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JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 2686228X     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Artikel yang dimuat melalui proses Blind Review oleh Jurnal JOSH, dengan mempertimbangkan antara lain: terpenuhinya persyaratan baku publikasi jurnal, metodologi riset yang digunakan, dan signifikansi kontribusi hasil riset terhadap pengembangan keilmuan bidang teknologi dan informasi. Fokus Journal of Information System Research (JOSH)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 16 Documents
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Search results for , issue "Vol 7 No 3 (2026): April 2026" : 16 Documents clear
Implementasi Algoritma Kriptografi RSA untuk Keamanan Transmisi Data pada Sistem Monitoring Energi Listrik Berbasis IoT Rajawali Rajawali; Syamsul Bahri; Kasliono Kasliono
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.8232

Abstract

Data security is a crucial issue in Internet of Things (IoT) systems used to monitor electricity consumption. This study aims to enhance the security of data transmission in an IoT-based electricity monitoring system by implementing the Rivest–Shamir–Adleman (RSA) cryptographic algorithm. Data from the PZEM-004T sensor is encrypted using the RSA public key and verified with a digital signature before being transmitted to the server. The system was tested under two conditions: without encryption and with RSA encryption, including a simulated ARP spoofing attack using Ettercap. The results show that the system successfully rejected manipulated data, with a packet loss rate of 2.08%, which is categorized as “very good” based on the TIPHON standard, and achieved a throughput of approximately 9.88 bit/s. The implementation of RSA proved effective in maintaining data integrity and authenticity, thereby improving the reliability of the IoT-based electricity monitoring system.
Implementasi Model Deep Learning MobileNetV2 untuk Klasifikasi Citra Melanoma Berbasis Web Deva Safara Alfan; Intan Kumalasari
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.8848

Abstract

Melanoma is one of the most aggressive types of skin cancer with a high mortality rate if not detected at an early stage. In primary healthcare facilities, the lack of dermoscopy equipment causes examinations to rely solely on visual assessment, which may lead to diagnostic errors, particularly false negatives. This study aims to develop a web-based early melanoma detection system as a tool to assist initial screening. The proposed method implements a deep learning model based on the MobileNetV2 architecture using a transfer learning approach with pre-trained ImageNet weights. The dataset used in this study consists of melanoma and notmelanoma images from HAM10000, while the nonskin class is obtained from CIFAR-10 to help the model distinguish between skin lesion images and non-skin images. The dataset is divided into 70% training data, 20% validation data, and 10% testing data. Evaluation results show that the model achieves an accuracy of 90% in multiclass classification, while binary evaluation focusing on melanoma detection yields an accuracy of 90.48%, precision of 81.75%, recall of 91.96%, and an F1-score of 86.50% on the test data. The model is then implemented in a web-based system capable of displaying skin lesion classification results along with a confidence score in real time. The findings indicate that the developed system can perform automated image analysis and has the potential to be used as a supporting tool for early melanoma screening.
Implementasi Metode SAW pada Sistem Seleksi Siswa Baru Berbasis Web Nabil Ahyan Annakhief; Sri Lestari
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9352

Abstract

New student admission is a crucial process in educational institution management because it determines the quality of accepted students. The Mathlaul Anwar Foundation offers several selection pathways: scholarships, report card grades, achievement pathways, and transfer pathways. Currently, the selection process is still conducted manually, resulting in various problems such as delays in data processing, potential calculation errors, lack of objectivity, and low transparency of selection results. This research aims to develop a web-based New Student Selection System using the Simple Additive Weighting (SAW) method as a decision support system to assist in the ranking process and determine student graduation objectively and measurably. The research methods used include observation, interviews, and documentation. The system development utilizes the Waterfall model, which consists of the stages of needs analysis, design, implementation, testing, and maintenance. The implementation results show that the system is able to reduce the selection process time from an average of 5 days to 2 days (a time efficiency of 60%). The process of calculating grades and ranking, which was previously done manually for approximately 120 minutes for 100 applicants, can be accelerated to approximately 15 minutes using the system (an efficiency increase of 87.5%). System testing using the Black Box method on 20 test scenarios showed a 100% functional success rate according to user requirements. In addition, the results of the SAW method calculation validation showed 100% accuracy compared to manual calculations. Thus, the application of the SAW method in the web-based new student selection system has been proven to be able to increase the efficiency, accuracy, objectivity, and transparency of the selection process at the Mathlaul Anwar Foundation.
Penerapan Metode Association Rule Mining Menggunakan Algoritma Equivalence Class Transformation Dalam Menganalisis Pola Stok Obat Aniq Astofa
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9432

