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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 77 Documents
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Search results for , issue "Vol 6 No 2 (2025): Januari 2025" : 77 Documents clear
Klasfikasi Tingkat Kematangan Roasting Biji Kopi Berbasis Deep Learning dengan Arsitektur MobileNet Firmansyah, Tegar; Kurniawan, Rudi; Hidayat, Asep Toyib
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6811

Abstract

Coffee is one of the most widely consumed beverage ingredients in Indonesia and has high economic value to improve the community's economy and as a source of foreign exchange. The roasting process is an important stage in coffee processing because it affects the aroma and flavor of coffee. What is often encountered is that visually determining the level of coffee roasting is often inaccurate and prone to human error. To overcome this problem, this study uses a deep learning approach with a transfer learning method based on MobileNet architecture to classify the level of coffee roasting maturity based on digital images. MobileNet was chosen due to its lightweight and fast architecture, suitable for implementation on mobile devices. This research aims to compare the performance of the model in detecting coffee roasting level automatically, efficiently, and objectively. With this approach, it is expected that coffee enthusiasts and producers can easily recognize the type of coffee roasting, support product quality consistency, and reduce dependence on experts in the roasting process. This study analyzed the performance of the classification model with the results showing excellent performance. The model achieved a total accuracy of 99.50%, with consistently high precision, recall, and f1-score values across all classes, including several classes with perfect scores (1,000). Evaluation using ROC curves and AUC also demonstrated the model's ability to distinguish between the two classes.
Perbandingan Algoritma K-Nearest Neighboor dan Naive Bayes Dalam Prediksi Penyakit Ginjal Kronis Pada Lansia Simbolon, Marco Duran; Bukit, Dimas Dimanta; Purba, Rian Elby; Ketaren, Faisal Haries; Prabowo, Agung
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.5938

Abstract

Chronic kidney disease is a serious illness that requires early diagnosis to improve treatment outcomes, especially in the elderly. The main challenge in diagnosing this disease lies in the fact that symptoms often do not appear until the disease has reached an advanced stage, which necessitates the use of accurate prediction methods. Additionally, the dataset's limited size, consisting of only 195 patient records, may affect the algorithm's ability to identify patterns. Choosing the appropriate algorithm is also a challenge, as some algorithms have limitations in handling complex medical data. This study aims to evaluate the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in predicting chronic kidney disease. The dataset was analyzed using Weka Waikato software and tested using the 9-fold cross-validation method. The best results were obtained using the Naïve Bayes algorithm, with an accuracy of 97.4359%. Based on these results, it can be concluded that both algorithms can be used to predict chronic kidney disease in the elderly. However, to further improve prediction accuracy, proposed solutions include expanding the dataset with more diverse data and optimizing the algorithm's hyperparameters. On the other hand, the Naïve Bayes algorithm demonstrated higher accuracy compared to KNN in this study, making it the more recommended choice.
Implementasi Sistem Pengambilan Nomor Antrean Online dengan Pendekatan Waterfall dan Keamanan MFA Bleskadit, Adri Agustinus; Dewi, Christine
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6187

Abstract

In the digital era, information technology plays an important role in increasing the efficiency of various sectors, including public services. One of the problems faced by the XYZ office in tax services is taking queue numbers. Long queues often cause long waiting times for visitors and reduce company efficiency, which ultimately impacts public satisfaction and perceptions of public services. An efficient queuing system not only improves the user experience but also the productivity of the institution. However, manual systems are often slow, prone to errors, and less flexible, so digital-based solutions are needed. This research aims to design a website-based queue number retrieval system using the waterfall method. To ensure the security of user data, the system is equipped with a Multi-Factor Authentication (MFA) feature, which increases the protection of user data from unauthorized access. This system was built using the PHP programming language and is supported by the XAMPP device as a local server. Tools such as Entity Relationship Diagrams (ERD) and Unified Modeling Language (UML) are used to design data structures and system flows effectively. It is hoped that this research will provide a practical solution to make it easier to collect queue numbers online, reduce waiting times, and increase user satisfaction and safety at the XYZ office.
Prediksi Spasial Kerapatan Vegetasi Perkotaan dengan Pendekatan Algoritma Time Series Untuk Mendukung Pertumbuhan Ekonomi Hijau Pratama, Yudistira Bagus; Dalimunthe, Nurzaidah Putri; Sukma, Mega
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6251

