RBAC model by integrating hierarchical roles and document lifecycle as a more adaptive and context-aware access control mechanism. The research method employs a prototyping approach with iterative development, along with quantitative evaluation through access scenario testing, performance measurement, and business process efficiency analysis. The evaluation is conducted using multiple scenarios representing various combinations of roles, hierarchical structures, and document states. The results indicate that the proposed model is able to reduce unauthorized access across all testing scenarios, improve approval process efficiency by 35%, and minimize access errors during testing. The main contribution of this study lies in the integration of hierarchical organizational structures and document states into the RBAC model, resulting in a more flexible, adaptive, and effective access control mechanism that meets the requirements of modern document management systems.

RANCANG BANGUN ALAT PENGUNCI PINTU OTOMATIS BERBASIS PENGENALAN WAJAH MENGGUNAKAN RASPBERRY PI

siti Ayu Luffiyati Hamid — 2026-07-29

Door security systems in government institutions generally still rely on conventional locks, which have several limitations, including the risk of being lost, easily duplicated, and the inability to automatically verify user identities. These limitations allow unauthorized visitors or individuals to enter restricted rooms without an authentication process, potentially disrupting office activities. This study aims to design and develop an automatic door locking system based on facial recognition using a Raspberry Pi as an alternative security solution. An experimental research method was employed by integrating a Raspberry Pi 3 Model B+, a USB camera, a 5V relay module, and a solenoid door lock as the hardware components, while the software was developed using the Python programming language, the OpenCV library, and the Local Binary Pattern Histogram (LBPH) algorithm for facial recognition. The proposed system detects a user's face and compares it with the registered facial database before automatically unlocking the door for authorized users. Experimental results indicate that the system achieved a facial recognition accuracy ranging from 40% to 90%, with performance influenced by lighting conditions, facial orientation, and the use of accessories such as eyeglasses. In addition, the system successfully controlled the automatic door locking mechanism according to the authentication results. Therefore, the developed system can serve as a practical and effective alternative to conventional door locking systems for improving access control in office environments

PERANCANGAN SISTEM INFORMASI MANAJEMEN LOGISTIK OBAT MASUK DAN OBAT KELUAR

Iqbal Dzulfiqar Isakandar — 2026-07-29

PT AAN is a pharmaceutical distribution company that still relies on a manual record-keeping system using physical logbooks and Excel files. This practice has led to a high risk of data inaccuracies, shipping delays, and difficulties in real-time inventory tracking. This study aims to design a web-based Monitoring System for Incoming and Outgoing Drug Distribution to enhance efficiency, accuracy, and real-time oversight in the company's pharmaceutical logistics management. The system was developed using a prototyping approach, which encompasses requirement analysis, conceptual design using Unified Modeling Language (UML) modeling, prototype construction, system evaluation, and functionality testing using the Black Box Testing method. The design results demonstrate that the proposed system successfully maps two main user roles (Admin and Warehouse Staff) with features for automatic stock validation and data redundancy elimination, while achieving a 100% success rate in interface functionality testing via the Black Box method. In conclusion, the implementation of this web-based monitoring system design provides a significant contribution by minimizing human errors, accelerating distribution workflows, and optimizing the accuracy of month-end stock reporting at PT AAN.

PERANCANGAN SISTEM INFORMASI TRACKING PENJUALAN PRODUK ATK BERBASIS WEB

Lela Nurlaela — 2026-07-30

The rapid advancement of information technology has encouraged business owners to adopt information systems to improve the effectiveness and efficiency of data management. Toko Meta, continues to face challenges in managing product data, sales transactions, sales tracking, and sales reporting. These limitations result in inefficient data retrieval and hinder effective sales monitoring. This study design a web-based Sales Tracking Information System for Toko Meta. A qualitative research method was employed, with data collected through observation, interviews, and literature. System requirements were analyzed using the PIECES framework, while system development followed the System Development Life Cycle (SDLC) methodology. The application was developed using the Laravel framework, PHP programming language, and MySQL database. The novelty of this study lies in the integration of a sales tracking feature with product management, sales transactions, and reporting within a single web-based application, enabling more effective sales monitoring for store owners. The resulting system provides features for user authentication, product management, sales transaction processing, sales tracking, and sales reporting. The implementation improves data management efficiency, accelerates information retrieval, facilitates sales monitoring, and generates more accurate and integrated sales reports.

EVALUASI KINERJA MODEL YOLOv11 UNTUK KLASIFIKASI TINGKAT KEMANISAN BUAH NANAS

Willy Muhammad Fauzi — 2026-07-30

Sweetness is a primary determinant of pineapple quality, yet its conventional measurement requires cutting the fruit open and is therefore unsuitable for large-scale, non-destructive sorting. Existing image-based studies on pineapple sweetness generally treat the task as whole-image classification on pre-isolated fruit and report aggregate accuracy, leaving unclear how a model that must localize and classify simultaneously behaves at category boundaries. This study aims to measure and diagnose the performance limits of a single-view YOLOv11 detector in classifying pineapples into three sweetness categories, namely Asam (sour), Manis Ideal (ideal) and Sangat Manis (very sweet). A dataset of 4,475 annotated instances was trained for 50 epochs and evaluated using precision, recall, mean average precision, a confusion matrix, and confidence-threshold sensitivity curves. The model reached an overall mean average precision of 0.555 at an intersection-over-union threshold of 0.5, with a peak F1-score of 0.58; per class, Asam was the most reliable (0.695) while Manis Ideal (0.505) and Sangat Manis (0.465) lagged behind. At the default threshold only 32 to 64 percent of instances per class were classified correctly, far below the recall range observed on the training curves. The novelty of this work lies in the diagnostic characterisation of that gap: errors concentrate almost symmetrically between visually adjacent categories, not between the extremes, and reported recall substantially overstates behaviour at a real operating threshold. A single viewpoint without class-imbalance handling therefore cannot reliably separate boundary categories, which gives the empirical basis for the multi-view refinement pursued next.

PERANCANGAN SISTEM INFORMASI PENGELOLAAN DATA PENINGKATAN KAPASITAS SDM APARATUR SIPIL NEGARA BERBASIS DATA MINING MENGGUNAKAN ALGORITMA NAIVE BAYES

Antonius Ivan Dwiarta Putra — 2026-07-30

Data management regarding capacity building for State Civil Apparatus (ASN) human resources at the Directorate General of Regional Development, Ministry of Home Affairs, currently faces challenges in data processing, information presentation, and decision-making due to the suboptimal use of information technology. This study aims to develop a web-based information system for managing apparatus capacity-building data by employing the Naïve Bayes algorithm as a classification method to support decision-making. The research adopts a quantitative approach utilizing data mining methods, encompassing data collection, preprocessing, splitting data into training and testing sets, building a classification model using the Naïve Bayes algorithm, and evaluating the model via a Confusion Matrix. The system was developed using the Python programming language and the Streamlit framework, and was implemented using apparatus capacity-building data from the 2024–2026 period. The results demonstrate that the system can integrate data management processes, classify capacity-building levels into Low, Medium, and High categories, and automatically generate analytical dashboards and reports. Model evaluation yielded an accuracy rate of 66.67%, indicating that the Naïve Bayes algorithm delivers satisfactory classification performance to support decision-making in managing apparatus capacity building. Consequently, the developed system can enhance the effectiveness, efficiency, and accuracy of data management while supporting the monitoring and evaluation of competency development programs for the apparatus at the Directorate General of Regional Development, Ministry of Home Affairs.