Abstract

Poorly planned drug inventory management often leads to imbalances between patient needs and the availability of medicines in clinics. This issue generally arises because transaction data has not been optimally utilized as a basis for decision-making. The purpose of this study is to identify patterns of drug associations by applying Association Rule Mining techniques using the Equivalence Class Transformation (ECLAT) algorithm. The research adopts a quantitative approach, utilizing one year of drug transaction data. The analysis reveals several combinations of medicines that are frequently prescribed together by healthcare providers. These association patterns provide valuable insights into prescribing tendencies within the clinic. By understanding the most common combinations, managers can plan drug procurement more accurately and efficiently. The information obtained not only helps anticipate the risk of stock shortages but also prevents excessive inventory that could result in waste. Thus, the application of the ECLAT algorithm proves effective in enhancing drug inventory management. Furthermore, the findings of this study can serve as a foundation for developing more efficient procurement strategies, ultimately improving the quality of healthcare services in clinics. Overall, leveraging transaction data through Association Rule Mining contributes significantly to evidence-based decision-making. This demonstrates that integrating data analysis techniques with inventory management can create a healthcare system that is more responsive, efficient, and patient-centered.
Evaluation of Service Quality Gaps in Pos Express Services Using the SERVQUAL Method Muhamad Alif Fitrah Adriansyah; Rahayu Amalia; Ari Muzakir
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9466

Abstract

This study aims to analyze the service quality of Pos Express in South Sumatra by applying the SERVQUAL method to identify gaps between customer expectations and perceptions. A quantitative approach was employed by distributing structured questionnaires to 120 respondents selected through purposive sampling. The measurement instrument was developed based on five SERVQUAL dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The results indicate that customer expectations were consistently higher than perceived service performance across all dimensions. The largest negative gap values were found in the responsiveness (-0.73) and reliability (-0.72) dimensions, indicating weaknesses in service response time, complaint handling, delivery punctuality, and information accuracy. Meanwhile, the empathy dimension recorded the smallest gap (-0.29), suggesting relatively positive interpersonal interactions between staff and customers. To support data processing and analysis, a web-based evaluation system was developed to automate SERVQUAL calculations and reporting. The system facilitated efficient data management and improved the accuracy of service quality analysis. Overall, the findings highlight the need for service improvement, particularly in enhancing operational reliability and responsiveness. This study provides empirical evidence to support service quality management and decision-making in regional postal services.
Improving the POSPAY Mobile Interface Using User-Centered Approach with User Experience Questionnaire Evaluation Tasya Arnomel Mareta; Evi Yulianingsih; Ari Muzakir
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9474

Abstract

Digital public service applications require interfaces that are clear, efficient, and consistent to support fast and accurate transactions. In the PT Pos Indonesia service environment, POSPAY users may experience difficulties in locating core services, understanding menu structures, and completing tasks efficiently due to navigation and interface consistency issues. This study aims to improve the POSPAY mobile interface using a user-centered approach and to evaluate user experience using the User Experience Questionnaire. The study involved 20 participants (staff and customers). Observation and semi-structured interviews were conducted to elicit user needs, which were translated into prioritized requirements and implemented in a high-fidelity clickable prototype developed with Figma. Participants completed standardized task scenarios before completing the questionnaire. The results show positive mean scores in five dimensions, with Perspicuity (1.70) and Efficiency (1.55) as the highest, followed by Attractiveness (1.45), Dependability (1.20), and Stimulation (1.05). Novelty (0.65) remained neutral, indicating that the proposed interface is perceived as functional but not strongly innovative. The main contribution of this study is a context-specific requirement set and traceable mapping between user needs and prototype features for POSPAY in a postal service setting, supported by quantitative user experience evidence to prioritize interface refinement and implementation decisions at PT Pos Indonesia.
Pemanfaatan Algoritma FP-Growth pada Teknik Data Mining untuk Mengidentifikasi Pola Stok Produk Elektronik Irawaty Irawaty
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9517

