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INDONESIA
Journal of Information Technology
Published by Universitas Bhinneka Nusantara
ISSN : 23031425     EISSN : 2580720X     DOI : https://doi.org/10.32664/j-intech
Core Subject : Science,
Computer Science & IT
Journal of Information and Technology is a journal published by Bhinneka Nusantara University, Malang. The scope of this journal includes IT Governance, IS Strategic Planning, IS Theory and Practices, Management Information System, IT Project Management, Distance Learning, E-Government, Information Security and IT Risk Management, E-Business / E-Commerce, Big Data Research, and other related topics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 307 Documents
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Webqual 4.0 Analysis of Java Dancer Resto and Cafè Ordering Website Rizki Nur Iman; Muhammad Jibril; Rizqiyatul Khoiriyah; Fachrudin Pakaja
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.2044

Abstract

Java Dancer Resto and Cafè is one of the restaurants in Malang City that has utilized a website as a means of online ordering, this study aims to analyze the quality of usability, information, interaction services, and the overall ordering website of Java Dancer Resto and Cafè  using the WebQual 4.0 method. The research method is quantitative descriptive using a survey method to 100 consumers, data analysis using the average and percentage of user satisfaction with the Kaplan and Norton interval scale. The results showed that the overall quality of the Java Dancer Resto and Cafe ordering website was considered very good by users with an average score of 4.34. The usability aspect received the highest rating with a score of 4.41, indicating ease of use and intuitive navigation. The quality of information received a score of 4.35, indicating high accuracy and relevance of information. The service interaction aspect received a score of 4.25 and is still in the very good category, but requires improvement, especially in service personalization and transaction security.
Application of the K-Nearest Neighbor (K-NN) Algorithm for Detecting Banana Harvest Feasibility Citra Citra; Arnah Ritonga; Arnita Arnita; Said Iskandar Al Idrus; Debi Yandra Niska
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2064

Abstract

This study focuses on detecting banana harvest feasibility at the green-ripe stage, an area often overlooked in previous studies that focus only on general ripeness. The objective of this research was to develop a system based on the K-Nearest Neighbor (K-NN) algorithm to classify bananas as “Ready for Harvest” or “Not Ready for Harvest” using digital image processing. The system utilizes Hue Saturation Value (HSV) for color analysis and Gray Level Co-occurrence Matrix (GLCM) for texture identification. Unlike other methods, the combination of HSV and GLCM provides richer, complementary features, improving classification accuracy. The study was conducted at a banana plantation in Kwala Bekala Village, Medan Johor District, with 200 banana images taken from five different locations. The K-NN algorithm, with a value of K = 3, was chosen to avoid tie votes and ensure computational efficiency. The system achieved an accuracy of 94%, with precision of 93.5%, recall of 92.8%, and an F1-score of 93%. In beta testing with 33 respondents (18 farmers and 15 non-farmers), the system achieved a user satisfaction rate of 90%. Misclassifications occurred due to factors such as lighting variations and background noise. The study demonstrates the practical benefit of using the K-NN algorithm for determining the optimal harvest time, helping farmers make more accurate decisions, reducing waste, and increasing market competitiveness. This research fills the gap in detecting green-ripe bananas, providing an innovative solution to optimize harvest timing in the agricultural industry.
Identifikasi Tandan Buah Segar (TBS) Kelapa Sawit Layak Jual dengan Algoritma K-Nearest Neighbors Dechy Deswita Indriani.S; Kana Saputra S; Said Iskandar Al Idrus; Susiana Susiana; Adidtya Perdana
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2066

Abstract

Indonesia is the world's largest palm oil producer, with annual production reaching more than 45 million tons. The quality of oil palm fresh fruit bunches (FFB) determines the quality of the oil produced. The quality of FFBs can be seen through their maturity and health. Fruit that is not ripe, overripe, or contaminated with mold can reduce oil quality due to high levels of free fatty acids (FFA). This research aims to build a classification model of FFB marketability using the K-Nearest Neighbors (K-NN) algorithm with RGB and GLCM features. Image data was collected from the plantation, then processed through the stages of preprocessing, feature extraction, and normalization. The model was tested in three approaches, namely using RGB-GLCM combination features, RGB only, and GLCM only, with various data sharing scenarios, namely 70:30, 80:20, and 90:10, as well as varying k values, namely k = 3, 5, 7, 9. The evaluation results show that the RGB-GLCM feature combination model in the 80:20 data sharing scenario and k = 5 value is the most optimal model, with accuracy reaching 88%. In addition to providing high accuracy, this model also shows good stability compared to the RGB and GLCM models alone. This proves that the use of a combination of features is more effective and reliable in identifying the marketability of oil palm FFB compared to the use of a single feature.
Application of K-Nearest Neighbor Algorithm for Estimating Fishery Product Quality at BPPMHKP Pontianak Michelson Febrianto; Achmad Zakki Falani
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2071

