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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 795 Documents
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Sistem Pengukuran Tingkat Kemiripan Judul dan Abstrak Skripsi Menggunakan Algoritma Winnowing dan Dice Similarity Sucipto, Hadi; Indriyanti, Aries Dwi; Mauludi, Kartika; Mujianto, Ahmad Heru; Rizal, Muhammad Fatkhur; Mashuri, Chamdan
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5948

Abstract

Thesis is a scientific paper that must be made by undergraduate stratum 1 academics at the UNHASY information technology faculty. One of the problems with writing a thesis at the Faculty of Information Technology, UNHASY, is that the process of checking the similarity of titles and abstracts is still manual so it takes a long time to check. From the existing problems, the research was carried out with the aim of providing a system for measuring the level of similarity of titles and abstracts of web-based thesis using the Winnowing and Dice Similarity Algorithm. The algorithm used in this research is the winnowing algorithm which is used as document fingerprinting. The title and abstract of the submitted thesis will be processed through several stages, namely Whitespace insensitive, forming an n-gram sequence, rolling hash, splitting the hash value into several windows and then taking the smallest fingerprint from each window. In the final stage, the similarity value is sought by using the dice coefficient. The results of this study are a sub-system that can check the similarity of titles and abstracts using the winnowing algorithm. This algorithm does not have specific provisions regarding how many gram, window, and prime values that must be applied so that the author tests using 10 test data to determine the effect of these values so that the smallest value is obtained from the input value gram = 5, window = 4 and prime = 3, which is 10.82%, while the largest result, which is 38.16%, is obtained from the input value of gram = 4, window = 3 and prime = 3.
Pemanfaatan Matrix Factorization Berbasis Android Berdasarkan Preferensi Pengguna untuk Optimasi Promosi Objek Wisata Saputra, Widodo; Dewi, Rafiqa
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5954

Abstract

This research aims to optimize the promotion of tourist attractions in Simalungun Regency and Pematangsiantar City by utilizing Artificial Intelligence (AI) technology based on Matrix Factorization. The lack of information about tourist spots in these areas has resulted in many attractions being relatively unknown to the public, making current promotional efforts less effective. Through the application of AI, it is hoped that the promotion of tourist attractions will become more efficient, providing users with a better experience in finding destinations that match their preferences. The data used in this study was collected through web scraping from Google Maps, including information about tourist spots such as name, location, rating, number of reviews, and category. This data was then processed using Matrix Factorization, which analyzes user preferences based on their visit history and ratings of tourist attractions. The ultimate goal is to generate personalized and relevant recommendations. The research was implemented in the form of an Android application, allowing users to easily access tourism recommendations via mobile devices. Testing results showed that the recommendation system successfully provided suggestions for tourist attractions that matched users' needs. The model was evaluated using Root Mean Squared Error (RMSE), yielding a result of 0.0835 on training data and 0.2362 on validation data, demonstrating good performance. Overall, this research proves that Collaborative Filtering technology based on Matrix Factorization is effective for tourism recommendation systems.
Implementasi CRISP-DM Pada Analisis Pembangunan Pendidikan Prasekolah Menurut Kabupaten/Kota di Indonesia Iranti, Putri Chandra; Kurniawan, Dedy; Sanjaya, M Rudi; Rifai, Ahmad; Syahbani, M Husni; Hartono Cahyadi, Gabriel Ekoputra; Sari, Purwita
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5957

Abstract

Preschool education through Kindergarten (TK) plays a crucial role in child development in Indonesia, yet unequal access remains a significant issue. This study evaluates the need for preschool infrastructure development using the K-Means clustering algorithm implemented through RapidMiner. Regional clustering is based on the number of students, number of TK schools, Human Development Index (HDI), poverty rate, population size, and unemployment rate. The CRISP-DM methodology is applied, involving stages of understanding, preparation, modeling, evaluation, and deployment. Data from the Central Bureau of Statistics (BPS) and the Ministry of Education's Dapodik system are utilized, incorporating Z-transformation normalization and data cleansing. The clustering results reveal three main clusters with the lowest Davies-Bouldin Index (DBI) at K=3, scoring 0.205. With a total of 514 districts/cities in Indonesia, the results of the needs of each cluster were obtained, namely Cluster 0 consisting of 402 districts/cities requiring increased participation, Cluster 1 covering 49 districts/cities requiring educational facilities, Cluster 2 covering 63 districts/cities requiring the construction of new schools. This study provides valuable insights into addressing disparities in preschool education access and offers guidance for better resource allocation and policy decisions aimed at improving early childhood education infrastructure.
Analisis Prediksi Banjir di Indonesia Menggunakan Algoritma Support Vector Machine dan Random Forest Purnomo, Indarto Aditya; Indra, Jamaludin; Awal, Elsa Elvira; Rohana, Tatang
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5958

