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Ipung Dwiansyah
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JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Published by Universitas Muhammadiyah Jember
ISSN : 25025724     EISSN : 25415735     DOI : https://doi.org/10.32528/justindo
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
JUSTINDO is a scientific journal managed by the informatics engineering study program at the University of Muhammadiyah Jember as a publication media for research articles in the field of systems and information technology which covers the following topics: Software engineering, Games, Information Retrieval, Computer networks, Telecommunication, Internet, Internet of Things, Cloud Computing, Wireless technology, Network security, Multimedia technology, Mobile Computing, Parallel / Distributed Computing, Development, management and utilization of Information Systems, Organizational Governance, Enterprise Resource Planning, Enterprise Architecture Planning, e-Businness, e-Commerce, e-Learning, Data mining, Text mining, Machine Learning, Data warehouse, Online Analytical Processing, Artificial Intelligence, Decision Support System, and Mathematics. JUSTINDO is issued twice a year in February and August. The editor invites research lecturers, reviewers, practitioners, industry, and observers to contribute to this journal. JUSTINDO provides a platform for scientists and academics throughout Indonesia to promote, share and discuss new issues and the development of information systems and information technology. JUSTINDO aims to achieve the theory and application of this sophisticated field. In 2017, JUSTINDO already has an ISSN both printed and online, for ISSN (Print) is 2502 - 5724 and for ISSN (Online) is 2541 - 5735
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 58 Documents
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Web-Based E-Learning Information System with Multi-Role Monitoring Feature at SMP Ibrahimy 2 Sukorejo Rosita Natania Maulani; Achmad Baijuri; Firman Santoso
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5566

Abstract

Learning at SMP Ibrahimy 2 Sukorejo is still conducted conventionally, resulting in limited material access outside school hours and minimal learning monitoring. This study designs and implements a web-based e-learning information system using the Waterfall method. The system accommodates five actors (Admin, Teacher, Student, Class Teacher, Curriculum Vice Principal), with novelty in multi-role monitoring features and integration of five roles in a single platform. Main features include learning material management, online assignment submission, UTS/UAS evaluation with automatic grade calculation (Final Grade = (Assignment Score + Exam Score) / 2), real-time notifications, and per-role monitoring dashboards. Functional testing using Black Box Testing on 20 scenarios across 8 modules yielded a 100% success rate (20/20 scenarios matched expected outcomes). Usability testing using System Usability Scale (SUS) with 15 respondents produced a mean score of 81.3, categorized as Excellent. The system was successfully implemented as an integrated digital platform supporting the teaching and learning process at SMP Ibrahimy 2 Sukorejo.
Student Graduation Prediction at Ibrahimy University Using K-Nearest Neighbor (KNN) Algorithm Herlinatus Safira Muasolli; Achmad Baijuri; Fajriyanto
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5567

Abstract

Student graduation is an important indicator of a university's success in delivering quality education. Ibrahimy University faces challenges in objectively and proactively predicting student graduation, as academic evaluation processes remain conventional and reactive. This study aims to build a student graduation prediction system using the K-Nearest Neighbor (KNN) algorithm based on academic data including GPA, credits, attendance, and number of failed courses. The dataset consists of 150 student records from Ibrahimy University, developed using the Knowledge Discovery in Database (KDD) framework. Data was split into 80% training and 20% testing with StandardScaler normalization. The optimal k value was searched from k=1 to k=15. Results show that k=1 achieved the highest accuracy of 96.67%. The system is deployed as an interactive web application using Streamlit, enabling non-technical users such as lecturers and academic administrators to monitor student graduation potential more effectively and data-driven.
The Influence of the Faculty of Sharia and Islamic Economics Information System (SIFASE) on Student Satisfaction at the FSEI Ibrahimy University Situbondo Syamhadi; Nanda Hidayan Sono; Nadzirotul Fithriyah; Helyatin Nisyak; Muhammad Fauzen Adiman
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5759

Abstract

This study aims to analyze the effect of the Faculty of Sharia and Islamic Economics Information System (SIFASE) on student satisfaction at the Faculty of Sharia and Islamic Economics (FSEI), Ibrahimy University Situbondo. SIFASE was developed to support various academic services that were not fully accommodated by the Academic Information System (SIAKAD), including field practice management, community service programs, thesis submission, and graduation administration. This research employed a quantitative approach using accidental sampling techniques. The population consisted of 192 seventh-semester students, while 99 respondents were successfully analyzed. Data were collected through observation, questionnaires, and documentation. Data analysis was conducted using simple linear regression preceded by validity, reliability, normality, and linearity tests. The results indicate that SIFASE has a positive and significant effect on student satisfaction, with a t-value of 8.330 and a significance level of 0.000 < 0.05. Furthermore, the coefficient of determination (R²) value of 0.417 indicates that SIFASE explains 41.7% of the variation in student satisfaction, while the remaining 58.3% is influenced by other factors outside the research model. These findings demonstrate that SIFASE contributes to improving academic service quality and student satisfaction within FSEI Ibrahimy University Situbondo.
Application for Local Coconut Quality Classification Using the MobileNetV2 Convolutional Neural Network (CNN) Algorithm Ferdiansyah; Muh Rasyid Ridha; Dwi Yuli Prasetyo
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5773