Abstract

The urgency of this research is based on data from the Pangkalpinang City Population and Civil Registration Service in 2020, the population reached 218,569 people and continued to grow to 232,915 people in 2023. The importance of monitoring vegetation density in the context of green economic growth, which requires careful evaluation of the balance between economic development and environmental conservation. With rapid urban growth, the Pangkalpinang city government requires a variety of approaches to accurately predict vegetation density. By identifying the factors that affect land vegetation density, this study aims to develop a machine learning model that can predict land vegetation density conditions over a certain period of time in the future. This research method involves collecting spatial vegetation density data over a period of 11 years using remote sensing technology or remote monitoring, such as satellite imagery. Furthermore, time series data will be analyzed and modeled using machine learning techniques, focusing on algorithms that can overcome the spatial and temporal dynamics of vegetation density. Machine learning algorithms, especially time series algorithms such as Autoregressive Integrated Moving Average (ARIMA) will be used to build a spatial prediction model for vegetation density. The results of this study indicate that the use of ARIMA is able to produce an accurate prediction model in projecting vegetation density in Pangkalpinang City. The ARIMA model shows strong performance with low error metrics, indicating its effectiveness in making accurate predictions for the given data set. The results of this study are expected to provide valuable information for the Pangkalpinang city government in making decisions related to environmental management and green economic development. By involving collaboration between researchers with three complementary expertise including computer science, civil engineering and natural resource conservation and policy makers.
A Qualitative Study on Justice and Fairness Perception Through Kohlberg's Theory using Video Games Wibowo, Tony; Deli, Deli; Hanita, Hanita
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6666

Abstract

This study explores how narrative-driven video games can enhance adolescents' moral reasoning, focusing on justice and fairness through Kohlberg’s moral development theory. Addressing the challenge of fostering advanced moral reasoning in youth, it highlights the limitations of traditional methods and the potential of video games to immerse players in ethical dilemmas. Using a qualitative approach, 35 adolescents played a story-based video game and participated in interviews and group discussions. Thematic analysis revealed that 60% began with pre-conventional reasoning, emphasizing individual rewards, but many advanced to conventional reasoning (Stage 4) after gameplay. A smaller group (15%) demonstrated post-conventional reasoning (Stage 5), considering fairness and abstract principles. While 40% found moral options confusing, 11.43% formed emotional connections with the narrative, underscoring the role of storytelling in fostering empathy and reflection. The findings suggest that thoughtfully designed video games can bridge gaps in moral education, offering engaging contexts for ethical exploration. This research supports integrating such games into curricula to enhance moral and cognitive growth in adolescents.
Sistem Otentifikasi Otomatis Kendala Perangkat Jaringan Menggunakan NDLC Hadi, Muhammad Fawazi; Vidiasari, Viviana Herlita; Lauwl, Christoper Michael; Husain, Husain; Amin, Farda Milanda
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6685

Abstract

The authentication system is a monitoring system where if there is a network problem, it can provide information quickly. The research was conducted at Bumigora University, using 3 buildings as research materials implemented in the form of topology design. The monitoring is carried out to anticipate lost connections or network disconnections caused by certain factors. The Network Development Life Cycle (NDLC) method is the appropriate method for conducting this analysis, because it focuses on parameters such as network reliability and the ability to secure data transmission. The stages of the NDLC method consist of analysis, design, simulation prototype, implementation, monitoring and management, but in this study the author only used 3 stages, namely analysis, design, simulation prototype and implementation. The NDLC method has been proven to increase network security and reliability, as well as minimize downtime due to device failure or disconnection of data transmission. Automatic authentication implemented through NDLC allows real-time device monitoring, by connecting the API with telegram. Telegram will provide notifications in the form of network condition statuses that are experiencing problems. So that the IT team can control the condition of network devices through telegram notifications. This can facilitate network management and efficiency of checking time in maintaining the stability and performance of the network as a whole.
Penerapan Metode Monte Carlo dalam Memprediksi Suhu Daerah Perkotaan Marpaung, Tulus Joseph; Marpaung, Rony Genevent
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6693

Abstract

Changes in temperature patterns due to climate change are a global challenge that requires in-depth analysis, especially in tropical regions such as the city of Medan, Indonesia. This research aims to project future temperature patterns using the Monte Carlo simulation method, utilizing historical data on daily average temperatures from the Meteorology, Climatology and Geophysics Agency (BMKG). A probability-based Monte Carlo method is used to analyze the future temperature distribution, applying the normal distribution as the basic model. Parameters such as mean and standard deviation are calculated accurately, and thousands of iterations are performed to ensure stable and representative simulation results. The analysis process is carried out using Python and supporting libraries such as NumPy, SciPy, and Matplotlib, which provide flexibility and efficiency in environmental data processing. The results of this study show that the Monte Carlo method can produce future temperature distributions that reflect daily temperature variations as well as the probability of extreme events. These predictions provide important insights for various sectors, including health, energy and urban planning, in developing strategic plans to deal with the impacts of climate change. This research confirms that Monte Carlo simulation is an effective approach for analyzing climate data in tropical regions. Additionally, this research opens up opportunities for further development, such as integrating additional data and adapting the model to different environmental scenarios to improve prediction accuracy and relevance of results.

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