Abstract

Natural disasters frequently occur in Indonesia, such as floods, landslides, and volcanic eruptions. Geological factors, such as the convergence of four major tectonic plates, make Indonesia vulnerable to natural disasters. Statistical data from the National Disaster Management Agency show an increase in flood occurrences each year, peaking in 2021 with 1,794 incidents. Early anticipation is necessary to minimize the impact of natural disasters, and predictive patterns are becoming new knowledge for preventing and managing these disasters. This study applies the Support Vector Machine and Random Forest algorithms. The results of this study predict that the largest number of floods from 2024 to 2026 in Indonesia will occur in Aceh with 240 floods, North Sumatra with 215 floods, West Java with 210 floods, and Central Java with 160 floods. The best algorithm comparison results were achieved with Random Forest, which had an accuracy of 99.6% and an average RMSE value of 3.834.
Toxicity Score and Sentiment Classification of Backpacker Content Reviews using SVM enhanced by SMOTE Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5961

Abstract

This research explores the dynamics of backpacker tourism in Indonesia by analyzing online content from various regions, including Bandung, Dieng, Borobudur, Ijen, Bromo, Tumpak Sewu, Malang, Banyuwangi, and Bali. Using the Digital Content Reviews and Analysis Framework, the study systematically processed user-generated content to assess sentiment and toxicity levels. The analysis revealed that while most interactions were non-toxic, there were occasional spikes in harmful language, particularly in the categories of profanity and identity attacks. For example, toxicity scores in Malang, Banyuwangi, and Bali averaged 0.06995, with peaks reaching 0.78207, underscoring the need for ongoing content moderation. In addition, the study employed a Support Vector Machine (SVM) model enhanced by SMOTE to handle class imbalance. The model achieved an accuracy of 82.64% and a recall rate of 97.39%, demonstrating its effectiveness in identifying positive cases with minimal false negatives. The AUC scores, ranging from 0.970 to 0.979, indicated strong discriminatory power. These findings highlight the potential of using machine learning models to analyze large-scale, imbalanced datasets in tourism-related research. Overall, this study provides valuable insights into traveler perceptions of Indonesia’s backpacker destinations, emphasizing the importance of context in understanding online discourse. The integration of toxicity analysis and SVM modeling offers practical implications for improving tourism management, content moderation, and promoting sustainable tourism practices.
Pengukuran Kualitas Layanan Website Reglab Informatika Menggunakan Metode Webqual 4.0 Hadi, Wisnu Setiawan; Umar, Rusydi
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5963

Abstract

Reglab.tif.uad.ac.id is a website used as an information media in the informatics study program for practical purposes. There are several complaints from students about the reglab website, including the language is still mixed between Indonesian and English, the appearance of the website still has some images that do not load and the chat feature on the website does not exist. Based on these problems, a measurement of the quality of the reglab website service will be carried out using the webqual 4.0 method. The results of the webqual 4.0 measurement, namely the webqual 4.0 dimensions, partially or simultaneously show the relationship between the webqual dimensions, namely the usability dimension, information quality, and service interaction quality, which affect the user satisfaction dimension. Recommendations given to website developers are based on the lowest value of each webqual 4.0 dimension. From the usability dimension, the recommendation is to add reference links or books that according to the lecturers can help practicums. From the information quality dimension, it is an improvement in the appropriate format such as language. From the quality of service interaction, it is providing interactive chat or question and answer facilities to support the relationship between website users and the regla party. From the dimension of user satisfaction, when carrying out activities on the Reglab website, the speed of accessing the website is slow, then the developer can reduce the number of plugins used, programs and images can be optimized so that the website can be lighter when accessed, update the programming language used and carry out regular maintenance to check the website.
Analisis Perbandingan Metode TAM dan UTAUT Terhadap Tingkat Penerimaan Pengguna Aplikasi Easy Access Andre, Muhammad; Damayanti, Nita Rosa; Andri, Andri; Ibadi, Taqrim
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5964