Abstract

Microbiological quality is a key parameter in ensuring the safety and overall integrity of fishery products. One of the primary indicators in microbiological testing is the Total Plate Count (TPC), locally known in Indonesia as Angka Lempeng Total (ALT), which measures the concentration of aerobic microorganisms in a sample. This study aims to develop a predictive system for ALT values in fishery products using the K-Nearest Neighbor (KNN) algorithm as the main classification approach. The dataset was derived from microbiological test results conducted at the BPPMHKP Laboratory in Pontianak between 2020 and 2024. Data preprocessing included converting ALT values from scientific text format to numeric values, applying Min-Max normalization, splitting the dataset into training and testing subsets, and implementing the KNN algorithm with K = 3. Predictions were generated by calculating the Euclidean distance between each test sample and the training set, selecting the three nearest neighbors, and averaging their ALT values. The proposed system achieved a prediction accuracy of 98.66% compared to actual ALT measurements. Based on the microbiological threshold of 5.0 × 10⁵ colony-forming units per gram (CFU/g) used by BPPMHKP, the system effectively estimated product quality according to safety standards. These findings indicate the potential of the system to be developed into an application-based decision-support tool for government laboratories and quality control agencies. Keywords: Total Plate Count, K-Nearest Neighbor, Prediction, Fishery Products, Accuracy
language Inggris Moch Bagus Tri Cahyo; Hamzah Setiawan; Ika Ratna Indra Astutik
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2083

Abstract

This study aims to analyze the differences in scalability and performance between a traditional monolithic system hosted on a Virtual Private Server (VPS) and a cloud-native serverless architecture using AWS services for an automotive workshop information system. An experimental method was employed using a post-test only control group design. Performance testing was conducted with K6 as the stress testing tool under a ramp-up load pattern of up to 60 Virtual Users (VU) to simulate peak traffic conditions, while Grafana was used for real-time monitoring and visualization of system metrics.The results indicate that under peak load scenarios, the cloud-native architecture reduced the average response time by 89.1% (from 6.05 seconds to 657.10 milliseconds) and eliminated the error rate completely (from 0.154% to 0%), compared to the monolithic system. Additionally, the throughput improved by 38.2%, demonstrating better responsiveness and stability. These findings confirm that serverless cloud-native systems offer superior scalability and reliability in handling dynamic and high-demand workloads, making them well-suited for public service platforms such as automotive workshop information systems.
Traffic Accident Severity Classification System Using Random Forest Algorithm Ega Muhammad Atsir; Nurmalitasari Nurmalitasari; Aprilisa Arum Sari
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2089

Abstract

Traffic accidents pose a major concern in many countries, including Indonesia, causing considerable losses, injuries, and fatalities each year. Properly classifying the severity of these incidents is essential for authorities to establish preventive actions, apply effective countermeasures, and improve overall road safety. Conventional statistical techniques often fall short in capturing the intricate relationships among multiple influencing variables, such as weather, driver experience, vehicle type, number of vehicles, and casualty figures. To address this limitation, this study proposes a machine learning–based classification method using the Random Forest algorithm, known for its robustness in handling complex and high-dimensional data while identifying nonlinear patterns. The model was trained on a traffic accident dataset from Kaggle and incorporated important features, including driver age group, driving experience, type of vehicle, lighting and weather conditions, type of collision, number of vehicles involved, and casualties. The proposed system achieved 81% accuracy, 75% weighted precision, 81% weighted recall, and a weighted F1-score of 77%, demonstrating reliable performance in predicting accident severity levels Slight Injury, Serious Injury, and Fatal Injury. And providing useful insights for data-driven planning in traffic safety management.
Improving Software Defect Prediction Performance Using C4.5 Based Ensemble Learning with AdaBoost and Bagging Techniques Dede Wintana; Dinar Ismunandar; Eka Herdit Juningsih
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/k7fyc413