Abstract

Quality classification of local fibrous coconuts is still largely performed manually, a process that requires precision and significant time, while also potentially leading to inconsistencies in quality assessment. This study aims to develop an Android-based quality classification application for local fibrous coconuts using the Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture. The research involved several stages: dataset collection (coconut images), preprocessing, data augmentation, model training, model testing, and implementation of the model into an Android application using TensorFlow Lite. The model was developed to classify coconut quality into three categories: immature, mature, and reject. The results demonstrate that the MobileNetV2 model effectively learned the visual characteristics of local fibrous coconuts, yielding optimal classification performance based on evaluations using accuracy, loss, precision, recall, F1-score, and a confusion matrix. The developed model was successfully implemented in an Android application, enabling automated classification using images captured via the device's camera or selected from its gallery. The findings indicate that the developed application offers a practical and efficient solution for the rapid and consistent quality identification of local fibrous coconuts. According to the research provided, the dataset consisted of 3,000 images of local fibrous coconuts, comprising 1,000 images each for the immature (semi-ripe), mature (ripe), and reject (damaged) classes. The dataset was split into 70% training data (2,100 images), 15% validation data (450 images), and 15% testing data (450 images). Following the training process using the CNN algorithm with the MobileNetV2 architecture, the model achieved a test accuracy of 96% and a test loss of 0.1098, demonstrating excellent classification performance in distinguishing between the three coconut quality classes.
Optimization of a Hybrid CNN-SVM Model for Pineapple Ripeness Classification Fanisha Juliananda Putri; Muh. Rasyid Ridha; Fitri Yunita
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5890

Abstract

Pineapple ripeness classification plays an important role in maintaining fruit quality and supporting the sorting process. Conventional ripeness assessment is commonly performed manually, which may lead to subjectivity and inconsistent results. This study aims to develop a Hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model for classifying pineapple ripeness levels based on digital images. The dataset consisted of 3,745 pineapple images categorized into five ripeness classes: unripe, under-ripe, half-ripe, ripe, and rotten. Before model training, the images underwent preprocessing and data augmentation using rotation, horizontal flip, zoom out, brightness adjustment, and darkness adjustment techniques to increase dataset diversity. CNN was employed as a feature extractor to capture the visual characteristics of pineapples, while SVM was used as the classifier to determine the ripeness class. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results showed that the proposed Hybrid CNN-SVM model achieved an accuracy of 92%, outperforming CNN (87%), K-Nearest Neighbor (86%), Random Forest (84%), and SVM (71%). These findings indicate that the combination of CNN and SVM effectively improves pineapple ripeness classification performance. The proposed model has the potential to be implemented as an automated, fast, and objective fruit-sorting support system.
Digital Customer Experience of UNIPI Admission Website: A Technology Acceptance Model Analysis Dikdik Firman Sidik; Muhammad Akil Hi Umar; Ihsan; Fadhil Qonia Zulfa; Agung Febriyadi Fazrin
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5935

Abstract

The digital transformation of academic services requires that university admission websites not only function informatively but also provide a positive digital experience. This study aims to analyze the influence of perceived usefulness and perceived ease of use on behavioral intention, mediated by Digital Customer Experience (DCX), within the Technology Acceptance Model (TAM) framework. An explanatory quantitative approach was employed through a survey of 200 prospective students using the admission website of Universitas Persatuan Islam. Data were analyzed using SEM-PLS with SmartPLS 4.0. The results indicate that perceived usefulness (β=0.452) and perceived ease of use (β=0.381) significantly affect DCX. DCX significantly influences behavioral intention (β=0.347) and partially mediates the effects of both exogenous variables. The model exhibits strong predictive power (R² DCX=0.648, R² BI=0.701). These findings offer an applicable DCX-TAM evaluation model to improve digital admission services in higher education.
Implementation of a Web-Based Car Rental Management System Using Agile Methods Febrian Chandra; Bias Yulisa Geni
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5117

Abstract

This study aims to design and implement a web-based car rental management system integrated with real-time vehicle monitoring to improve operational efficiency at PT. Mitra Link Gerak. The main problem raised is the use of manual processes in managing vehicle data, customers, and rental transactions that have the potential to trigger service delays, inaccurate recording, and difficulties in fleet monitoring. As a solution, this study applies the Agile software development method which is iterative and adaptive. This system is built using the Laravel framework on the backend side, Vue for the frontend, and MySQL as the database. The main functionalities produced include fleet data management, booking books, transaction recording, management reporting, and GPS-based vehicle tracking. System feasibility testing is carried out through black box testing to validate application functions, and the System Usability Scale (SUS) method to provide a level of usability from the end user's perspective. The test results show that all system functionality runs validly, while the SUS evaluation obtained an average score of 76, indicating the system is in the category of feasible (acceptable) and easy to operate. Thus, the developed system is proven to be functional and highly effective as a technological solution to support efficient service digitalization.
Design and Development of an Earthquake Early Warning System Based on IoT Using the Fuzzy Tsukamoto Method Muhammad Zulfikar Adipradana; Ari Eko Wardoyo; Taufiq Timur Warisaji
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5580

Abstract

The development of an Internet of Things (IoT)-based earthquake early warning system using the Tsukamoto fuzzy method in this study aims to reduce the risk of injuries or fatalities in tall buildings due to earthquakes. This early warning system uses an MPU6050 gyroscope sensor to measure building tilt during an earthquake. The data obtained from the sensor is then processed using an ESP8266 microcontroller. The Tsukamoto fuzzy algorithm is used to classify earthquake risk status into three categories: "Safe," "Alert," and "Danger." Warning notifications are sent in real-time to an application designed using Kodular. The main contribution of this research is the real-time application of the Tsukamoto Fuzzy method in an IoT-based early warning system, utilizing a gyroscope sensor to efficiently measure building tilt and rapidly send warnings. Test results show that the sensor can detect tilt well, in accordance with the predetermined tilt rules, and is able to send warning notifications to the application within 1-2 seconds across all nine test scenarios. However, this system has several limitations, such as the need for further research on a larger scale and in more varied environments. This system provides a solution for disaster relief efforts for earthquake victims in tall buildings and can be further developed in the future.

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