Abstract

The Easy Access application is designed to facilitate employee attendance tracking, manage leave request letters, monitor employee working hours, and manage all employee personal data. However, despite these benefits, there are still issues present, such as several features that have not been optimized, including employee data fields that are empty or not available. This research is conducted to compare the TAM and UTAUT methods concerning the user acceptance level of the Easy Access application in the workplace of PT Semen Baturaja Tbk. The data analysis technique used in this study is SEM-PLS. Based on the comparison between the TAM and UTAUT methods, the hypothesis testing results indicate that all hypotheses in the TAM method are accepted, with the highest t-statistic reaching 10.257. Meanwhile, in the UTAUT method, four hypotheses are accepted with the highest t-statistic of 4.074, while one hypothesis is rejected, indicating that the social influence variable does not have a significant effect on behavioral intention, with a t-statistic of 1.554 < 1.98 and a p-value of 0.120 > 0.05. However, the UTAUT model shows an advantage in the R-square test, achieving the highest value of 56% for the user intention variable, while the TAM only reaches 49.6% for the same variable. This indicates that although the TAM model has more significant relationships between variables, the UTAUT model is better at explaining the variation in overall user acceptance of the application.
Implementasi Sistem Informasi Pelayanan Jasa Laundry Berbasis Web pada Track Laundry Arkanda, Rizal Naufal Farras; Fithri, Diana Laily
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5968

Abstract

The current development of Information Technology, especially in Indonesia, has developed rapidly. However, behind the progress of information technology which has now developed rapidly, until now most laundries do not have their own information system and still use manual systems, these laundries also do not have a website for their business, consumers will find it difficult to find information related to business profiles, service system, production systems, and types of services offered in laundry businesses. Track Laundry is a web-based application specifically designed to facilitate online laundry services by utilizing the website as part of the production, marketing and operational systems in a laundry business. Utilizing web-based information system design in laundry businesses can make it easier for customers to order services. The method used in this research is to use a waterfall model. This waterfall model provides a sequential software life flow approach starting from analysis, design, coding, testing and support stages. Built using Laravel 10, this application offers various advanced features that support laundry business operations efficiently and effectively. With Track Laundry, customers can easily order laundry services through a user-friendly interface. The ordering process is done quickly with just a few clicks, starting from selecting the type of service, pick-up and delivery schedule, to secure payment.
Analyzing Social Networks and Topic Clustering in Backpacker Tourism Content Reviews using K-means, Fast HDBScan, and Gaussian Mixture with Communalytic Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5969

Abstract

This research explores the integration of social network analysis and topic clustering techniques to provide novel insights into digital interactions and thematic trends within the context of backpacker tourism. Utilizing a structured framework, 3,575 records across three content IDs (c2ZMFDS_3rU, Sv_yxz7T8rU, and i9t9pbdo-bk) were processed and classified into 10 clusters using k-Means, Fast HDBScan, and Gaussian Mixture algorithms. Social network analysis was performed on 4,224 actor nodes and 395 edges, highlighting the role of key influencers in driving conversations while revealing the participation patterns of a larger, less engaged audience. The topic clustering revealed distinct themes, including budget travel, off-the-beaten-path destinations, and sustainable tourism, with each algorithm offering unique insights into the structure of the data. The novelty of this research lies in applying these computational methods to backpacker tourism, traditionally analyzed through qualitative approaches, to uncover how thematic discussions propagate within digital communities. By integrating these techniques, the study provides a deeper understanding of how key topics resonate with backpackers and how social interactions influence the spread of ideas. The findings offer valuable implications for content creators and tourism marketers seeking to engage this niche travel demographic more effectively. This work contributes a scalable, data-driven methodology for analyzing traveler behavior and preferences in virtual environments, enhancing the field of backpacker tourism research.
Optimasi Algoritma K-Nearest Neighbors Menggunakan Teknik Bayesian Optimization Untuk Klasifikasi Diabetes Sowabi, Nur Kholis; Widiastuti, Nur Aeni; Maori, Nadia Annisa
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5975

Abstract

Diabetes is one of the chronic diseases that affects millions of people worldwide. Early diagnosis is crucial to prevent long-term complications, but the main challenges lie in the complexity of medical data and selecting optimal parameters for classification algorithms. This research aims to optimize the K-Nearest Neighbors (KNN) algorithm using Bayesian Optimization to improve accuracy in diabetes classification. The dataset used is the "Early-stage Diabetes Risk Prediction" from the UCI Machine Learning Repository, preprocessed through normalization and categorical feature encoding. Bayesian Optimization was applied to find the optimal parameters, such as the number of neighbors (k) and the best distance metric. The results show that the optimized KNN achieved 91.34% accuracy, 100% precision, and a 93.23% F1-Score, demonstrating a significant improvement over the standard KNN model. In conclusion, KNN optimization with Bayesian Optimization proves effective in enhancing diabetes classification performance and can contribute significantly to early detection and disease management.

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