Abstract

Software defect prediction (SDP) plays a crucial role in improving software quality by enabling the early detection of faulty modules during the development phase. However, class imbalance within software defect datasets remains a significant challenge that adversely impacts prediction accuracy. This study aims to address this issue by implementing ensemble learning methods—specifically Bagging and AdaBoost—combined with the C4.5 decision tree algorithm to enhance classification performance. The research utilized five well-known datasets from the NASA MDP Repository (CM1, JM1, KC1, KC2, and PC1), each containing comprehensive software metrics and defect labels. The methodology involved several stages: data preprocessing (normalization and discretization), model training using 10-fold cross-validation, and performance evaluation through metrics such as accuracy and Area Under the Curve (AUC). Results indicate that both ensemble methods outperformed the standalone C4.5 algorithm across all datasets. Notably, the AdaBoost + C4.5 model yielded the highest accuracy in most scenarios, with the PC1 dataset reaching 97.20% accuracy. In comparison, C4.5 alone and C4.5 with Bagging recorded lower values, demonstrating the significant impact of adaptive weighting in AdaBoost. These findings affirm that ensemble learning, particularly AdaBoost, effectively mitigates the impact of class imbalance and improves prediction performance in SDP tasks.
Development of an Indonesian Hoax Detection System Using Logistic Regression Based on TF-IDF Dewa Samudra Anggeng Suryama; Singgih Jatmiko
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2127

Abstract

The massive spread of fake news (hoaxes) on digital platforms has become a serious challenge in Indonesia, with the potential to disrupt social stability and undermine public trust. This background drives the urgency of developing an automated system to combat disinformation. Unlike previous works relying on deep learning with high computational cost, this study demonstrates that a lightweight approach remains highly effective for Indonesian hoax detection. This study aims to develop and evaluate a lightweight and effective automatic classification system to detect Indonesian-language hoaxes using a machine learning approach. The method used is Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction to represent text content numerically, which is then classified using the Logistic Regression algorithm. This approach was chosen for its computational efficiency and ease of interpretation. The study utilizes a dataset collected from verified sources, consisting of 7,075 Indonesian-language news articles, which were divided into 80% training data and 20% test data. The evaluation results on the test data show excellent model performance, achieving an accuracy of 94.98%, a precision of 0.95, and an average F1-Score of 0.95. Specifically, the model demonstrated a strong ability to identify hoaxes with a recall value of 98% for the hoax class. This study concludes that the combination of TF-IDF and Logistic Regression is an efficient and accurate approach for Indonesian hoax detection, offering a practical solution that can be further developed to combat disinformation.
Blockchain Adoption Challenges for SMEs: A Systematic Literature Review Mukhlis Amien; Adnan Zulkarnain
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.2130

Abstract

Blockchain technology has emerged as a transformative force in various industries, with increasing interest from Small and Medium-sized Enterprises (SMEs). However, the adoption of blockchain by SMEs faces unique challenges that warrant comprehensive examination. This systematic literature review analyzes research on blockchain adoption challenges for SMEs published between 2019 and 2024. We reviewed 48 papers from major databases, including Scopus, Web of Science, and IEEE Xplore. Our analysis reveals three primary challenges: limited financial resources, lack of technical expertise, and regulatory uncertainty. The review also highlights a shift from conceptual to empirical studies over the period, indicating a maturing field of research. Geographically, 60% of studies focus on developing countries, particularly in Asia. Industry-wise, the supply chain sector dominates blockchain adoption research in SMEs (40%), followed by finance (25%) and manufacturing (20%). Despite challenges, significant benefits are identified, including enhanced supply chain transparency, improved operational efficiency, and better access to financing. This review contributes to the understanding of blockchain adoption in SMEs by synthesizing current knowledge, identifying research gaps, and proposing future research directions. Our findings provide valuable insights for researchers, practitioners, and policymakers involved infacilitating blockchain adoption among SMEs.
Classification of Stinging Nettle Plants Based on Leaf Images Using the CNN Method (Case Study: Biru-Biru Village) Daniel Sembiring; Insan Taufik; Hermawan Syahputa; Zulfahmi Indra; Kana Saputa S
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2131

Abstract

The high number of skin irritation cases among residents in Desa Biru-Biru due to direct contact with stinging nettle plants highlights the need for an automatic identification system to distinguish plant types. This study aims to develop a leaf image classification model for stinging nettle plants using the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture. The image dataset was collected directly from the study area and classified into four categories: Jelatang Ayam, Jelatang Gajah, Jelatang Niru, and Non-Nettle plants. The research stages include data collection and analysis, pre-processing (resizing, normalization, augmentation), data splitting (70:10:20), model training, performance evaluation (accuracy, precision, recall, and F1-score), and web-based system implementation. The test results show that the model achieved an accuracy of 98%, with the highest precision score of 0.98, recall score of 0.98, and F1-score of 0.98. The system has also been successfully implemented as an interactive web application that allows users to identify nettle plant types quickly and accurately. This research contributes to risk mitigation efforts related to harmful plants in rural environments through the application of digital image processing technology.

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