Abstract

Managing the availability of electronic product stock is a crucial issue in the retail world due to the high variety of products and dynamic consumer purchasing patterns. Inaccuracy in determining the amount of stock can lead to excess inventory or product shortages, which impacts on decreasing operational efficiency. This study aims to apply the FP-Growth algorithm in the data mining process to determine the pattern of electronic product stock availability based on purchase transaction data. The dataset used in this study consists of 150 electronic product purchase transaction data. The main problem faced is the lack of optimal utilization of transaction data to determine the relationship between products that are frequently purchased together. As a solution, this study applies the Frequent Pattern Growth (FP-Growth) algorithm because of its ability to find association patterns without the need to generate candidate itemsets, making it more efficient in data processing. The research process begins with calculating the frequency of item occurrences, determining the minimum support value of 20% (30 transactions), forming an FP-Tree, and mining frequent itemsets and association rules. The results show that Mouse, Laptop, and Keyboard are the items with the highest frequency, respectively 80%, 73%, and 70% of the total transactions. The Mouse–Laptop–Keyboard purchasing pattern has a support value of 55% with a confidence level of 80%. While the Mouse → Keyboard rule yields the highest confidence level of 85%. Based on these results, it can be concluded that the FP-Growth algorithm is effective in identifying purchasing patterns for electronic products and can be used as a basis for decision-making in prioritizing stock availability more precisely and data-driven.
Pengelompokan Tanaman Perkebunan Berdasarkan Produktivitas dan Luas Lahan dengan K- Means Clustering Ethaniel Williano Adhi Putra; Yunus Widjaja
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9518

Abstract

Plantation data in West Java was grouped based on land area and crop productivity using the K-Means method. This data was obtained from Open Data Jabar from 2022 to 2024 and analyzed using a quantitative approach. Three groups can be identified based on the clustering results: one group has high productivity but relatively limited land area, another has large land area but suboptimal productivity, and the last group has equally low productivity and land area. The results indicate that land area does not always correlate with productivity. This study emphasizes the importance of selecting relevant variables and using methods consistently to produce more accurate and understandable analyses.
Sistem Pendukung Keputusan Penentuan Siswa Magang Terbaik Menggunakan Metode Simple Additive Weighting SAW I Komang Sugiartha; Eka Fitri Rahayu
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9583

Abstract

Selecting the best interns is a crucial activity in assessing the success of the internship program at Budi Darma University. The manual assessment process often leads to subjectivity and is time-consuming. Therefore, a system capable of assisting in objective and efficient decision-making is needed. This study aims to develop a Decision Support System (DSS) for determining the best interns using the Simple Additive Weighting (SAW) method. The SAW method was chosen because it provides accurate results by summing the weighted scores of each alternative based on predetermined criteria. The assessment criteria used in this study include discipline, responsibility, communication skills, cooperation, and internship report results. Assessment data is processed by weighting each criterion, then calculated using the SAW formula to obtain each student's preference score. The results show that the system can assist the university in quickly and objectively determining the best interns. This system makes the assessment process more transparent, accurate, and supports data-driven decision-making.
Perbandingan K-Means dan DBSCAN dalam Analisis Pola Pergerakan Kapal Menggunakan Data Automatic Identification System (AIS) Darmansah, Darmansah; Handoko, Koko; Adhiatma, Novri; Simanjuntak, Pastima
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9363

Abstract

Batam waters are one of the busiest shipping lanes in Indonesia, with high ship traffic density and complex movement patterns. This condition requires data analysis techniques that can accurately identify and adapt ship movement patterns. The purpose of this study is to study ship movement patterns using Automatic Identification System (AIS) data, and also to see how the K-Means and DBSCAN algorithms work in the data clustering process. The AIS data used includes geographic coordinates, observation time, speed, and direction of ship movement in Batam waters. This study includes the application of the K-Means and DBSCAN algorithms, feature extraction and normalization, and data pre-processing to improve data quality. Internal validation metrics used to assess cluster quality are the Silhouette Score and the Davies–Bouldin Index. The results of the study show that the DBSCAN algorithm has a better level of cluster cohesion and separation between clusters than K-Means. The K-Means algorithm produces a Silhouette Score value of 0.48 and a Davies–Bouldin Index value of 0.91, while the DBSCAN algorithm produces a Silhouette Score value of 0.62 and a Davies–Bouldin Index value of 0.67. In addition, DBSCAN can find sound data of 19.96% of the data set, which indicates abnormal ship movements or does not form a certain density pattern. The results show that the DBSCAN algorithm analyzes ship movement patterns with AIS data in the Batam waters better than K-Means. This research is expected to be the basis for the development of maritime information systems that help monitor ship traffic, make decisions about safety, and manage waters.

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